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.
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Beverley J. McKeon is a Professor of Mechanical Engineering at Stanford University, previously holding the Theodore von Kármán Professorship in Aeronautics at Caltech. Her research focuses on fluid mechanics, particularly turbulence, flow control, and boundary layer dynamics. She earned her B.A. and M.Eng. from the University of Cambridge, and her Ph.D. from Princeton University. McKeon's work integrates experimental and theoretical approaches to manipulate wall-bounded flows for drag reduction and performance enhancement. Her research interests include resolvent analysis, high Reynolds number turbulence, and the application of machine learning to fluid dynamics. She has led interdisciplinary projects on morphing surfaces and viscoelastic turbulence. Awarded the Vannevar Bush Faculty Fellowship and PECASE, McKeon has been recognized for her teaching and mentoring. Her honors include Fellowships from the APS and AIAA. She chairs editorial boards for journals like Physical Review Fluids and has served on national committees for theoretical and applied mechanics. Her academic leadership includes roles as Deputy Chair of Caltech’s Division of Engineering and Applied Science and Associate Director of GALCIT. She advises numerous students and collaborates globally on initiatives like the Stories of Women in Fluids.
Dr. Lucas Pahl is a Lecturer in Economics at the University of Sheffield, School of Economics since 2024. His research specializes in mathematical game theory, particularly in equilibrium refinements, fixed point theory, and real algebraic geometry. He holds a PhD in Economics from the University of Rochester, an MSc in Mathematics from IMPA (Brazil), and a BSc in Social Sciences from the University of São Paulo. Previous positions: Postdoctoral Scholar at Hausdorff Institute for Mathematics and Institute for Microeconomics, University of Bonn His research interests include advanced topics like fixed point theory applications in game theory, algebraic topology intersections with economic models, and equilibrium refinement methodologies. Recent work explores information spillover in zero-sum games and polytope-form game structures. Key publications focus on O’Neill’s theorem extensions, sustainable equilibria analysis, and finite characterizations of perfect equilibria. He serves as a reviewer for top journals including Econometrica and Games and Economic Behavior. No scientific awards explicitly mentioned. His advisory and grant activities remain unspecified in current records. Labs/teams: No lab affiliations explicitly stated in provided information.
- Autonomous Robotics
- Control Systems
- Game Theory
- +۴ مورد دیگر
Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).
Freddy Bouchetمشاهده پروفایل
پژوهشگر
- Statistical Physics
- Climate Dynamics
- Turbulence
- +۵ مورد دیگر
Freddy Bouchet is a Directeur de Recherche at CNRS and a Professeur attaché at École Normale Supérieure de Paris (ENS-PSL). His work bridges mathematical physics, climate science, data science, and statistical mechanics , focusing on turbulent flows, climate extremes, and large deviation theory . He will lead the Laboratoire de Météorologie Dynamique (LMD) starting 2025. Research Themes : Statistical mechanics of geophysical flows (Jupiter's jets, ocean currents). Large deviation theory for rare events in turbulence and climate. Non-equilibrium phase transitions in atmospheric/oceanic systems. Ensemble inequivalence in systems with long-range interactions. Scientific Awards : Three Physicists Prize Collaborations : Tapio Schneider, Antoine Venaille, J. Laurie, O. Zaboronski, B. Dubrulle, A. Venaille. Labs & Teams : Climate and Statistical Mechanics group at ENS de Lyon Future director of Laboratoire de Météorologie Dynamique (LMD/IPSL) Publications span climate dynamics, turbulence, statistical mechanics, and large deviation theory , with applications to Jupiter's atmosphere, ocean vortices, and non-equilibrium systems . His work often challenges paradigms like Tsallis non-extensive statistics.
GONG Jiangbin serves as Professor and Head of Department at the National University of Singapore (NUS), holding the prestigious Provost's Chair Professorship (2020-2026). He is a Principal Investigator at the Centre for Quantum Technologies (CQT) with office S12-02-10 and contact email phygj@nus.edu.sg. Education: PhD, University of Toronto, Canada (2001) His research program centers on topological quantum phenomena, with primary focus on novel topological phases of matter and their applications in quantum computation and information transfer. The group actively investigates quantum dynamics control, quantum simulation frameworks for metrology/sensing applications, disorder physics in few/many-body systems, quantum chaos, and emerging quantum machine learning paradigms to bridge theoretical advances with practical quantum technologies. Analysis of recent publications (2018-2024) reveals consistent expertise in topological quantum systems, spanning non-Abelian braiding in Majorana time crystals, Thouless pumping with single spins, exotic Floquet semimetals, acoustic topological platforms, and KPZ physics in Anderson localization - demonstrating deep integration of theoretical modeling with experimental quantum platforms. Scientific Awards: Provost's Chair Professorship (2020-2026) National Research Foundation Investigatorship (class of 2017) No explicit information regarding student advising or specific research grants was provided in the source material, though his leadership role suggests significant mentorship responsibilities and grant oversight. Gong leads a multidisciplinary research group at CQT/NUS that synergizes theoretical quantum physics with experimental quantum technologies, maintaining active collaborations across quantum simulation, topological materials, and quantum information science to advance next-generation quantum computing architectures.
Klaus Mølmer is a Professor at the Niels Bohr Institute, University of Copenhagen, specializing in Quantum Optics and Photonics. His research spans quantum information, entanglement, and cavity QED, leveraging machine learning and Grover's algorithm for quantum state engineering. His recent work focuses on spin squeezing, Rydberg atom interactions, and mechanical resonator cooling. A leader in quantum simulation and superradiance, he collaborates on cavity-mediated emission and quantum network design. The 15 most recent articles highlight advancements in quantum state manipulation, entanglement protocols, and robust differential phase sensing. These studies bridge theoretical frameworks with experimental applications in cavity QED, Rydberg arrays, and zero-photon detection.
- Machine Learning
- Dynamical Systems
- Optimization
- +۳ مورد دیگر
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
Gabriele Farinaمشاهده پروفایل
استادیار
- Game Theory
- Machine Learning
- Optimization
- +۷ مورد دیگر
Gabriele Farina is an Assistant Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS), with additional affiliations at the Operations Research Center (ORC). Holding the X-Window Consortium Career Development Chair, his research focuses on theoretical and algorithmic foundations for learning and computational decision-making under imperfect information, integrating game theory, machine learning, optimization, and statistics. He previously served as a Research Scientist at Meta's Fundamental AI Research (FAIR) group, where he contributed to Cicero, a human-level AI agent combining strategic reasoning and natural language. Ph.D. in Computer Science from Carnegie Mellon University (advisor: Tuomas Sandholm) Facebook Fellowship (2019-2020) in Economics and Computation Recipient of multiple awards including ACM SIGecom dissertation award, NSF CAREER, and AI2050 Early Career Fellow His research spans four key areas: (1) No-Regret Learning Dynamics in extensive-form games; (2) Correlation and Mediated Equilibria in sequential decision-making; (3) Team Games and Team Max-Min Equilibria; and (4) Human Modeling and Equilibrium Perfection. His work addresses challenges in scalable equilibrium computation, stability of learning algorithms, and robustness to mistakes in multi-agent systems. Recent publications highlight advancements in polynomial-time equilibrium computation, cautious optimism algorithms, and connections between regret minimization and mirror descent. These contributions appear in top venues like COLT, NeurIPS, ICML, and AAAI, with keywords spanning game theory, optimization, and machine learning. NSF CAREER award AI2050 Early Career Fellow Facebook Fellowship ACM SIGecom dissertation award GameSec 2024 best paper award ICLR 2023 outstanding paper honorable mention His research group at MIT collaborates on projects involving strategic reasoning, human-level AI agents, and equilibrium refinements, with applications to games like Diplomacy and poker. Current efforts include developing faster algorithms for correlated equilibria and exploring connections between machine learning and economic theory.
Professor Paul Goulart is a full Professor of Engineering Science at the University of Oxford and Tutorial Fellow at St Edmund Hall, positions he has held since 2014. He leads research and teaching in robust optimization, control systems, and high-speed numerical methods, with applications spanning fluid flows, traffic networks, and economics. Education SB & MSc, Aeronautics and Astronautics – Massachusetts Institute of Technology (MIT) PhD, Control Engineering – University of Cambridge (Gates Scholar, 2007) Research Interests Professor Goulart’s work lies at the intersection of control engineering and optimization . His core expertise includes: Robust and high-speed convex optimization Model predictive control (MPC) and control barrier functions Neural-network-based control and system identification Optimization over traffic and economic networks Real-time and embedded optimization solvers These interests are reflected in prolific publication output and active supervision of doctoral researchers. Publications & Trends From 2020 to 2025 Professor Goulart has co-authored more than thirty papers. A dominant theme is the development of fast, reliable algorithms for conic optimization and robust control , often leveraging machine-learning techniques to enhance scalability and real-time performance. Recent works emphasize safety certificates, GPU-accelerated solvers, and neural-network controllers for uncertain systems. Awards & Honors Gates Cambridge Scholar (2003) Advising & Grants Professor Goulart actively seeks DPhil students in control engineering and optimization . He leads the Control Group within the Department of Engineering Science and has been involved in multiple industrially funded projects, although specific grant identifiers are not provided in the supplied text. Laboratory & Teams He is a member of the Control Group , Department of Engineering Science, University of Oxford, and serves as Secretary to the Governing Body of St Edmund Hall (Michaelmas Term 2024).
Thomas Demeester is an Associate Professor at the Internet Technology and Data Science Lab (IDLab), Ghent University - imec, Belgium. Appointed as Assistant Professor in 2019, he leads an AI research group focused on health applications and drug design, co-directing the Text-to-Knowledge research cluster with Prof. Chris Develder. His educational background includes: M.Sc. in Electrical Engineering from Ghent University (2005), completed with thesis work at ETH Zurich Ph.D. in Computational Electromagnetics from Ghent University (2009), funded by Research Foundation - Flanders (FWO) Demeester's research spans artificial intelligence with emphasis on deep learning and neuro-symbolic methods. Current tracks include energy-based models (Hopfield Networks, Deep Equilibrium Models), diffusion models for drug design, and clinical reasoning systems. His work bridges NLP, healthcare informatics, and generative AI with strong industry partnerships. Recent publications (2023-2025) reveal strategic expansion from NLP into health-centric AI: BioLORD biomedical encoders (2023), synthetic medical data frameworks (UAI/NeurIPS 2024), and novel diffusion model guidance (ICLR 2025). This evolution demonstrates convergence of generative modeling, clinical data analysis, and protein design. He actively mentors 24 PhD students across diverse AI domains: Current Research: Conversational agents, emotion analysis, clinical reasoning, antibody design, and diffusion model optimization Recent Graduates: Interpretable language models, biomedical semantics, task-oriented dialogue, and social media knowledge extraction Research is supported by imec funding and collaborations with Flemish biotech companies, building on his post-doctoral experience securing media-sector projects. Within IDLab, he co-leads the Text-to-Knowledge cluster driving NLP innovations for healthcare, legal, and economic applications.
Yiguang Ju is the Robert Porter Patterson Professor of Mechanical and Aerospace Engineering at Princeton University, affiliated with the HMEI Grand Challenges Program. His research focuses on plasma-assisted combustion, alternative fuels, and nano-material synthesis via flame processes. He investigates energy-efficient systems for microscale energy conversion, catalytic reactions, and low-temperature plasma chemistry. Research interests include non-equilibrium plasma dynamics, ammonia synthesis, and high-pressure oxidation kinetics. He develops advanced diagnostics like hybrid laser spectroscopy and machine learning models to study reaction mechanisms. Recent work explores plasma-enhanced combustion for hydrogen and alternative fuels, with applications in energy storage and emission reduction. His studies address challenges in plasma-chemistry interactions, material synthesis, and high-pressure combustion systems. His articles highlight innovations in plasma catalysis, combustion kinetics, and atmospheric chemistry. Collaborative projects include plasma-based material recycling and supercritical-pressure reactor analysis. He leads initiatives in clean energy technologies and sustainable chemical processes.
Oliver Schmitz is a Professor in the Department of Nuclear Engineering & Engineering Physics at the University of Wisconsin-Madison, where he leads research in plasma edge physics for magnetic confinement fusion and next-generation particle accelerators. His work bridges experimental plasma science, computational modeling, and diagnostic development with applications in both tokamaks and stellarators. Education: PhD (2006), Heinrich-Heine-Universität Diploma (2003), Rheinische Friedrich-Wilhelms-Universität Professor Schmitz's research focuses on 3D plasma edge transport phenomena, plasma-wall interactions, and helicon plasma generation for wakefield accelerators. His group employs advanced computational tools like EMC3-EIRENE for 3D plasma edge modeling and develops active spectroscopic diagnostics to measure plasma parameters through atomic emission analysis. Key themes include resonant magnetic perturbation effects in tokamaks, inherent 3D physics in stellarators, and high-density plasma sustainment for accelerator applications. He actively develops atomic models to interpret spectroscopic data and operates helicon plasma test stands for fundamental process studies. Recent publications reveal strong emphasis on experimental-computational integration for fusion boundary physics, with significant contributions to ITER divertor solutions, stellarator exhaust optimization, and plasma-facing materials. The work shows growing focus on wakefield accelerator diagnostics through helicon plasma sources and advanced spectroscopy, alongside persistent innovation in 3D modeling of plasma-material interfaces. Scientific Awards: 2020 Thomas and Suzanne Werner Chair Professorship 2018 UW Madison Teaching Academy Fellow 2017 ITER Science Fellowship & Vilas Mid-Career Award 2015 DOE Early Career Award & NSF CAREER Award 2011 Torkil Jensen Award (General Atomics) 2007 Günther-Leibfried-Preis (Jülich) Professor Schmitz directs multiple DOE/NSF-funded research programs including his UW Madison laboratory and AWAKE project contributions at CERN. He mentors graduate students through NE 890/990 thesis research courses and has developed nationally recognized K-12 outreach including the "Plasma Show" for elementary schools and "Plasma Academy" for high-school educators developing AP Physics curriculum modules. His leadership extends to university governance through the Kaufman seminar on academic leadership. His research group operates helicon plasma test stands and computational facilities for EMC3-EIRENE simulations, with current efforts focused on high-density plasma sources for accelerators and resilient divertor solutions for stellarators. The group maintains strong international collaborations with ITER, CERN, and major fusion facilities worldwide.
Ardalan Vahidi is a Professor of Mechanical Engineering at Clemson University, joining in 2005 after receiving his Ph.D. from the University of Michigan. His research focuses on optimal control, energy-efficient mobility, connected and automated vehicles, and human bioenergetics during exercise. Education: Ph.D. Mechanical Engineering, University of Michigan, Ann Arbor, 2005 M.Sc. Transportation Safety, George Washington University, 2001 M.Sc. Structural Engineering, Sharif University of Technology, 1998 B.Sc. Civil Engineering, Sharif University of Technology, 1996 Research Interests: His work integrates control theory with transportation systems to reduce energy use and emissions. He explores eco-driving algorithms, vehicle connectivity, and human factors in cycling performance, leveraging both modeling and extensive vehicle-in-the-loop experimentation. Publications Trend: Recent articles emphasize validated experiments on energy-efficient automated driving, cyclist fatigue modeling, and cooperative control strategies, demonstrating a shift toward cyber-physical validation and interdisciplinary sports science applications. Scientific Awards: Best Paper Award, Road User Measurement and Evaluation Committee, TRB 2024 2nd Best Paper Award, IEEE International Automated Vehicle Validation Conference 2023 ASME Automotive and Transportation Systems Best Paper Award 2020 & 2018 IFAC Young Author Award 2019 Advising & Grants: He mentors numerous graduate researchers and postdocs; prospective students are directed to an online form for open positions. His research has been supported by NSF, DOE, DOT, and industry partners, although specific grant details are not listed here. Labs & Teams: He leads the Clemson Vehicle & Energy Systems Laboratory, conducting vehicle-in-the-loop experiments and collaborating with interdisciplinary teams across mechanical engineering, transportation, and sports science.
Rachid Cherkaoui is a Senior Scientist at École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering, specifically within the Department of Electrical Engineering. He is actively associated with research units SEL-ENS, EDEY-ENS, and DESL, contributing to the Distributed Electrical Systems Laboratory (DESL). His work focuses on advanced power system optimization, smart grids, and energy market modeling. Ph.D. in Electrical Engineering, EPFL, 1992 M.S. in Electrical Engineering, EPFL, 1983 Dr. Cherkaoui's research interests include electrical power and distribution systems, distributed generation, energy storage, electricity market deregulation, and power system vulnerability mitigation. His work bridges theoretical modeling and real-world applications, particularly in flexibility provision, grid resilience, and market integration of renewable energy. His recent publications (2020–2025) reflect a strong focus on smart grid technologies, energy storage integration, and market mechanisms. Key themes include optimal dispatch of hybrid systems, TSO-DSO coordination, frequency control, and stochastic optimization under uncertainty. His work is frequently published in top-tier journals such as IEEE Transactions on Power Systems and IEEE Transactions on Smart Grid. ABB Swiss Award '83 Senior Member, IEEE Member, CIGRE Task Forces C5-2 IEEE Swiss Chapter Officer since 2005 Dr. Cherkaoui actively supervises doctoral students and collaborates extensively with researchers like Mario Paolone. He has contributed to numerous projects funded by industry, CTI/Innosuisse, and Horizon 2020. His research includes experimental validation and real-time control systems, particularly in hydropower and battery storage applications. He is also involved in national and international energy strategy discussions, including Switzerland's path to carbon neutrality.
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