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Martin RinardView profile

Professor

• Programming Languages
• Software Engineering
• Security & Cryptography
• +7 more

Martin Rinard is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Vertical AI Community of Research and the Systems Community of Research, focusing on advancing artificial intelligence, programming languages, security, and large-scale software systems. His work emphasizes approximate computing, program repair, and deployable machine learning through initiatives like the MIT Center for Deployable Machine Learning (CDML), which ensures robust AI systems for real-world applications. Rinard’s research spans programming languages, software engineering, and systems, with notable contributions to probabilistic programming, security protocols, and neural network verification. He has pioneered techniques for error detection, mitigation, and automated program synthesis. His recent award-winning work includes foundational advancements in program repair and approximate computing methodologies. Research Highlights: Program Repair, Approximate Computing, Probabilistic Programming, Cybersecurity, Machine Learning Safety, and AI-driven Systems. Leadership Roles: Director of MIT CDML, Leader of Vertical AI and Systems Communities at CSAIL. Rinard’s publications reflect a focus on bridging theory and practice, with contributions to compiler optimization, secure software design, and healthcare AI applications like cancer risk prediction models. His team actively develops tools for program analysis, including KumQuat for parallel Unix pipelines and AEDAM for adaptive error detection. Awards: 2025 SIGSOFT Outstanding Research Award for pioneering program repair and approximate computing. Labs/Teams: Core member of CSAIL, CDML, and the Computation Structures Group.

Elliott AshView profile

Assistant Professor

• Law and Economics
• Data Science
• Political Science
• +3 more

Elliott Ash is an Assistant Professor of Law, Economics, and Data Science at ETH Zurich's Center for Law & Economics. His research focuses on analyzing the workings of law and politics using econometrics, data science, and computational linguistics, with an emphasis on natural experiments to establish causal evidence. Prior to ETH Zurich, he held academic positions at the University of Warwick and Princeton University. He earned a Ph.D. in Economics and a J.D. from Columbia University. Education: Ph.D. in Economics, Columbia University J.D., Columbia University Research Interests: Elliott’s work bridges law, economics, and data science. He employs computational methods to study legal and political systems, emphasizing causal inference through natural experiments. His research explores how laws and policies function in practice, leveraging advanced analytics to uncover mechanisms beyond correlational patterns. Advising & Grants: No specific advising or grant information provided in the text. Labs/Teams: Affiliated with ETH Zurich’s Center for Law & Economics, focusing on interdisciplinary research at the intersection of law, economics, and data-driven methods.

Craig FoxView profile

Professor

• Decision Under Uncertainty
• Behavioral Economics
• Neuroeconomics
• +5 more

Craig Fox is the Harold Williams Chair and Professor of Management at the UCLA Anderson School of Management , with joint appointments in Psychology (UCLA College of Letters and Science) and Medicine (UCLA Geffen School) . He co-founded the Behavioral Science & Policy Association and serves as co-editor of its journal Behavioral Science & Policy , as well as co-director of UCLA's Interdisciplinary Research Group on Behavioral Decision Making . Education: Ph.D. and M.A. in Psychology, Stanford University (1990–1994) B.A. in Economics and Psychology, University of California, Berkeley (1989), Phi Beta Kappa, summa cum laude His research spans decision behavior under risk and uncertainty , integrating behavioral economics, social psychology, and cognitive neuroscience. Key areas include healthcare policy (reducing antibiotic overuse, opioid prescriptions), financial decision-making , and neuroimaging to understand risk preferences. Recent work focuses on choice architecture and partition dependence to improve decision-making systems. Analysis of his 15 most recent publications reveals expertise in machine learning (temporal modeling, explainable AI), medical decision-making (diabetes management, antibiotic stewardship), and cognitive neuroscience (brain imaging in risk assessment). Articles also address public policy (nudges for physicians), economics (market pricing biases), and organizational behavior (team scaling fallacies). Scientific Awards: Best Paper in Decision Analysis (2005, INFORMS) Co-founder, Behavioral Science & Policy Association Keynote speaker at UCLA Anderson TED Week As principal investigator at the Fox Uncertainty Lab , he leads research projects on risk perception , neuroeconomics , and behavioral interventions in healthcare and finance. His 2012–2024 publications demonstrate sustained innovation across health IT , machine learning , and decision science .

• Digital Manufacturing
• Supply Chain Management
• Artificial Intelligence
• +7 more

Professor Alexandra Brintrup is a faculty member at the Department of Engineering, University of Cambridge, specializing in Digital Manufacturing. She contributes to advancing supply chain management through cutting-edge artificial intelligence and data-driven methodologies. Her research spans multiple domains, including federated learning, graph neural networks, causal inference, and autonomous systems. She focuses on enhancing supply chain visibility, optimizing logistics through reinforcement learning, and developing trustworthy AI frameworks for industrial applications. The analysis of her recent publications (2022-2026) reveals expertise in privacy-preserved machine learning, synthetic data generation, neurosymbolic reasoning, and digital twin implementations. Her work addresses critical challenges in supply chain risk prediction, collaborative vehicle routing, and network resilience.

Jon KleinbergView profile

Professor

• Algorithms and Networks
• Societal Implications of Computing
• Machine Learning
• +7 more

Jon Kleinberg is the Tisch University Professor at Cornell University , with affiliations in the Department of Computer Science and Department of Information Science . His research explores the intersection of algorithms, networks, and societal systems, supported by grants from NSF, ONR, MacArthur Foundation, Packard Foundation, Simons Foundation, Sloan Foundation, and Vannevar Bush Faculty Fellowship. Education : Not explicitly detailed in the provided text. Research Interests : Algorithms, network theory, human-AI collaboration, computational social science, fairness in AI, and societal implications of large-scale information systems. Recent Publications : Focus on algorithmic fairness, human-AI delegation, generative model limitations, social network polarization, and technical aspects of decentralized decision-making. Key venues include NeurIPS , ICML , AAAI , EC , and FAccT . Scientific Recognition : NSF CAREER Award ONR Young Investigator Award MacArthur and Packard Fellowships Simons Investigator Vannevar Bush Faculty Fellowship Member of National Academy of Sciences, National Academy of Engineering, American Academy of Arts and Sciences, and American Philosophical Society Advising and Grants : Mentored numerous PhD students and postdocs, including Katherine Van Koevering, Katy Blumer, and Harini Suresh. Collaborated with institutions like Microsoft Research, Google, and Facebook on projects involving network analysis, algorithmic fairness, and social dynamics. Labs and Teams : Leads research groups focused on network theory, algorithmic fairness, and human-AI interaction, with collaborations across computational social science and machine learning communities.

• Machine Learning
• High-Dimensional Statistics
• Reinforcement Learning
• +4 more

Martin J. Wainwright is the Cecil H. Green Professor at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and the Department of Mathematics . He is also associated with the Statistics and Data Science Center , the Laboratory for Information and Decision Systems , and the Institute for Data, Systems and Society . His research bridges machine learning , high-dimensional statistics , and information theory , with a focus on theoretical guarantees for algorithms in reinforcement learning, optimization, and graphical models. Books : High-Dimensional Statistics: A Non-Asymptotic Viewpoint (2019, Cambridge University Press), Statistical Learning with Sparsity: The Lasso and Generalizations (2015, CRC Press). Research Themes : Statistical and computational trade-offs, robustness in adaptive learning, posterior contraction rates, and decentralized estimation. His recent work explores non-asymptotic analysis , stochastic approximation , and instance-dependent guarantees in reinforcement learning and optimization. Key contributions include minimax optimality in value estimation, variance-reduced Q-learning , and adaptive inference under elliptical constraints. Awards : IMS Medallion Lecturer , COPSS Presidents' Award , Loève Prize in Probability , Fellow of the Institute of Mathematical Statistics , NIPS Outstanding Paper Award .

Scott CunninghamView profile

Professor

• Economics
• Causal Inference
• Sex Work
• +7 more

Scott Cunningham is the Ben H. Williams Professor of Economics at Baylor University and a Visiting Professor in the Government department at Harvard University for the 2025–2026 academic year. His affiliations include the Texas Hunger Initiative (Research Fellow since 2017) and Harvard’s Government department. PhD in Economics, University of Georgia (2007) BA in Literature, University of Tennessee (1999) Research interests span causal inference, mental health in corrections, sex work economics, abortion policy, drug policy, violence, and AI applications in social sciences. His work bridges empirical methods with policy analysis, focusing on public health, criminal justice, and social policy outcomes. Recent publications include methodological guides (difference-in-differences, synthetic control) and policy analyses (methamphetamine control, ChatGPT forecasting, mental health assessments in jails). His 2021 book Causal Inference: The Mixtape provides accessible training in quantitative methods. He teaches: ECO 4349/5349 – Causal Inference ECO 1305 – Issues in Economics ECO 4343/5343 – History of Economic Thought At Harvard, he leads Gov50, a data-driven course satisfying Harvard’s Quantitative Reasoning with Data requirement. His pedagogical approach emphasizes practical coding (R), project milestones, and the PERMA model of well-being.

• Multi-Agent Systems
• Artificial Intelligence
• Agent-Based Simulation
• +5 more

Dr. Brian Logan is an Associate Professor in the Faculty of Science at Utrecht University, specializing in Computer Science and Intelligent Systems. His research focuses on Multi-Agent Systems, Artificial Intelligence, and formal verification of resource-bounded systems. Key research areas: Agent-Based Simulation, Reinforcement Learning, Temporal Logic, Norm Synthesis, Causal Modeling He has published extensively on intention progression, norm revision, and epidemic simulations, with recent work spanning 2025 to 2020. His articles explore topics like Probabilistic Strategy Logic, Reward Machines, and Behavioral Interventions in Pandemics. Email: b.s.logan@uu.nl , bslogan@uu.nl

David SeimView profile

Professor

• Labor Economics
• Public Economics
• Wealth Taxation
• +5 more

David Seim is a Professor in the Department of Economics at Stockholm University, with his workplace located in Room A660 at Universitetsvägen 10 A. His contact information includes email david.seim@su.se and phone number 08-16 35 39. Professor Seim's research focuses on labor economics and public economics, with particular expertise in Swedish economic policy. His work examines wealth taxation systems, employment protection legislation, income mobility patterns, and intergenerational transmission of economic beliefs and outcomes. He has conducted extensive research on how notice periods affect job transitions, the impacts of eliminating employment protection for older workers, and the relationship between inheritances and wealth inequality in Sweden. His recent publications demonstrate a consistent focus on empirical economic analysis of Swedish policy, with a blend of theoretical modeling and data-driven research. The articles reveal a strong emphasis on labor market dynamics, taxation policy, and the psychological factors influencing economic behavior and policy preferences. His work often bridges microeconomic theory with practical policy applications in the Swedish context. While specific scientific awards are not documented in the available information, Professor Seim's substantial publication record in high-impact economic research demonstrates significant scholarly contribution to the field. His work appears frequently in economic journals and policy reports, suggesting recognition within the academic community. As a professor at Stockholm University, Professor Seim likely supervises graduate students and contributes to the academic development of economics students, though specific details about his advisees are not available in the current documentation. His research agenda appears well-funded through standard academic channels that support economic research in Sweden.

Brian HanView profile

Assistant Professor

• Business Analytics
• Information Systems
• Operations Management
• +4 more

Brian Han is an Assistant Professor of Business Administration at the University of Illinois at Urbana-Champaign's Gies College of Business. He holds a Ph.D. in Data Sciences and Operations from the University of Southern California and a B.A. in Economics from Shanghai University of Finance and Economics. His research focuses on field experiments, econometrics, and data-driven optimization to explore retailing innovation and human-AI interaction. Key application areas include online-offline integration in retail/logistics and AI-human collaboration. Research Highlights: Experimental studies on last-mile delivery commercialization and autonomous delivery systems. Analysis of AI-enabled telemarketing and smart vending supervision mechanisms. Evaluation of pandemic impacts on e-commerce operations and post-pandemic supply chain strategies. Awards & Recognition: Third Place, CSAMSE Practice Award (2024) UIUC Excellence in Teaching Awards (2021, 2023, 2024) Second Place, INFORMS TIMES Best Dissertation Award (2021) WISE Best Student Paper Award (2019) Teaching: Teaches Operations Management (BADM 275) and Field Experiments & Causal Inference (BADM 569), receiving consistently high student evaluations.

• Operations Management
• Healthcare Analytics
• Pharmaceutical Industry
• +4 more

Ujjal Mukherjee is an Associate Professor of Business Administration at the Gies College of Business, University of Illinois Urbana-Champaign. His research spans interdisciplinary domains, with a focus on operational strategies in healthcare systems, pharmaceutical innovations, and data-driven decision-making in complex environments. He holds a leadership role in advancing methodologies for predictive analytics and computational drug discovery, with notable contributions to understanding drug resistance mechanisms in oncology. Dr. Mukherjee’s work integrates quantitative approaches from operations research, epidemiology, and bioinformatics. His recent studies address critical challenges such as optimizing testing strategies for pandemic control, modeling product recall decision-making in supply chains, and developing algorithms to identify novel drug combinations for treatment-resistant cancers. He collaborates extensively with medical institutions and public health agencies, contributing to real-world solutions for healthcare delivery and crisis management. Research Themes: Drug Resistance Mechanisms, Epidemic Modeling, Predictive Analytics, Operations in Healthcare, Computational Biology Affiliations: Gies College of Business, University of Illinois Contact: 306 Wohlers Hall, Champaign, IL | ukm@illinois.edu His publications demonstrate a commitment to translational research, bridging theoretical frameworks with practical applications in both business and life sciences domains. Current projects include optimizing school reopening strategies during pandemics and enhancing precision medicine through single-cell omics integration.

• Uncertainty Propagation
• Inverse Problems
• Optimal Learning
• +3 more

Dr. Ilias Bilionis is a Professor of Mechanical Engineering at Purdue University's West Lafayette campus, leading the Predictive Science Lab. His research focuses on uncertainty quantification, inverse problems, optimal learning, and materials design, with applications in computational engineering, human-machine interaction, and high-performance buildings. He holds a strong record of awards, including the 2008-2009 Olin Fellowship and multiple academic excellence awards from his early career. His work integrates machine learning, Bayesian methods, and physics-informed models to address complex engineering challenges. Notable areas include 4D flow MRI analysis, extraterrestrial habitat systems, and energy-efficient building technologies. He has contributed to advancing methodologies in stochastic processes, optimal transport theory, and computational fluid dynamics. Awards: Extensive recognition for academic excellence and research innovation, including awards from Cornell University and the National Technical University of Athens. Labs/Teams: Heads the Predictive Science Lab, focusing on data-driven modeling and uncertainty quantification. Grants/Projects: Leads initiatives in smart energy systems, space habitat resilience, and advanced manufacturing.

• Signal Processing
• Image Processing
• Computer Vision
• +4 more

Dr. Tania Stathaki is an Associate Professor (Reader) in Signal Processing at the Department of Electrical and Electronic Engineering, Imperial College London, within the Faculty of Engineering. She holds a PhD in Signal Processing from Imperial College London (1994) and a Master’s in Electronics and Classical Piano Performance. Her research focuses on Signal/Image Processing, Computer Vision, and AI applications in medical imaging, remote sensing, and autonomous systems. She has held academic roles at Imperial College, Brunel University, and the University of Pireus. Education: PhD in Signal Processing, Imperial College London (1994) MEng in Electronics and Computer Engineering, National Technical University of Athens (1991) Advanced Diploma in Classical Piano Performance, Orfeion Athens College of Music (1991) Research Interests: Development of AI-driven methods for few-shot learning, invariant representations, and medical imaging analysis. Applications in pedestrian detection, forest fire risk assessment, and archaeological artifact grouping. Signal processing innovations for EEG-based hazard perception and crack detection in infrastructure. Recent Work Trends: Her 2024–2025 publications emphasize few-shot learning, synthetic data augmentation, and causal knowledge distillation, reflecting a focus on robust, efficient, and domain-adaptive AI systems. Key contributions include novel frameworks for medical image segmentation and 3D archaeological reconstruction. Grants & Labs: Her research leverages grants for projects in autonomous vehicle safety, forest surveillance, and smart concrete quality control. She collaborates with Imperial’s Communications and Signal Processing Research Group and leads teams on multiscale deformable transformers and LiDAR-based tree species classification.

Francis DitragliaView profile

Associate Professor

• Econometrics
• Causal Inference
• Public Policy Analysis
• +5 more

Francis Ditraglia is an Associate Professor of Economics at the University of Oxford and a Tutor/Fellow in Economics at Lady Margaret Hall. He also serves as a visiting researcher at the Federal Reserve Bank of Chicago. His research focuses on applying econometric methods to address economic and public policy questions, with expertise in causal inference, instrumental variables, measurement error, and Bayesian inference. His applied work includes projects on lead poisoning prediction via machine learning, pawnbroking experiments in Mexico City, and paramilitary violence modeling in Colombia. His methodological contributions emphasize improving estimation techniques through innovative frameworks like focused information criteria and disciplined identification beliefs in linear models. Notably, he received the Public Policy Challenge Fund Support Award for impactful policy-related research. He co-founded SQARE.org and maintains a blog (econometrics.blog) discussing econometric methods, statistics, and R programming. Frank's work bridges theory and practice, combining rigorous statistical innovation with real-world applications in public health, development economics, and historical institutional analysis. His recent projects highlight cross-disciplinary approaches, integrating machine learning with traditional econometric techniques to solve complex societal challenges.

• Computer Vision
• Image Processing
• Pattern Recognition
• +5 more

Prof Qiang Wu is a Professor and Deputy Head of School (Research) at the School of Electrical and Data Engineering, University of Technology Sydney. He is a core member of the Global Big Data Technologies Centre. His research focuses on computer vision, image processing, pattern recognition, and machine learning, with applications in video surveillance, biometrics, and human-computer interaction. He holds a B.Eng. and M.Eng. from Harbin Institute of Technology and a Ph.D. from UTS. Prof Wu has led industry collaborations with organizations like Meat & Livestock Australia, Canon, and Westpac Bank. He has authored over 300 publications, including works on pedestrian detection, virtual clothing fitting, and gait recognition patented in the U.S. He serves as an Associate Editor for IEEE Transactions on Multimedia and is a Senior IEEE Member. His research has been funded by grants from bodies like the Australian Egg Corporation and the Australian Research Council. He advises on PhD and Master’s students in computer vision and related areas.