Maarten de Hoop is the Simons Chair and Professor of Computational and Applied Mathematics at Rice University, part of the George R. Brown School of Engineering. He holds visiting roles at MIT and the Chinese Academy of Sciences. His research spans seismic wave analysis, inverse problems, deep learning, and planetary seismology. He earned his Ph.D. in Technical Sciences from Delft University of Technology (1992), and earlier degrees from Utrecht University. Notable awards include the 1996 J. Clarence Karcher Award and 2001 Fellowship from the Institute of Physics. His work integrates computational mathematics with geophysics, focusing on extracting signal information from large datasets, developing novel inverse scattering methods, and applying deep learning to geoscience challenges. Recent studies include transformer models for in-context learning, semialgebraic neural networks, and seismic waveform foundation models like SeisLM. He leads the Geo-Mathematical Imaging Group, fostering interdisciplinary projects in planetary missions and data-driven discovery.
Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.
Erik P.M. Vermeulen is a Full Professor of Business and Financial Law at Tilburg University and a key researcher at the Tilburg Law and Economics Center (TILEC) in the Netherlands. He also serves as Senior Counsel Corporate/Vice President at Philips International B.V. , focusing on corporate governance, M&A, and securities law. His academic work spans comparative company law, innovation, and sustainable legal frameworks. Current Affiliation: Professor at Tilburg Law School Practice: Corporate governance and venture capital at Philips His research explores how legal structures influence corporate competitiveness and innovation, particularly in digitalization and AI. He emphasizes the intersection of Law & Technology , Responsible Organizations , and Entrepreneurial Finance . Recent article trends include insights on: 2025: AI-driven corporate transformation and global compliance 2024: Gen X's tech struggles and urban well-being 2020: Evolution of governance frameworks He directs Tilburg's International Business Law Program and advises SMEs through the International Venture Club . His global teaching spans institutions in Colombia, Belgium, Japan, and Israel.
Miyuki Hino is an Assistant Professor in the Department of City and Regional Planning and an Adjunct Assistant Professor in the Environment, Ecology, and Energy Program at the University of North Carolina at Chapel Hill. She holds a Ph.D. in Environment and Resources from Stanford University and a B.S. in Chemical Engineering from Yale University. Her research focuses on climate hazards, governance, and public policy , with emphasis on equitable adaptation to climate change. Key areas include sea level rise impacts, flood risk on property markets, and managed retreat strategies. She has conducted extensive work on floodplain development policies, household relocation programs, and community resilience frameworks. Dr. Hino's interdisciplinary approach integrates environmental science, urban planning, and social equity . She collaborates with academic and municipal partners, such as the Center for Urban and Regional Studies and Annapolis, MD local governments, to develop actionable solutions for climate adaptation. Her work bridges technical analyses (e.g., sensor networks, machine learning) with policy design to ensure both effectiveness and justice in climate responses. Recent projects emphasize preventing future 'trapped households' by analyzing zoning policies and market dynamics that drive risky development. She advocates for climate-smart growth strategies to balance economic needs with environmental safety, while addressing disparities in vulnerability across communities. Her research has been featured in Science Advances , Nature Climate Change , and interdisciplinary journals. She actively engages with policymakers to translate findings into practical measures, such as equitable buyout programs and floodplain management reforms.
Bryan H. Choi is an Associate Professor of Law at the University of Colorado Law School , where he bridges law and computer science to address software and AI safety. His work on software liability has influenced national cybersecurity strategy discussions. As an Adviser for the ALI Principles Project on Civil Liability for Artificial Intelligence , he shapes legal frameworks for emerging technologies. Education : JD and AB in Computer Science from Harvard University; clerkships with U.S. Court of Appeals judges Leonard I. Garth and William C. Bryson. Roles : Former joint appointment at Ohio State University Law School and Computer Science Department; Faculty Fellow at UPenn's CTIC; Director of Law and Media at Yale's ISP. Research Focus : Choi's scholarship examines software liability , AI accountability , and privacy law through interdisciplinary lenses. He critiques institutional approaches to software safety and advocates for empirical legal frameworks over participation-based models. Recent Articles address AI malpractice , NIST software standards , and forensic tool validation , reflecting trends in AI regulation and cyber-physical system liability . His 2021 NSF grant funded technical-legal methods for safety-critical systems. Awards & Grants : National Science Foundation (NSF) Grant (2021) Adviser, ALI Principles Project on Civil Liability for Artificial Intelligence Community Engagement : Active in Law and Computer Science communities , serving on committees for the ACM Symposium , Cybersecurity Law and Policy Scholars Conference , and co-organizing the AAAI Bridge Program on AI and Law .
Florian Schäfer is an Assistant Professor at the School of Computational Science and Engineering at Georgia Tech. His research spans numerical computation, statistical inference, and competitive games, with applications in materials science, turbulence modeling, computer graphics, and computational geometry. He will join the Courant Institute at NYU in September 2025. PhD in Applied and Computational Mathematics, Caltech Bachelor’s and Master’s in Mathematics, University of Bonn His work focuses on information geometric mechanics to design structure-preserving numerical methods for continuum mechanics. This includes: State-of-the-art solvers for elliptic PDEs via Gaussian elimination and conditional independence Efficient multi-agent optimization algorithms Information geometric regularization for compressible fluid dynamics Enabling the first compressible fluid simulation exceeding 100 trillion grid cells His recent research trends integrate: Machine learning for materials science (e.g., active learning, Bayesian approaches) Stochastic modeling of microstructures and phase-field problems Neural operators for super-resolution fluid dynamics Generative models for polycrystalline material datasets Optimal transport and diffusion models for conditional density transformations High-performance computing at extreme scales Florian collaborates with researchers including Houman Owhadi, Jessie Liu, Spencer Bryngelson, Tamer Zaki, and Ali Mani. He actively presents at conferences like SIAM and UCLA seminars, and is recruiting PhD students for work at the Courant Institute starting 2025.
Univ-Prof. Dr. med. Malek Bajbouj serves as Director of the Institute for Affective Neuroscience and Emotion Modulation at Charité – University Medicine Berlin's Campus Benjamin Franklin (CBF), operating within the Department of Neurology, Neurosurgery and Psychiatry (CC 15). His position integrates clinical leadership with translational neuroscience research focused on severe mental illnesses. Dr. Bajbouj's research program centers on affective neuroscience and emotion dysregulation mechanisms in psychiatric disorders, particularly schizophrenia spectrum conditions and depression. He pioneers multimodal intervention approaches combining neuromodulation (tDCS), oxytocin augmentation, mindfulness therapies, and digital health tools. His work emphasizes translational biomarker development using neuroimaging, machine learning, and physiological stress parameter analysis to personalize treatment for treatment-resistant populations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) novel treatment combinations for negative symptoms in schizophrenia (oxytocin + mindfulness, yoga therapy); (2) real-world implementation of neuromodulation (at-home tDCS protocols, technical efficacy monitoring); and (3) global mental health responses to crises (pandemic impacts on vulnerable groups, culturally adapted refugee interventions). His methodology consistently employs rigorous randomized controlled trials with embedded biomarker studies. As director of his eponymous institute, Dr. Bajbouj leads a multidisciplinary team conducting neuroimaging studies, clinical trials, and international collaborations focused on emotion modulation pathways. The institute coordinates research across CC 15's clinical infrastructure at CBF Building V, with particular emphasis on bridging laboratory neuroscience with clinical psychiatry through the DepressionDC and OXYMIND trial frameworks.
Christophe Ancey is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he serves in the School of Architecture, Civil and Environmental Engineering (ENAC), specifically in the Institute of Civil Engineering (IIC) and the Environmental Hydraulics Laboratory (LHE). He also holds teaching appointments in SGC-Teaching and EDME-Teaching departments at EPFL. His office is located at GC A1 401, Station 18, 1015 Lausanne, Switzerland. Dr. Ancey holds both a PhD and engineering degree from Ecole Centrale de Paris and Grenoble National Polytechnic Institute. After completing his doctoral work (1994-1997) on rheology of granular flows under Pierre Evesque, he worked as a researcher at Cemagref before joining EPFL in 2004. He directs the Environmental Hydraulics Laboratory and serves as associate editor for Water Resources Research, a leading journal in hydrology. Dr. Ancey's research spans fluid dynamics, rheology, and hydraulics with emphasis on geophysical flows and natural hazards. His work focuses on three interconnected themes: rheology of concentrated suspensions (particularly granular flows in avalanches and mudflows), inverse problems in rheology (determining microscopic behavior from macroscopic measurements), and particle entrainment in turbulent suspensions (erosion and sediment transport processes). His recent publications (2022-2025) demonstrate continued innovation in sediment transport modeling, experimental techniques like PIV for complex flows, and integration of machine learning approaches. The research shows progression from fundamental fluid mechanics to practical applications in natural hazard assessment and river engineering, with particular attention to granular segregation, avalanche-obstacle interactions, and river morphodynamics. Associate Editor, Water Resources Research Co-founder of Toraval, an engineering consulting firm specializing in avalanche risk management Dr. Ancey teaches courses on Fluid Mechanics, Flood and Dam Break Waves, Hydrological Risks and Structures, and Similarity and Transport Phenomena in Fluids. He has supervised numerous PhD students whose work spans environmental hydraulics, granular flows, and sediment transport, with current students including Chen Yanan, Farazande Sofi, and Giboulot Axel Loïc among others.
Nils Holzenberger is an Assistant Professor at Télécom Paris, France, since February 2023, affiliated with the Data Intelligence Graphs (DIG) research team within the Information Processing and Communication Laboratory (LTcI). His work bridges artificial intelligence, natural language processing, and legal domains through neuro-symbolic approaches to statutory reasoning, particularly in tax law. Education: PhD in Computer Science, Johns Hopkins University (2017-2022) Master's in Engineering, Mines ParisTech (2013-2017) Preparatory Classes, Lycée Louis-le-Grand (2011-2013) Holzenberger's research centers on legal artificial intelligence with emphasis on statutory reasoning limitations in large language models. He pioneered the SARA dataset for tax law reasoning and LegalBench benchmark, developing hybrid symbolic-neural frameworks that expose LLMs' shortcomings in precise legal interpretation. His work integrates Prolog solvers with NLP techniques to create executable tax code mappings and contract analysis tools, establishing foundational methods for verifiable legal AI systems. Analysis of his 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) Tax law reasoning benchmarks exposing LLM hallucinations, (2) Neuro-symbolic integration for statutory interpretation, and (3) Low-resource template extraction for legal documents. His work consistently demonstrates that pure neural approaches fail at precise legal reasoning, necessitating symbolic grounding for reliable legal AI applications. The DIG research team at Télécom Paris, where Holzenberger leads legal AI initiatives, is actively hiring faculty for neuro-symbolic projects. While specific grant details aren't public, his collaborations with HEC Paris, Copilex startup, and featured podcast appearances indicate substantial industry-academia engagement in legal tech development. Holzenberger directs the legal AI vertical within LTcI's DIG team, focusing on data intelligence for statutory reasoning. His group develops tools for tax minimization strategy discovery, contract analysis, and legal information extraction, maintaining close ties with legal practitioners through projects like the Prolog-based tax code interpreter. The team's infrastructure supports both academic research and startup partnerships in computational law.
Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Andrea Liu is the Hepburn Professor of Physics at the University of Pennsylvania, leading the Department of Physics and Astronomy. As Director of the Penn Center for Soft and Living Matter, she bridges physics, biology, and materials science. She joined Penn in 2004 after faculty roles at UCLA (1994-2004) and postdoctoral research at Exxon and UCSB. Her research focuses on theoretical studies of soft and living matter, particularly jamming transitions, glass physics, and emergent phenomena in biological systems. She pioneers the application of machine learning to physical systems, designing self-learning materials and circuits. Education Ph.D., Cornell University (1989) B.A., University of California, Berkeley (1984) Research Interests Soft matter: Glass transition, jamming, and plasticity in disordered solids Living matter: Collective behavior in tissues, fluidization mechanisms, and biopolymer networks Machine learning: Physical implementations, energy-efficient circuits, and adaptive systems Her work combines analytical theory and computation to explain how complex systems achieve functionality through structural and dynamical principles. Publications Trends Recent work emphasizes physical learning networks, clogging dynamics in granular systems, and biophysical tissue mechanics. Key themes include emergent learning in analog systems, topology-driven material design, and interdisciplinary approaches to biological and engineering challenges. Awards 2025 American Physical Society Leo P. Kadanoff Prize 2021-2025 Simons Investigator in Theoretical Physics Member, National Academy of Sciences (2017) Labs & Teams Her research group collaborates on the Center for Soft and Living Matter, advancing theoretical frameworks for adaptive materials and biological systems. Ongoing initiatives focus on machine learning-informed materials design and experimental validation of theoretical models.
Antonio A. Casilli is a Professor of Sociology at Télécom Paris (Institut Polytechnique de Paris) and a researcher at the Interdisciplinary Institute of Innovation (i3) . He is an associate member of LACI-IIAC (formerly the Edgar Morin Center) at EHESS and serves as a Faculty Fellow at the Nexa Center for Internet and Society (Polytechnic University of Turin) since 2018. Research Focus: Digital labor, platform economy, AI ethics, and the social implications of technology Awards: Prix Étoiles de la SCAM, Prix Meilleur Documentaire de l'Année (Bangkok), selected as Nature Journal Top Science Pick His work analyzes how digital platforms reshape labor dynamics, privacy norms, and social inequalities. Recent studies explore global AI data work, including fieldwork in Latin America, Africa, and Europe. He advocates for ethical frameworks recognizing human labor behind automation. Media Engagement: Regular contributor to France Culture , Le Monde , Wired , and Rue89 . Authored En attendant les robots (2019) and Schiavi del clic (2020), investigating hidden digital labor structures.
Gerold Schneider is an Associate Professor at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences and Faculty of Business, Economics and Informatics . He leads the Text Crunching Center (TCC) , focusing on interdisciplinary research at the intersection of NLP, Digital Humanities, and Health Data Science. Research Interests His work spans Text Analytics , Digital Humanities , Corpus Linguistics , and Health Data Science , with applications in: Biomedical NLP (e.g., Alzheimer’s detection, clinical trials) Digital Humanities projects (e.g., analyzing Charles Dickens, UN archives) Migration discourse framing across languages Adversarial data collection for hate speech detection Interdisciplinary methodologies for digital unstructured data Recent Publications 2025–2024 research highlights include annotated corpora for preclinical and neurological studies, AI-driven analysis of historical linguistic variation, and innovative tools for language learners. His NLP applications address health diagnostics, ethical AI, and cross-lingual political discourse. Labs & Teams As TCC leader, he spearheads collaborative projects within the Digital Society Initiative (DSI) communities (AI & Law, Health, Ethics, etc.), integrating computational methods with humanities and health research.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, Language Technologies Institute, leading the L3 Lab. His research focuses on bridging informal and formal reasoning with AI, spanning machine learning for mathematics and code, inference algorithms, and AI agents. PhD in Computer Science from New York University (advised by Kyunghyun Cho) Postdoctoral work at University of Washington (advised by Yejin Choi) His work explores AI-driven formal methods for mathematics and code generation, test-time compute scaling, and algorithms enabling AI improvement over time. Recent publications analyze reasoning evaluation, premise selection, and automated proof optimization in systems like Lean. Key article trends include neural theorem proving, code generation, and inference-time compute optimization. Awards: NVIDIA AI Labs Pioneering Research Awards (2017, 2018), NAACL 2025 Best Paper. Current advisees include PhD students Pranjal Aggarwal, Weihua Du (co-advised with Yiming Yang), Andre He (co-advised with Daniel Fried), and Seungone Kim (co-advised with Graham Neubig). He co-organizes workshops like Autoformalization for the Working Mathematician (ICERM 2025) and VerifAI: AI Verification in the Wild (ICLR 2025), and teaches Advanced NLP at CMU.
