Ajit Rajwade is a Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay. His research spans Artificial Intelligence , Compressed Sensing , and Medical Imaging , with affiliations to the Centre for Machine Intelligence and Data Science and the Koita Centre for Digital Health . Education : PhD in Computer and Information Science and Engineering, University of Florida (2010) MSc in Computer Science, McGill University (2004) BTech in Computer Engineering, University of Pune (2002) His research interests focus on intelligent data acquisition, particularly in neural network analysis , graph signal processing , and medical imaging . He develops compressed sensing algorithms for inverse problems like tomography and MRI reconstruction , alongside applying group testing to pandemic response. Scientific awards include the Prof. S. P. Sukhatme Award (2024) and Departmental Teaching Excellence (2019). His publications demonstrate expertise in image restoration , noise modeling , and epidemiological algorithms . He has advised PhD students like Sabyasachi Ghosh and Jerin Geo James , with a focus on computationally efficient methods in medical imaging and machine learning .
Prof. Olga Sorkine Hornung is a Full Professor of Computer Science at ETH Zürich, leading the Interactive Geometry Lab. She holds a BSc and PhD from Tel Aviv University (2000 and 2006) and conducted postdoctoral research at Technical University Berlin. Her research focuses on computer graphics, geometric modeling, and geometry processing, with applications in shape editing, digital fabrication, and animation. She has received numerous accolades, including the ACM Fellowship (2020), ERC Consolidator Grant (2020), and the Golden Owl Teaching Award (2021). Her work bridges theoretical foundations and practical algorithms, addressing challenges in parameterization, surface compression, and interactive design tools. Her research interests span: Computer Graphics & Visualization Geometric Modeling & Processing 3D Content Creation & Digital Fabrication Garment Design & Simulation Human Motion Analysis & Animation Awards and grants include: 2024: Best Paper Honorable Mention (EUROGRAPHICS) 2023: Member of Swiss Academy of Engineering Sciences (SATW) 2020: ERC Consolidator Grant 2017: Rössler Prize (ETH Zurich) Her lab focuses on developing novel methods for interactive geometry processing, with recent advancements in garment modeling (e.g., AIpparel, Rags2Riches) and motion retargeting systems like WalkTheDog. She actively collaborates on interdisciplinary projects, including biomedical applications and sustainable fashion technology.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Bradley Olsen is a Professor of Chemical Engineering at the Massachusetts Institute of Technology (MIT), holding the Alexander and I. Michael (1960) Kasser Chair in Chemical Engineering. He is affiliated with MIT's School of Engineering and directs research in the Plastics and the Environment Program. His academic career spans over two decades with numerous prestigious appointments and recognitions. Olsen earned his S.B. from MIT in 2003 followed by a Ph.D. from the University of California Berkeley in 2007. His educational background is complemented by postdoctoral fellowships including NIH and Beckman Institute Postdoctoral Fellowships (2008-2009) and the Hertz Fellowship (2003-2007). Research Interests Professor Olsen's research focuses on designing materials to address important challenges while understanding the fundamental science necessary for materials design. His primary research areas include block copolymers, soft condensed matter physics, protein-based materials, and bioelectronics. His group specializes in polymer networks, protein-polymer conjugates, self-assembly phenomena, and sustainable polymer development. The research has significant implications for biomaterials, sustainable polymers, and advanced materials design. Publication Trends Analysis of Professor Olsen's recent publications reveals a strong focus on polymer network topology, protein-polymer conjugates, and sustainable materials. His work increasingly integrates computational methods with experimental approaches, particularly in polymer characterization and data science applications to materials science. Recent publications show growing emphasis on biodegradable polymers, polymer informatics, and biomedical applications of advanced materials. Scientific Recognition Professor Olsen has received numerous prestigious awards throughout his career, including: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters/Biomacromolecules/Macromolecules Young Investigator Award (2021) AIChE Owens Corning Early Career Award (2019) American Physical Society Dillon Medal (2018) Alfred P. Sloan Research Fellow in Chemistry (2014) Advising and Funding Professor Olsen has secured significant research funding from multiple federal agencies including NSF, NIH, AFOSR, and DOE. His group has produced numerous high-impact publications across top journals in polymer science, materials science, and chemistry. He has advised multiple graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. The MIT OGE's Committed to Caring Honor (2019) recognizes his excellence in graduate student mentoring. Research Infrastructure Professor Olsen leads a research group with capabilities spanning polymer synthesis, protein engineering, materials characterization, and computational modeling. His lab maintains strong collaborations with other MIT departments, national laboratories, and international research institutions. The group participates in several interdisciplinary initiatives including the Plastics and the Environment Program and has developed significant data infrastructure for polymer science through projects like CRIPT and BigSMARTS.
Alexis Battle is an Associate Professor at Johns Hopkins University with appointments in Biomedical Engineering , Computer Science , and Genetic Medicine (secondary). She directs the Malone Center for Engineering in Healthcare and serves as Deputy Director of the Data Science and AI Institute . Educated at Stanford University (PhD in Computer Science, 2013), Battle transitioned to academia after leadership roles at Google. Research Focus: Battle’s work bridges genomics and machine learning , emphasizing the impact of genetic variation on human health. Her lab develops tools like Watershed to predict functional effects of rare variants, aiming to enhance rare disease diagnosis. Key themes include non-coding DNA analysis , personalized genomics , and systems biology , with applications in cardiovascular disease and neurodegenerative disorders . Publications & Awards: Over 60 peer-reviewed articles in journals like Nature , Science , and Genome Biology , with recent emphasis on single-cell transcriptomics , multiomics integration , and telomere biology . Recipient of the President’s Frontier Award (2022), Microsoft Investigator Fellowship (2019), and Searle Scholar (2016). Scientific Awards: 2022 President’s Frontier Award 2019 Microsoft Investigator Fellowship 2019 Johns Hopkins Discovery Award 2017 Johns Hopkins Catalyst Award 2016 Searle Scholar Advising & Funding: Mentors 11 PhD students, 3 undergraduates, and postdoctoral fellows. Her research is funded by NIH, Searle Scholars, and institutional grants. The Battle Lab collaborates on projects like the GTEx Consortium , focusing on gene regulation and clinical genomics .
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.
Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.
Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.
Artur W. Dubrawski is an Alumni Research Professor of Computer Science and Director of the Auton Lab at Carnegie Mellon University's School of Computer Science. He leads interdisciplinary research on Artificial Intelligence, Machine Learning, and Robotics with real-world applications in healthcare, nuclear safety, food safety, and counter-human trafficking. His work focuses on bridging gaps between data-driven AI and empirical sciences through probabilistic modeling, predictive analytics, and time-series intelligence. Lab: Auton Lab (founded 1993) Collaborations: Allegheny County Health Department, USDA, CDC, U.S. Army Research Impact: AI for wastewater-based COVID-19 forecasting, radiological inspection systems, and hospital infection detection His students and affiliates include current PhD candidates Angela Chen, Emma Erickson, Cecilia Morales, Willa Potosnak and past researchers like Benedikt Boecking (co-inventor of Interactive Weak Supervision). The lab has spun off startups like Marinus Analytics (IBM XPrize finalists) and developed open-source tools like auton-survival for survival analysis. Key Grants: $10.5M U.S. Army contract for AI-driven predictive maintenance research.
Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at the University of Chicago Booth School of Business and a Professor of Economics and the Peter de Florez Professor of EECS at the Massachusetts Institute of Technology . His work bridges machine learning , behavioral science , and computational medicine , focusing on social problems like discrimination , poverty , and health equity . Research Interests : Behavioral economics, algorithmic fairness, poverty, AI in healthcare, and policy evaluation. Teaching : Courses on Artificial Intelligence and Algorithmic Solutions to Human Problems. Publications : Over 150 papers in journals like Science , Quarterly Journal of Economics , and Nature Medicine , with recent work on AI-driven healthcare disparities and behavioral economics. Scientific Awards : MacArthur ‘Genius’ Grant, Infosys Prize, ‘Top 100 Thinker’ (Foreign Policy Magazine), ‘Young Global Leader’ (World Economic Forum). Organizations : Co-founder of ideas42 (behavioral science non-profit), J-PAL (randomized trials in development), and Dandelion Health (healthcare data for AI). Serves on the MacArthur Foundation board.
Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.
Liping Liu is a Professor in the Department of Management at The University of Akron's College of Business. He holds a Ph.D. in Business from the University of Kansas (1995), Master of Engineering in Systems Engineering (1991), and dual bachelor's degrees in Applied Mathematics (1986) and River Dynamics (1987). Ph.D., University of Kansas MS, Huazhong University of Science and Technology B.E., Wuhan University BS, Huazhong University of Science and Technology His research spans Artificial Intelligence , Electronic Business , Systems Analysis , Data Quality , and Belief Function Theory . He pioneered coarse utility theory and linear belief functions , now taught in top Ph.D. programs across multiple disciplines. Key trends in his publications include Belief Function Applications (2012-2024), Medical Data Systems (2003-2015), and Decision Theory (2004-2014). Recent works focus on Gamma Belief Functions (2024) and computational improvements in linear belief function operations (2019-2016). Scientific contributions recognized via: Microsoft Azure Educator Grant (2014-2016) Inclusion in Who's Who in America (2010-2013) and Who's Who in the World (2011-2013) As an editor and committee member for major conferences (INFORMS, AMCIS, Belief Functions conferences), he bridges academic research with practical systems implementation in e-business and healthcare domains.
Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.
