Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science and the Department of Electrical and Computer Engineering at the University of Maryland, College Park . He earned his Ph.D. in Computer Science from Dartmouth College and holds prior degrees from Shanghai Jiao Tong University. His research focuses on advancing the foundations and applications of artificial intelligence, particularly in machine learning, data mining, natural language processing, computer vision, and biomedical informatics . His work integrates large-scale optimization, fairness, and robustness in deep learning systems. Heng Huang’s recent publications demonstrate a strong trend in large language models, federated learning, model watermarking, continual learning, and medical image analysis . His work appears consistently in top venues like NeurIPS, ICML, CVPR, ICLR, and MICCAI, reflecting a broad impact across theoretical and applied AI. He actively mentors students and postdocs, seeking highly motivated researchers in machine learning and related domains. His work has significant implications for healthcare, privacy, and trustworthy AI.
Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .
Apostolos Fasianos is a Lecturer in Economics at Brunel University London, specializing in macroeconomic implications of household financial behavior. Prior roles include economist positions at the Hellenic Ministry of Finance (2017-2020) and Central Bank of Ireland (2016-2017) , with collaborative research spanning the Bank of England and Reserve Bank of New Zealand . PhD in Economics, University of Limerick MSc in Economic Development, University of Glasgow MPhil in Economics, University of Athens Research focuses on household finance , housing economics , monetary policy , and economic inequalities . Recent work explores AI-enabled technological shocks on UK labor markets via Bayesian VAR modeling and textual patent analysis. Publications span topics like wealth inequality , housing market asymmetries , and financialization trends . Selected publications highlight interdisciplinary approaches, merging macroeconomic theory with empirical analysis of crises (e.g., Covid-19 ), housing markets, and historical financial trends. Key methodologies include textual analysis , VAR modeling , and spatial econometrics . Active in policy analysis, Fasianos represented Greece in international forums such as the EPC - Ageing Working Group and OECD Working Party 1 . Current projects include a 2023-2024 BRIEF AWARDS grant on AI’s macroeconomic impacts.
Theo Damoulas is a Professor of Machine Learning at the University of Warwick with a joint appointment in the Department of Computer Science and Statistics. He is a Turing AI Fellow (2021-2026) through UK Research and Innovation, an ELLIS member, and a Visiting Professor at New York University's Center for Urban Science and Progress (CUSP). He founded and leads the Warwick Machine Learning Group and has directed major projects at The Alan Turing Institute including Project Odysseus and the London Air Quality project. Education includes: PhD in Probabilistic Multiple Kernel Learning (University of Glasgow, 2009) MSc in Informatics (Distinction, University of Edinburgh, 2004) MEng in Mechanical Engineering (1st Class, University of Manchester, 2003) His research focuses on probabilistic machine learning and Bayesian statistics, emphasizing the integration of structural priors, spatiotemporal dependencies, physical laws, and causal relationships. Key applications include Digital Twins, urban science, and computational sustainability. His work advances robust and scalable inference methodologies for complex real-world systems. Publications demonstrate strong emphasis on Bayesian methods, spatiotemporal modeling, and uncertainty quantification, with applications spanning battery modeling, urban mobility, federated learning, and causal inference. Recent work shows increased focus on physics-informed models, federated learning frameworks, and causal abstraction techniques. Major scientific awards: Turing AI Acceleration Fellowship (2021-2026) Best Paper Awards (Wilkes 2024, AISTATS 2022, IEEE ICMLA 2010) ACM SIGMOD Most Reproducible Paper (2017) Dissertation Award (Classification Society 2012) Teaching Excellence nominations (Warwick 2015-2017) He actively advises PhD students and secured significant grants including the £multi-million Turing AI Fellowship. Current doctoral researchers investigate federated learning, causal inference, and spatiotemporal modeling. He leads the Warwick Machine Learning Group, a cross-departmental team developing foundational ML methods for scientific and societal challenges.
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.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
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.
Wei Sun is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, where he also serves as the Director of the Siemens Digital Grid Lab. His research focuses on power system restoration, self-healing smart grids, cyber-physical security, and renewable energy integration. Dr. Sun received his Ph.D. from Iowa State University in 2011, and his M.S. and B.S. from Tianjin University in 2007 and 2004, respectively. Prior to joining UCF, he was an Assistant Professor at South Dakota State University (2013-2015), a power system engineer at Alstom Grid (2011-2012), a visiting scholar at the University of Hong Kong (2011), and an intern at California Independent System Operator (2010). His research interests include: Power System Restoration and Self-healing Smart Grid Resilient and Secure Critical Infrastructure Cyber-Physical Systems Renewable Energy and Microgrid Distributed Energy Resources Integration Dr. Sun's recent publications demonstrate strong focus on cyber-physical security in power systems, distributed energy resource integration, and resilient grid operations. His work shows increasing emphasis on AI and machine learning applications for grid security and resilience, particularly in the context of high renewable penetration. His notable scientific awards include: Microsoft Software Engineering Innovation Foundation Award (2014) Best Paper Award, 2019 IEEE PES ISGT Asia Mentor of the Year, UCF Graduate Student Association (2019) Dr. Sun has successfully secured multiple research grants totaling millions of dollars from agencies including the US Department of Energy, National Science Foundation, Florida Center for Cybersecurity, and Microsoft. He currently serves as PI or Co-PI on several major projects including "Secure and Resilient Operations Using Open-Source Distributed Systems Platform (OpenDSP)" funded by the Department of Energy. He leads the Siemens Digital Grid Laboratory at UCF, which is equipped with utility-grade software and hardware including Spectrum Power Microgrid Management System, Power System Simulator for Engineering, and Siemens Distribution Feeder Automation. The lab provides capabilities for both software modeling and hardware-in-the-loop testing of power systems.
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
Aad van der Vaart is a Professor of Stochastics at Leiden University's Mathematical Institute. He was awarded the prestigious NWO Spinoza Prize in 2015 for groundbreaking work in mathematical statistics, particularly Bayesian methods applied to medical imaging, genetic data, and complex models. His research bridges pure mathematical theory with applied domains like neuroscience and astronomy. Research Interests : Van der Vaart focuses on infinite-dimensional Bayesian statistics, nonparametric models, and statistical genetics. His work emphasizes rigorous mathematical analysis of prior distributions and their impact on data-driven conclusions. Applications include gene network modeling and PET scan image reconstruction. Key Contributions : Authored influential books on estimation theory; pioneered modern Bayesian approaches to high-dimensional data. His Spinoza Prize funds will support interdisciplinary research and hiring new talent in statistical methods. Awards : NWO Spinoza Prize (2015), recognized as a global leader in statistical theory. Future Directions : Expanding into astronomical data analysis and medical applications, leveraging Bayesian frameworks for big datasets.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
