Jelani Nelson is a Professor and Department Chair in the UC Berkeley EECS Department (College of Engineering). His work focuses on theoretical computer science , particularly algorithms , data streams , dimensionality reduction , and privacy-preserving computation . Advising : Current students include Ishaq Aden-Ali, Xin Lyu, Mihir Singhal, and Hongxun Wu (co-advised with leading researchers). Education : PhD from MIT (George M. Sprowls Award), M.Eng from MIT. Research Highlights : Developed foundational results in Johnson-Lindenstrauss dimensionality reduction (optimality, sparse embeddings). Advancements in differential privacy (lower bounds, private mean estimation, threshold learning). Pioneering work on streaming algorithms for heavy hitters, norm estimation, and graph problems. Innovations in compressed sensing and oblivious subspace embeddings . Scientific Awards : PODS Best Paper Award (2011, 2022) IBM Pat Goldberg Memorial Best Paper Award (2011) George M. Sprowls Award for MIT doctoral thesis (2009) NeurIPS 2020 Spotlight Presentation
Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk
Prof. David Kerr is a Professor at the University of Münster's Department of Mathematics, within the Faculty of Mathematics and Computer Science. His research focuses on operator algebras, ergodic theory, and mathematical physics. He has contributed to projects such as the CRC 1442 on entropy and group dynamics, and has organized conferences including 'Group Actions: Dynamics, Measure, Topology' in 2022. His work spans topics like C*-algebras, sofic groups, and dynamical systems. Education: Not explicitly detailed in the text, but his career indicates advanced training in mathematics. Research Interests: Operator algebras, ergodic theory, entropy theory, and their applications to group actions and dynamical systems. Grants/Projects: Involved in CRC 1442 projects D04 and D05, exploring entropy, C*-algebras, and dynamical tilings. Labs/Teams: Collaborates with researchers like Hanfeng Li and Robin Tucker-Drob on projects involving operator algebras and group dynamics.
Dr. Yi-Feng Chen is a Research Assistant Professor and Master's Supervisor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech as a postdoctoral fellow in November 2020 and was promoted to Research Assistant Professor in February 2023. His academic journey includes interdisciplinary training across engineering, neuroscience, and biomedical applications. Dr. Chen's educational background includes: Ph.D. in Engineering from Wuhan University of Technology (2014-2017), supervised by Professor Quan Liu M.Sc. from Wuhan University of Technology (2011-2014), supervised by Professor Zhou Zude B.Sc. from Wuhan University of Technology (2007-2011) He also participated in exchange programs at Yuan Ze University in Taiwan (2012) and the University of Auckland in New Zealand (2015). Dr. Chen's research spans the intersection of biomedical engineering, neuroscience, and artificial intelligence, with particular focus on brain-computer interfaces and rehabilitation technologies. His work combines advanced signal processing techniques with clinical applications, especially in decoding neural signals for movement intention and monitoring brain states during anesthesia. His research has significant implications for neurorehabilitation, assistive technologies, and intraoperative neurophysiological monitoring. His recent publications demonstrate a clear trajectory toward increasingly sophisticated neural decoding techniques, with a focus on coordinated limb movements and practical rehabilitation applications. The work shows progression from basic EEG signal processing to complex bimanual movement decoding and robot-assisted rehabilitation systems, reflecting a translational research approach from basic science to clinical applications. Dr. Chen has secured significant research funding as principal investigator and core contributor on multiple projects: National Natural Science Foundation of China Youth Science Fund Project (2024-2026) Guangdong Natural Science Foundation General Project (2024-2026) Ministry of Science and Technology National Key R&D Program Project (2023-2026) Shenzhen Science and Technology Innovation Commission Key Project (2022-2025) As a Master's Supervisor, Dr. Chen mentors graduate students in biomedical engineering with focus on neural engineering and rehabilitation robotics. His laboratory collaborates closely with clinical partners to ensure research relevance to real-world medical challenges, particularly in neurorehabilitation and intraoperative monitoring.
Gary Pielak is a Kenan Distinguished Professor of Chemistry, Biochemistry, and Biophysics at the University of North Carolina at Chapel Hill, with a joint appointment in the School of Medicine. His research focuses on high-resolution protein NMR studies in living cells and the biophysics of tardigrade desiccation-tolerance proteins, bridging structural biology and molecular biophysics. Education: BS in Chemistry from Bradley University (1977), PhD in Biochemistry from Washington State University (1983), Postdoc at the University of British Columbia (1983-1986), Postdoc at Oxford University (1986-1988). Research Interests center on understanding protein structure, stability, and function in physiologically relevant environments. Key areas include: In-Cell NMR: Quantifying protein behavior in living cells using advanced NMR techniques. Macromolecular Crowding: Studying synthetic polymers and proteins as crowding agents to mimic cellular environments. Tardigrade Biology: Exploring desiccation-tolerance mechanisms in intrinsically disordered proteins from water bears. Recent Publications highlight interdisciplinary trends, combining AI-driven stability prediction, solid-state NMR for dry protein analysis, and molecular glass/gel applications for preservation. His Scientific Awards include: NIH Pioneer Award DuPont and Morrow Young Faculty Awards Multiple UNC Mentorship Awards Mentorship is a cornerstone, with a focus on training graduate students and advancing NMR methodologies. His group employs Research Methods : 19F, 1H, 15N, and 13C NMR Circular Dichroism and Calorimetry Protein Expression in E. coli
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.
David Kutasov is a Professor in the Department of Physics at the University of Chicago, affiliated with the Enrico Fermi Institute. His research focuses on string theory and quantum field theory, particularly addressing dynamics of strongly coupled systems, supersymmetry breaking, black hole physics, and cosmological singularities. Kutasov has contributed to understanding the interplay between string theory and field theory, including mechanisms for vacuum selection in early universe scenarios and brane dynamics. His work explores theoretical frameworks such as holography, time-dependent backgrounds, and tachyon condensation, with applications to particle physics and cosmology. Key research directions include analyzing string theory's predictions for nature and applying string-based insights to experimental particle physics and cosmic phenomena. Notable contributions span topics like D-brane interactions, non-supersymmetric vacua, and dualities in Chern-Simons theories. Kutasov's publications often bridge abstract string theory constructs with observable phenomena, emphasizing tools for analyzing string theory's implications in diverse physical contexts. Despite extensive contributions, no specific scientific awards are explicitly listed in the provided materials. His research remains active across multiple frontiers of theoretical physics, maintaining a strong focus on foundational questions in high-energy physics.
Baharan Mirzasoleiman is an Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), where she leads the BigML research group. Prior to joining UCLA, she was a postdoctoral research fellow in Computer Science at Stanford University working with Jure Leskovec. She received her Ph.D. in Computer Science from ETH Zurich advised by Andreas Krause. Her research focuses on addressing sustainability, reliability, and efficiency of machine learning, with particular emphasis on improving big data quality by developing theoretically rigorous methods to select the most beneficial data for efficient and robust learning. Her work spans several critical areas including data efficiency, robustness against label noise and data poisoning, and addressing spurious correlations in machine learning models. She has made significant contributions to understanding how neural networks exploit spurious features that correlate with certain categories during training but fail to generalize to minority groups. Professor Mirzasoleiman's research demonstrates how theoretically grounded approaches can lead to practical improvements in model robustness and efficiency across various applications including medical diagnosis and environmental sensing. Her work has resulted in the development of the SpuCo package, a Python library that provides modular implementations of state-of-the-art methods to address spurious correlations, along with controllable synthetic datasets like SpuCoMNIST and large-scale vision datasets like SpuCoAnimals. She has received numerous prestigious awards including the ETH medal for Outstanding Doctoral Thesis, being selected as a Rising Star in EECS by MIT, an NSF Career Award, a UCLA Hellman Fellows Award, and an Okawa Research Award. Her students have also received multiple fellowships and awards including Amazon Doctoral Student Fellowships and an OpenAI Superalignment Fast Grant. Professor Mirzasoleiman actively contributes to the academic community through invited talks at major conferences including ICML, ICLR, NeurIPS, and KDD, as well as co-organizing workshops on new frontiers in adversarial machine learning and sparsity in neural networks. She has developed educational resources including tutorials on Foundations of Data-efficient Learning presented at ICML 2024.
Jakob Foerster is an Associate Professor at the University of Oxford's Department of Engineering Science and a Supernumerary Fellow at St Anne's College. He leads the FLAIR lab, focusing on multi-agent reinforcement learning (MARL), human-AI coordination, and AI foundational research. Previously, he was a Research Scientist at Facebook AI Research (FAIR) and holds a DPhil from Oxford. His work has been cited over 5,000 times and includes seminal contributions like QMIX and the Hanabi Challenge. Research interests span compute-efficient scaling of AI, MARL applications in finance and bio, and ethical AI. He actively collaborates across academia and industry, co-organizing workshops like NeurIPS' Emergent Communication. His lab emphasizes open-ended RL, environment design, and scalable algorithms. Notable awards include the CIFAR AI Chair (2019) and NeurIPS Best Paper Runner-Up (2018). Current efforts include FLAIR's research on zero-shot coordination and the JaxMARL framework. He advises students in Oxford's Engineering DPhil and AIMS CDT programs.
Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor at Cornell Tech (Cornell University) and a faculty member in the Department of Computer Science. He co-founded and co-directs the Initiative for CryptoCurrencies and Contracts (IC³) and serves as Chief Scientist at Chainlink Labs. His research focuses on blockchain technology, cryptocurrency, smart contracts, applied cryptography, authentication, and privacy. Juels holds a Ph.D. in Computer Science from UC Berkeley and previously held roles as Chief Scientist and Director of the RSA Laboratories at EMC/Dell EMC until 2013. His work spans foundational contributions to decentralized systems, including Chainlink’s oracle networks and Ekiden’s privacy-preserving smart contracts. Juels has authored influential books like *The Oracle* (2024) and *Tetraktys* (2009), blending cryptography with thriller narratives. He advises numerous students and alumni, many of whom have joined prominent blockchain firms like Chainlink Labs, Mysten Labs, and Espresso Systems. Recent publications explore topics such as liquidity in blockchain assets, oracle network security, and economic analysis of smart contracts. His research emphasizes practical applications of cryptographic principles to real-world challenges in digital systems and decentralized finance. Juels’ lab collaborates on projects like HyDRA (automated bug bounty systems) and Lanturn (economic security metrics). He actively participates in academic and industry partnerships, bridging theoretical computer science with applied blockchain innovation.
Prof. Harry Hyungryul Baik is a Tenured Associate Professor at KAIST's Department of Mathematical Sciences since 2017. He holds a PhD from Cornell University (2014) and a B.S. from KAIST (2009), advised by William Thurston, John Hubbard, and Dylan Thurston. His research focuses on geometric topology, geometric group theory, and low-dimensional topology, with notable contributions to mapping class groups, Kleinian groups, and Teichmüller theory. Education: PhD in Mathematics (Cornell, 2014), B.S. in Mathematics (KAIST, 2009). Key research areas include asymptotic translation lengths, laminar groups, and circular orders of groups. He co-leads the KAIST-KIAS joint research group 2K-GATE as Director, emphasizing collaboration between topologists. Research highlights: Characterization of Fuchsian groups via laminations, unsmoothability of mapping class group actions on 1-manifolds, and exponential torsion growth in random 3-manifolds. His work bridges topology with dynamical systems and geometric group theory, often involving collaborations with institutions like KIAS and MPIM. Awards include the Sangsan Prize (2018), Young-KAST membership (2020–2023), and multiple grants from Samsung and POSCO. He advises 7 PhD students and has mentored 15+ alumni, many of whom hold postdoc positions globally. His lab actively hosts conferences like the KAIST Geometric Topology Fair. Labs/Teams: Director of 2K-GATE (KAIST-KIAS), core member of the KAIST Topology Research Group, collaborator with international networks including the Harvard-MIT-Princeton topology axis.
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Brandon Seward is an Associate Professor of Mathematics at the University of California San Diego (UC San Diego), where they conduct research and teach in the Department of Mathematics. They use the pronouns they/them or he/him . Education Ph.D. in Mathematics, University of Michigan , 2015 Research Interests Their work lies at the intersection of ergodic theory, topological dynamics, descriptive set theory, and group theory . They investigate geometric, combinatorial, and entropic properties of actions of countable groups, with special emphasis on the divide between amenable and non-amenable groups. Publications & Research Impact Across 27 refereed papers (2014-2024), Seward has advanced entropy theory for non-amenable groups, Borel combinatorics of group actions, and structure theorems for measure-preserving actions. Their work has appeared in top journals such as Inventiones Mathematicae , Journal of the American Mathematical Society , and Duke Mathematical Journal . Scientific Awards Michael Brin Dynamical Systems Prize for Young Mathematicians (2018) – awarded for outstanding contributions to dynamical systems. Teaching & Mentorship Seward regularly teaches core undergraduate courses (e.g., Math 142A Introduction to Analysis) and organizes the UC San Diego Group Actions Seminar , a weekly research forum featuring international speakers. They serve as a faculty mentor and are actively involved in graduate student supervision and seminar coordination. Contact & Office Email: bseward@ucsd.edu Office: AP&M 5739, 9500 Gilman Drive, La Jolla, CA 92093-0112
