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.
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.
David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.
Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Amy R Greenwald is a Professor of Computer Science at Brown University. Her research spans artificial intelligence, algorithmic game theory, and computational economics, with a focus on multiagent reinforcement learning and market equilibrium computation. Education PhD, New York University (1999) MS, Cornell University (1995) MS, Oxford University (1992) BS, University of Pennsylvania (1991) Research Focus Greenwald's work explores strategic interactions in computational systems, including: Game-theoretic modeling of multiagent systems Algorithmic approaches to market equilibrium Simulation-based equilibrium learning Stackelberg game formulations for hierarchical decision making Applications to supply chain negotiations and economic design Her recent publications emphasize tractable equilibrium computation, social influence in economic models, and advanced reinforcement learning techniques for strategic settings. Teaching CSCI 0100 - Data Fluency for All CSCI 0180 - Computer Science: An Integrated Introduction CSCI 1440 - Algorithmic Game Theory CSCI 2440 - Advanced Algorithmic Game Theory CSCI 2951Z - Advanced Algorithmic Game Theory
Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Laura Elo serves as Professor of Computational Medicine and Head of the Medical Bioinformatics Centre at the University of Turku, Finland. She concurrently holds the position of Research Director at Turku Bioscience Centre and acts as InFLAMES Flagship Contact, driving interdisciplinary biomedical research initiatives. Her academic foundation includes a PhD in Applied Mathematics (2007) and Adjunct Professorship in Biomathematics (2011), establishing her quantitative expertise before transitioning into biomedical applications. Her research program focuses on transforming molecular and clinical datasets through statistical modeling and advanced machine learning . Key thrusts include robust computational tools for proteome/epigenome analysis, AI-driven digital health diagnostics, and computational systems immunology for immune-mediated diseases. This work directly addresses challenges in reproducibility and scalability of high-throughput biotechnology data. Analysis of her recent publications reveals dominant themes in type 1 diabetes biomarker discovery , multi-omics integration , and immune system modeling , with strong emphasis on clinical translation through collaborations with experimental and medical teams. Her scientific recognition includes: JDRF Career Development Award Professor Elo actively trains MSc/PhD students and postdoctoral fellows while leading major research initiatives including ERC grants. Her teaching portfolio spans Bioinformatics Journal Club, AI in Diagnostics, and Systems Biology courses. The Elo Lab (https://elolab.utu.fi) operates as a hub for computational biomedicine, developing open-source tools like CellRomeR while maintaining close ties with Turku Bioscience Centre's experimental facilities for validating computational predictions in immunology and metabolic disease contexts.
Daniel Sanz-Alonso is an Assistant Professor in the Department of Statistics at the University of Chicago since 2018, affiliated with the Committee on Computational and Applied Mathematics. He previously held a postdoctoral position in Brown University’s Division of Applied Mathematics and contributed to their Data Science Initiative. His research focuses on integrating predictive mathematical models with large datasets, addressing challenges in inverse problems, data assimilation, and scientific machine learning. Education: BSc from University of Valladolid (2012), PhD in Mathematics and Statistics from University of Warwick (2016). Awards include the José Luis Rubio de Francia Prize (2020) for Spanish mathematicians under 32 and an NSF CAREER Award (2023). He has been an Associate Editor of the SIAM/ASA Journal on Uncertainty Quantification since 2025. Funding support comes from the National Science Foundation, National Geospatial-Intelligence Agency, Department of Energy, and BBVA Foundation. His interdisciplinary work bridges data science, machine learning, and partial differential equations, with applications in weather forecasting and geophysical sciences.
Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.
Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.
Murat Erdogdu is an Assistant Professor at the University of Toronto, jointly appointed in the Department of Computer Science and Department of Statistical Sciences . He is also a faculty member at the Vector Institute and holds the CIFAR Chair in Artificial Intelligence . PhD in Statistics, Stanford University (advised by Mohsen Bayati and Andrea Montanari) MSc in Computer Science, Stanford University BSc in Electrical Engineering and Mathematics, Bogazici University His research focuses on machine learning theory , high-dimensional statistics , and optimization . He has contributed to understanding gradient-based algorithms, sampling methods, and feature learning in structured data. Recent publications address problems in: Heavy-tailed sampling Mean-field Langevin dynamics Robust feature learning Minimax regression Stochastic optimization under infinite noise Scientific Awards: CIFAR Chair in Artificial Intelligence ICLR 2023 Spotlight NeurIPS 2019 & 2021 Spotlights He teaches graduate courses like STA 414/2104 (Statistical Methods for Machine Learning II) and STA 4273 (Modern Learning Theory) , emphasizing probabilistic modeling and theoretical analysis.
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 .
Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.
David W. Jacobs is a Professor in the Department of Computer Science at the University of Maryland, with a joint appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). He also served as the interim Director of the University of Maryland Center for Machine Learning starting in 2018. University: University of Maryland School: College of Computer, Mathematical, and Natural Sciences Department: Department of Computer Science Academic Rank: Professor Education: He received his B.A. from Yale University, and M.S. and Ph.D. in Computer Science from MIT. Research Interests: His research primarily focuses on computer vision and machine learning, particularly visual object recognition, lighting variation modeling, 3D reconstruction, perceptual organization, motion understanding, and the integration of vision with graphics and human-computer interaction. A major applied contribution is the development of Leafsnap , an electronic field guide app for plant identification, which has been downloaded over 1.5 million times and used in biodiversity and educational contexts. Publication Trends: His recent scholarly output centers on deep learning, convolutional networks, residual architectures, generative models (especially GANs), and interpretability. His work often bridges theoretical insights with practical applications in vision and AI. Scientific Awards: Honorable Mention, Best Paper Award, CVPR 2000 Best Student Paper Award, UIST 2003 Best Paper Award, Eurographics 2016 2011 Edward O. Wilson Biodiversity Technology Pioneer Award for Leafsnap Teaching and Advising: He has taught advanced courses such as CMSC 422 (Introduction to Machine Learning) and CMSC 828L (Deep Learning). He mentors students through course projects and research, though specific advisees are not listed. He has collaborated with institutions like Columbia University and the Smithsonian on impactful interdisciplinary projects. Labs and Teams: He is affiliated with UMIACS and leads research efforts in vision and learning, contributing to the University of Maryland Center for Machine Learning. His team has developed several mobile applications including Leafsnap, Birdsnap, and Dogsnap, demonstrating a strong focus on real-world deployment of vision technology.
