Steven D. Levitt is a Professor at the University of Chicago 's Booth School of Business and director of the Becker Center on Chicago Price Theory . His work spans economics, criminology, education, and behavioral science, with a focus on empirical analysis of real-world issues. Education: BA from Harvard (1989), PhD from MIT (1994) Key Research Areas: Crime economics, educational incentives, behavioral economics, and market dynamics Scientific Awards: 2004 John Bates Clark Medal, Time Magazine's 100 Most Influential People (2006) Levitt's article portfolio includes groundbreaking studies on topics like early childhood education (CogX program), abortion's impact on crime , cheating detection algorithms , and behavioral economics in education . His work often challenges conventional wisdom through unconventional data analysis. Prior research collaborations with Roland Fryer , John List , and Chad Syverson have produced influential papers on racial disparities , real estate markets , and juvenile crime . His NBER working papers demonstrate consistent methodological rigor and innovation.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.
Paul-Christian Burkner is a researcher in the Department of Computer Science at Aalto University. His work focuses on Bayesian statistical methods, computational modeling, and probabilistic programming. He collaborates with Professor Aki Vehtari's research group and has published extensively on topics like model sensitivity, spatiotemporal analysis, and variable selection techniques. His research interests include: Bayesian inference and model comparison Computational statistics Probabilistic programming Machine learning algorithms Statistical modeling in social sciences Neuroimaging data analysis Recent publications demonstrate expertise in simulation-based calibration, spatiotemporal modeling, and Gaussian process approximations. Collaborations span psychology, neuroscience, and machine learning domains. Contact: ext-paul-christian.burkner@aalto.fi
Prof. Rama Cont is a Statutory Professor of Mathematics at the University of Oxford and a Professorial Fellow at St Hugh's College . He serves as Director of the Centre for Doctoral Training in Mathematics of Random Systems , Faculty Member of the Stochastic Analysis Group , and Senior Research Fellow at the Institute for New Economic Thinking . Additional roles include Director of the Oxford Martin Programme on Systemic Resilience , Principal Investigator at the Oxford Suzhou Centre for Advanced Research , and Editor-in-Chief of Mathematical Finance . His research interests span pathwise methods in stochastic analysis, rough analysis, functional Ito calculus, mathematical modeling in finance, systemic risk, and data-driven decision systems. Recent publications focus on causal transport, rough volatility, and deep residual networks, reflecting his interdisciplinary approach to mathematics and finance. Functional Ito calculus and pathwise integration Rough volatility and financial market dynamics Systemic risk in financial networks Deep learning applications to finance and stochastic processes He has received prestigious awards including the Louis Bachelier Prize , SIAM Fellowship, Royal Society APEX Award, and IMA Fellowship. His editorial roles and seminar leadership underscore his influence in mathematical finance and stochastic analysis.
Justin Sirignano is a Professor of Mathematics at the University of Oxford, affiliated with the Mathematical Institute. His research bridges Applied Mathematics, Machine Learning, and Financial Mathematics, developing novel mathematical frameworks and computational methods. Education: B.A. in Mathematics, Princeton University PhD in Mathematics, Stanford University Chapman Fellow, Imperial College London His research focuses on theoretical and applied aspects of machine learning, particularly in mean-field analysis of neural networks , deep learning for PDEs/SDEs , and scientific machine learning . He has pioneered methods for solving complex financial and scientific problems using data-driven approaches. His recent publications emphasize recurrent neural networks, reinforcement learning, and PDE closure models with applications in turbulence simulation and hypersonic flows. These works span numerical methods, optimization, and stochastic processes. Scientific Awards: 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize Grants & Collaborations: He has secured over $16.5 million in funding from agencies like ONR, NSF-EPSRC, and DoE. His PhD students hold positions at J.P. Morgan, Bank of America, and other institutions. Labs & Teams: He leads research groups in Machine Learning and Mathematical Finance at Oxford, collaborating with institutions like Notre Dame, Boston University, and UIUC.
Dr Estara Arrant is a Cambridge University Library Leverhulme Early Career Research Fellow and Trinity Hall Postdoctoral Research Associate. She merges Semitic linguistics with data science to study medieval Jewish and Islamic textual cultures. PhD (University of Cambridge, 2021): Computational analysis of Cairo Genizah Torah codices MPhil (University of Oxford, 2018): Linguistic structure of Qur’ānic creation narratives BA: History & Modern Hebrew Her groundbreaking work combines machine learning and bioinformatics algorithms to analyze textual evolution. Current projects include: Developing the first NLP model for medieval Judaeo-Arabic Creating digital tools for computerized stemmatology She actively teaches Python and R as an Cambridge Digital Humanities associate, and her research has been published by Open Book Publishers and Brill .
Kjell Jorner is an Assistant Professor of Digital Chemistry in the Institute for Chemical and Bioengineering at ETH Zurich's Department of Chemistry and Applied Biosciences. His research group focuses on integrating computational methods and machine learning to address challenges in chemical synthesis, materials design, and reaction prediction. Education: PhD from Uppsala University (Photochemistry of aromatic compounds) Postdoctoral studies at AstraZeneca UK (Reaction prediction using computational chemistry and ML) Postdoctoral studies at University of Toronto (Molecular design of catalysts and organic electronic materials) Research Interests: Professor Jorner's work bridges computational chemistry, machine learning, and experimental design. Key areas include: Development of quantum mechanics-machine learning hybrid approaches for reaction feasibility prediction Inverse molecular design of functional materials (e.g., singlet-fission systems) Computational catalyst optimization and high-throughput screening methods Digital tools for chemical education and cheminformatics Publication Trends (2023-2025): Recent articles demonstrate a strong focus on machine learning applications in chemistry, including reaction prediction algorithms, catalyst design frameworks, and automated molecular generation. A recurring theme is the development of computational tools to accelerate materials discovery and optimize chemical processes. Laboratory & Team: Leads the Digital Chemistry research group at ETH Zurich (HCI E 137) exploring computational approaches to chemical challenges.
Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.
Olaf Ronneberger is an associate professor at the Albert-Ludwigs-Universität Freiburg and works at Google DeepMind . His research focuses on deep learning architectures , AI applications to scientific problems , and protein structure prediction . He leads seminars on deep learning and 3D image analysis, emphasizing vision-language integration and generative models. His publications include foundational work on U-Net architectures for biomedical image segmentation, AlphaFold 3 for biomolecular interaction prediction, and Gemini models for multimodal AI systems. Key subfields span medical imaging , protein folding , and vision-language models . Co-developer of U-Net , a widely used biomedical image segmentation framework. Contributor to AlphaFold 3 for structural biology. Research on Gemini 1.5/2.5 models for multimodal reasoning.
Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.
Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.
Aleksandr (Sasha) Aravkin is Associate Professor in the Department of Applied Mathematics and Adjunct Associate Professor of Health Metrics Sciences, Mathematics, and Statistics at the University of Washington. He serves as Director of Mathematical Sciences at the Institute for Health Metrics and Evaluation (IHME), where he leads the Mathematical Sciences and Computational Algorithms team and contributes to strategic direction for applying mathematical sciences to analytic challenges. Dr. Aravkin earned his PhD in Mathematics (Optimization) and MS in Statistics from the University of Washington in 2010, following a BSc in Mathematics and Computer Science in 2004. His educational background forms the foundation for his interdisciplinary research approach. His research expertise spans large scale optimization, machine learning, data science, convex and variational analysis, algorithm design, robust statistics, inverse problems, and uncertainty quantification . These methodologies are applied across diverse domains including health metrics, computational medicine, tracking and navigation, seismic imaging, computational finance, and neuroscience. Dr. Aravkin has pioneered approaches for fusing physics-based and data-driven models, enabling innovative solutions to complex problems. Dr. Aravkin's publication record demonstrates a strong focus on health metrics and Global Burden of Disease studies, with recent work emphasizing meta-analytic approaches for risk-outcome relationships. His research spans epidemiological modeling, health effects of various exposures, and forecasting disease burden across populations. The publications reveal a consistent pattern of high-impact work in top journals including The Lancet and Nature Medicine, often addressing critical public health questions through rigorous statistical and mathematical frameworks. At IHME, Dr. Aravkin has led significant projects including the Burden of Proof Studies (2019-2024) which analyzed 153 risk-outcome pairs, and COVID-19 Modeling (2020-2023) where his team developed forecasting models and excess mortality estimation methods. He has also been instrumental in developing the Evidence Score model, requiring novel algorithmic approaches. His work bridges theoretical mathematical sciences with practical applications to improve global health policy and practice.
Roel Leus is a full professor at KU Leuven's Faculty of Economics and Business (FEB), part of the Operations Research and Statistics Research Group (ORSTAT). He holds roles such as Program Director for the Business Engineering programs and Chairman of the KU Leuven Advisory Committee for the Chinese Region. He earned his PhD in Applied Economics from KU Leuven in 2003, focusing on project planning under uncertainty. His research emphasizes operations research and management, particularly scheduling, project planning, and decision-making under uncertainty. Education: PhD in Applied Economics (KU Leuven, 2003); Master's in Business Engineering (Handelsingenieur, KU Leuven, 1998). He has held academic positions since 2003, including adjunct professorships at Beijing Jiaotong University. His administrative roles include heading ORSTAT research group (2012–2016) and program directorships. Research Interests: Sequencing and scheduling, project planning under uncertainty, discrete optimization, and practical quantitative decision support. He has supervised 12 graduated PhD students as primary supervisor and contributed to numerous publications in top journals like INFORMS Journal on Computing and European Journal of Operational Research. Teaching: Courses include 'Introduction to Operations Research,' 'Operations Research,' and 'Applications of Operations Research.' He coordinates master's theses in Data Science and Business Analytics, focusing on practical optimization problems. Grants and Projects: Acquired over €2 million in research funding from private companies, the National Bank of Belgium, and KU Leuven. His work spans satellite scheduling, supply chain management, and cross-docking logistics. Labs/Teams: Active in ORSTAT, collaborating on projects like drone-assisted delivery and robust scheduling algorithms. His research bridges theoretical advancements with real-world applications in logistics, manufacturing, and aerospace.
Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.
Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.
