جستجوی استاد راهنمای دانشگاهی

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

Ariel Pakesمشاهده پروفایل

استاد

• Econometrics
• Industrial Organization
• Economics of Technological Change
• +۳ مورد دیگر

Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'

Andrew Postlewaiteمشاهده پروفایل

استاد

• Game Theory
• Behavioral Economics
• Social Norms
• +۳ مورد دیگر

Andrew Postlewaite is the Harry P. Kamen Professor of Economics and Professor of Finance at the University of Pennsylvania's School of Arts and Sciences. He holds dual roles in the Department of Economics and the Ronald O. Perelman Center for Political Science and Economics. As a distinguished academic, he is a member of the American Academy of Arts and Sciences and serves as a director at the National Bureau of Economic Research (NBER). Additionally, he is the founding editor of the American Economic Journal: Microeconomics and a research associate at Penn's Population Studies Center and Institute for Law and Economics. Postlewaite's research focuses on game theory, behavioral economics, and social norms, with contributions to mechanism design, repeated games, and intergenerational transmission of preferences. His work bridges theoretical foundations with applied economic analysis, particularly in areas like consumer behavior, decision-making, and institutional design. He has held visiting positions at institutions including Caltech, Harvard, and Stanford, reflecting his academic prominence. His scholarly contributions include pioneering studies on future-oriented decision-making, interdependent valuations in markets, and equilibrium concepts in repeated games. Recent publications explore topics such as the impact of local media accessibility in the digital age and the welfare implications of consumption fluctuations. His editorial leadership at journals like Econometrica and International Economic Review underscores his influence in shaping economic discourse. Awarded prestigious fellowships and editorial roles, Postlewaite's career exemplifies a blend of rigorous theoretical research and practical applications in economics. His current research trends emphasize behavioral insights and the methodological underpinnings of economic models, reflecting a commitment to advancing both theory and real-world relevance.

David Alvarez-Melisمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Optimal Transport
• +۴ مورد دیگر

David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.

Navid Azizanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Control Systems
• +۵ مورد دیگر

Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.

Metin Uyanikمشاهده پروفایل

مدرس ارشد

• Economic Theory
• Game Theory
• Microeconomic Theory
• +۳ مورد دیگر

Dr. Metin Uyanik is a Senior Lecturer at the School of Economics, The University of Queensland. He holds a PhD from Johns Hopkins University (2016) and previously worked as a Postdoctoral Research Fellow at the University of Rochester's Wallis Institute of Political Economy. His research focuses on Economic Theory, Game Theory, Political Economy, Computational Economics, and Mathematical Economics. His work has been published in top-tier journals such as Economic Theory , Games and Economic Behavior , and Journal of Mathematical Economics . Education: Bachelor of Economics, Bogazici University Masters (Research) of Economics, Koc University Doctor of Philosophy, The Johns Hopkins University Research Interests: Dr. Uyanik's research bridges theoretical and applied economics, emphasizing foundational mathematical approaches. His work explores general equilibrium theory, discontinuous games, topological methods in economics, and welfare economics. Key themes include the analysis of non-convex economies, fixed-point theorems, and the integration of axiomatic frameworks with behavioral assumptions. Teaching: He teaches Advanced Microeconomics, Economics for Commerce, and Introduction to Strategic Thinking at UQ. His pedagogical focus includes mathematical rigor and practical applications of economic theory. Collaborations & Future Work: Collaborations with scholars like M. Ali Khan and Richard P. McLean highlight his engagement with complex theoretical frameworks. Ongoing projects include studies on large production economies, myersonian common knowledge, and computational methods in equilibrium analysis.

Eduardo Azevedoمشاهده پروفایل

استاد

• Microeconomic theory
• Market design
• Selection markets
• +۴ مورد دیگر

Eduardo Azevedo is the John M. Bendheim and Thomas L. Bendheim Professor of Business Economics and Public Policy at the Wharton School , University of Pennsylvania. He holds a courtesy appointment as Professor of Economics and was awarded the 2016 Sloan Foundation Fellowship. His research integrates economic theory with practical applications across science and business domains. His research interests include: Market design Selection markets Social science genetics Experimental economics Game theory Recent publication trends focus on: Economic theory applications to healthcare and digital markets Empirical Bayes methods in A/B testing Adverse selection in insurance markets Strategic behavior in two-sided matching Evolutionary behavioral economics He serves as an instructor for BEPP2500 - Managerial Economics , emphasizing real-world application of microeconomic theory to business problems. His work also involves software development for economic research, including MATLAB-based empirical Bayes tools for analyzing treatment effects in large-scale experiments. Scientific awards : Sloan Foundation Fellow (2016)

• Plasma Physics
• Plasma Technology
• Thin Film Technology
• +۶ مورد دیگر

Indrek Jõgi is an Associate Professor of Plasma Technology at the University of Tartu's Institute of Physics within the Faculty of Science and Technology. He serves as Assistant Director of the Institute of Physics and Programme Director of the Doctoral Programme in Chemical and Physical Sciences. His academic career spans over 15 years at the University of Tartu, with progressive roles from Research Fellow to his current Associate Professor position. Dr. Jõgi earned his PhD in Physics (Optics and Spectroscopy) from the University of Tartu in 2007, following a Master's degree in Applied Physics in 2003 and a diploma in Physical Information Technology in 2001. His doctoral research focused on conduction mechanisms in thin atomic layer deposited films containing TiO 2 . His research interests center on plasma physics and technology, particularly the electrical properties of thin metal-oxide films, plasma-chemistry, and thermodynamically non-equilibrium plasma properties. His work bridges fundamental plasma physics with practical applications in materials science, nuclear fusion technology, and biomedical applications. He specializes in plasma diagnostics using Laser-Induced Breakdown Spectroscopy (LIBS) for fusion reactor materials analysis and has made significant contributions to understanding ionization processes in various gas mixtures. His recent publications demonstrate a strong focus on plasma applications for nuclear fusion materials analysis, particularly using LIBS techniques for detecting hydrogen isotopes and impurities in fusion reactor wall materials. His work spans plasma diagnostics, thin film deposition techniques like atomic layer deposition, and biomedical applications of plasma technology including cancer cell treatment research. His scientific awards include: IOP Outstanding Reviewer Award for Journal of Physics D: Applied Physics (2022) IOP Outstanding Reviewer Award for Journal of Physics D: Applied Physics (2018) III award in the PhD student category at the National Contest of Students on Scientific Research (2007) Dr. Jõgi serves in significant administrative roles including Assistant Director of the Institute of Physics and Programme Director for the Doctoral Programme in Chemical and Physical Sciences. He is the Estonian representative in the Governing Board of Fusion for Energy and the General Assembly of EUROfusion consortium. He also represents Estonia in COST Actions 23139 (from 2025) and previously served as Vice STSM Coordinator for COST Action 19110 (2020-2024). With approximately 90 peer reviews completed according to Web of Science, he is an active contributor to scholarly discourse in his field. He leads the Laboratory of Plasma Physics at the University of Tartu and is a member of the International Scientific Committee for the HAKONE symposium series on High Pressure Low Temperature Plasma Chemistry. His research team collaborates extensively with international fusion research facilities including WEST tokamak in France and Magnum-PSI in the Netherlands.

Beverley J. McKeonمشاهده پروفایل

استاد

• Fluid Mechanics
• Turbulent Boundary Layers
• Flow Control
• +۵ مورد دیگر

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.

Zach Y. Brownمشاهده پروفایل

استادیار

• Industrial Organization
• Health Economics
• Applied Microeconomics
• +۳ مورد دیگر

Zach Y. Brown is an Assistant Professor of Economics at the University of Michigan’s Department of Economics within the College of Literature, Science, and the Arts (LSA), and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). He holds a tenure-track position and focuses on Industrial Organization and Health Economics, with an emphasis on information frictions and healthcare market dynamics. Education: Ph.D. in Economics from Columbia University (2017), B.A. in Economics and Physics (minor) from University of California - Berkeley (2007). His research interests include algorithmic pricing strategies, healthcare policy, and the economic implications of market structures. For instance, he explores how pricing algorithms affect competition in digital markets and investigates disparities in healthcare access and outcomes, particularly in Medicare. His work frequently bridges theoretical models with empirical analysis to inform policy debates. Recent studies highlight his contributions to understanding the effects of hospital payment caps on pricing, the role of broadband access in healthcare, and racial disparities in medication for opioid use disorder. His 2023 paper on algorithmic pricing won the American Economic Journal’s Best Paper Award in Microeconomics, underscoring his impact on digital market research. Teaching includes courses like Government Regulation of Industry (undergraduate) and Industrial Organization II (Ph.D. level), reflecting his expertise in applied economics and policy analysis. Prior to his academic role, he served as a Staff Economist at the Council of Economic Advisers, bringing practical policy experience to his academic work. Brown is affiliated with the NBER, contributing to their research initiatives. His current projects include examining market power in index funds and insurer competition in Medicaid markets, further expanding his interdisciplinary approach to economic challenges.

Dimitrios P. Tsomocosمشاهده پروفایل

استاد

• Central Banking
• Banking and regulation
• Incomplete asset markets
• +۷ مورد دیگر

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.

• Game Theory
• Bargaining Theory
• Coalition Formation
• +۴ مورد دیگر

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.

Oliver Schmitzمشاهده پروفایل

استاد

• Plasma Edge Physics
• Plasma Wall Interaction
• Three-Dimensional Plasma Edge Transport
• +۶ مورد دیگر

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 Vahidiمشاهده پروفایل

استاد

• Optimal Control and Estimation
• Energy Efficient Mobility
• Connected and Automated Vehicles
• +۲ مورد دیگر

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.

Aaron Rothمشاهده پروفایل

استاد

• Algorithms
• Machine Learning
• Differential Privacy
• +۱۰ مورد دیگر

Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, affiliated with the Department of Computer and Information Science in the School of Engineering and Applied Science. He holds a secondary appointment in the Department of Statistics and Data Science at the Wharton School and is associated with several research centers including PRiML, the Warren Center for Network and Data Sciences, and the AMCS program. He received his PhD from Carnegie Mellon University under Avrim Blum and was a postdoc at Microsoft Research New England. His research focuses on algorithms and machine learning, particularly in private data analysis, fairness in machine learning, game theory, mechanism design, and learning theory. His work bridges theoretical computer science with societal concerns, advocating for ethically aware algorithm design. He co-authored the book The Ethical Algorithm with Michael Kearns, which explores how to embed social values like privacy and fairness into algorithmic systems. His recent publications show a strong trend toward uncertainty quantification, multicalibration, conformal prediction, and fairness in reinforcement learning and high-dimensional settings. He frequently publishes in top-tier venues such as STOC, FOCS, ICML, NeurIPS, and COLT, often with a focus on rigorous theoretical foundations with practical implications. Hans Sigrist Prize Presidential Early Career Award for Scientists and Engineers (PECASE) Alfred P. Sloan Research Fellowship NSF CAREER award Google Faculty Research Award Amazon Research Award Yahoo Academic Career Enhancement award Roth has advised numerous PhD students and postdocs, many of whom now hold academic or industry research positions. He is also an Amazon Scholar at AWS and has served in advisory roles for companies like Apple, Facebook, Leapyear, and Spectrum Labs. He has been active in organizing workshops and tutorials on differential privacy, fairness, and adaptive data analysis, and has given keynotes at major conferences and institutions worldwide. He leads research groups and collaborates widely across Penn, focusing on responsible AI, privacy, and algorithmic fairness. His lab produces foundational work on calibration, unlearning, privacy-preserving learning, and equitable decision-making systems.