Dr. Manish Kumar is a Professor in the Department of Mechanical Engineering at the University of Cincinnati, leading the Cooperative Distributed Systems (CDS) Laboratory and the UAV MASTER Lab. He specializes in robotics, unmanned aerial systems (UAVs), and swarm systems, with a focus on decision-making, control in complex systems, and multi-sensor data fusion. His research has been supported by grants from the National Science Foundation, Department of Defense, and industry partners. Education: Ph.D. in Mechanical Engineering, Duke University (2004) M.S. in Mechanical Engineering, Duke University (2002) Research Interests: Dr. Kumar's work addresses challenges in autonomous systems, including UAV coordination, swarm robotics, and emergency response technologies. He integrates machine learning, control theory, and optimization to develop robust systems for applications such as wildfire management, telehealth, and industrial automation. Grants & Labs: Directed projects funded by federal agencies (NSF, DoD) and industry, totaling over $5M. Co-directs the Collaboratory for Medical Innovation and Implementation and the UAV MASTER Lab. Key Contributions: Pioneered algorithms for UAV path planning and swarm coordination. Developed PDE-based models for epidemic spread prediction and control.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
Virginia Smith is the Leonardo Associate Professor of Machine Learning at Carnegie Mellon University's School of Computer Science, with a courtesy appointment in the Electrical and Computer Engineering Department. She leads research addressing critical challenges in machine learning systems, particularly focusing on safety and efficiency. Her research interests span federated and collaborative learning, efficient training methods, data privacy, and AI safety. Recent work has explored topics such as model unlearning, LLM security, and resource-efficient distributed learning systems. She has made significant contributions to understanding how to make machine learning systems more robust, private, and efficient while maintaining performance. Professor Smith's publication record demonstrates a clear progression toward addressing practical challenges in deploying machine learning systems at scale. Her recent work shows strong emphasis on large language model safety, privacy-preserving techniques, and efficient distributed learning approaches. The research spans theoretical foundations to practical implementations, with numerous papers appearing in top-tier venues including NeurIPS, ICML, ICLR, and MLSys. AFOSR Young Investigator Award Sloan Research Fellowship 2023 Samsung AI Researcher of the Year Best Paper Award at ICML 2025 Exploration in AI Workshop Outstanding Paper Award at MLSys 2023 As an educator, Professor Smith mentors numerous PhD students and postdocs while teaching advanced machine learning courses at CMU. She serves as Program Chair for ICML 2025 and co-organizes a semester program on Federated and Collaborative Learning at the Simons Institute. Her research group maintains strong collaborations with industry partners including Amazon, where she has received research awards.
Jiang Wang is the Mizuho Financial Group Professor at the MIT Sloan School of Management, where he has been a faculty member since 1990, progressing from Assistant Professor to his current named professorship. He holds appointments in the Finance department and maintains an active research program in financial economics. Massachusetts Institute of Technology, Sloan School of Management (2005-present) MIT Sloan School of Management: Assistant Professor (1990-1994), Associate Professor (1994-1998), Professor (1998-1999), NTU Professor (1999-2005) Wang's research focuses on financial economics, asset pricing, market liquidity, trading volume, and financial market microstructure , with significant contributions to understanding information dynamics in markets. His work bridges theoretical models with empirical analysis, particularly in Chinese capital markets. Wang has developed influential theories on liquidity, trading volume, and market efficiency that have shaped modern financial economics. His recent publications demonstrate continued scholarly productivity, with research spanning market uncertainty, circuit breakers, repo markets, and Chinese financial markets. Wang's work integrates theoretical modeling with empirical validation, maintaining relevance to both academic discourse and practical market concerns. China Economics Prizes (2018) Smith Breeden Prize (2007, 2006) New York Stock Exchange Award FAME Research Prize (2004) Trefftz Award, Western Finance Association (1990) Wang has advised numerous doctoral students and supervised significant research projects, though specific student names aren't listed in the available materials. His extensive grant history includes multiple NSF awards and industry-sponsored research. Wang has held leadership positions including President of the Western Finance Association (2017-2018) and Director of the China Center for Financial Research at Tsinghua University (2002-2014). His academic service includes editorial roles for major finance journals and advisory positions with institutions including the Federal Reserve Bank of New York, Nasdaq Stock Market, and China Securities Regulatory Commission.
Yuchen Liu is an Assistant Professor in the Department of Computer Science and Department of Electrical & Computer Engineering (by courtesy) at North Carolina State University. He earned his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology. His research spans networking, machine learning, and cybersecurity, focusing on wireless systems, digital twins, and networked agentic systems. Research areas: Networking (3D UAV networks, mmWave/THz communication, cybersecurity), Machine Learning (generative AI, LLMs, reinforcement learning), Digital Twins (synchronization optimization, edge caching), Software Development (differentiable simulators, open-source testbeds) His recent publications emphasize neurosymbolic AI, diffusion models for wireless systems, and multi-agent approaches to spectrum sensing. Articles highlight applications in UAV networks, vehicular security, satellite localization, and federated learning defenses. Honors include NSF CAREER (2025), NVIDIA Academic Grant (2025), NCSU Carla Savage Award (2025), and multiple IEEE/ACM Best Paper Awards. Current projects are supported by NSF CNS (#2312138), NSF SaTC (#2350075), and NSF NAIRR Pilot Demonstration (#2506757) grants.
Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.
Nati Srebro is a Professor at the Toyota Technological Institute at Chicago with a cross-appointment as a Part-Time Professor in the Department of Computer Science and Committee on Computational and Applied Mathematics at the University of Chicago. He earned his PhD from MIT in 2004 and has held previous positions including post-doctoral fellow at the University of Toronto, Visiting Scientist at IBM, and Associate Professor at the Technion. Professor Srebro's research focuses on methodological, statistical and computational aspects of Machine Learning and Optimization. His work spans foundational contributions to learning theory, matrix reconstruction, and optimization techniques. He is particularly known for introducing the use of nuclear norm for machine learning, work on wider Markov networks, and advancing our understanding of the relationship between learning and optimization. His current research interests include understanding deep learning through optimization, distributed and federated learning systems, algorithmic fairness, and practical adaptive data analysis. His publication record shows consistent contributions to core machine learning conferences and workshops, with recent work focusing on symmetric and asymmetric hashing techniques, matrix parameter learning, and optimization methods. The publications demonstrate a strong theoretical foundation with practical applications across various machine learning domains. Professor Srebro has been actively involved in several research programs including the Federated and Collaborative Learning program (Spring 2026, as Visiting Scientist and Program Organizer), Modern Paradigms in Generalization (Fall 2024), and multiple summer clusters on Deep Learning Theory and Fairness. His program participation reflects his leadership in emerging areas of machine learning research. Contact: nati@ttic.edu | (773) 834-7493 | Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637
Professor Omer Rana serves as Professor of Performance Engineering and International Dean for the Middle East at Cardiff University's School of Computer Science and Informatics. He also holds the prestigious position of Cross-Council Research Director for the UK National Edge AI Hub, demonstrating his leadership in national research initiatives. Previously, he led the Complex Systems research group and served as Dean of International for the Physical Sciences and Engineering College at Cardiff University. Professor Rana is a Fellow of both the Learned Society of Wales and the Higher Education Academy, and serves on the Advisory Board of the Welsh Ethnic Minority Professors Initiative (WEMPI). His research expertise centers on the intersection of intelligent systems and high performance distributed computing, with particular focus on applying intelligent techniques to resource management in distributed systems. His scholarly contributions span edge computing, cloud computing, Internet of Things (IoT), artificial intelligence, cybersecurity, federated learning, privacy-preserving systems, and sustainable computing. Professor Rana has published extensively in top-tier journals and conferences, with recent work emphasizing practical applications in industrial automation, smart buildings, and sustainable computing solutions. Professor Rana's publication record demonstrates consistent leadership in edge computing and distributed systems research, with a growing emphasis on practical implementations across diverse application domains. His work bridges theoretical computer science with real-world technological challenges, particularly in industrial automation, smart environments, and sustainable infrastructure. Fellow of the Learned Society of Wales Fellow of the Higher Education Academy Professor Rana actively supervises postgraduate students and has secured significant research funding for projects related to edge computing, IoT, and distributed systems. His international collaborations span Europe, Asia, and the Middle East, reflecting his global influence in the field. He serves as a sought-after keynote speaker, workshop chair, and panel moderator at major international conferences including IEEE/ACM Utility and Cloud Computing (UCC) and IEEE Edge Computing. He leads multiple research initiatives, most notably as Cross-Council Research Director for the UK National Edge AI Hub, where he shapes national research directions in edge computing and AI. His work has practical applications across industrial automation, smart building management, electric vehicle infrastructure, and sustainable computing solutions.
Prof. Dr. Dirk Heckmann is a prominent academic and legal expert currently serving as a member of the Bavarian Institute for Digital Transformation (bidt) Board of Directors and holding the Chair of Digital Law and Security at the Technical University of Munich . As a constitutional lawyer and judge at the Bavarian Constitutional Court, he bridges legal expertise with digital policy through advisory roles in the Federal Government's Data Ethics Commission and the Chancellor's National IT Summit. In 2020, he established the TUM Center for Digital Public Services to advance legally sound, common-good-oriented digitalization. His research focuses on digital education , digital administration , and healthcare digitalization , with particular emphasis on legal certainty for digital systems . Recent projects like ReDiKo (Regulating Digital Communication Platforms) and AFFAIRE (AI Regulation for Examinations) highlight his commitment to evidence-based digital governance. He has authored the juris Practical Commentary on Internet Law since 2021, shaping Germany's digital legal discourse. Professor Heckmann actively engages in public debates about digital sovereignty and platform regulation , most notably at the 2025 Nuremberg Digital Festival where he will discuss open-source foundations for digital sovereignty. His work spans constitutional law , data ethics , and AI policy , with recent publications analyzing social media harassment, AI's impact on education, and digital violence legislation.
Professor Will Bateman is a distinguished academic at the Australian National University (ANU) College of Law, where he serves as a Professor and recently completed his term as Associate Dean (Research) from 2021 to 2024. He is also a Chief Investigator for the ANU Grand Challenge project "Humanising Machine Intelligence" and a Fellow at the Gradient Institute, a leading ethical AI research organization based in Sydney. Professor Bateman's educational background is impressive, having earned a PhD and LLM (Hons) from the University of Cambridge and a BA/LLB (Hons) from the Australian National University. Prior to his academic career, he worked in appellate litigation, commercial disputes, and banking as a solicitor at Herbert Smith Freehills, and served as an associate to Justice Stephen Gageler AC of the High Court of Australia and Justice Steven Rares of the Federal Court of Australia. Professor Bateman's research spans two major interdisciplinary domains that sit at the intersection of law with finance and technology. His work on financial regulation focuses on the legal aspects of central banking, sovereign debt markets, digital currencies, and sustainable investing. He has provided expert evidence to the UK Parliament's House of Lords Inquiry into Quantitative Easing, and has collaborated with major financial institutions including the Federal Reserve Bank of New York and the Bank of England. His research on artificial intelligence examines regulatory frameworks for AI in the public sector, with collaborations including the Minderoo Foundation and the Gradient Institute. His recent publications demonstrate a remarkable breadth across legal theory, financial regulation, and AI governance. The articles reveal a consistent theme of examining how traditional legal frameworks adapt to new financial technologies and monetary policy challenges. His work bridges theoretical legal scholarship with practical policy implications, as evidenced by his numerous government consultations and collaborations with central banks worldwide. 2020 Yorke Prize by the University of Cambridge for his work on public finance and constitutionalism Top 10 all-time most downloaded SSRN paper on central banking ("Central Bank Money: Liability, Asset, or Equity of the Nation?") Professor Bateman actively supervises research students, currently mentoring Benjamin Ettinger who is pursuing a PhD on "Legal Method, Cartels and Public Monopolies: A View From the High Court 1908 - 1948." He has secured significant research funding, including projects funded by the Economic and Social Research Council (UK), the German Research Foundation (Deutsche Forschungsgemeinschaft), and The Minderoo Foundation. His "Rebuilding Macroeconomics Initiative: Legal and Economic Conceptions of Money" received £245,000, while the "FA Mann" project was funded with €620,000 (approximately A$1,012,500). He leads the "Humanising Machine Intelligence" project, an ambitious interdisciplinary initiative involving computer scientists, mathematicians, philosophers, sociologists, psychologists, and lawyers aimed at developing democratically legitimate machine intelligence. He also co-led a major project with the University of Western Australia to formulate model legal frameworks for AI regulation in the public sector, funded by The Minderoo Foundation.
Amrita Roy Chowdhury is an Assistant Professor in the Department of Computer Science at the University of Michigan, Ann Arbor. Her research focuses on developing systems that enable safe, decentralized data analytics while ensuring provable privacy guarantees through the synergy of differential privacy and cryptography. Key research areas: Data Privacy, Cryptography, Secure Data Analytics, and Privacy-Preserving Machine Learning. Recent work explores prompt sanitization for LLMs (NDSS 2026), robust graph analysis (ASIACCS 2025), and metric differential privacy (CCS 2024). She has received awards including Best Paper at Private ML@ICLR'24 and Best Poster at ITA'23. Current Ph.D. advisees include Mushtari Sadia, Yiyi Sun, and Samanway Sadhu. Her work spans conferences like IEEE S&P, CCS, USENIX Security, and ICML.
Edouard Oyallon is a CNRS Researcher at Sorbonne University's MLIA team within the Institute of Intelligent Systems and Robotics (ISIR). His research focuses on machine learning foundations, particularly the symmetries of deep neural networks, and large-scale distributed/decentralized training algorithms. He has contributed to frameworks like Kymatio for wavelet scattering transforms and collaborates on projects such as SHARP (Frugal Learning) and ADONIS (ANR-funded). He advises multiple PhD and postdoctoral researchers and teaches advanced deep learning courses at Institut Polytechnique de Paris (IPP). Grants include the ADONIS project (ANR/Sorbonne) and participation in VHS and CoCa4AI initiatives. His work spans theoretical and applied aspects, with recent emphasis on optimizing LLM training at exascale. He maintains active roles in academic service, including organizing workshops on federated learning and graph machine learning.
Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.
Yusupova Guzel Fatehovna is an Associate Professor at the Department of Applied Economics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE), where she has been working since 1998 with 28 years of scientific and teaching experience. She also serves as a Senior Research Fellow at the Institute for Enterprise and Market Analysis, specifically in the Laboratory of Competition and Antimonopoly Policy. Her research interests focus on Industrial Organization, Competition Policy, Antitrust Economics, Digital Markets, Market Analysis, and Economics of Network Effects, with particular attention to Russian industrial markets. Her professional expertise stems from her Candidate of Economic Sciences degree (2007) in Economics and Management of the National Economy, with a dissertation on "Boundaries of Russian markets and competition," and her Associate Professor title awarded in 2013. Dr. Yusupova's publication record shows a consistent focus on competition policy and market analysis, with her most recent work examining AI applications in cartel detection, digital platform competition, and the nuances of antitrust enforcement in specialized markets. Her research demonstrates progression from foundational industrial organization topics to increasingly complex digital market challenges. Honorary certificate of the Ministry of Science and Higher Education of the Russian Federation (November 2022) Gratitude of HSE (March 2022) Gratitude of the Faculty of Economic Sciences HSE (January 2021) Winner of the Competition for the best Russian-language scientific works of HSE employees - 2021 Multiple academic bonuses for publications and contributions to HSE reputation (2010-2024) As an educator, Dr. Yusupova teaches graduate and undergraduate courses including Analysis of Industry Markets and Competition Policy, Introduction to the Economy of Digital Platforms, and Theory of Industrial Markets. Her teaching reflects her research expertise, bridging theoretical industrial organization concepts with practical antitrust applications. She has participated in numerous international conferences and research projects focused on competition policy effectiveness, with particular emphasis on Russian market contexts and transition economy challenges.
Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .
