[{"content":" Claire David Experimental particle physicist who left a faculty position to train hundreds of African students in Artificial Intelligence.\nRarely sits still.\n\u0026ldquo;Claire\u0026rsquo;s superstrength is to connect people.\u0026rdquo;\nMax Welling\n","externalUrl":null,"permalink":"/","section":"","summary":"","title":"","type":"page"},{"content":"I am a scientist who enjoys fiddling with particle detectors, playing with data, and sharing the beauty of physics, algorithms, and my latest adventures in nature.\nInitially trained as an engineer, I got intrigued by data analysis, so switched to fundamental science and realized a dream: working on one of the largest detectors at CERN.\nIn 2023, while in my third year as assistant professor, I made a bold move. I asked for a leave of absence to pursue two interests at once: learning the math behind machine learning and teaching in Africa. I eventually resigned my tenure-track position in Canada to relocate to Cape Town and help launch a new master’s in AI for science.\nThere I found a new passion: mentoring. After colliding particles, I can\u0026rsquo;t resist colliding talented students with scholars.\nCurious about my research, teaching, talks, or my recent cycling journey across Canada? Reach out here. I\u0026rsquo;m open to teaching missions, a physics-ML project, a field trip to help climate scientists, or any adventure I haven\u0026rsquo;t thought of, with or without a bike.\nIn Time and Space # This interactive map traces my missions and the places I\u0026rsquo;ve lived. Click a year-button, then the pins for more details.\n\u0026times; Zoom out pinch to zoom I try to limit my flights to long teaching missions. Teaching Philosophy # How do I teach? I start with a question.\nFrom basic definitions to the very concept I\u0026rsquo;m about to explain, I ask first. I let students think, alone and in groups, and collect their first intuitions. Only then do I start the lecture: knowledge arrives as an answer, never as \u0026ldquo;What is this for?\u0026rdquo; A tree is better planted if a hole has been dug beforehand. And by asking guiding questions, students learn how to learn, to think on their own, as future autonomous researchers, or whatever they choose to become.\n\u0026ldquo;Don\u0026rsquo;t forget to have fun.\u0026rdquo;\nScience can be frustrating as well as exhilarating. Pushing oneself can be thrilling, but competition brings stressful deadlines and insane workload. I remind my students to cherish the joy of learning before everything else. It\u0026rsquo;s not innocent. The more they enjoy it, the more they do it. They gain skills, they get noticed. And most importantly, they gain clarity on what they really want to do, what they don\u0026rsquo;t, and which opportunities fit their technical curiosity as well as their human values.\nIn Pictures # The ATLAS detector in its underground cavern, CERN, 2012 Seminar on the DUNE experiment at CIPMA, Benin, 2023 Students of the \u0026#34;Introduction to Machine Learning\u0026#34; course at AIMS Senegal, 2024 AI for Science\u0026#39;s inaugural cohort, AIMS South Africa, 2023–24 Landesliga triathlon, Hamburg, 2018 Unicycling in Muizenberg, South Africa, 2023 Handstand at sunrise, Muizenberg, South Africa, 2023 Lake Louise by bike, Canada, 2026 \u0026times; \u0026#8249; \u0026#8250; In Motion and Arts # I\u0026rsquo;ve been lucky to train, perform, and compete in various activities alongside my studies and work missions:\nTrained as a kid in circus arts at Le Lido, Toulouse (1993–1999) Theatre in high school (1999–2003), stand-up in engineering school (2006–2009) Partner dance: boogie, Lindy Hop, salsa, bachata, kizomba (since 2004) Triathlon in Hamburg with team VAF Mädels (2016–2019). Landesliga winners (2017) Qualified for the gravel (2024, 2026) and road (2026) cycling world championships, age-group category.1 Climbed Mount Cameroon, 4,095 m (2025) I don\u0026rsquo;t fly for leisure, so I did not compete.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n","externalUrl":null,"permalink":"/about/","section":"About","summary":"","title":"About","type":"about"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":" Illustration: Sandbox Studio/Fermilab Role Co-PI, with two other PIs Years 2019 – 2022 Link DUNE\u0026#39;s official website: dunescience.org The Deep Underground Neutrino Experiment (DUNE) is a complex of detectors under construction in the United States. DUNE is dedicated to studying one mysterious elementary particle: the neutrino. They are the most abundant matter particles in the universe, yet very difficult to detect. DUNE will measure how neutrinos change over time, a phenomenon known as oscillations. It will also be able to observe neutrinos from a core-collapse supernova in the Milky Way, allowing us to peer inside a newly formed neutron star and potentially witness the birth of a black hole. Inside the cryostat of ProtoDUNE, a prototype built at CERN to validate the liquid-argon chamber technology at scale.Credit: CERN The giant DUNE Far Detector modules will record images of particle tracks created by neutrinos colliding with argon atoms.Credit: Fermilab In 2019, while I was faculty at York University, I co-launched the DUNE-Canada effort with two other principal investigators. We secured three-year funding from the Natural Sciences and Engineering Research Council (NSERC) to start the first Canadian effort, then five-year funding from the Canada Foundation for Innovation (CFI) to build a cryogenic facility for ArgonCube, a research and development initiative designing the advanced Liquid Argon Time Projection Chamber (LArTPC) technology used for the Near Detector.\nDUNE in 2 minutes\nA great animation in this short video from Fermilab ↗\nWhat is so special about neutrinos?\nMy improvised take as a (very amateur) YouTuber: video ↗\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/dune-canada/","section":"Research","summary":"","title":"Bringing Canada into DUNE, an international flagship neutrino experiment","type":"research"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Role Team Leader Years 2016 – 2019 Links Proceedings of PSD11 ↗: \u0026#34;A new strips tracker for the upgraded ATLAS ITk detector\u0026#34; JINST 13 (2018) C01003 ↗ During my postdoctoral fellowship at the Deutsches Elektronen-Synchrotron (DESY) in Germany, I worked on the ATLAS upgrade of its inner detector, which is a subsystem at its centre that reconstructs the trajectories of the charged particles created at each collision.\nWhy upgrade? The Large Hadron Collider at CERN near Geneva, Switzerland, will operate from 2030 in a new regime of more intense collisions. It\u0026rsquo;s the High-Luminosity LHC project. The luminosity is a quantifier proportional to the number of collisions that occur in a given amount of time. More collisions, more data. And with more data, more possibility to discover a rare new process we haven\u0026rsquo;t seen yet.\nThing is: some parts of the LHC detectors are not currently ready for this harsher environment. They need to be rebuilt with better readout systems for both withstanding and recording more collisions. ATLAS is no exception: its inner tracker needs to be replaced. Prototypes of the new inner tracker (ITk) were built at DESY when I started my postdoc and needed to be tested. I led a team of 3 engineers, 3 technicians and 4 students to perform the first thermal characterization of the support structures holding the silicon sensors. The goal is to ensure there will not be overheating of the sensors due to the radiative environment or the powered electronics. With a custom testbench, we successfully delivered several measurement campaigns in various conditions, all matching the simulations.\nThe prototype of the inner tracker strip sensor before (top) and after (bottom) being sprayed black, since such a coating makes temperature measurements with an infrared camera more accurate.Credit: Claire David, DESY. The test chamber and instrumentation devices. The prototype and infrared camera are both inside the dark chamber, shielded from external sources of heat and in a controlled, low humidity environment.Credit: Claire David, DESY. Results from the simulation (left) and the thermal measurements (right), presented in October 2018 for the Preliminary Design Review at CERN.Image: Yu-Heng Chen, Claire David, DESY. Learn more\nWatch the second part of the tele-colloquium I gave for McGill University\u0026rsquo;s Physical Society in 2020: Recording (from 29:39) ↗ \u0026larr; Back ","externalUrl":null,"permalink":"/research/atlas-itk/","section":"Research","summary":"","title":"Characterizing the ATLAS inner tracker (ITk) prototype with an infrared camera","type":"research"},{"content":" Connections are everything.\nFirst name Last name Email How would you like to be addressed? (optional) No preference Informally, by my first name Formally (please specify in your message, thanks) Topic Choose a topic\u0026hellip; Collaboration Teaching invitation Speaking invitation Job opportunity A physics question! Other Your message Something went wrong. Please try again in a moment.\nSend message Thanks for reaching out! ","externalUrl":null,"permalink":"/contact/","section":"Contact","summary":"","title":"Contact","type":"contact"},{"content":" Jump to section Education Appointments Professional Development Skills Research Engineering Experience Research Supervision Teaching Experience Student Placement Academic Service Grants \u0026amp; Awards Presentations Publications Proceedings Other Activities Education Appointments Professional Development Skills Research Engineering Experience Research Supervision Teaching Experience Student Placement Academic Service Grants \u0026amp; Awards Presentations Publications Proceedings Other Activities Download PDF Last updated: October 2026\nEducation 2010 – 2016 PhD in Experimental Particle Physics, University of Victoria, Canada\nSupervisors: Rob McPherson \u0026amp; Michel Lefebvre\nGPA: 8.63/9\nBased at CERN (2012) for authorship qualification, ATLAS Experiment\n2005 – 2010 Master of Engineering in Applied Physics, INSA Toulouse, France\n4th year: exchange semester on Fundamental Physics (in German), Humboldt-Universität Berlin\n5th year: specialization in Nanophysics \u0026amp; Instrumentation\nRank: top 5%\nAppointments 2023 – 2025 Academic Director, AI for Science*, AIMS South Africa\nResident Researcher in Machine Learning, AIMS South Africa\n* MSc program funded by Google DeepMind: ai.aims.ac.za\n2022 – 2023 Visiting Scholar, AIMS South Africa\nMcDonald Institute Ambassador\nCanada's astroparticle physics network; sponsored fellow\n2019 – 2023 Assistant Professor, York University, Canada, jointly appointed as\nResearch Scientist, Fermilab, USA\nATLAS Experiment, CERN \u0026amp; Deep Underground Neutrino Experiment, Fermilab\n2016 – 2019 Postdoctoral Researcher, DESY, Hamburg, Germany\nATLAS Experiment, CERN\n2010 – 2016 Graduate Research Associate, University of Victoria, Canada\nATLAS Experiment, CERN; based at TRIUMF\nProfessional Development 2026 Technical AI Safety, Blue Dot Impact\n6-week course with weekly facilitated discussions; certificate of completion\nSkills Research expertise Collider physics, machine learning, advanced data analysis, statistical inference\nProgramming Python (NumPy, PyTorch, scikit-learn), C, C++, ROOT, LabVIEW, Unix shell\nCommunication University-level instruction, public speaking, science popularization\nMentoring Academic coaching, student placement across international institutions\nLanguages French (native), English (fluent), German (fluent), Spanish (highly proficient)\nResearchHighlights 2020 – 2022 Co-convener of Training \u0026amp; Documentation, DUNE Computing Consortium\nDeep Underground Neutrino Experiment, Fermilab; arXiv:2310.07342\n2019 Co-founded DUNE-Canada group\nDeep Underground Neutrino Experiment, Fermilab\n2018 Observation of the Higgs boson production mode with a top-antitop quark pair\nATLAS Experiment, CERN LHC; Phys.Lett. B 784 (2018) 173\n2016 – 2019 Led thermal validation and radiation length measurements of the ATLAS ITk silicon strip sensor support structure\nATLAS Experiment, CERN LHC; ATLAS-TDR-025\n2015 – 2016 pMSSM reinterpretation of LHC Run 1 SUSY searches; featured in CERN Courier\nATLAS Experiment, CERN LHC; JHEP 10 (2015) 134\nCurrent Projects 2025 – 2026 Automatic mapping of confidence sets to confidence intervals for intractable statistical models\nWith Prof. Harrison B. Prosper (Florida State U.) \u0026amp; Prof. Olivia Prosper-Feldman (U. of Tennessee)\nExpected completion: December 2026\nAs Resident Researcher in Machine Learning at AIMS South Africa 2025 – 2026 Physics-Informed Neural Network as solver of the photon orbit equation around a black hole\nWith Tlotlo Oepeng (formerly AIMS South Africa) \u0026amp; Prof. H. B. Prosper (FSU)\nPaper submitted to the Machine Learning for Physical Science (ML4PS) 2026 workshop\n2025 Graph autoencoders for anomaly detection in particle physics\nWith Raktim Mukherjee (Universität Heidelberg), Prof. H. B. Prosper (FSU)\nAs Research Scientist at Fermilab (York University, Toronto, Canada) 2020 – 2023 DUNE Computing Consortium\nLed first R\u0026amp;D on an interactive analysis facility for DUNE using columnar computing\nRe-designed and co-organized three DUNE Computing tutorials → Front. Big Data 8 (2025) 1497622\nModernized DUNE Computing wiki; designed \u0026amp; deployed DUNE's automated glossary\n2019 – 2023 DUNE-Canada group\nSecured 3-year NSERC funding to launch first Canadian effort (3 PIs)\nSecured 5-year CFI infrastructure funding for a cryogenic facility for ArgonCube R\u0026amp;D\nAs Postdoctoral Researcher at DESY, Hamburg, Germany 2017 – 2019 Observation of tt̄h (→ bb̄) process, ATLAS Experiment, CERN LHC\nDeveloped interface for Matrix Element Method in official analysis code\nValidated Boosted Decision Trees' input distributions across Monte Carlo generators\nLed fake lepton background estimate, one-lepton channel → Phys.Lett. B 784 (2018) 173\n2016 – 2019 ATLAS Inner Tracker upgrade for High-Luminosity LHC\nLed a team (3 engineers, 3 technicians, 4 students) to deliver first measurements of the prototype's thermal performance; developed infrared camera data acquisition interface\nLed first measurements of radiation length (X/X0) using EUDET telescopes\nCreated custom entries of prototype's components in the production database\nAs Graduate Student Research Associate, University of Victoria, Canada 2013 – 2015 Search for electroweak Supersymmetry, ATLAS Experiment, CERN LHC\nMain analyzer and editor of internal note of the search in the h → bb̄ channel\nReinterpreted the search as part of Run,1 summary paper in the context of the 19-parameter pMSSM\nJHEP 10 (2015) 134\n2012 – 2013 ATLAS Liquid Argon Calorimeter Operation\nSoftware on-call expert during 2012 data taking (leading to the Higgs boson discovery)\nCo-organized software expert training; created web-portal for shifters in control room\nDeveloped ATLAS Online Software plugin revision to filter error messages\nEngineering Experience 2010(5 months) ATLAS Forward Calorimeter upgrade, TRIUMF, Vancouver, Canada\nInstrumented a radiation hardness test for diamond sensors using TRIUMF's cyclotron proton beam; created acquisition interface with TestPoint\nDeveloped and tested the data analysis framework with ROOT → JINST 6 P05011 (2011)\n2009(3 months) Pulse-shape modelling for IceCube, DESY, Berlin, Germany\nDeveloped data analysis framework to measure the transfer function of IceCube Neutrino Observatory's photomultiplier modules, improving detector simulations\n2008(2 months) Afterglow tests monitoring, Hilger Crystals Company*, UK\nMonitored scintillation crystals afterglow tests under X-Rays; created a PhP/MySQL database for managing the test results\n* developed crystals for the BaBar experiment\n2007(1 month) French National High Magnetic Field Laboratory, Toulouse, France\nExperimentations on the scotch-tape method for graphene exfoliation\nResearch SupervisionAt AIMS South Africa 2024 – 2026 Postdoc Ryan Atkin, AIMS South Africa\nMain supervisor; anomaly detection for ATLAS High-Level Triggers\n2025(2 months) MSc Raktim Mukherjee, visitor from Universität Heidelberg, Germany\nSupervisor; investigation of Graph Auto-Encoder for anomaly detection for ATLAS triggers\nAt York University 2021 – 2023 Postdoc Tejin Cai, York University\nMain supervisor; interactive analysis facility for DUNE on Compute Canada cloud\nNow: software engineer at Black Duck\n2020 MSc Alfred Ferwerda, York University\nThesis committee member; CERN ALPHA Experiment\n2019 – 2020 PhD Darij Starko, York University\nLocal supervisor; ATLAS qualification task, Flavour tagging group, CERN\nNow: data science consultant at Wyatt AI Inc.\n2021 BSc Amin Jafarov, York University, NSERC USRA recipient\nMain supervisor; searches for long-lived particles using ML\nNow: Medical School, University of Central Lancashire, UK\n2021 BSc Anita Nasiri, York University\nCo-op research project supervisor; long-lived particle event 3D visualisation in ATLAS\n2020 BSc Majd Salam, York University\nCo-op research project supervisor; first ML classifier for ATLAS long-lived particle search\nNow: PhD candidate, Theoretical Astrophysics, Western University, Canada\n2020 BSc Damith Deshan, York University\nCo-op research project supervisor; automatic scans of chip pack labels for ATLAS ITk production database\nAs postdoctoral researcher at DESY 2016 – 2019 PhD Jan-Hendrik Arling, DESY\nLocal technical supervisor; ATLAS ITk upgrade\nNow: junior staff at DESY\n2018 BSc Anna Beck, DESY summer student\nSupervisor; ATLAS ITk thermal characterization\nPhD at University of Warwick; now postdoctoral researcher at MIT\n2017 BSc Maximilian Caspar, DESY summer student\nSupervisor; ATLAS ITk upgrade, instrumentation\nPhD at Universität Wuppertal/DESY; now consultant at Basycon\n2016 – 2017 PhD Yasiel Delabat Diaz, DESY\nLocal supervisor; ATLAS qualification task, ITk upgrade\nPhD at Humboldt-Universität/DESY; now software developer at Safetec\nAs research assistant at University of Victoria 2011 BSc Philipp Koenig, University of Victoria\nCo-op project co-supervisor; ATLAS Higgs group\nPhD at Universität Bonn; now senior data analyst at Syna\nTeaching ExperienceInvited Lectures Oct. 2025 Introduction to Artificial Intelligence \u0026amp; Data Science, AIMS Cameroon\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 108 students\nSkill course, required for all MSc streams\nSep. 2025 Introduction to Machine Learning (modernized version), AIMS South Africa\nMSc course AI for Science; 10 lectures (20 hours) + tutorials (5 hours); 33 students\nCo-teaching with Prof. Harrison Prosper (FSU)\nJul. 2025 Basic Concepts in Machine Learning, EASY-ML* Summer School, Nairobi, Kenya\n10 lectures (15 hours) + tutorials (5 hours); 35 students\n* East-African School for Young Researchers on Advanced Machine Learning; CNRS, France\nSep. 2024 Introduction to Machine Learning, AIMS South Africa\nMSc course AI for Science; 15 lectures (30 hours) + tutorials (10 hours); 21 students\nDelivered again in December–January for 10 students who joined late due to visa delays\nApr. 2024 Machine Learning by Hand, AIMS South Africa\nMSc course AI for Science; 15 lectures (30 hours) + tutorials (10 hours); 14 students\nAdapted content for students joining in January due to visa delays\nFeb. 2024 Introduction to Artificial Intelligence \u0026amp; Data Science, AIMS Cameroon\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 35 students\nJan. 2024 Introduction to Machine Learning, AIMS Senegal\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 20 students\nSep. 2023 Introduction to Machine Learning, AIMS South Africa\nMSc course AI for Science; 15 lectures (30 hours) + tutorials (10 hours); 16 students\n+ 24 auditing students from the Mathematical Science stream\nMay 2023 Introduction to Machine Learning \u0026amp; Data Science, AIMS Ghana\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 19 students\nMar. 2023 Learn the Math behind Neural Networks, University of Johannesburg, South Africa\nOne-day workshop: theory (3 hours) and practice in Python (3 hours); 22 participants\nOpen to all academic levels from 4th year undergraduate\nFeb. 2023 Machine Learning in Particle Physics, AIMS South Africa\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 33 students\nJan. 2023 Machine Learning in Particle Physics, AIMS Senegal\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 19 students\nDec. 2022 Introduction to Machine Learning, ASP2022*, Gqeberha, South Africa\nTwo morning sessions (6 hours); 40 participants\n* African School of Physics: africanschoolofphysics.org\nJan. 2019 Life of a Particle: Stochastic Modelling in Particle Physics, AIMS Ghana\nGraduate course; 15 lectures (30 hours) + tutorials (10 hours); 38 students\nCo-taught with Dr. Samuel Meehan\nTeaching at York University Fall 2021 Special Relativity and Introduction to Modern Physics (PHYS2040)\nSecond year undergraduates in Physics \u0026amp; Engineering Majors (online); 57 students\nFall 2020 Special Relativity and Introduction to Modern Physics (PHYS2040)\nSecond year undergraduates in Physics \u0026amp; Engineering Majors (online); 64 students\nFall 2019 Special Relativity and Introduction to Modern Physics (PHYS2040)\nSecond year undergraduates in Physics \u0026amp; Engineering Majors; 58 students\nPrevious Teaching Experience Sep. 2018 Autumn School for High Energy Physics, Maria Laach, Germany\nInvited group coordinator; tutoring graduate students projects in experimental particle physics; 15 participants\nJan. 2016 TRIUMF Junior Research School, Vancouver, Canada\nInvited lecturer: “The Higgs boson mechanism”; 10 Singaporean college students\nJan. 2015 TRIUMF Junior Research School, Vancouver, Canada\nInvited lecturer: “The Higgs boson mechanism”; 10 Singaporean college students\nStudent PlacementAs Academic Director at AIMS South Africa 2024 – 2025 33 students guided to MSc graduation, AI for Science\nSelected placements:\nAhmed Nadir, Research Intern, ML \u0026amp; Global Health Network, University of Oxford; active learning for survey design\nAntony Gitau, PhD student, University of South-Eastern Norway; AI for medical imaging\nBenedict Ejelonu, G-RIPS Fellow, Tohoku University, Japan; AIMS–Tübingen Data Science \u0026amp; AI Fellow\nHarlette Denebeye, Marie Skłodowska-Curie Doctoral Researcher, Université Paris-Saclay/CEA-CNRGH; trustworthy AI\nJosiah Kimani, AI for Weather Prediction researcher, AfriClimate AI; 2nd prize poster, Deep Learning Indaba 2025\nKoomi Toussaint Amoussouvi, AI for Weather Prediction researcher, AfriClimate AI; 1st prize poster, Deep Learning Indaba 2025\nMushtaha El-Amin, Visiting Researcher, LiGHT Laboratory, EPFL; global health \u0026amp; humanitarian response technologies\nOmer Ebead, Research Fellow, Cooperative AI Foundation; multi-agent privacy; intern at InstaDeep on MARL\nOnintsoa Rakotondranisa, PhD student, University of Cape Town; astrophysics\nRodeo Toha, Head of data and AI at Grow-UP AI, Abomey-Calavi, Benin\nTim Hitge, Research Assistant, Imperial College London; surrogate models \u0026amp; simulation-based inference; paper + poster at NeurIPS 2025\n2023 – 2024 30 students guided to MSc graduation, AI for Science inaugural cohort\nSelected placements:\nAbel Legese, Senior AI Research Engineer, Ersilia; open-source drug discovery\nCarlin Foka, PhD student, Centre for Epidemic Response \u0026amp; Innovation (CERI)\nCatherine Essuman, PhD candidate (from Sept. 2026), Ontario Tech University, Canada; Canada Impact+ Research Award recipient\nDaniel Rajaonarivelomanantsoa, Research Fellow, InstaDeep; first Malagasy oral presenter at NeurIPS 2025; PhD, Stellenbosch University\nDariose Diffo, PhD student, Università di Bologna; AI \u0026amp; computational methods for astronomy and physics\nHaingo Andry, Recipient of the 2025 Google PhD Fellowship, Stellenbosch University/CERI\nHerman Tesso, Doctoral Researcher, Aalto University, Finland; first-author paper at AABI workshop, ICLR 2025; simulation-based inference \u0026amp; spatial statistics\nIsaac Omolayo, PhD student, Universität Tübingen; contrastive learning for connectome-constrained neural networks, fruit fly visual system\nShâma Wache, PhD student, TU Chemnitz, Germany; inverse problems in medical imaging\nTiana Ramonjison, MSc student, Simon Fraser University/TRIUMF; dark matter detector design\nInformal Mentoring \u0026amp; Connections 2025 Catherine Essuman \u0026amp; Pelumi Aderinto, AI for Science alumni\nLed on my behalf the tutorial “The Math Behind Neural Networks” at Deep Learning Indaba, Lagos, Nigeria\n2025 Angel Bashyal, MSc in Astrophysics, Tribhuvan University, Nepal\nMet at BCVSPIN 2024; connected to potential supervisors via McDonald Institute network\nRecipient of Swiss Government Excellence Scholarship; PhD in Astrophysics, start Sept. 2026\n2025 Nishan Lamichhane, MSc student, Tribhuvan Vishwavidalaya, Nepal\nConnected with Prof. Aaron Vincent, Queen's University, for honour's research project\nNow accepted for a PhD in Physics \u0026amp; Astronomy at Queen's University; start Sept. 2026\n2024 Jeffrey Katabo, MSc student, AIMS Cameroon\nConnected with Prof. Joel Zylberberg (CIFAR Fellow, York University); meta-learning for models of primate visual cortex (AIMS capstone project)\nNow pre-doctoral research fellow, Freie Universität Berlin / Bernstein Center for Computational Neuroscience\n2023 Ufuk Çakr, AIMS–Universität Heidelberg research intern\nDPhil student, Intelligent Earth CDT, University of Oxford; RL for wildfire response\nAcademic ServiceService to AIMS 2024 – 2026 Designed and led student onboarding programme, AIMS South Africa\nWeek-long induction: campus life, values, systems, cultural exchange, wellness walks\n4 editions: Sept. 2024, Jan. 2025, Sept. 2025, Jan. 2026\n2023 – 2025 Founded student clubs to foster initiative and campus life, AIMS South Africa\nStudent-run, with dedicated budget; adapted each year to cohort interests\n2023–24: Arts, Fitness, Hike \u0026amp; Walk, Paper Reading, Running\n2024–25: Board Game, Cultural, Dance, Media, Running, Soccer\n2023 – 2025 Co-created formal lecturer contracts and tutor agreements, AIMS South Africa\nService to Fermilab/York University 2020 – 2021 Member, Fermilab Neutrino Division postdoc hiring committee\n2020 – 2021 Co-chair, Physics \u0026amp; Astronomy Department Weekly Colloquium, York University\n2019 – 2020 Member, Faculty of Science Award Committee, York University\nService to the Field 2024 Session co-chair, Sustainability Track, 42nd International Conference on High Energy Physics (ICHEP), Prague\nFirst dedicated sustainability track at ICHEP; environmental impact of HEP accelerators, detectors and computing\n2022 – 2023 Sustainable HECAP+ Initiative\nCreator of sustainable-hecap-plus.github.io; advocate for sustainable practices in high-energy physics\n2022 Judge, Best Student Competition, Particle Physics division\nCanadian Association of Physicists' Congress, Hamilton, Canada\n2021 – 2022 Elected Vice-Chair, Particle Physics Division, Canadian Association of Physicists (CAP)\n2021 Session convener, Neutrino session, CAP Congress 2021\n2020 – 2022 Co-convener, DUNE Training \u0026amp; Documentation working group, Fermilab\n2019 – 2020 Institute contact (York University), ATLAS Inner Tracker production database\nMemberships 2024 – 2025 Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX)\nMember of Management Team\n2023 – 2026 South African Institute of Physics (SAIP)\n2020 – 2022 Canadian Association of Physicists (CAP)\n2019 – 2022 Institute of Particle Physics (IPP), Canada\nPeer Review 2026 Reviewer, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NOW)\nDutch Research Council\n2025 Nominated reviewer, Machine Learning for Physical Sciences Workshop (ML4PS), NeurIPS 2025\nReviewed 2 papers\n2024 Nominated reviewer, Machine Learning for Physical Sciences Workshop (ML4PS), NeurIPS 2024\nReviewed 2 papers\n2019 Referee, Journal of Instrumentation\nNominated by peers\n2017 Referee, Journal of Instrumentation\nNominated by peers\nEditorial Work 2021 Selected reviewer, DUNE Vertical Drift Conceptual Design Report\nChapter: Trigger \u0026amp; Data Acquisition\n2019 – 2021 Editorial Board Member, ATLAS Collaboration\n“Configuration and performance of the ATLAS b-jet triggers in Run,2”; Eur. Phys. J. C 81 (2021) 1087\nEvent Organization 2025 International Masterclass “Hands-on Particle Physics”, AIMS South Africa\nFirst edition hosted by AIMS; 25 high school students from 8 Cape Town schools\nOrganized in partnership with IPPOG; live conference with CERN\n2023 – 2024 Siyakhula Festival, AIMS 20th Birthday Celebration, Muizenberg, South Africa\nOrganizing committee member; 6-day festival, March 17–22, 2024\n2021 DUNE Computing Tutorial, extended version (online)\nCo-organizer; 3-day format with interactive platform, quizzes and expert Q\u0026amp;A\n2021 Poster session organizer, TIPP 2021 (online)\nInternational Conference on Technology and Instrumentation in Particle Physics; GatherTown platform\n2021 DUNE Computing Tutorial, one-day workshop (online)\nMain organizer; teaching basics of DUNE computing to new members\n2019 ATLAS Higgs \u0026amp; Top Quark Workshop (HTop), DESY, Hamburg, Germany\nMember of the Local Organizing Committee\nOther Service 2007 – 2008 Founder \u0026amp; spokesperson, Commission Réussite, INSA Toulouse\nDesigned \u0026amp; implemented programme where students in difficulty choose a mentor among professors\nApproved by administration as a dedicated commission; system still active in 2026\nGrants \u0026amp; AwardsResearch Grants 2022 – 2027 Canadian Foundation for Innovation (CFI) John R. Evans Leaders Fund (JELF) (PI, CAD $125,000)\nNext-generation neutrino detector for DUNE\n2022 – 2023 Compute Canada Fast Track Competition, one-year extension (PI, CAD $23,339)\n2021 – 2022 Compute Canada Resources Competition (PI, CAD $32,225)\nInteractive Analysis Facility for DUNE\n2021 – 2022 NSERC Discovery COVID-19 Extension (non-competitive, CAD $50,000)\n2020 – 2023 NSERC Subatomic Physics Discovery Grant (Co-PI, CAD $465,000)\nNeutrinos exposed: from the cosmos to deep underground\n2020 – 2021 NSERC Subatomic Physics Discovery Grant (PI, CAD $50,000)\nHiggs boson and top quark in ATLAS: gateway to new physics\n2019 – 2023 York University Internal Research Fund, Startup (CAD $110,000)\nAwards \u0026amp; Recognitions 2025 Breakthrough Prize in Fundamental Physics\nShared with ATLAS, CMS, ALICE and LHCb collaborations (13,508 researchers)\nbreakthroughprize.org\n2020 York University Minor Research Grant \u0026amp; Junior Faculty Fund (CAD $3,100)\n2017 – 2018 DESY Bonus Merit (3,200)\nThermal validation of ATLAS ITk prototype\n2016 – 2017 DESY Bonus Merit (3,200)\nFirst infrared measurements of ATLAS ITk sensor structures\n2016 DESY Postdoctoral Fellowship, High Energy Physics division\n2011 – 2012 Nora and Mark de Goutiere Memorial Fellowship (CAD $10,000)\n2010 University of Victoria Entrance Award (CAD $10,000)\nNominated for Vanier Scholarship, Dept. of Physics \u0026amp; Astronomy, UVic\nPresentationsInternational Conferences 2026 High Dimensional Problems for Statistical Methods in Fundamental Physics, BIRS, Banff, Canada\nInvited talk: “Leveraging Machine Learning, and its limits” (co-presenter: Anthony Ozerov)\n2025 32nd International Workshop on Deep Inelastic Scattering (DIS2025), Cape Town, South Africa\nInvited plenary talk: “Diversity, Equity and Inclusion in Physics”\n2023 11th Large Hadron Collider Physics Conference (LHCP), Belgrade, Serbia\nInvited talk: “Statistics on participation of emerging countries in LHC and other experiments”\n2020 30th International Linear Accelerator Conference (LINAC), Liverpool, UK (online)\nInvited panelist: “Women in Science and Engineering: Should I stay or should I go now?”\n2019 Conference on Flavor Physics and CP Violation (FPCP), Victoria, Canada\nInvited talk: “Higgs boson couplings to quarks at the ATLAS experiment”\n2018 Conference on Standard Model at LHC, Berlin, Germany\nYoung Scientist talk: “Search for tt̄H production in the H→ bb̄ channel”\n2017 11th International Conference on Position Sensitive Detectors (PSD11), Milton Keynes, UK\nInvited plenary talk: “A new strips tracker for the upgraded ATLAS ITk detector”\n2016 Lake Louise Winter Institute, Alberta, Canada\nInvited plenary talk: “Search for supersymmetry with the ATLAS detector in final states with leptons, jets and missing transverse momentum”\n2014 International Conference on High-Energy Physics (ICHEP), Valencia, Spain\nPoster: “Search for chargino and neutralino production with a Higgs boson in the decay chain with one or three leptons final state events”\n2013 International Conference on New Frontiers in Physics (ICNFP), Kolymbari, Greece\nInvited talk: “ATLAS results in SUSY searches for 3rd generation squarks and electroweak production”\nFunding Agencies \u0026amp; Institutional Reviews 2021 Institute of Particle Physics (IPP) Annual General Meeting (online)\nInvited talk: “Deep Underground Neutrino Experiment: current status, Canadian effort \u0026amp; funding”\n2018 DESY Program-Oriented Funding (POF) Evaluation, Hamburg, Germany\nInvited poster: “ATLAS and CMS tracker end-caps for the High-Luminosity LHC” (co-author Dr. J. Keaveney); official tour guide for Helmholtz Association reviewers\n2016 Helmholtz Programme Matter and the Universe, Mainz, Germany\nInvited poster: “Anatomy of ATLAS and CMS strip tracker end-caps for High-Luminosity LHC”\n2013 TRIUMF International Review Committee, Canada\nInvited poster: “Search for Higgs production in supersymmetric decay cascades with ATLAS”\n2013 VIP Visit, ATLAS Control Room, CERN, Geneva, Switzerland\nInvited speaker for P. Young (Chair, TRIUMF Board of Management) \u0026amp; G. Patry (CEO, Canada Foundation for Innovation)\nLocal \u0026amp; Regional Conferences 2021 Canadian Association of Physicists Congress (CAP), online\nInvited talk: “Deep Underground Neutrino Experiment: Review and Recent Progress”\n2021 Third Summer Particle Astrophysics Workshop, Queen's University, Canada (online)\nPlenary talk: “The Deep Underground Neutrino Experiment: DUNE”\n2020 Winter Nuclear \u0026amp; Particle Physics Conference (WNPPC), Banff, Canada\nPlenary talk: “Higgs boson physics and upgrade work with ATLAS at the Large Hadron Collider”\n2019 HTop Workshop, DESY, Hamburg, Germany\nPlenary talk: “Small backgrounds in the leptonic channels”\n2018 Preliminary Design Review ITk Strip Local Support Structures, CERN\nFinal presentation of results on thermo-mechanical petal prototypes; passing review\n2018 Forum on Tracking Detector Mechanics, Valencia, Spain\nPlenary talk: “Thermo-mechanical characterization of Petals as Local Support Structures for the ATLAS ITk Strip Detector”\n2018 HTop Workshop, CERN, Switzerland\nInvited talk: “Fakes and other backgrounds in tt̄H(bb̄) leptonic channels” (co-author J. Mellenthin)\n2014 LHCC, CERN, Switzerland\nPoster: “Search for charginos, neutralinos and sleptons in one or two leptons final state events with ATLAS” (co-author R. Ueno)\n2011 ATLAS Canada Workshop, Vancouver, Canada\nTalk: “Search for SUSY Higgs in the bb̄ final state”\nPublic Talks 2026 Career Thursday, Qhubeka Youth Development Programme, Langa, South Africa\nFeatured guest; Q\u0026amp;A on experimental physics \u0026amp; AI in education; organized by Just Grace\n2023 Université Kongo, Mbanza-Ngungu, Congo DRC\nCo-talk with Dr. Nour Skaf: “From the smallest to the largest: a journey from particle physics to astrophysics”\n2023 Dakar Academy, Senegal\nInvited speaker: experimental particle physics to 200+ college-preparatory students\n2022 Neutrino Day, Sanford Underground Research Facility, Lead, South Dakota, USA\nKeynote speaker at Lead's Opera House; VIP underground visit with YouTuber Physics Girl (Dianna Cowern)\nLive radio broadcast for South Dakota radio about DUNE and Neutrino Day\n2021 Royal Astronomical Society of Canada, Toronto, Canada\nInvited speaker, Speaker's Night livestream: “What is DUNE? Embark on the underground telescope to pierce the secrets of matter!” [YouTube]\n2015 Science Innovation \u0026amp; Technology Speaker Series, Richmond, BC, Canada\n“The Triumphant Discovery of the Higgs Boson”\n2014 Cafés Scientifiques, Alliance Française, Vancouver, Canada\n“La triomphante découverte du boson de Higgs”; organized by the French Consulate\nInvited Colloquia 2026 Integrative Think Tank (ITT), AIMS South Africa\nFive-day industry-academia workshop; invited talk: “How I became a scientist: the backstory not read on my CV”\n2025 AIMS Cameroon, Limbé, Cameroon\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2025 AIMS Cameroon, Limbé, Cameroon\n“How I became a scientist: the backstory not read on my CV”\n2024 AIMS Cameroon, Limbé, Cameroon\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2024 AIMS Cameroon, Limbé, Cameroon\n“How I became a scientist: the backstory not read on my CV”\n2023 Université de Montréal, Département de Physique, Canada\n“Half a year in Africa: AI, AIMS \u0026amp; opportunities”\n2023 East African Institute for Fundamental Research (EAIFR), Kigali, Rwanda\nCo-talk with Dr. Nour Skaf: “From the smallest to the largest: a journey from particle physics to astrophysics”\n2023 University of Ghana, Department of Physics, Accra, Ghana\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2023 AIMS Rwanda, Kigali, Rwanda\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2023 AIMS Cameroon, Limbé, Cameroon\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2023 Université de Lomé \u0026amp; CERME, Lomé, Togo\n“The ATLAS Experiment at CERN: a detector, an analysis and an upgrade program”\n2023 International Chair in Mathematical Physics \u0026amp; Applications, Cotonou, Benin\n“A journey inside matter: experimental particle physics with ATLAS and DUNE”\n2023 AIMS South Africa, Muizenberg, South Africa\n“How I became a scientist: the backstory not read on my CV”\n2020 McGill Physical Society Colloquium, Montréal, Canada (online)\n“Data analysis and detector development with ATLAS at the Large Hadron Collider” [YouTube]\n2019 York University, Dept. of Physics \u0026amp; Astronomy Colloquium, Toronto, Canada\n“Sorting needles in a haystack and cooling a sandwich: data analysis and detector development with ATLAS”\nPublications 2026 David C., Oepeng T., Prosper H., “PINN-Slicer: Exploiting Rotational Symmetry to Solve the Photon Orbit Equation Around a Black Hole”\nSubmitted to the ML4PS 2026 workshop\n2025 Reinsvold Hall A., Skidmore N., Benelli G., Carlson B., David C. et al., “Training and onboarding initiatives in high energy physics experiments”\nFront. Big Data 8 (2025) 1497622\n2025 Sustainable HECAP+ Initiative, “Environmental sustainability in basic research: a perspective from HECAP+”\nJINST 20 (2025) P03012; www.hecap.eco\n2022 DUNE Collaboration, “Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC”\nJINST 17 (2022) P01005\n2021 ATLAS Collaboration, “Search for exotic decays of the Higgs boson into long-lived particles in pp collisions at √s=13,TeV using displaced vertices in the ATLAS inner detector”\nJHEP 11 (2021) 229\n2021 DUNE Collaboration, “Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report”\nInstruments 5 (2021) 31\n2020 Poley L., Sawyer C. et al., “The ABC130 barrel module prototyping programme for the ATLAS strip tracker”\nJINST 15 (2020) P09004\n2020 DUNE Collaboration, “Long-baseline neutrino oscillation physics potential of the DUNE experiment”\nEur. Phys. J. C 80 (2020) 978\n2018 ATLAS Collaboration, “Observation of Higgs boson production in association with a top quark pair at the LHC with the ATLAS detector”\nPhys. Lett. B 784 (2018) 173–191\n2018 ATLAS Collaboration, “Search for the SM Higgs boson produced in association with top quarks and decaying into a bb̄ pair in pp collisions at √s=13,TeV with the ATLAS detector”\nPhys. Rev. D 97 (2018) 072016\n2018 Albertsson K. et al., “Machine Learning in High Energy Physics Community White Paper”\nJ. Phys.: Conf. Ser. 1085 (2018) 022008\n2017 ATLAS Collaboration, “Technical Design Report for the ATLAS Inner Tracker Strip Detector”\nCERN-LHCC-2017-005 ; ATLAS-TDR-025\n2015 ATLAS Collaboration, “Summary of the ATLAS experiment's sensitivity to supersymmetry after LHC Run,1 – interpreted in the phenomenological MSSM”\nJHEP 10 (2015) 134\n2015 ATLAS Collaboration, “Search for direct pair production of a chargino and a neutralino decaying to the 125,GeV Higgs boson in √s=8,TeV pp collisions with the ATLAS detector”\nEur. Phys. J. C 75 (2015) 208\n2013 ATLAS Collaboration, “Search for chargino and neutralino production in final states with one lepton, two b-jets consistent with a Higgs boson, and missing transverse momentum with the ATLAS detector in 20.3,fb-1 of √s=8,TeV pp collisions”\nATLAS-CONF-2013-093\n2011 Axen D. et al., “Diamond detector irradiation tests at TRIUMF”\nJINST 6 (2011) P05011\nProceedings 2025 David C., “What is AI, what is it not, how we use it in physics and how it impacts… you”\nBCVSPIN 2024: Particle Physics and Cosmology in the Himalayas, Kathmandu, Nepal\narXiv:2504.01827\n2024 David C., “Statistics on participation of emerging and developing countries in LHC and other experiments”\nPoS(LHCP2023) 450 (2024) 062\n2018 David C., “A new strips tracker for the upgraded ATLAS ITk detector”\nProceedings of PSD11\nJINST 13 (2018) C01003\n2016 David C., “Search for chargino and neutralino production with a Higgs boson in the decay chain in one or three leptons final state events with ATLAS”\nNucl. Part. Phys. Proc. 273–275 (2016) 2400–2402\n2014 David C., “New ATLAS results in SUSY searches for 3rd generation squarks and electroweak production”\nEPJ Web of Conferences 71 (2014) 00034\nOther ActivitiesPanels 2025 SA-CERN 15 Year Celebrations, iThemba LABS, South Africa\nInvited panelist: “Fundamental Science for South African Development”\n2023 Science \u0026amp; Technology Week, Kinshasa, Congo DRC\nInvited panelist: “Science and technology for sustainable development in Africa” (in French)\n2023 11th Large Hadron Collider Physics Conference (LHCP), Belgrade, Serbia\nInvited panelist: “Challenges for emerging countries to join/participate in LHC experiments”\n2020 30th International Linear Accelerator Conference (LINAC), Liverpool, UK (online)\nInvited panelist: “Women in Science and Engineering: Should I stay or should I go now?”\n2014 Screening of “Particle Fever”, Vancouver International Film Festival, Canada\nInvited panelist with movie producer \u0026amp; Prof. David Kaplan\n2011 Emily Carr University of Art and Design, Vancouver, Canada\nPanel speaker: “But ℏ? Particle Interactions in Physics and Arts: Artistic Transformation”\nScience Festivals \u0026amp; Tours 2023 Village of Sciences, École de la Gombé, Kinshasa, Congo DRC\nIntroduced CERN and ATLAS to 10,000+ high school students; trained selected learners as booth animators\n2017 DESY Open Day (DESYDAY), Hamburg, Germany\nOrganizer of ATLAS exhibition \u0026amp; infrared camera booth; 20,000+ visitors\n2015 – 2016 TRIUMF Tour Guide, Vancouver, Canada\nMonthly tours for high schools, graduate students and the general public\n2014 ATLAS Masterclass, University of Victoria, Canada\nOne-day hands-on for high-school students; invited talk: “Data analysis with ATLAS at the LHC”; organized by VISPA\n2013 TRIUMF Open House, Vancouver, Canada\nVolunteer at the ATLAS booth; 1,200+ visitors\nOnline \u0026amp; Media 2023 ATLAS Virtual Visit, ATLAS Control Room, CERN\nLycée Liziba, Kinshasa, Congo DRC; girls' secondary school [atlas.cern]\n2023 ATLAS Virtual Visit, ATLAS Control Room, CERN\nMalaika School, Lubumbashi, Congo DRC; free education for 400+ girls [atlas.cern]\n2021 Allan I. Carswell Observatory, York University, Canada\nTeleTube series: “The Triumphant Discovery of the Higgs Boson at the Large Hadron Collider” [YouTube]\n2020 “The Drift” series, Sanford Underground Research Facility, USA\n“What is so special about neutrinos?” on SURF's YouTube channel [YouTube]\n2020 Neutrino Day, SURF's Free Science Festival (online)\n“DUNE scientists answer your questions” [YouTube]\n2013 ATLAS Virtual Visit, ATLAS Control Room, CERN\nEurope Day Symposium, Vancouver; TELUS World of Science Celebration, Vancouver [video]\n","externalUrl":null,"permalink":"/cv/","section":"CV","summary":"","title":"CV","type":"cv"},{"content":" Credit: TRIUMF Role Intern Year 2010 Link JINST paper (2011) ↗ During the last year of my Master of Engineering, I did a five-month internship at TRIUMF, Canada’s national particle accelerator centre. The mission was to help instrument a test of how radiation-hard diamond sensors would be. Those sensors were studied for an ATLAS sub-system called the Forward Calorimeter, positioned at each end of the ATLAS detector, right around the beam pipe. These zones are subject to a high-radiation environment, primarily caused by the debris of the collisions. Some ATLAS readout systems thus need to work and withstand the harsh environment without being damaged. Diamonds are good for that, but the research needed measurements.\nThe testbench made use of accelerated protons from TRIUMF’s 18-metre-diameter cyclotron, the largest in the world in its category. A cyclotron is a type of circular accelerator made of two ‘D’-shaped halves, in which particles are accelerated along a spiral trajectory, from the centre to the edge.\nI used TestPoint, a data-acquisition software, to create the test user interface. Then I developed the analysis software using ROOT, a CERN software framework widely used in high-energy physics.\nThe tests ran after my departure from TRIUMF, but the team included me in what ended up being my first publication!\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/diamond/","section":"Research","summary":"","title":"Diamond detector irradiation tests at TRIUMF","type":"research"},{"content":" Photo: Reidar Hahn, Fermilab Role Co-convener, DUNE Computing Training \u0026amp; Documentation subgroup Years 2020 – 2022 Links Paper: \u0026#34;Training and Onboarding initiatives in High Energy Physics experiments\u0026#34; Frontiers in Big Data (2025) ↗ DUNE automated glossary: abc.dunescience.org ↗ Coffea Python toolkit: As part of the Computing Consortium, I co-convened the Training and Documentation subgroup. We redesigned the onboarding and co-organized three tutorial sessions to help newcomers grasp the DUNE analysis workflow. The tutorials focused mostly on LArSoft, the software framework for event generation, detector simulation and reconstruction used to analyze data from Liquid Argon Time Projection Chambers (LArTPCs). I also modernized the DUNE Computing wiki, and designed and deployed DUNE\u0026rsquo;s automated glossary.\nMy team led the first R\u0026amp;D on an interactive analysis facility for DUNE using columnar computing. This was done using the Coffea Python toolkit, a prototype package to supply high-energy collider physics (HEP) experiments with big-data technologies for physics analyses.\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/dune-computing/","section":"Research","summary":"","title":"DUNE Computing: new training, a glossary, and R\u0026D on columnar data analysis","type":"research"},{"content":" Role Co-lead Years 2025 – 2026 Status Paper submitted Team Prof. Harrison B. ProsperFlorida State University Tlotlo Openg2023–24 AI for Science AIMS alumnus Link Submitted to the Machine Learning for Physical Sciences (ML4PS) 2026 workshop. Physics-informed neural networks (PINNs) are a type of Machine Learning algorithm that include physics knowledge directly in their optimization process. In other words: the quantity to minimize, which measures how well the model fits the data, receives extra terms that enforce known constraints and established results. PINNs are mostly used to solve partial differential equations, which are the mathematical translation of how physical systems evolve in space and time.\nIn this project, we developed a method to compute the orbit of a photon, a particle of light, approaching a black hole. A black hole warps spacetime so strongly that its gravity bends even the path of a massless photon. Depending on its starting distance and angle, the photon is deflected away, or it spirals in and is swallowed. The one exception is a perfectly circular orbit, possible only at a special distance and angle. The orbit equation comes from Albert Einstein\u0026rsquo;s General Relativity.\nBut while classical solvers would need to be recomputed each time the initial conditions are new (distance to the black hole and incident angle), PINN-Slicer is trained once for all possible initial conditions, and can then give any orbit with an RMS deviation below 10−3.\nThe trick is to exploit a feature of the photon orbit differential equation: it is an autonomous differential equation, that is to say invariant under translations of the independent variable (azimuthal angle $\\phi$). In other words, the coordinate system can be rotated to a different starting azimuthal angle. Therefore, it is possible to train the PINN only over a thinner range of the angle $\\phi$ (a $\\phi$-slice) and deploy it recursively across the desired angular domain.\nTechnical details We solve the null geodesic equation in the Schwarzschild metric, which describes a non-rotating, uncharged, spherically symmetric black hole of Schwarzschild radius $r_s$. The initial conditions are the distance $r_0$ from the centre and the incident angle $\\delta_0$, converted into $u_0 = r_s/r_0$ and $v_0 = du/d\\phi$, where $\\phi$ is the azimuthal angle in spherical polar coordinates. Our method accurately reproduces a custom fourth-order Taylor-series solver, and it generalizes to any second-order autonomous ODE of the form $\\ddot{u} = G(u)$, where $G(u)$ does not depend explicitly on the independent variable $\\phi$. \u0026larr; Back ","externalUrl":null,"permalink":"/research/pinn/","section":"Research","summary":"","title":"Exploiting Rotational Symmetry to Solve the Photon Orbit Equation Around a Black Hole","type":"research"},{"content":" Role Supervisor Years Nov – Dec 2025 Status Exploratory, concluded Team Raktim MukherjeeResearch intern, AIMS South AfricaHeidelberg University Prof. Harrison ProsperCo-supervisor (remote)Florida State University A graph is a way to represent data: dots (nodes) connected by straight lines (edges). There is an entire branch of mathematics dedicated to them, called graph theory.\nHere we want to represent a particle physics collision as a graph, then squeeze this graph into a compacted version with fewer dimensions (called a latent space, storing the essential information) to then decode it back into a graph. We do this encoding-decoding because it forces the model to capture the most important patterns among a large collection of collisions. As physicists, one collision can\u0026rsquo;t tell us much about new particles or interactions, but with many collisions we can apply statistical tools to test theories or spot something interestingly unusual. The decoding part is the challenge: it is far from trivial and can become very computationally intensive if not implemented cleverly.\nIn November and December 2025, Raktim Mukherjee, then an undergraduate student at Heidelberg University, spent two months at AIMS South Africa working on optimizing a so-called autoencoder that takes a graph as input and outputs a graph. Using real data from the ATLAS experiment at CERN, he found an optimized architecture for the available dataset. At the end of his stay, he was invited to give a talk summarizing his optimization at a technical meeting of the GELATO group at CERN, which develops anomaly-detection algorithms for the ATLAS trigger system.\nProf. Harrison Prosper, invited co-supervisor on this project, later informed us that a Google Summer of Code team had worked on exactly this problem. Moreover, transformers are gaining momentum on the graph decoding problem. The time frame of the project did not allow us to investigate this direction further.\n\u0026ldquo;This short-term project highly impacted my research experience, and Dr. David just pushed me to perfection. I also got acquainted with Prof. Harrison Prosper, who was not only supportive during the project but also very inspiring.\u0026rdquo;\nRaktim Mukherjee\nRaktim is now a graduate student at the University of Heidelberg working within the ALICE Collaboration.\nTechnical details The goal is to map a graph to a graph with high fidelity through a latent space of fixed dimension, as fast as possible. Fully-connected graph models scale as $N^2$ in the number of particles $N$; in the context of fast event generation we target a method scaling as $N \\times n$, where $n$ is a fixed number of nearest neighbours, thus effectively linear in $N$.\nRaktim optimized the architecture using a grid search on a set of 2,000 graphs. He found the best results (highest AUC) for an autoencoder with 3 encoder and 3 decoder layers, with the middle layer having half the nodes of the first. Among the convolution types, SAGEConv avoids the over-smoothing effects observed with GCNConv and GINConv.\nThe open questions at the end of his stay were on finding the optimal $p_T$ threshold range: higher thresholds improve AUC but reduce statistics. It was found that the overlap removal changes the classifier\u0026rsquo;s emphasis on different features. It was still a debate whether it was necessary to implement it.\nOne important issue regarding the decoding of graphs is handling the permutation invariance: a graph is to be seen as a 3D object with nodes connected with edges. The order in which items are listed does not matter. But for a computer, there needs to be a list. So some arbitrary order. The self-attention mechanism, the key algorithm at the core of transformers, is naturally permutation-invariant.\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/gae/","section":"Research","summary":"","title":"Graph Autoencoders for Particle Collisions","type":"research"},{"content":" The effect of the Machine Learning course on AIMS Ghana’s cohort, April 2023. Credit: AIMS Ghana Role Course designer and instructor Years Created in 2022Revised in 2025 Audience/Level Graduate Prerequisites Undergraduate calculus \u0026amp; basic programming (Python) Format 30 hours of lectures5–10 hours of tutorialsShorter versions on request Links Course website ↗ Slides are internal, but if you are curious, feel free to contact me. This course provides the basic conceptual and technical knowledge to anyone starting out in machine learning. It begins with intuition: students first guess how the main algorithms work. Then the mathematics is thoroughly derived, step by step. Finally, the students implement the algorithms from scratch, i.e. writing the equations and the algorithmic logic in Python themselves.\nFrom linear and logistic regression to boosted decision trees and neural networks, the goal is to show there is no magic, but beautiful (and actually simple) mathematics at play, and to give students full control over how their code is written and works.\nThe students learn how to assess the performance of their models and optimize them. They learn how a ROC curve is built, what under- and overfitting mean, and what the bias/variance tradeoff is, again intuitively, then mathematically. Time permitting, unsupervised techniques are covered.\nFor courses delivered at AIMS, the students work on a STEP, i.e. a “Study Through Exploration and Presentation.” They pick a topic they want to understand in greater depth, build a small demo (with rules on AI use so as not to delegate the thinking process), and give a 10-minute presentation in the last lecture, solo or in small groups.\nAssessment combines multiple-choice quizzes, audits of the tutorials (randomly selected students are tested orally on the code they submitted), and a final exam: oral for cohorts under 40, written for larger ones.\nTestimonials # Feedback collected from students after the course and shared with their permission.\nIt was the best introductory ML course I have ever taken. […] Dr. Claire structured the course to make it very interactive, hands-on, and exploratory, down to the quality of the assessments and assignments. One thing that stood out to me was how much the assessments really test your honest understanding and intuition of the concepts taught, rather than just rote memorisation. Dr. Claire’s introduction to linear regression, decision trees, MLPs, deep neural networks, and the rigorous “pen-on-paper” session on formulating the derivatives per layer of backpropagation really drives home the point that understanding the basics gives a strong foundation for research.\nJoshua Oluwatimileyin Salako, AI for Science MSc student, AIMS South Africa, 2025–2026 Building machine learning models by hand gave us a much deeper understanding of the mathematical intuition behind the algorithms instead of simply relying on libraries and automation. Beyond the technical knowledge, the course encouraged curiosity, critical thinking, and confidence in exploring AI further. This course has strengthened my motivation to continue working in AI and Data Science […] If I’m working with Deep Learning neural network[s] now (PINNs), it’s because of my understanding of the course concepts and building [the latter] by hand.\nErnest Katembo Muhasa, MSc student, AIMS Cameroon, 2025–2026 It was a unique experience taking the introductory course in Machine Learning with you. I really appreciated your teaching style, which consistently focused on developing our intuition regarding each course concept; your enthusiasm was contagious. It was clear that your goal wasn’t just to teach the course, but above all to ensure that everyone got something out of it. I also appreciated the final assessment in the form of an interview, as it helped me reflect on what I’d learned with you over the three weeks.\nMamady Konaté, MSc student, AIMS Senegal, 2023–2025 What marked me most about Prof. Claire David’s course at AIMS Ghana was the strong connection between theory and practical application. […] Thanks to your course, I became more actively involved in applying Machine Learning to solve complex biological problems.\nAfola Kossi Mawouena Samuel, MSc student, AIMS Ghana, 2022–2023 [Claire] taught us the fundamentals through her enthusiastic explanations and her signature teaching stick, encouraging us to dive deep into the mathematics of learning theory and the algorithms. […] Our class was full of brilliant people coming from all around Africa, but being in an entirely new and different environment, everyone was stressed during the first few days. […] her lectures were amazing, not only because of the fantastic content but also because of how she helped us come together to form a strong community.\nToavina Leong Pock Tsy, AI for Science MSc student, AIMS South Africa, 2025–2026 Before coming to AIMS, I had no experience with Machine Learning, but your course gave me a very solid foundation. At first, I found ML quite challenging, even while the course was ongoing. However, during the review block, my perspective completely changed when I realized I could seamlessly apply the concepts and techniques from your course in other data science classes. […] What stood out most to me was how interactive your classes were. You were always open to questions and took the time to explain concepts clearly.\nIsrael Oluwamayomikun Alabi, MSc student, AIMS Cameroon, 2025–2026 I absolutely loved your in-person Intro to ML course! It was brilliantly clear and simplified, so everyone from different backgrounds could follow along easily. You were so passionate, dedicated, and masterful with every tiny detail. Your infectious energy made it truly exciting! The class ignited my passion for ML years ago, and I’m still applying those skills today.\nAnn Wangui, MSc student, AIMS South Africa, 2022–2023 A transformative three weeks. Coming from brief online courses, this was my first deep dive into actual ML theory. Working through backpropagation on paper before building neural networks from scratch bridged the gap between surface-level concepts and true understanding. A phenomenal curriculum capped off by a great hands-on project.\nHassan Suliman, AI for Science MSc student, AIMS South Africa, 2025–2026 Every class was a relaxing moment for us, not because the lesson was that easy but because Prof Claire was full of energy, full of good vibe […] the fact that she always uses the sentence “don’t forget to have fun” was a kind of reminder that, hey guys, this course is not here to stress you even if it can be difficult. […] I cannot forget the chocolate part: she was giving chocolate for every good suggestion you do, for every typo found in her notes […] in terms of teaching skill, Prof Claire deserves a big A for me.\nJordan De Kenang Tagni, MSc student, AIMS Cameroon, 2025–2026 What I appreciated most was [Prof. Claire’s] ability to combine kindness, discipline, and high academic standards. Her teaching encouraged us to think deeply instead of simply memorizing concepts. Beyond the course itself, she created a very positive atmosphere between students, encouraged communication and teamwork, and was always supportive and approachable. She contributed a lot to making the AIMS experience both academically strong and human.\nMahugnon Aime Loick Gohouede, AI for Science MSc student, AIMS South Africa, 2025–2026 You made difficult concepts easier to understand, and I appreciated how supportive and engaging your teaching was throughout the course. The course helped me identify my interests and improved my confidence in machine learning.\nOphelia Dankyi, AI for Science MSc student, AIMS South Africa, 2024–2025 Prof. Claire is exceptional, passionate, always available and present for us. She gave me confidence in my ability to learn in the field of AI and to understand the basics of ML and algorithms. Thanks to her explanations, I finally found it easy to grasp the complex concepts behind neural networks and to code from scratch. What impressed me was her organisation and motivation, which inspired everyone to follow along and learn with joy.\nJosué Romaric Edou, AI for Science MSc student, AIMS South Africa, 2025–2026 Your course was one of the most engaging and refreshing experiences I had at AIMS. I especially appreciated the way you made complex ideas approachable while still encouraging us to think deeply and independently. Your enthusiasm, humour, and openness created an atmosphere where learning felt both challenging and enjoyable.\nCharles Aveved, MSc student, AIMS Cameroon, 2025–2026 Your teaching helped me understand the fundamental principles of AI and Machine Learning […] I particularly appreciated the clear explanations and practical approach you used during the course. The knowledge I gained continues to support my academic and research journey in cybersecurity and AI.\nKokeb Gebremedhin Gebrehiwot, MSc student, AIMS Senegal, 2023–2025 The course “Introduction to Machine Learning and Artificial Intelligence” taught me almost everything I needed to know as a beginner in the field. Taking this course in the early days of my Masters program helped me to appreciate the subsequent courses. Your patience, eloquence, expertise and availability made the course an exceptional one.\nPriscilla Obo Otoo, MSc student, AIMS Cameroon, 2025–2026 \u0026larr; Back ","externalUrl":null,"permalink":"/teaching/intro-to-ml/","section":"Teaching","summary":"","title":"Introduction to Machine Learning","type":"research"},{"content":" Role Instructor Years 2019–2021 Audience/Level Second-year undergraduatesPhysics or Engineering majors Prerequisites First-year Physics Format Semester-long course80-minute lectures twice a week50-minute weekly tutorial In this introduction to the theories of relativity and quantum mechanics, the relativistic concepts of space, time and energy are explained and the quantum nature of radiation and matter is discussed.\nTopics # Special relativity Modern physics Before Einstein\nBasics of special relativity\nTwin paradox and Doppler effect\nRelativistic dynamics Experimental basics of quantum theory\nThe particle nature of radiation\nTowards a theory of the atom\nMatter waves Extract of lecture slides, chapter 1, Doppler derivationCredit: Claire David Extract of lecture slides, chapter 2, energy spectrumCredit: original figure from OpenStax, University Physics Volume 3 (CC BY 4.0) ↗, modified by Claire David Testimonials # Feedback collected from students after the course and shared with their permission.\nProf. David is genuinely one of the best physics professors I’ve ever had. She taught me quantum mechanics in my second year of university, and I still remember how she managed to make something so confusing feel so simple and easy to understand. I’m really grateful I had the chance to learn from her, and I wish more professors shared her enthusiasm.\nElham Rahimi, Math Teacher in Al-Bayan Bilingual School in Kuwait Professor Claire David truly was one of the best professors I had the pleasure of being taught by during my undergraduate degree at York University. She cared deeply about the performance and well-being of her students both in and beyond the classroom.\nDarren Singh, Associate Manager, Reporting and Analytics, Enterprise Risk Management Fittingly for a relativity course, one of your strong points was showing perspective, the \u0026#34;reference frames\u0026#34; of different physicists, so to speak. You walked us through how classical and modern physics tackled the same problems under different conditions, so the equations made sense in context.\nWhat stood out most, though, was how inspiring the course was. Puzzles like the twin paradox are the kind of questions that awaken curiosity, and that curiosity matters more than how easy or hard a course is, because it\u0026#39;s what drives students to find the answers to even the hardest problems.\nGiulio Gesuelli \u0026larr; Back ","externalUrl":null,"permalink":"/teaching/sr-qm/","section":"Teaching","summary":"","title":"Introduction to Special Relativity \u0026 Modern Physics","type":"research"},{"content":" Role Course designer and instructor Years Created in 2023 Audience/Level From fourth-year undergraduates Prerequisites Undergraduate calculus \u0026amp; basic programming (Python) Format One-day workshop3–4 hours of theory (morning)3–4 hours of coding (afternoon) Link Based on my full course: Introduction to Machine Learning ↗ Sections Basics \u0026amp; Neural Networks This day workshop is a shorter version of my 30-hour course Introduction to Machine Learning, but entirely focused on neural networks. Participants get the basics (linear and logistic regression), then a thorough walk through the mathematics of forward and backward propagation. After the blackboard derivations, the second part of the workshop is a hands-on session where participants enter all equations ‘by hand’ in a Python code. They go back home with a little neural network built from scratch on a Jupyter notebook, without using any fancy ML library.\nThe goal is to show that the foundations behind any neural network, from simple to the latest LLM transformer, come from the simple application of the chain rule, put at scale.\nWorkshop hosted in March 2023 by the University of Johannesburg Nuclear Research Centre and the Data Science Across Disciplines Research Group, both in the Faculty of Engineering and the Built Environment.Credit: Simon Connell A non-negligible feature of this workshop: it is way more fun and enriching if attendees span many ages and stages as well as disciplines and specialties. I had a blast pairing honours undergraduates with emeritus professors, both new to machine learning, for impromptu yet sparkling collaboration.\nIf you are interested in hosting this at your institution, please contact me.\n\u0026larr; Back ","externalUrl":null,"permalink":"/teaching/ltmbnn/","section":"Teaching","summary":"","title":"Learn The Math Behind Neural Networks!","type":"research"},{"content":" Role Co-LecturerCourse designed by Dr. Sam Meehan Year 2019 Audience/Level Graduate Prerequisites Undergraduate math Format 30 hours of lectures5–10 hours of tutorials From its birth in the heart of the Large Hadron Collider’s detectors to its propagation and — spoiler alert — death in readout material, the course follows the journey of a particle and introduces stochastic modelling techniques with numerous hands-on sessions in Python. Joy scattering at the end of the Life of a Particle course, AIMS Ghana, 2019.Credit: Claire David \u0026larr; Back ","externalUrl":null,"permalink":"/teaching/life-particle/","section":"Teaching","summary":"","title":"Life of a Particle: stochastic modelling in particle physics","type":"research"},{"content":" Role Analyser Years 2017 – 2019 Links ttH(bb) search paper (2018) ↗ ttH observation paper (2018) ↗ During my postdoctoral fellowship at DESY, I contributed to the discovery of a rare process: the Higgs boson production in association with a top-quark pair, or ‘ttH’ (quarks are constituents of protons and neutrons). The Higgs boson is a particle playing a key role in giving mass to the other elementary constituents of matter. The measurement gives access to the so-called ‘top Yukawa coupling,’ which is a coefficient linked with the interaction strength between the Higgs boson and the top quark. This is an important measurement in the theory of particle physics. Combined results of the ttH production cross section. More explanation below.Image: ATLAS Collaboration/CERN. The cross section relates to the rate of the process (how often it occurs). It is compared with the prediction of the Standard Model (SM), the theoretical framework of particle physics. A ratio of 1 (vertical red line) means that it is consistent with theoretical predictions. The horizontal spread is due to uncertainties (an interval of possible values as no detector is 100% precise). The analysis had different channels depending on the Higgs boson’s decay mode. I was involved in the one where the Higgs boson decays into a pair of bottom and antibottom quarks, the first line ‘ttH(bb)’ in the plot.\nLearn more\nThis analysis was refined by ATLAS collaborators later, and in 2024 a more precise result was published. Here is the ATLAS briefing ↗ (accessible for newcomers).\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/atlas-tthbb/","section":"Research","summary":"","title":"Observation of the Higgs boson's production mode 'ttH' in the bottom-quark decay channel","type":"research"},{"content":" Credit: Benjamin Eberhardt, IceCube/NSF Role Intern Year 2009 Link icecube.wisc.edu ↗ During the fourth year of my Master of Engineering, I was an exchange student at Humboldt-Universität zu Berlin and got an internship at DESY-Zeuthen, Germany\u0026rsquo;s national research centre for fundamental science. There I worked on the electronics of the IceCube Neutrino Observatory, whose focus is to record signals from high-energy neutrinos coming from outer space. The neutrino is an elementary particle very abundant in the universe, yet barely interacting with matter. Most of them go through Earth without being stopped! To get a chance of an interaction, the trick is to go big: the IceCube detector is an arrangement of 5,160 digital optical modules (DOMs) buried between 1,450 and 2,450 metres deep in the Antarctic ice, covering a volume of a cubic kilometre! Presentation of the IceCube Neutrino Observatory at the South Pole. Click to enlarge.Credit: IceCube My role was to measure the transfer function of the DOMs\u0026rsquo; analog signal output. In short, the transfer function is the shape of the DOM\u0026rsquo;s signal, its intensity over time, which the module\u0026rsquo;s electronics digitize and send to the surface. Getting that shape right matters because if the intensity goes over a given threshold, a trigger flags the signal as interesting for physics, and saves it. I used the ROOT toolkit to analyze data produced by spare modules at the DESY-Zeuthen laboratory. I summarized my results in an internal report that was uploaded to the IceCube intranet. The digital optical module (DOM) is a vital part of the neutrino telescope, collecting information about neutrino interactions. The DOMs were made by scientists in the U.S., Germany, and Sweden.Credit: IceCube Collaboration DOMs ready for testing before being shipped to the South Pole. I worked on spare modules at DESY-Zeuthen (not pictured).Credit: DESY In October 2026, IceCube was in the spotlight when the Nobel Prize in Physics was awarded to Francis Halzen, of the University of Wisconsin–Madison and principal investigator of the IceCube Neutrino Observatory, \u0026ldquo;for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophysical origin.\u0026rdquo;\nPress release on the Nobel Prize website ↗\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/desy-zeuthen/","section":"Research","summary":"","title":"Pulse-shape modelling for the IceCube Neutrino Observatory","type":"research"},{"content":" Machine Learning Exploring machine learning techniques to help fundamental physics, from theoretical models to experimental data. PINN-Slicer Exploiting symmetry, general relativity in a neural network to get any photon orbit around a black hole 2025 – 2026 Graph Autoencoders for Particle Collisions Compressing a particle collision into a graph, and decoding it back, to spot the unusual 2025 DUNE The Deep Underground Neutrino Experiment (DUNE) is a future leading-edge detector complex studying neutrinos, hosted at Fermilab near Chicago and at the Sanford Underground Research Facility in South Dakota. DUNE Canada Co-launching the Canadian DUNE effort 2019 – 2022 DUNE Computing Designing new training \u0026amp; onboarding, a glossary, and R\u0026amp;D on columnar data analysis 2020 – 2022 ATLAS ATLAS is one of the largest particle detectors ever built, recording proton-proton collisions at CERN\u0026#39;s Large Hadron Collider. Learn more about ATLAS and a summary of my years working on it here. ttH(bb) analysis Observation of the Higgs boson produced in association with a top-quark pair 2017 – 2019 Detector development, ATLAS upgrade Characterizing the ATLAS inner tracker (ITk) prototype with an infrared camera 2016 – 2019 SUSY analysis Search for supersymmetric particles with a Higgs boson in the decay chain 2011 – 2016 Diamond detector irradiation tests at TRIUMF Instrumenting a radiation hardness test using TRIUMF’s cyclotron proton beam 2010 IceCube My first particle physics experience started at DESY in Zeuthen, near Berlin, Germany. I was still an engineer-padawan, working on the analog front-end electronics of the detector modules for the IceCube Neutrino Observatory, located in Antarctica. Pulse-shape modelling for IceCube Measuring the transfer function of photomultiplier modules 2009 ","externalUrl":null,"permalink":"/research/","section":"Research","summary":"","title":"Research","type":"research"},{"content":" Role Analyser Years 2011 – 2016 Links Analysis paper (2015) ↗ pMSSM summary paper (2015) ↗ PhD Thesis (2016) ↗ My PhD work was about analysing data from the ATLAS Experiment at the Large Hadron Collider to search for supersymmetric particles.\nWhat is Supersymmetry? Or SUSY, for short? It is a theoretical framework yet to be confirmed experimentally. The mathematics of Supersymmetry is really elegant, and the role of supersymmetric particles would fix a lot of inconsistencies in the current theory of particle physics. So it was just about finding it in the data. There was lots of hope in the community, however no SUSY particles have been discovered so far. Instead of claiming a discovery, physicists proceed to an exclusion, which is still a scientific result. Experimentalists provide to theorists \u0026rsquo;limits\u0026rsquo; on either an energy range or a cross-section (linked to how often those particles would be produced) where we can say, with high confidence, that the SUSY particles do not existing.\nI worked on a specific scenario where supersymmetric particles (charginos and neutralinos for the technical names) are produced along with a Higgs boson, which in turn decays into a pair of bottom and antibottom quarks. As no excess was found, i.e. no additional data collisions over the background predictions were observed, we proceeded to an exclusion. I was part of the analysis team that set the first limits on this decay chain.\nFollowing the null results of SUSY searches during the Run 1 data taking campaign (2010–2012), I joined the effort on the summary work that explored the impact of 22 ATLAS analyses on more than 310,000 cosmologically viable SUSY models. Because the limits of each individual analysis are typically presented in the context of simplified SUSY models, the resulting numbers are more optimistic than realistic. The goal of the summary paper was to reinterpret (recalculate) the exclusion limits for many possible combinations of SUSY particle masses from a model called the pMSSM. At the time, the pMSSM was a widely used candidate model within the SUSY framework.\nI was responsible for the reinterpretation of the analysis from my PhD (referred to as the ℓh channel in the publication). The resulting publication, very well received in the theory community, was described as “the most comprehensive ATLAS summary to date”, as quoted in CERN Courier of November 2015.\nFeynman diagram for the direct pair production of supersymmetric particles (χ̃₁± and χ̃₂⁰) with a Higgs boson (h) produced and decaying into a pair of bottom and antibottom quarks (bb).Credit: ATLAS Collaboration This animation showcases the pMSSM models (dots) that survive, i.e. not excluded by the pMSSM reinterpretation. The horizontal axis is the mass of the lightest supersymmetric particle, which is stable. The vertical axis is the dark matter relic density, which is the amount of dark matter not annihilated and remaining in our Universe.Credit: ATLAS Collaboration Excluding is less exciting than claiming a discovery, but this is also how science progresses. An exclusion range helps theorists refine their models. So far, no SUSY signals have been observed. Particle physics still keeps both theorists and experimentalist in the game of unveiling the unknown. Hopefully the High-Luminosity LHC, operational around 2030, will shed light on how to expand our current understanding of the Universe.\nLearn more\nThe pMSSM summary paper featured in CERN Courier: Issue of November 2015 (p.9) ↗\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/atlas-susy/","section":"Research","summary":"","title":"Search for Supersymmetry with a Higgs boson in the decay chain","type":"research"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"Talks: coming soon.\n","externalUrl":null,"permalink":"/talks/","section":"Talks","summary":"","title":"Talks","type":"talks"},{"content":" Statistics \u0026amp; ML Introduction to Machine Learning From intuition to derivation and code implementation ‘by hand’ 2023–2026 Learn The Math Behind Neural Networks! It is not magical, it is mathematical 2023 Physics Introduction to Special Relativity \u0026amp; Modern Physics Time, space, radiation \u0026amp; energy revisited 2019–2021 Life of a Particle Stochastic modelling in particle physics 2019 ","externalUrl":null,"permalink":"/teaching/","section":"Teaching","summary":"","title":"Teaching","type":"research"},{"content":" Approved July 1, 1997(construction approval) Completed 2008 Dimensions 46 × 25 × 25 m Mass 7,000 tonnes Collaboration 5,500\u0026#43; scientists Official website atlas.cern Lying in an underground tunnel of 27 kilometre circumference under the French-Swiss border near Geneva, the Large Hadron Collider (LHC) accelerates particles close to the speed of light and lets them smash together at the centre of big detectors. The task for experimental particle physicists is to reconstruct the trajectories and identify the particles produced during these collisions, to then measure their properties for ultimately inferring on the presence of new processes in subatomic physics.\nATLAS (A Toroidal LHC ApparatuS) is an arrangement of concentric sub-detectors, each recording specific information from the particles produced in a collision. It is the largest volume particle detector ever constructed. This short video ↗ from the great movie Particle Fever ↗ gives an idea of what it feels like seeing ATLAS for the first time.\nAerial map of the Large Hadron Collider, a 27-kilometre circular accelerator located in a tunnel 100 metres underground near Geneva, across the French–Swiss border. Depicted are also the interaction points where the four major particle detectors are situated.Credit: CERN. The ATLAS detector assembled with its calorimeter (inner cylinder) that measures the energy of particles. Humans for scale.Credit: Maximilien Brice/CERN. The ATLAS detector under construction in 2005. The eight large magnets (toroids) are used to bend the path of charged particles, which helps identify them. Standing in the middle, for scale: Roger Ruber, who led the commissioning of another magnet from ATLAS, the solenoid (not pictured).Credit: CERN/Maximilien Brice I started my Ph.D. just before the big LHC\u0026rsquo;s success in 2012: the discovery of the Higgs boson, a long-sought particle responsible for giving massive particles their observed masses (the understanding of mass at the subatomic). I was relieved as my Ph.D. analysis required the presence of the Higgs boson! Moving on as a postdoc, I contributed to the observation of a rare process, also involving the Higgs boson: its production in association with a top quark pair (quarks are constituents of protons and neutrons). I also had fun playing with an infrared camera to characterise the thermal performance of the next generation of particle detectors; this was part of the ATLAS upgrade program, preparing for an enhanced collision phase called the High-Luminosity LHC Project.\nWhat fascinates me with collider physics:\nThe mere fact that we, as a species, are building the most gigantic and complex machines to discover the tiniest pieces of matter to ultimately answer a very simple question: what is everything made of?\nLearn more\nAbout the Large Hadron Collider (CERN\u0026rsquo;s website) and frequently asked questions ↗.\nDiscover ATLAS subsystems and take a virtual tour ↗\n\u0026larr; Back ","externalUrl":null,"permalink":"/research/atlas-experiment/","section":"Research","summary":"","title":"The ATLAS detector at the CERN Large Hadron Collider","type":"research"}]