Zongyi Li is a Research Fellow at Massachusetts Institute of Technology , hosted by Kaiming He. They are currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech (2019-2025), mentored by Anima Anandkumar and Andrew Stuart. Ph.D. candidate: Computing and Mathematical Sciences, Caltech (2019-2025) B.Sc. in Computer Science and Mathematics with a Jazz minor from Washington University in St. Louis (2015-2019) They focus on Neural Operators for learning solution operators in Partial Differential Equations (PDEs) , particularly in fluid mechanics and earth science . Their work models physical simulations with chaotic behaviors and complex geometries, showing applications in weather forecasting , carbon storage , and aerodynamics simulation . Publications emphasize resolution-invariant models , chaotic systems , and zero-shot super-resolution capabilities. Their research combines Fourier analysis , graph networks , and physics-informed loss functions to achieve state-of-the-art performance in PDE solving with up to 1000x speedup over traditional solvers. Fellowships: Kortschak Scholarship PIMCO Fellowship Amazon AI4Science Fellowship Nvidia Fellowship MIT Novo Nordisk AI Fellowship Code & Open-Source: Co-developer of the NeuralOperator library Implementations for Fourier Neural Operators , Graph Neural Operators , and Tensorized Neural Operators Media Recognition: Quanta Magazine MIT Tech Review NVIDIA Features Towards Data Science
Charles D. Sprenger is a Professor of Economics at the California Institute of Technology (Caltech), where he has served since 2020 and held the position of Executive Officer from 2022 to 2025. He is affiliated with Caltech's Division of the Humanities and Social Sciences (HSS) and holds key roles at the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). His external appointments include Board of Editors for the American Economic Review and Associate Editor roles for the Journal of the European Economic Association and Quantitative Economics . His educational background includes a B.A. from Stanford University (2002), an M.Sc. from University College London (2005), and a Ph.D. from the University of California, San Diego (2011). These credentials established his foundation in economic theory and experimental methodology. Sprenger is a leading behavioral and experimental economist specializing in intertemporal decision making and choices under uncertainty. His research designs innovative experiments across diverse contexts—from food deserts in the United States to polio vaccination drives in Pakistan—to test the validity of standard economic models. His work consistently reveals significant deviations from rational choice theory, particularly regarding time inconsistency, risk preferences, and reference-dependent behaviors. He has pioneered methods for measuring time preferences and testing cumulative prospect theory, with implications for public policy and behavioral interventions. Analysis of his 15 most recent publications (2015-2024) shows a cohesive research program centered on behavioral anomalies in decision making. His work bridges laboratory precision with real-world field applications, demonstrating how psychological factors like procrastination and loss aversion manifest in high-stakes environments. Key trends include the development of tailored incentive structures, validation of rank-dependent utility models, and exploration of dynamic inconsistency across domains including health, finance, and public policy. His notable recognition includes: Sloan Foundation Fellowship (2016-2018) Sprenger actively contributes to the academic community through editorial leadership and collaborative research. His work has been featured in Caltech news for projects like "Reducing Procrastination with Tailored Incentives" (2023) and the graduate summer program "Experimental Economics in Theory and Practice" (2023). Though specific advisees aren't listed, his teaching of advanced courses like Experimental Economics (SS 212 abc) indicates mentorship of graduate researchers. He secures significant research funding through fellowships and institutional support, enabling large-scale field experiments. As a core member of CTESS, Sprenger leads a multidisciplinary team conducting cutting-edge experimental economics research. His lab integrates theoretical modeling with empirical validation, focusing on how behavioral insights can improve policy design in areas like tax compliance, vaccination programs, and financial decision making. Current work emphasizes scalable interventions for procrastination and preference-based incentive customization.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.
Anand Bhojan is an Associate Professor (Educator Track) at the Department of Computer Science, School of Computing, National University of Singapore (NUS). He is a member of the Communication and Internet Research Lab and serves on the Graduate Studies Committee. Dr. Bhojan is also the founder of Anuflora Systems and Virtual and Augmented Reality Labs (www.varlabs.org), and serves as Associate Editor of Computers and Electrical Engineering Journal, Elsevier, and Vice President of International Researchers Club, Singapore. Dr. Bhojan earned his Ph.D. in Computer Science & Engineering from NUS in 2011, where his thesis was nominated for the Best PhD Thesis Award. He also holds a Professional Master's in Computer Applications from Bharathidasan University (1999), a Bachelor's degree in Computing with Gold Medal (University topper) from Bharathiar University (1994), and a Postgraduate Certificate in Teaching Higher Education from University of Sheffield, UK (2003). His research spans multiple domains including Distributed Computing and Wireless Networks (IoT, Security, Blockchain), Artificial Intelligence (Generative AI, FinTech), and Entertainment Computing with focus on Games, VR/AR/Metaverse technologies. Dr. Bhojan leads the Metaverse Foundry research group which focuses on content generation for games & XR simulations across multiple domains including entertainment, healthcare, and architecture, while also experimenting with innovative teaching methods for entertainment media technologies. Dr. Bhojan's recent research has pioneered hybrid rendering techniques that strategically combine ray tracing and rasterization to create more realistic video game graphics without compromising performance. His work addresses critical challenges in real-time rendering, particularly in depth of field and motion blur effects, with the goal of making Hollywood-quality graphics accessible on a wider range of hardware. Earlier work focused on energy efficiency in mobile gaming, including power management techniques and latency optimization for cloud gaming. Among his notable achievements: 2012 Nominated for Best PhD Thesis Award (Wang Gungwu Medal & Prize), NUS 2011 Dean's Graduate Research Achievement Award (PhD), SoC, NUS 2006 Best R&D Project award from TOTE Board, Singapore 1995 Gold medal for first Rank (out of 4000) in Computing, Bharathiar University Dr. Bhojan has served as Organizing Chair and Program Chair for multiple international conferences and has delivered keynote talks at IEEE/ACM International Conferences. He teaches courses including Computer Networks Practice, Game Development, Interaction Design for Virtual and Augmented Reality, and Game Development Project. He leads the Metaverse Foundry research group and the Virtual and Augmented Reality Labs, where undergraduate and graduate students have won multiple research and innovation awards. His research bridges entertainment, education, and emerging technologies with practical applications in making immersive experiences more accessible across different hardware capabilities while maintaining energy efficiency.
Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University, directing the Interactive Perception and Robot Learning Lab. Previously, she was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems (2012-2017). She holds a PhD from KTH Royal Institute of Technology (Stockholm) and degrees from Chalmers University and TU Dresden. Her research focuses on perception, learning, and real-time multi-modal methods for autonomous robotic manipulation and grasping, aiming to bridge principles of human sensorimotor coordination with robotic implementation. Education: PhD in Robotics (KTH), MSc in Art & Technology (Chalmers), Diploma in Computer Science (TU Dresden) Research interests include developing goal-directed, real-time robotic systems capable of meaningful feedback for execution and learning. Key areas are dexterous manipulation, imitation learning, and cross-embodiment policy transfer. Notable contributions include the TidyBot platform and work on force-aware surgical robotics. Awards include the 2019 IEEE ICRA Best Paper Award, 2019 IEEE RA Early Career Award, and 2020 RSS Early Career Award. Her lab explores intersections of robotics, ML, and computer vision. Advising: Actively mentoring students/postdocs in manipulation, perception, and learning. Grants and collaborations span NSF, Stanford AI Lab, and industry partnerships. Future work emphasizes robust real-world deployment and human-robot collaboration. Labs/Teams: Leads the Interactive Perception and Robot Learning Lab, contributing to Stanford’s AI ecosystem. Previously managed the MPI AMD group, fostering interdisciplinary research in autonomous systems.
John Duchi is an Associate Professor at Stanford University, holding positions in the Department of Statistics and the Department of Electrical Engineering (with a courtesy appointment in Computer Science). He is affiliated with the School of Engineering. His research focuses on statistical learning, optimization, information theory, and computation, with an emphasis on balancing computational efficiency, privacy, and robustness. Key interests include developing algorithms for large-scale optimization, privacy-preserving techniques, and tools for evaluating machine learning systems' validity. Education: BS and MS in Computer Science (Stanford University, 2007–2008), MA in Statistics (UC Berkeley, 2012), and PhD in EECS (UC Berkeley, 2014). Research Interests: His work addresses three core areas: (1) optimizing trade-offs between computational resources and statistical performance, (2) creating scalable optimization methods, and (3) quantifying confidence in machine learning systems. Recent publications highlight contributions to privacy in federated learning, robust statistical validation, and uncertainty quantification. Articles: His recent work explores topics like distribution-free M-estimation, private federated learning, and instance-optimal mechanisms for statistical estimation. These studies emphasize privacy, robustness, and scalable solutions for modern data challenges. Advising & Grants: While no advisees are listed, his research has been supported by grants focused on optimization, privacy, and statistical theory.
Prof. Bernt Schiele is a Max Planck Director at the Max Planck Institute for Informatics and holds a Professorship at Saarland University. His research focuses on understanding multimodal sensor data, with key areas in computer vision, 3D object recognition, and machine learning. He leads the Computer Vision and Machine Learning group, addressing challenges in sensor fusion, scene understanding, and human activity recognition. Schiele has held academic roles at TU Darmstadt, ETH Zurich, and MIT, and contributes to top journals like IEEE Transactions on PAMI and conferences like ECCV. His work emphasizes robust models, interpretability, and domain adaptation for real-world applications. Education: PhD (1997, Grenoble), MSc (1994 Karlsruhe/1993 Grenoble) Key Positions: MIT (1997-2000), ETH Zurich (1999-2004), TU Darmstadt (2004-2010) Research interests span 3D scene understanding, multimodal sensor processing, and machine learning techniques for large-scale data. His recent work advances robust object detection, explainable AI, and domain-invariant training methods. He also chairs major conferences like ECCV 2018 and co-chairs ICCV 2011. Publications highlight innovations in interpretable vision transformers, certified explanations, and test-time adaptation. Despite no listed awards, his contributions shape foundational areas of computer vision and multimodal AI.
Danai Koutra is an Associate Professor in Computer Science and Engineering at the University of Michigan, Ann Arbor, and an Amazon Scholar. Her research focuses on large-scale graph mining, graph neural networks, and interpretable machine learning methods for understanding complex networks. Key roles include leading the GEMS Lab and contributing to projects like DeltaCon (graph similarity) and VoG (graph summarization). She holds a PhD from Carnegie Mellon University and has authored over 80 publications in top venues like KDD, SDM, and NeurIPS. Educations: PhD in Computer Science, Carnegie Mellon University (2015) MS in Computer Science, Carnegie Mellon University (2015) Diploma in Electrical & Computer Engineering, National Technical University of Athens (2010) Research Interests: Her work spans graph mining, anomaly detection, knowledge graph completion, and applications in neuroscience, healthcare, and social networks. Recent projects include MAGNET (multi-agent graph networks) and GT2VEC (multimodal graph-text encoders). Grants & Awards: Recipient of the 2025 PECASE award, NSF CAREER Award (2019), and the 2016 ACM SIGKDD Dissertation Award. Active in organizing conferences like KDD and ECML/PKDD. Labs & Teams: Directs the GEMS Lab, collaborating on projects like FIDDLE (clinical data preprocessing) and SpecGreedy (dense subgraph detection). Engages in interdisciplinary efforts, including M-DICE (urban mobility analysis with Detroit).
Arno De Caigny is an Associate Professor at IÉSEG School of Management in France, specializing in Marketing Analytics. He holds a Ph.D. in Sales and Marketing from the University of Lille and Masters in Economics/Mathematics and Finance from Ghent University. His professional experience includes work as a Business Analyst at Deloitte. His primary research interests include customer churn prediction, AI applications in marketing, explainable AI for business, and life event-based marketing. He develops advanced machine learning models for customer behavior prediction and retention strategies. De Caigny's recent publications demonstrate strong focus on developing interpretable machine learning models for business applications, particularly in customer churn prediction and financial decision support. His work increasingly incorporates deep learning and natural language processing techniques.
Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.
Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.
Farhad Rachidi-Haeri is a Titular Professor and Head of the Electromagnetic Compatibility (EMC) Group at EPFL. His expertise spans EMC research, lightning electromagnetics, time reversal techniques, and fault location in power systems. He has led the EMC Group since the 1980s, with funding from the Swiss National Science Foundation, European Union, and private sector collaborations. His work involves international partnerships with institutions like the University of Toronto and KTH. Education: PhD in Electrical Engineering from EPFL (1991), M.S. from EPFL (1986). Roles: President of Swiss National Committee of URSI (2012–2020), Editor-in-Chief of IEEE Transactions on EMC (2013–2015), and member of the Academy of Sciences of Bologna Institute (2019). Research Focus: Lightning interaction with infrastructure, electromagnetic field modeling, time reversal applications for fault detection, and high-frequency transient analysis. His work bridges theoretical physics and engineering, addressing challenges in power systems, lightning protection, and aerospace. Awards: IEEE EMC Technical Achievement Award (2005), Berger Award (2016), and Distinguished Honorary Professor at Tsinghua University (2024). Over 400 peer-reviewed papers and 500 conference contributions reflect his prolific research output. Labs/Teams: Leads the EMC Laboratory at EPFL, focusing on experimental and numerical studies of electromagnetic phenomena. Collaborates with global networks on projects like Laser Lightning Control and structural lightning protection for wind turbines.
Moshe E. Ben-Akiva is the Edmund K Turner Professor at the Massachusetts Institute of Technology (MIT), affiliated with the School of Engineering and the Department of Civil and Environmental Engineering. He holds a B.S. from Technion-Israel Institute of Technology (1968), and M.S. and Ph.D. degrees in transportation systems from MIT (1971, 1973). His research focuses on transportation systems analysis, intelligent transportation systems, demand modeling, econometrics, and infrastructure management. He has been recognized with prestigious awards, including election to the National Academy of Engineering (2025) for contributions to transportation systems modeling and demand analysis. His work spans theoretical and applied domains, including agent-based microsimulation for freight logistics, tradable credit schemes for congestion management, and behavioral dimensions of transport decarbonization. Ben-Akiva collaborates with industry and policymakers to design sustainable mobility solutions. His notable publications include foundational texts on discrete choice analysis and stated preference elicitation. He advises on transportation policy, urban planning, and emerging mobility technologies such as automated vehicles and urban air mobility. Current research explores impacts of automated mobility-on-demand systems, real-time tolling strategies, and e-commerce delivery demand modeling. His team develops tools like SimMobility Freight, an agent-based urban freight simulator. He remains active in teaching, focusing on demand modeling and econometrics courses at MIT.
