Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)
Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.
Qi Long is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Biostatistics, Epidemiology and Informatics (Perelman School of Medicine), Department of Computer and Information Science (School of Engineering and Applied Science), and Department of Statistics and Data Science (The Wharton School). He serves as Founding Director of the Center for Cancer Data Science, Associate Director of the Penn Institute for Biomedical Informatics, and Associate Director for Quantitative Data Science at the Abramson Cancer Center. His research bridges statistical and machine learning (ML/AI) method development with biomedical applications, focusing on precision medicine and population health. Education : Ph.D. (2005) and M.S. (2003) in Biostatistics from University of Michigan; B.S. (1998) in Computer Science from University of Science and Technology of China. Research interests include: Robust statistical and ML/AI methods for big health data (-omics, EHRs, imaging, mHealth) Multimodal data integration and subgroup heterogeneity analysis Missing data, causal inference, Bayesian methods, and clinical trials Data privacy, algorithmic fairness, and responsible AI in healthcare Foundation models and agentic AI for biomedicine His publications focus on privacy-preserving AI, fairness-aware ML, and integrative models for multi-omics and EHRs. Recent work explores LLMs and watermark detection in hybrid human-AI settings. Scientific Awards : Elected fellow: AAAS, ASA, IMS, ISI, AMIA He leads large NIH- and ARPA-H-funded initiatives, directing statistical coordinating centers for national clinical trials. His lab trains numerous PhD/Master’s students and postdocs, many of whom hold prestigious academic or industry positions.
Anjalie Field is an Assistant Professor in the Computer Science Department at the Whiting School of Engineering, Johns Hopkins University. She is also affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). Dr. Field completed her PhD at the Language Technologies Institute at Carnegie Mellon University under Yulia Tsvetkov, where she was a member of TsvetShop. Prior to joining Johns Hopkins, she was a postdoctoral researcher in the Stanford NLP Group and at the Stanford Data Science Institute, working with Dan Jurafsky and Jennifer Eberhardt. She also spent time as a visiting student at the University of Washington from 2021 to 2022. Her research focuses on the ethics and social science aspects of natural language processing, developing computational models to address societal issues like discrimination and propaganda while critically assessing and improving privacy, transparency, and fairness in AI pipelines. Her work spans social media analysis, bias detection in multilingual content, police accountability systems, and ethical considerations in large language model applications. Dr. Field's publications demonstrate a consistent focus on identifying and addressing biases in language technologies, with particular attention to racial, gender, and cultural dimensions. Her recent work has expanded into domain-specific applications of NLP in astronomy, healthcare, and social justice contexts, showing the breadth of impact that ethical AI considerations can have across disciplines. Among her notable recognitions are: AI2050 Fellow by Schmidt Sciences 2022 Wikimedia Foundation Research Award of the Year Best Paper nomination at Socinfo (2020) Dr. Field teaches AI Ethics and Social Impact (Fall 2023; Fall 2024) and NLP for Computational Social Science (Spring 2024; Spring 2025). She plans to take PhD students for the 2024-2025 admissions cycle. Her work bridges technical NLP research with important social considerations, making significant contributions to both the technical community and broader societal discourse around AI ethics. She is an active member of the Center for Language and Speech Processing, where she collaborates with researchers working at the intersection of language technologies and real-world applications. Her research focuses on developing methods that not only advance NLP capabilities but also ensure these technologies serve diverse communities equitably.
Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.
Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships
Wang Yingji is a tenured professor and doctoral supervisor at the School of Art and Media, Tongji University. He holds a PhD in Literary Studies and has served as director of the China Culture and Art Communication Research Center, member of the China Artists Association, and editorial board member of "Media Criticism" journal. BA: Chinese Language and Literature, Renmin University of China (1995) MA: Journalism, Beijing Normal University (2004) PhD: Literary Studies, Beijing Normal University (2007) His research focuses on the intersections between Journalism and Communication, Media Phenomenology, Philosophy of Technology, and Traditional Chinese Culture. His work explores embodied cognition in media interaction, technological mediation of human perception, and historical epistemology of media systems. The articles demonstrate his expertise in media phenomenology (Hubert Dreyfus analysis), digital reading behavior studies, VR technology ideologies, and historical reconstruction of Chinese communication theories. His publications span from 2016-2022 with consistent CSSCI indexing. National Social Science Fund General Project: "Chinese Modern Public Opinion Thought History" (2021) Major Project Subtopic: "Chinese Media Archaeology in Civilizational Diversity" (2020) Ministry of Education Project: "Husserl's Media and Communication Thought" (2020) Completed NSFC Project: "Rumor Propagation and Governance in Public Emergencies" (2011) As educator, he teaches courses like "Chinese Classical Media Art", "Media Research Methods", and "Communication Theory". His administrative roles include leadership positions in the Chinese Society of Journalism History and multiple journal editorial boards.
Professor Li Hui serves as the executive dean of the School of Network and Information Security at Xidian University, where he holds the position of second-level professor and doctoral supervisor. He is nationally recognized as a distinguished teacher and serves in multiple prestigious roles including member of the National Steering Committee for Postgraduate Education in Cryptography, inaugural president of ACM SIGSAC CHINA, and director of several major academic societies related to cryptography and information security. Professor Li's research spans cryptographic information security, privacy computing, information theory, and coding theory, with significant contributions to network and cyberspace security. His work demonstrates a strong focus on both theoretical foundations and practical applications, particularly in developing security protocols for emerging technologies like blockchain, federated learning systems, and IoT environments. His research output shows consistent innovation in balancing security requirements with computational efficiency across diverse application domains. With over 300 publications and more than 15,000 Google Scholar citations (H-index 60), Professor Li's scholarly impact is substantial. His recent publications demonstrate increasing emphasis on privacy-preserving machine learning, secure multi-party computation, and cryptographic protocols for distributed systems, reflecting the evolving security challenges in the AI era. Three second-class national teaching achievement awards Special prize and first-class national teaching achievement awards Four first-class provincial and ministerial science and technology progress awards Privacy Computing Theory award (Qian Weichang Chinese Information Processing Science and Technology Award) Multiple patents with over 80 granted inventions Professor Li leads the Cyber Changan Team and serves as head of the Shaanxi Provincial Innovation Team for Mobile Internet Security. He has successfully supervised numerous doctoral and master's students who have gone on to win prestigious competitions like the National College Student Information Security Competition. His research is supported by major national grants including a National Key R&D Program project and key projects from the National Natural Science Foundation of China.
Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.
Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE/CREST, École Nationale de la Statistique et de l'Administration Économique since September 2020. Previously, he was a postdoctoral researcher at the Laboratory for Computational and Statistical Learning at the University of Genoa (2019-2020). He completed his PhD in Statistics at École Polytechnique under the supervision of Stéphane Gaïffas and Erwan Scornet. His educational background includes a Master's degree in Mathematics with specialization in Probability and Random Models (2016), a Master's degree in Fundamental Mathematics (2015), and a Bachelor's degree in Mathematics from Pierre and Marie Curie University and École Normale Supérieure (2013). Dr. Mourtada's research focuses on the intersection of statistics and learning theory, with particular interest in high-dimensional statistics, online learning, and density estimation. His work explores the complexity of prediction and estimation problems through rigorous theoretical analysis. His research spans statistical learning theory, robust statistics, and the theoretical foundations of machine learning algorithms. His publication record since 2017 demonstrates consistent contributions to top-tier venues in statistics and machine learning, with recent work focusing on universal coding, aggregation methods, robust regression, and the theoretical analysis of kernel methods and random forests. His research shows a progression from online learning and expert aggregation to more complex statistical learning problems involving high-dimensional data and model misspecification. He teaches courses including Statistical Learning Theory for Master 2 Data Science students and Probability Theory at ENSAE. His teaching spans theoretical foundations of machine learning and core probability concepts for advanced statistics students.
Adela Isvoranu serves as an Assistant Professor in the Department of Psychology at the National University of Singapore (NUS), where she investigates developmental pathways from mental health to mental illness using complexity-based frameworks. Her work challenges traditional syndrome-focused approaches by emphasizing symptom interactions within psychopathological networks. Academic Background: Ph.D. (cum laude) from University of Amsterdam M.Sc. from University of Amsterdam B.Sc. (Hons.) from University of Leeds Her research program integrates network analysis with clinical psychology to model mental health conditions as dynamic systems. This approach examines fuzzy boundaries between diagnostic categories and aims to develop novel intervention strategies through collaborations with clinical psychologists, practitioners, and methodologists. Her methodological innovations focus on translating complex network theory into practical clinical applications. Recent publications reveal a strong trajectory in advancing network psychometrics methodology while applying these frameworks to psychosis and trauma research. The 2022 book establishes foundational R-based tools for behavioral scientists, the 2021 methodological paper provides crucial estimation guidelines, and the 2017 psychosis study demonstrates clinical utility in mapping trauma-symptom pathways. Scientific Awards: No awards explicitly mentioned in source material While specific student mentorship details are unavailable, her collaborative research environment with clinical teams and methodologists indicates active supervision of research projects. Her work is supported through university affiliations with infrastructure including dedicated research space (AS4-02-32) and computational resources for network analysis. Research Environment: Operates within NUS Psychology Department's clinical research ecosystem Collaborates with GROUP Investigators consortium Integrates statistical methodology with clinical practice Maintains research website (www.adelaisvoranu.com) for dissemination
Professor James Im serves as Professor of Materials Science in the Departments of Earth and Environmental Engineering and Applied Physics and Applied Mathematics at Columbia University, with an office at 1106 S.W. Mudd (Mail Code 4701). His academic career spans over three decades at Columbia, where he progressed from Assistant Professor (1991-1994) to Associate Professor (1995-2002), and ultimately to full Professor (2002-present), including a tenure as Chair of the Materials Science and Engineering Program (2002-2014). His educational background includes a B.S. with Distinction in Materials Science from Cornell University (1984) and a Ph.D. in Electronic Materials from MIT (1989), followed by postdoctoral research at Caltech (1989-1991). Cornell University: B.S. Materials Science (1984) MIT: Ph.D. Electronic Materials (1989) Caltech: Postdoctoral Scholar (1989-1991) Im's research centers on ultra-rapid phase transitions in beam-irradiated thin films, specifically focusing on laser crystallization of silicon films , energy-beam-induced melting and solidification , and nucleation in discontinuous phase transitions . His work employs experimental, computational, and theoretical approaches to develop innovative semiconductor materials for advanced displays, solar cells, and integrated circuits. Notably, his invention of Sequential Lateral Solidification (SLS) technology has been licensed to major display manufacturers (Samsung, LG, Sharp) and implemented in products by Apple, Blackberry, and Nokia. Current research focuses on advancing the Spot-Beam Crystallization (SBC) platform using fiber lasers for next-generation microelectronics. His publication record spans environmental aerosol studies (2019-2024), oilfield operations technology (2002-2014), and foundational atmospheric research (1980s), reflecting interdisciplinary expertise bridging materials science, environmental engineering, and petroleum technology. The most recent works emphasize low-cost sensor development and aerosol monitoring. Professional recognition includes membership in prestigious societies: Bohmisch Physical Society Sigma Xi Alpha Sigma Mu Materials Research Society American Physical Society Im's research group maintains strong industry connections through technology licensing and collaborative projects, particularly in display manufacturing. His leadership as former department chair demonstrates administrative commitment alongside scientific innovation. The laboratory leverages state-of-the-art laser systems and beam delivery optics for materials development, with recent focus shifting toward environmental monitoring applications while maintaining core semiconductor research.
Rachel Hess, MD, MS is Professor of Population Health Sciences and Internal Medicine and Associate Vice President for Research-Health Sciences at the University of Utah Schools of the Health Sciences. She co-directs the Utah Clinical and Translational Science Institute and was founding Chief of the Division of Health System Innovation and Research (2014-2022). A board-certified General Internist and internationally recognized health-services researcher, she focuses on translating evidence into practice through health-information technology and patient-centered outcomes. Education & Training MD – University of New Mexico School of Medicine MS Clinical Research – University of Pittsburgh Fellowship – General Internal Medicine & Women’s Health, University of Pittsburgh / VA Pittsburgh Medical Center Chief Residency – Internal Medicine, Western Pennsylvania Hospital Residency – Internal Medicine, Temple University Hospital BA Mathematics – Washington University in St. Louis Research Focus Dr Hess’s program is dedicated to improving patient-centered outcomes by leveraging implementation science, health-information technology, and patient-reported measures. Her work spans: Design and nationwide deployment of EHR-integrated clinical decision support for cancer genetics, lung-cancer screening, heart-failure management, and antibiotic stewardship. Large multi-site pragmatic trials (ADAPTABLE, RECOVER, BRIDGE, MAINTAIN) examining effectiveness, equity, and scalability of digital-health interventions. Women’s health across the lifespan, including studies on menopause, sexual function, and post-COVID sequelae. Advanced analytics linking patient-reported outcomes (PROs) with healthcare utilization and cost. Scientific Awards & Recognition Board Certification, American Board of Internal Medicine (Internal Medicine) Leadership of NIH RECOVER Consortium adult cohort—one of the largest studies of Long COVID worldwide Principal investigator on >$50 million in federal and foundation funding (PCORI, NHLBI, NCI, AHRQ, CDC) Leadership & Service As Associate Vice President for Research she sets strategic priorities for the Schools of Medicine, Nursing, Pharmacy, Dentistry, and Health. She co-chairs the Utah Clinical and Translational Science Institute, oversees campus-wide clinical-trials infrastructure, and mentors interdisciplinary teams spanning informatics, behavioral science, epidemiology, and clinical medicine. Laboratories & Teams Dr Hess leads the Health System Innovation and Research (HSIR) group—an interdisciplinary unit of data scientists, implementation researchers, clinicians, and patient partners—dedicated to rapid-cycle testing and national scale-up of digital-health solutions.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Jonathan Cullen is Professor of Sustainable Engineering at the University of Cambridge and President of Fitzwilliam College, specializing in resource efficiency and decarbonization through top-down analysis of industrial material and energy systems. His work bridges academic research with industry applications across energy-intensive sectors. Education: Bachelor's in Chemical and Process Engineering, University of Canterbury, New Zealand MPhil in Engineering for Sustainable Development, University of Cambridge PhD in Engineering Fundamentals of Energy Efficiency, University of Cambridge His research develops metrics for quantifying energy and material consequences of production systems, focusing on circular economy implementation, minimum energy requirements, and zero-carbon transition pathways. Key applications target cement, steel, plastics, and petrochemicals where he pioneers methods like exergetic analysis and material flow accounting to expose carbon lock-ins and circularity opportunities. Recent publications reveal three dominant trends: (1) Frameworks for theoretical minimum energy requirements across industrial processes, (2) Geopolitical analysis of critical mineral flows and ownership structures, and (3) Circular economy metrics for plastics and construction materials. These consistently employ system-scale modeling validated through industry partnerships. Research Funding: Lead: C-THRU ($4M, VKRF) - carbon clarity in petrochemical supply chains Co-I: UK FIRES (£5.2M, EPSRC) - industrial decarbonization program Co-I: CirPlas (£1.25M, UKRI) - plastic waste elimination 7+ projects (EPSRC, Innovate UK, Horizon 2020) Academic Leadership: Teaching: Energy Systems and Policy (MPhil in Energy Technologies) Undergraduate supervision in Materials/Mathematics Graduate Tutor at Fitzwilliam College IPCC AR6 Lead Author (Industry Chapter) He directs the Resource Efficiency Collective, which develops open-source tools like Mat-dp for material demand projections and Starter Data Kits for energy planning. Current work focuses on scaling circular business models for construction retrofitting and quantifying geopolitical risks in critical mineral supply chains.
