Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.
Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .
Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.
David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.
Baharan Mirzasoleiman is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where she leads the BigML research group. Prior to joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. She received her Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. Her research focuses on addressing sustainability, reliability, and efficiency of machine learning, with particular emphasis on improving big data quality by developing theoretically rigorous methods to select the most beneficial data for efficient and robust learning. Her work spans several critical areas including data efficiency, robustness against label noise and data poisoning, and addressing spurious correlations in machine learning models. She has made significant contributions to understanding how neural networks exploit spurious features that correlate with certain categories during training but fail to generalize to minority groups. Professor Mirzasoleiman's research demonstrates how theoretically grounded approaches can lead to practical improvements in model robustness and efficiency across various applications including medical diagnosis and environmental sensing. Her work has resulted in the development of the SpuCo package, a Python library that provides modular implementations of state-of-the-art methods to address spurious correlations, along with controllable synthetic datasets like SpuCoMNIST and large-scale vision datasets like SpuCoAnimals. She has received numerous prestigious awards including the ETH medal for Outstanding Doctoral Thesis, being selected as a Rising Star in EECS by MIT, an NSF Career Award, a UCLA Hellman Fellows Award, and an Okawa Research Award. Her students have also received multiple fellowships and awards including Amazon Doctoral Student Fellowships and an OpenAI Superalignment Fast Grant. Professor Mirzasoleiman actively contributes to the academic community through invited talks at major conferences including ICML, ICLR, NeurIPS, and KDD, as well as co-organizing workshops on new frontiers in adversarial machine learning and sparsity in neural networks. She has developed educational resources including tutorials on Foundations of Data-efficient Learning presented at ICML 2024.
Suzanne S. Lee is an Associate Professor of Finance at the Scheller College of Business, Georgia Institute of Technology, where she has been a faculty member since 2005. She also serves as the Ph.D. Coordinator, playing a key role in graduate education and research training. Her educational background is highly quantitative and interdisciplinary: Ph.D., University of Chicago MBA, University of Chicago M.S. in Statistics, University of Chicago Dr. Lee's research lies at the intersection of asset pricing and financial econometrics, with a strong focus on jump processes, market microstructure, and derivative markets. She investigates how sudden price movements (jumps) impact asset returns, risk, and information flow in financial markets. Her work extends to cryptocurrency, currency markets, and carry trade strategies, combining theoretical rigor with empirical validation using high-frequency data. The analysis of her recent publications (2008–2024) reveals a consistent and influential research program centered on detecting and modeling jumps in financial time series. Her work spans equity, currency, and cryptocurrency markets, often employing advanced nonparametric and econometric techniques. A recurring theme is the role of jumps in pricing anomalies, risk measurement, and market efficiency, with increasing attention to digital assets in recent years. Dr. Lee is actively engaged in the academic community through editorial service: Associate Editor, Journal of Banking and Finance Associate Editor, Asia-Pacific Journal of Financial Studies She has presented her research at premier conferences such as the American Finance Association, European Finance Association, Econometric Society, and Society for Financial Econometrics. Her publications appear in the most prestigious journals in finance and econometrics, including the Journal of Finance , Review of Financial Studies , Journal of Financial Economics , and Journal of Econometrics . While specific grant details are not listed, her sustained publication record in top journals indicates significant research funding and academic impact. She advises Ph.D. students through her role as Ph.D. Coordinator, though individual advisees are not named in the text. Dr. Lee's work contributes to both theoretical and applied finance, improving our understanding of market dynamics, risk modeling, and asset pricing under extreme events. Her research has practical implications for risk management, trading strategies, and financial regulation.
Christian Wolf is an Assistant Professor at the Massachusetts Institute of Technology (MIT) Department of Economics and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). His work bridges macroeconomics, monetary economics, and econometric methodology. Research Interests: Wolf specializes in macroeconomics and monetary policy , with a focus on econometric techniques like structural vector autoregressions (VARs) and local projections . His research explores policy counterfactuals, heterogeneous-agent models, and fiscal-monetary interactions. Recent Publications: His work spans topics such as equivalence between fiscal and monetary tools in HANK models, robust identification in VARs, and the interplay between inequality and macroeconomic dynamics. Articles appear in journals like Econometrica , Journal of Political Economy , and American Economic Review . Awards: Faculty Research Fellow, NBER Contact: ckwolf@mit.edu | Office: E52-554, MIT
Junjian Qi serves as the Hohbach Endowed Associate Professor in the Department of Electrical Engineering and Computer Science at South Dakota State University's College of Engineering, holding this position since 2023. His academic journey includes prior appointments as Assistant Professor at Stevens Institute of Technology (2020-2023) and University of Central Florida (2017-2020), along with research roles at Argonne National Laboratory and University of Tennessee. His educational background includes: Ph.D. in electrical engineering from Tsinghua University, Beijing, China (2013) B.E. in electrical engineering from Shandong University, Jinan, China (2008) Dr. Qi's research centers on electric power systems resilience, with particular expertise in cascading failure mechanisms, microgrid control architectures, cyber-physical security vulnerabilities, and synchrophasor applications. His work integrates advanced data analytics and machine learning techniques to enhance grid stability against extreme weather events and cyber threats. Current investigations focus on developing distributed control strategies for inverter-dominated grids and modeling system interdependencies during failure propagation. Analysis of his 15 most recent publications (2021-2024) reveals a strong methodological shift toward data-driven approaches for power system challenges. Key trends include machine learning applications for cascading failure prediction, novel distributed control frameworks for AC/DC microgrids, and cybersecurity enhancements for inverter-based resources. His work consistently bridges theoretical models with real-world utility data, particularly evident in multiple Best Paper Award-winning publications analyzing actual outage sequences. Dr. Qi's scientific recognition includes: NSF CAREER Award (2020) Three consecutive Best Paper Awards at IEEE PES General Meetings (2022-2024) World's Top 2% Scientist designation in energy (2020-2023) IEEE PES Outstanding Working Group Award (2023) Multiple journal Best Paper Awards (IEEE Transactions on Power Systems, Journal of Modern Power Systems) He currently leads significant research initiatives including an NSF CAREER project ($500k) on cascading failure analysis and an NSF collaborative grant ($219k) for grid stability, alongside previous DOE funding ($1.8M) for cybersecurity of distributed energy resources. His service includes editorial roles for IEEE Transactions on Power Systems and IEEE Power Engineering Letters, plus leadership in IEEE PES technical committees focused on voltage control and smart grid security.
Henrik Boström is a Professor of Computer Science specializing in Data Science Systems at the Division of Software and Computer Systems, KTH Royal Institute of Technology. His research focuses on trustworthy machine learning , with emphasis on conformal prediction (for confidence-calibrated predictions) and explainable AI . He is the developer of Python packages crepes (conformal classifiers/regressors) and xrf (explainable random forests). His primary research domains include: Developing robust methods for uncertainty quantification in predictive models Creating interpretable machine learning frameworks Optimizing ensemble techniques for high-dimensional data Applying ML to healthcare informatics and industrial diagnostics Analysis of his recent publications reveals strong emphasis on: (1) advancing conformal prediction theory for trustworthy AI, (2) enhancing interpretability of complex models like random forests and GNNs, and (3) developing efficient algorithms for uncertainty-aware learning in domains including healthcare, graph data, and high-dimensional regression. He serves as examiner for multiple degree projects and teaches courses including Programming for Data Science (ID2214) and Research Methodology and Scientific Writing (II2202) . He leads development of open-source tools for conformal prediction and model interpretation.
Tamara Broderick is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, specializing in machine learning and statistics. Her research focuses on developing methods for uncertainty quantification in data analysis, Bayesian nonparametrics, and scalable inference algorithms. She leads a research group advising PhD students and postdocs in statistical machine learning. Her work spans Bayesian modeling, variational inference, spatial statistics, and applications in epidemiology and environmental science. Recent projects involve uncertainty-aware forecasting, robustness analysis of statistical methods, and efficient algorithms for high-dimensional inference. Broderick teaches Bayesian Modeling and Inference and contributes to MIT's statistics and data science initiatives.
Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)
John Cotter is a Full Professor of Finance and Chair in Quantitative Finance at University College Dublin's Smurfit School of Business. He holds a PhD from Queen's University Belfast and prior academic roles include Associate Professor (2006-2012) and Senior Lecturer (2004-2006). His research focuses on volatility modeling, risk management, and asset pricing with applications in equity, real estate, and derivative markets. Cotter directs the Centre for Financial Markets and the Financial Mathematics Computation Cluster (FMC2), a multi-university research initiative funded by Science Foundation Ireland. Education: BComm and MEconSc from University College Cork, PhD in Finance from Queen's University Belfast. Research interests span asset pricing, volatility modeling, risk management, and financial market integration. His work has been published in top journals like Journal of Banking and Finance and Journal of International Money and Finance . He has secured grants including the ADAPT Phase 2 project (2021-2026). Cotter advises the European Securities Markets Authority (ESMA) and has consulted for numerous organizations globally. Notable awards include the UCD Outstanding Educator Teaching Award and UCD School of Business Research Contribution Award. He serves as Associate Editor for three journals and has supervised numerous PhD students through FMC2.
Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Eli Ben-Michael is an Assistant Professor jointly appointed in the Heinz College of Information Systems and Public Policy and the Department of Statistics & Data Science at Carnegie Mellon University. He is affiliated with the CMU-NIST AI Measurement Science & Engineering Cooperative Research Center (AIMSEC), contributing to cutting-edge research at the intersection of statistics, policy analysis, and artificial intelligence. His educational background includes a PhD in Statistics from U.C. Berkeley and undergraduate studies at Columbia University where he earned a dual degree in Computer Science and Statistics. Prior to his current position, he completed a postdoctoral fellowship at Harvard University's Institute for Quantitative Social Science and Department of Statistics. Ben-Michael's research focuses on developing innovative statistical and computational methods for causal inference and policy evaluation, with particular emphasis on integrating machine learning techniques to address complex problems in public policy and social science. His work bridges theoretical statistics with practical applications in healthcare, criminal justice, education, and social policy. Current research directions include safe policy learning, sensitivity analysis for clustered data, and methodological innovations for the synthetic control method. His publication record shows a strong trajectory in top-tier journals including Journal of the American Statistical Association, Journal of the Royal Statistical Society, and Proceedings of ICML. Recent work demonstrates increasing focus on policy-relevant applications including abortion legislation impacts, pre-trial risk assessment, and healthcare disparities, while maintaining methodological rigor in causal inference frameworks. Ben-Michael has developed open-source software tools including augsynth and multical R packages, which implement his methodological contributions for synthetic controls and multilevel calibration weighting. These packages have been adopted by researchers in multiple disciplines for causal inference applications.
