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.
Dimitrios P. Tsomocos is a Professor of Financial Economics at Saïd Business School and a Fellow in Management at St Edmund Hall, University of Oxford. He holds a BA, MA, MPhil, and PhD from Yale University and previously worked at the Bank of England. He serves on editorial boards including Annals of Finance and Economic Theory, and is a Senior Research Associate at the Financial Markets Group at the London School of Economics. His educational background includes: University of Oxford: M.A. by resolution, 2002 Yale University: Ph.D. in economics, 1996 Yale University: M.Phil. in economics, 1992 Yale University: M.A. in economics, 1990 Yale University: B.A. in economics, 1989 Professor Tsomocos is a mathematical economist specializing in Central Banking, Banking and regulation, Incomplete asset markets, Systemic risk, Financial instability, and Issues of new financial architecture. His research focuses on contagion, financial fragility, interbank linkages, and the impact of the Basel Accord using General Equilibrium models with incomplete asset markets, money, and endogenous default. He is working toward designing a new paradigm of monetary policy, financial stability analysis, and macroprudential regulation. His recent publications show a consistent focus on financial stability, banking regulation, and the interaction between monetary policy and financial stability. The research spans theoretical modeling of bankruptcy and default in general equilibrium frameworks, practical applications to bank regulation, analysis of commodity cycles in emerging economies, and policy responses to crises like the COVID-19 pandemic. His work frequently employs quantitative methods and general equilibrium modeling to address pressing issues in financial economics. His scientific achievements include: 2004 Bank Sabatell prize for the best work on the economics of banking (for "Book vs. Fair Value Accounting in Banking and Intertemporal Smoothing") Co-development of the Goodhart-Tsomocos model of financial fragility (2003) Testimony to House of Lords for the Economic and Financial Affairs and International Trade Sub Committee's report (2011) Appointment to Research Advisory Board, Central Bank of Russian Federation (2018) Professor Tsomocos has advised numerous PhD students and collaborated extensively with central banks worldwide. He has served as an economic advisor to a major political party in Greece and regularly provides commentary on the Greek economy. His research has had substantial policy impact, with the Goodhart-Tsomocos model implemented by more than ten central banks including the Bank of Bulgaria, Bank of Colombia, Bank of England, and Bank of Korea. He continues to collaborate with researchers from the ECB, Central Bank of the Russian Federation, and Bank of Chile on updated versions of his financial fragility model. He co-developed the Goodhart-Tsomocos model of financial fragility while working at the Bank of England, which has been implemented at various central banks globally. His research group at Oxford continues to refine this model and apply it to contemporary financial stability challenges.
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.
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.
Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.
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.
Juan Camilo Gómez is an Associate Professor with tenure at the University of Washington Bothell's School of Business, specializing in game theory, bargaining, and microeconomic theory. He earned his Ph.D. in Economics from the University of Minnesota and his B.Sc. in Mathematics from Universidad de los Andes in Colombia. Education: Ph.D. Economics (1998–2003), University of Minnesota, Minneapolis, MN B.Sc. Mathematics (1990–1996), Universidad de los Andes, Bogotá, Colombia Research Interests: His research focuses on foundational and applied aspects of game theory, including bargaining models, coalition formation, cooperative solution concepts, and general equilibrium. He explores how efficiency can be achieved in strategic interactions, especially when agents may behave manipulatively or when traditional balance conditions do not hold. His work contributes to understanding how coalitions form and how outcomes can be predicted or designed to ensure equitable and efficient allocations. He also investigates the implications of reference points in bargaining and the role of aspirations in shaping cooperative behavior. Publications Overview: Across his publications, a clear trajectory emerges from foundational theoretical work—such as axiomatizing core extensions in cooperative games—to applied models that predict coalition formation and bargaining outcomes. His research consistently bridges rigorous mathematical frameworks with practical economic questions, including market interpretations of cooperative solutions and strategic pricing models like 'Pay What You Want'. Awards and Honors: 2010 MBA Professor of the Year, University of Washington Bothell Teaching and Advising: Gómez has taught a wide range of courses from undergraduate calculus and mathematical economics to graduate-level game theory and quantitative methods for business. He has held teaching roles at institutions including Macalester College, University of Copenhagen, Universidad de los Andes, and University of Minnesota. He has also co-directed undergraduate theses, such as that of Santiago Saavedra, and served on numerous academic committees including MBA Admissions and faculty hiring. Labs and Teams: While no specific lab is mentioned, his collaborative working papers with researchers like Camelia Bejan and P.V. Balakrishnan suggest active participation in research networks focused on game theory and economic modeling.
Mark Trodden is the Dean of the School of Arts & Sciences and Thomas S. Gates Jr. Professor of Physics and Astronomy at the University of Pennsylvania. He previously served as the Fay R. and Eugene L. Langberg Professor of Physics, Department Chair, and Co-Director of the Center for Particle Cosmology. His career includes faculty roles at Syracuse University (2000–2009) and visiting positions at Case Western Reserve University and Cornell University. Ph.D. and M.Sc. in Physics, Brown University (1992–1995) Advanced Study in Mathematics, University of Cambridge (1990–1991) M.A. in Mathematics, University of Cambridge (1987–1990) Trodden’s research focuses on the intersection of cosmology and particle physics, addressing fundamental questions such as the nature of dark energy, dark matter, the baryon asymmetry of the universe, inflation, and modified gravity theories. His work explores how cosmological data can constrain physics beyond the Standard Model and general relativity. His publications span topics like dark energy models , inflationary spacetimes , topological defects , and BPS states in supersymmetric theories , reflecting his expertise in connecting high-energy physics to cosmological observations. At Penn, Trodden has held editorial roles for journals like Physics Letters B and Journal of Cosmology and Astroparticle Physics , and has contributed to collaborative workshops advancing cosmology and particle physics.
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.
