Yanzhi Wang is a Professor in the Department of Electrical and Computer Engineering at Northeastern University , affiliated with the Institute for Experiential AI and the Institute for the Wireless Internet of Things . He holds a PhD from the University of Southern California (2014). His research focuses on real-time AI systems, deep neural network compression, neuromorphic computing, and non-von Neumann architectures. Notable projects include NSF-funded initiatives on age-inclusive urban design, superconducting computing (DISCoVER), and edge device optimization (PatDNN). He has received prestigious awards such as the Army Research Office Young Investigator Award and the Constantinos Mavroidis Translational Research Award. His work emphasizes algorithm-hardware co-design for energy efficiency, with grants from NSF, ARO, and industry partners like Google. Recent research trends reflect his focus on accelerating vision transformers, diffusion models, and large language models for edge computing. He has pioneered methods like AutoViT and Fastcar, addressing latency and resource constraints in mobile platforms. Collaborations span academia and industry, driving innovations in superconducting circuits and neuromorphic systems.
James Glass is a Senior Research Scientist at the Massachusetts Institute of Technology (MIT) and heads the Spoken Language Systems Group within MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also affiliated with the Harvard-MIT Division of Health Sciences and Technology. His research spans automatic speech recognition, multimodal learning, and spoken language understanding, with applications in healthcare and video analysis. Education: SM and PhD in Electrical Engineering and Computer Science from MIT His work focuses on paralinguistic speech analysis, health markers in speech, and the intersection of speech and natural language processing. Recent trends emphasize audio-visual alignment, recursive reasoning, and AI applications in cognitive disorder diagnosis. Scientific awards include IEEE Fellow, ISCA Fellow, and Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence. His group explores unsupervised learning, speaker verification, and social text analysis. James leads the Spoken Language Systems Group at CSAIL, collaborating with institutions like IBM and Harvard-MIT Division of Health Sciences and Technology. His research integrates vision-language models, neural audio codecs, and self-supervised frameworks.
Terrence Chapman is a Professor of Government at the University of Texas at Austin, affiliated with the Robert S. Strauss Center for International Security and Law, Center for European Studies, and Clements Center on History Strategy, and Statecraft. His research focuses on international organizations, conflict resolution, and political economy frameworks. Key research areas include: International institutional legitimacy Global regulatory regimes Public opinion in international affairs Conflict management mechanisms Formal modeling of diplomatic interactions Recent scholarly trends reveal expertise in climate governance negotiations, digital surveillance politics, and regulatory effectiveness. His work spans theoretical modeling and empirical analysis of international collaboration dynamics. Scientific recognition includes: 2011-2012 APSA Conflict Processes Section Book Award Teaching portfolio covers: Science Fiction and Politics International Organization and Law Research Methods in International Politics Previously served as associate/senior editor for International Studies Quarterly .
Arman Cohan is an Assistant Professor of Computer Science at Yale University, affiliated with the School of Engineering & Applied Science. His research focuses on the intersection of Machine Learning and Natural Language Processing (NLP), particularly in language modeling, representation learning, retrieval systems, and applications in specialized domains such as scientific text processing. He earned his Ph.D. in Computer Science from Georgetown University and has received notable awards, including the Dr. Harold N. Glassman Distinguished Doctoral Dissertation Award (2019) and the EMNLP 2017 Best Long Paper Award. His work emphasizes ethical AI, robustness of LLMs, and interdisciplinary applications in healthcare, science, and education. Cohan's research group, the Yale NLP Lab, develops advanced techniques for multi-document summarization, adversarial fact-checking, and LLM-driven tools for scientific discovery. Recent projects include frameworks like SciBERT, Longformer, and ChemAgent, which enhance domain-specific reasoning and safety in AI systems. His publications address challenges in table reasoning, uncertainty expression, and multimodal reasoning, with applications in medical decision-making and educational problem-solving. He collaborates on initiatives like the Roberts Innovation Fund to advance AI in healthcare and environmental technology.
Jeff B Murray serves as Professor and Department Chair in the Department of Marketing at the Sam M. Walton College of Business, University of Arkansas, teaching in the full-time MBA, Executive MBA, and Marketing Doctoral programs. His leadership extends to international doctoral seminars across Europe and Australia, reflecting his scholarly influence. His academic foundation includes a Ph.D. from Virginia Tech. His research program examines critical marketing, interpretive consumer research, and philosophy of science through frameworks like Consumer Culture Theory and Transformative Consumer Research. Murray's scholarly contributions reveal a trajectory from foundational critical theory toward contemporary applications in arts markets, sustainable fashion, and digital consumption. His work consistently bridges philosophical inquiry with empirical consumer phenomena, emphasizing emancipatory interests and societal transformation. Recent publications demonstrate growing interdisciplinary engagement with sociology, anthropology, and sustainability studies. Outstanding All-Around Professor Award (2002) Charles and Nadine Baum Faculty Teaching Award (2002) Editorial board service for leading journals including Journal of Consumer Research He mentors doctoral students now holding faculty positions globally, with recent workshops in Germany, France, and Scandinavia highlighting his international impact. His professional engagements span the American Marketing Association, Association for Consumer Research, and American Sociological Association, where he contributes to critical theory development through initiatives like Consumer Culture Theory. Murray maintains active scholarly networks through Transformative Consumer Research and regularly presents at international conferences, advancing critical perspectives on marketing's societal role.
James A. Evans is a Professor at the University of Chicago, where he serves as Director of the Knowledge Lab and Faculty Director of the Masters Program in Computational Social Science. He is also an External Professor at the Santa Fe Institute. His research bridges computational methods with social theory to analyze collective cognition, innovation, and knowledge production across science, technology, and broader societal domains. Director, Knowledge Lab Faculty Director, Masters Program in Computational Social Science External Professor, Santa Fe Institute Evans’s research explores how social and technical institutions shape discovery processes, utilizing machine learning, network modeling, and large-scale data analysis. His work spans fields like computational social science, sociology of science, and data science, focusing on team dynamics, peer review, and the global structure of scholarship. His recent publications examine team size effects on innovation, discursive influence in academia, and the interplay between tradition and novelty in research strategies. Articles trend toward interdisciplinary approaches combining social theory, computational methods, and science policy. Evans supports novel observatories for human understanding through crowdsourcing, sensor networks, and semantic modeling. He has received funding from the National Science Foundation, National Institutes of Health, and Air Force Office of Scientific Research, with findings featured in major media outlets like Nature , Science , and The New York Times .
Jiliang Tang is an MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), part of the College of Engineering. He holds a PhD from Arizona State University (2015) and previously worked as a research scientist at Yahoo Research. His research focuses on graph machine learning, trustworthy AI, and applications in education and biology. He has received numerous awards, including the 2022 AI's 10 to Watch, IAPR J.K. Aggarwal Award, and NSF CAREER Award. Education: PhD in Computer Science, Arizona State University, 2015 (Advisor: Huan Liu) Research Interests: Graph Neural Networks (GNNs) and Deep Learning on Graphs Trustworthy AI: Safety, Robustness, and Fairness AI+X Applications: Education Technology and Biological Data Analysis His work bridges theoretical advancements and practical applications, with contributions to graph representation learning, privacy in generative models, and educational AI systems. Awards & Recognition: Over 8 best paper awards (or runner-ups) Rock Star Award from Association of Chinese Scholars in Computing Extensive media coverage for innovations in AI education and biology Grants & Projects: NSF CAREER Award (2019) for research on signed networks Co-PI on a $1.7M grant for 5G research Leadership in projects like DSE Lab and Data Science initiatives Labs & Teams: Directs the Data Science and Engineering (DSE) Lab at MSU, focusing on advancing AI for real-world challenges. The lab collaborates with industry leaders and publishes widely in top conferences (e.g., KDD, SIGIR, ACL).
Professor Duc Duy (Louis) Nguyen is Professor in Finance at Durham University Business School. Previously, he was Associate Professor at King's College London and Assistant Professor at University of St Andrews. His research has been featured in Forbes, Harvard Business Review, and BBC. He holds a BSc in Computing from National University of Singapore, MSc in Accounting, Finance, and Management from University of Bristol, and PhD in Finance from University of Edinburgh. Research foci include: Climate risk integration in mortgage markets and corporate finance Impact of social policies (e.g., marriage equality) on credit access Corporate governance and misconduct prevention in banking Local information environments and fraud detection Cross-cultural dimensions of executive decision-making His publication portfolio demonstrates consistent examination of how institutional frameworks (regulation, culture, governance) shape financial behaviors and market outcomes, with recent emphasis on climate finance and social equity impacts. Awards include: David Hume Publication Prize (2015) Semi-finalist, FMA Europe Best Paper Award (2018) Finalist, FMA Asia/Pacific Best Paper Award (2019) Best Registered Report on "Politics and Corporate Power" Vietnam Symposium in Climate Transition Best Paper Award (2024) He currently supervises PhD candidates Bingzhi Zhang and Jing Wei. His externally funded research includes grants from British Academy/Leverhulme Trust and Carnegie UK Trust. He serves as Associate Editor for European Journal of Finance and British Accounting Review. As frequent speaker at central banks and policy forums (Federal Reserve Banks of New York & St. Louis), he translates research insights into regulatory practice and policy development.
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.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.
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
Dr. Jarno Hoekman is an Associate Professor in Innovation Studies at the Copernicus Institute of Sustainable Development, Utrecht University. His research focuses on the dynamics and governance of science in the context of innovation, regulation, and societal challenges, with particular emphasis on sustainability and health. He is affiliated with both the Copernicus Institute of Sustainable Development and the Utrecht collaborating Centre for Pharmaceutical Policy & Regulation in the Department of Pharmaceutical Sciences. Dr. Hoekman serves as the programme leader of the research master in Innovation Sciences and teaches academic integrity courses to Master students and PhD candidates. Dr. Hoekman's research is organized around three interconnected themes: Science in transition , examining how scientific practices and institutional arrangements are evolving through open science, interdisciplinary research collaborations, and alternative recognition systems; Regulatory science and innovation , investigating innovations in tools and standards for assessing risks and benefits of new technologies, including safety assessment methods and regulatory authorization standards; and Geography of science , exploring the role of location factors in scientific knowledge production and diffusion. Analysis of Dr. Hoekman's recent publications (2022-2024) reveals a strong interdisciplinary focus spanning pharmacology, toxicology, innovation studies, and science policy. Key research trends include examining conditional marketing authorization pathways, post-approval studies for conditionally authorized medicines, animal-free safety testing methodologies, and the societal impact of transdisciplinary research projects. His work frequently employs mixed-methods approaches and involves multi-stakeholder perspectives to address complex regulatory and innovation challenges. Dr. Hoekman actively contributes to policy-relevant research, including major European Commission studies on pharmaceutical legislation. His collaborative work extends across multiple institutions and involves interdisciplinary teams working on pharmaceutical policy, sustainable innovation, and science-society interactions. As a member of the Utrecht Young Academy and the UU Open Science Platform, he engages with broader academic community initiatives related to research integrity and open science practices.
Jie Bai is an Associate Professor of Public Policy at Harvard Kennedy School (HKS), focusing on firms and markets in developing economies. His research addresses barriers to firm growth, market frictions, and policy design for private sector development in regions like China, East Africa, and Southeast Asia. Methodologically, he combines randomized control trials, quasi-experiments, and structural modeling from industrial organization and international trade. He holds a Ph.D. in Economics from MIT (2016) and previously worked at Microsoft Research New England before joining HKS in 2017. His work emphasizes collaboration with governments and NGOs to evaluate industrial and trade policies. Key research themes include collective reputation in trade, environmental policy impacts, corruption dynamics, and child labor economics. His recent publications analyze China's dairy industry reputation, Vietnam's firm corruption patterns, and Zambia's product choice perceptions. He co-founded initiatives like the China Econ Lab and China and the Global Economy project to foster research on China's role in global economics. Teaching includes advanced microeconomic analysis and game theory. No grants or labs are explicitly listed in the provided texts.
John Gallemore is an Associate Professor of Accounting at the University of North Carolina Kenan-Flagler Business School, where he focuses on how corporate tax policy and enforcement shape financial reporting behaviors. His research, published in top journals like the Journal of Accounting & Economics and Contemporary Accounting Research , explores tax policy, regulatory impacts, and banking sector dynamics. He teaches courses such as Strategic Cost Analysis and Performance Evaluation in the MBA program and has been recognized for teaching excellence, including the Emory Williams Award and being named one of Poets & Quants’ 'Best 40 Under 40' professors. Gallemore holds a PhD, MBA, and BSBA from UNC Chapel Hill. His career includes roles at the University of Chicago Booth School of Business before joining UNC in 2021. Awards include the Deloitte Foundation Doctoral Fellowship and FASB Doctoral Consortium Fellowships. His research has been featured in media outlets like the Washington Post and Bloomberg. Key research interests include corporate taxation, regulatory spillovers, AI’s impact on white-collar work, and tax avoidance strategies. His recent articles analyze tax policy expectations, bank regulatory oversight effects, and anti-tax avoidance regulation’s role in market concentration.
