جست‌وجوی استاد و پژوهشگر

Stephanie Gilمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics and Control
• Computer Science
• +۴ مورد دیگر

Stephanie Gil is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on artificial intelligence, robotics, and distributed systems, particularly addressing challenges in multi-agent coordination, resilience to adversarial attacks, and wireless communication for autonomous systems. She leads the REACT Lab, advancing research in resilient multi-robot networks and cyber-physical systems. Her work integrates machine learning, control theory, and wireless sensing to solve problems such as whale tracking via autonomous robots, proactive multi-robot routing, and decentralized exploration without explicit information exchange. She has received prestigious awards, including the DARPA Young Faculty Award (2024) and the Amazon Research Award (2021). Key research areas include resilient distributed optimization, trust-centered coordination in multi-agent systems, and leveraging wireless signals (e.g., WiFi-CSI) for sensing and bearing estimation. Her contributions span both theoretical frameworks and practical implementations, with a focus on real-world applications like autonomous rideshare routing and environmental monitoring. Dr. Gil’s research also explores trust and cybersecurity in dynamic networks, with publications on crowd vetting, malicious robot detection, and adaptive communication strategies. She collaborates on interdisciplinary projects, such as Project CETI, combining AI and robotics for ecological studies.

Karl Henrik Johanssonمشاهده پروفایل

استاد

• Networked and distributed control
• Cyber-physical systems
• Cyber-secure control systems
• +۴ مورد دیگر

Karl Henrik Johansson is a Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm, Sweden, where he also serves as the Founding Director of Digital Futures. He is a Fellow of both IEEE and the Royal Swedish Academy of Engineering Sciences, and has held leadership positions including Immediate Past President of the European Control Association and IEEE Control Systems Society Vice President Diversity, Outreach & Development. Dr. Johansson earned his MSc in Electrical Engineering and PhD in Automatic Control from Lund University. His academic journey includes visiting positions at prestigious institutions such as UC Berkeley, Caltech, and NTU. His research focuses on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. His work investigates fundamental challenges in connecting physical world systems through communication networks, exploring how wireless communication and sensor technology can enhance system robustness, reliability, energy efficiency, and safety. Current research directions include security of cyber-physical systems, distributed optimization, multi-agent systems, and applications to intelligent transportation and energy networks. Analysis of his recent publications reveals a strong focus on distributed optimization algorithms, secure networked control, multi-agent systems, and applications to transportation and energy networks. His work increasingly integrates machine learning techniques with traditional control theory, addressing challenges in privacy-preserving distributed computation, resilient state estimation, and resource allocation in complex networked systems. IEEE Control Systems Society Hendrik W. Bode Lecture Prize (2024) Swedish Research Council Distinguished Professor (2018-2027) Wallenberg Scholar (2009-2026) IFAC Young Author Prize IEEE CSS Distinguished Lecturer (2017-2019) IFAC Outstanding Service Award IEEE Fellow Dr. Johansson has supervised over 100 postdocs and PhD students, with many now holding prominent positions at institutions worldwide. His research has been supported by significant grants including the Swedish Research Council Distinguished Professor Grant (2018-2027), multiple Wallenberg Foundation grants, and numerous EU and national research projects. He has directed major research centers including ACCESS Linnaeus Centre (2009-2016) and Strategic Research Area ICT TNG (2013-2020). His research group operates within the Digital Futures initiative and maintains strong connections with industry partners through projects like the Integrated Transport Research Lab (supported by Scania and Ericsson) and Smart Mobility Lab. The group actively collaborates with international institutions and participates in major EU-funded projects addressing challenges in cyber-physical systems, transportation, and energy networks.

Soroosh Shafieeمشاهده پروفایل

استادیار

• Optimization under Uncertainty
• Robust Optimization
• Optimal Transport
• +۳ مورد دیگر

Soroosh Shafiee is an Assistant Professor in the School of Operations Research and Information Engineering at Cornell University since July 2023. Before joining Cornell, he held postdoctoral positions at the Tepper School of Business (Carnegie Mellon University) and the Automatic Control Laboratory (ETH Zurich). He earned a B.Sc. and M.Sc. in Electrical Engineering from the University of Tehran and a Ph.D. in Operations Research from École Polytechnique Fédérale de Lausanne (EPFL). His research focuses on optimization under uncertainty, robust optimization, optimal transport, and their applications in machine learning and finance. Specific interests include designing algorithms for data-driven optimization, analyzing statistical and computational complexity, and exploring nonconvex optimization structures. Swiss National Science Foundation Early PostDoc Mobility Fellowship (2020) PhD Thesis Distinction Award, EPFL (2020) His work bridges theoretical foundations with practical applications, contributing to areas such as distributionally robust optimization, Wasserstein-based methods, and scalable algorithm development. His research has been published in top venues like Journal of Machine Learning Research and Operations Research .

Rohit Kannanمشاهده پروفایل

استادیار

• Learning + Optimization
• Optimization Under Uncertainty
• Global Optimization
• +۲ مورد دیگر

Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.

Dominic Liao-McPhersonمشاهده پروفایل

استادیار

• Control Theory
• Optimization
• Algorithmic Control
• +۷ مورد دیگر

Dominic Liao-McPherson serves as an Assistant Professor in the Department of Mechanical Engineering within the Faculty of Applied Science at the University of British Columbia. His research bridges algorithmic control, optimization theory, and computational engineering with practical applications across robotics, energy systems, and aerospace domains. His academic background includes a BASc from the University of Toronto, PhD from the University of Michigan, and postdoctoral training at ETH Zürich: BASc (University of Toronto) PhD (University of Michigan) Postdoc (ETH Zürich) Dr. Liao-McPherson's research centers on developing real-time computational decision-making algorithms for physical systems. His work spans predictive and constrained control (including model predictive control and reference governors), real-time embedded optimization, and game-theoretic coordination mechanisms for multi-agent systems. Key application areas include energy grids, autonomous vehicles, additive manufacturing, and aerospace systems, with past projects covering spacecraft landing, engine emissions control, and aircraft upset recovery. His methodology emphasizes rigorous stability analysis, constraint satisfaction, and practical implementation on resource-constrained hardware. Analysis of his 2020-2022 publications reveals a strong focus on advancing optimization-based control frameworks. His work consistently addresses stability guarantees and constraint handling in real-time systems, with increasing emphasis on distributed algorithms for multi-agent coordination. The research demonstrates a clear trajectory from theoretical algorithm development (e.g., FBstab solver) toward experimental validation in complex engineering systems like diesel engines and autonomous networks. No scientific awards are documented in the provided materials. Regarding academic advising and research funding, the source text contains no information about current students, grant awards, or sponsored research projects. He directs the Algorithmic Optimization and Control Lab (AOCL) at UBC, as evidenced by his research website (aocl.mech.ubc.ca). The lab specializes in developing computationally efficient control algorithms for embedded systems, with particular expertise in handling physical constraints and coordination challenges in multi-agent environments across energy, manufacturing, and robotics applications.

• Decision and Control
• Game Theory
• Multi-Agent Systems
• +۷ مورد دیگر

Jeff Shamma is the Department Head and Professor of Industrial and Enterprise Systems Engineering (ISE) at the University of Illinois at Urbana-Champaign, holding the Jerry S. Dobrovolny Chair. He is also courtesy Professor in Aerospace Engineering and Mechanical Science and Engineering. Formerly, he held the Julian T. Hightower Chair at Georgia Institute of Technology and faculty positions at KAUST. Dr. Shamma earned his PhD in Systems Science and Engineering from MIT (1988) and a BS in Mechanical Engineering from Georgia Tech (1983). He is a Fellow of IEEE and IFAC, recipient of the IFAC High Impact Paper Award, AACC Donald P. Eckman Award, and NSF Young Investigator Award. His research spans Decision and Control , Game Theory , and Multi-Agent Systems , focusing on human-machine networks, distributed autonomy, and adaptive robotic systems. Recent work examines crowd dynamics, risk-sensitive control, and feedback linearization for constrained optimization. Jeff has served as Editor-in-Chief of IEEE Transactions on Control of Network Systems (2020–2024) and held editorial roles in journals like Annual Reviews in Control and IEEE Transactions on Robotics . His 15 most recent publications (2024–2025) analyze learning dynamics, multi-agent optimization, and UAV-crawler systems, reflecting trends in autonomous systems, game-theoretic modeling, and industrial inspection technologies. Scientific distinctions include: Fellow of IEEE and IFAC IFAC High Impact Paper Award (2020) AACC Donald P. Eckman Award (1996) NSF Young Investigator Award (1992) Mohammed Dahleh Distinguished Lecture Award (2013) Dr. Shamma advises current PhD students Hassan Abdelraouf, Aya Hamed, and Nawaf Otaibi, with former advisees including Sarah Toonsi (2025) and Fat-hy Rajab (2025). His lab integrates theoretical research with applied projects like FalconScan, a UAV-crawler system for industrial inspection, and develops magnetic legs for curved surface UAV landing.

• Mathematical Statistics
• Model Selection
• Robust Estimation
• +۳ مورد دیگر

Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.

Andrea Goldsmithمشاهده پروفایل

استاد

• Wireless Communications
• Information Theory
• Communication Theory
• +۵ مورد دیگر

Andrea Goldsmith is the Dean of the School of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical and Computer Engineering at Princeton University. Previously, she held the Stephen Harris Professorship at Stanford University and remains Harris Professor Emerita there. Her research focuses on information theory, communication theory, signal processing, and their applications to wireless communications, interconnected systems, and neuroscience. She founded Plume WiFi and Quantenna, Inc., and serves on the boards of Medtronic and Crown Castle Inc. Education: B.S., M.S., and Ph.D. in Electrical Engineering, University of California, Berkeley (1986–1994) Research Interests: Her work bridges theoretical foundations with practical applications in wireless systems, including MIMO communications, cognitive radio, and the integration of machine learning in communication protocols. She also explores the intersection of wireless technology with biomedical systems and neuroscience, emphasizing innovations like smart buildings and in-body networks. Key Contributions: Authored seminal textbooks, including Wireless Communications and MIMO Wireless Communications . Inventor on 29 patents, with significant industry impact through startups. Recipient of prestigious awards such as the IEEE Sumner Award, ACM Athena Lecturer Award, and Marconi Prize. Labs & Leadership: Leads the Wireless Systems Lab at Princeton, advancing cutting-edge wireless technologies. Chair of the IEEE Board of Directors Committee on Diversity, Inclusion, and Ethics.

Paul G Dupuisمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Control Theory
• +۴ مورد دیگر

Paul G Dupuis is the IBM Professor of Applied Mathematics at Brown University. His research focuses on applications of probability theory, stochastic processes, control theory, and numerical methods. He holds affiliations with the American Mathematical Society, Society for Industrial and Applied Mathematics (SIAM), and the Institute for Mathematical Statistics (IMS). His work emphasizes large deviation theory, Markov chain approximations, Monte Carlo simulation, and partial differential equations. Education: Ph.D. in Applied Mathematics from Brown University (1985), M.S. from Northwestern University (1982), and B.S. from Brown University (1981). Research Interests: Control of deterministic and stochastic processes, differential games, numerical methods, operations research, and stochastic processes. His contributions include foundational work on large deviation theory, risk-sensitive control, and queueing networks. Awards: Elected SIAM Fellow (2010), Fellow of the Institute for Mathematical Statistics (2011), IBM Professor of Applied Mathematics (2012), and AMS Fellow (2014). Previously held an NSF Postdoctoral Fellowship (1985-1988). Grants: Current funding from the Army Research Office and National Science Foundation. Key collaborations include work on stochastic approximation, constrained diffusions, and reflected Brownian motion. Teaching: Courses include Operations Research: Probabilistic Models, Information Theory, and Advanced topics in Probability and Stochastic Control.

Nikolai Matniمشاهده پروفایل

استادیار

• Machine Learning
• Control Theory
• Robotics
• +۱۰ مورد دیگر

Nikolai Matni is an Assistant Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He holds a secondary appointment in the Department of Computer and Information Science and is a member of the Applied Mathematics and Computational Sciences (AMCS) graduate group. His research focuses on integrating learning, optimization, and control for safety-critical and data-driven cyber-physical systems, with applications in robotics, autonomy, and distributed control. Secondary Appointment: Computer and Information Science (University of Pennsylvania) Labs/Centers: GRASP Lab, PRECISE Center His research bridges machine learning, robust control, and autonomous systems, particularly in developing guaranteed-safe strategies for cyber-physical systems. He emphasizes the importance of reliability and robustness in learning-based control for applications like self-driving vehicles and agile robots, where failures could be catastrophic. Recent publications highlight advancements in vision-based robotic control, neural ODEs for motion planning, adversarial exploration strategies, and stability-constrained learning. These works span conferences such as ICRA, CoRL, WACV, IROS, and L4DC, with a focus on safety-critical applications. Notable scientific awards include the NSF CAREER Award, George S Axelby Award (for his work on System Level Synthesis), AFOSR YIP award, and Google Research Scholar Award. He was also elevated to IEEE Senior Member. Matni advises Ph.D. students in Computer and Information Science (CIS), Electrical and Systems Engineering (ESE), and Applied Mathematics and Computational Sciences (AMCS). His teaching includes courses like ESE 2030 (Linear Algebra with Engineering/AI applications) and others focused on control theory and robotics.

Alfred Chongمشاهده پروفایل

دانشیار

• Actuarial Science
• Financial Mathematics
• Quantitative Risk Management
• +۵ مورد دیگر

Dr. Alfred Chong is an Associate Professor in the Department of Actuarial Mathematics and Statistics at Heriot-Watt University (HWU). Previously, he served as an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) and co-founded the Illinois Risk Lab. His research focuses on Actuarial Science, Financial Mathematics, and Quantitative Risk Management, addressing emerging risks like cyber, pandemic, and climate risks, leveraging machine learning, optimization, and stochastic control. He holds a PhD from The University of Hong Kong and King's College London, and is an Associate of the Society of Actuaries. Chong actively contributes to academic governance, including roles in the EPSRC Mathematical Sciences Early Career Forum and the Maxwell Institute's Data and Decisions research theme. Education: PhD in Actuarial Science, University of Hong Kong & King's College London Research Interests: Chong explores risk sharing mechanisms, forward preferences in insurance, and mitigation strategies for large-scale risks. His work integrates data analytics and machine learning to solve decision-making challenges, such as cybersecurity risk assessment, pandemic resource allocation, and climate risk modeling. Recent projects include incident-specific cyber insurance design and delegated investment strategies for retirement savings. Awards: Michael V. Colla Prize for Mathematics Related to Medicine (2022) Best of 2020 in the Annual Meeting of the Casualty Actuarial Society (2021) Advising & Grants: Chong supervises PhD students in holistic risk management, forward preferences, and reinforcement learning applications. He has secured grants supporting interdisciplinary research in risk modeling and insurance innovation. Labs & Teams: Co-founder of the Illinois Risk Lab (UIUC), now leading research at HWU's Actuarial Mathematics & Statistics department. Engaged with the International Centre for Mathematical Sciences for knowledge exchange initiatives.

Amir Rahmatiمشاهده پروفایل

استادیار

• System Security
• Internet of Things (IoT) Security
• Augmented Reality (AR) Security
• +۳ مورد دیگر

Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security and Privacy Lab and contributes to the Stony Brook National Security Institute . His research focuses on system security , with specific emphasis on the security and privacy challenges of emerging technologies such as IoT , AR , and ML systems . Teaching: Instructor of SBU102: Computer Security (Spring 2025) Collaborations: Frequent collaborations with institutions like University of Michigan, University of Toronto, and IEEE/USENIX conferences. Rahmati’s research addresses security vulnerabilities in resource-constrained devices and real-world ML applications. His work includes adversarial robustness in neural networks, attack synthesis on medical devices, and privacy-preserving frameworks for IoT ecosystems. Trends in his publications reveal a focus on practical system design to mitigate security threats in cyber-physical systems , augmented reality , and blockchain technologies . Prospective students: Rahmati seeks researchers with expertise in hardware/software, machine learning, network protocols, and security to tackle system-stack challenges in his lab.

• Computational Fluid Dynamics
• Scientific Machine Learning
• Data-Driven Modeling
• +۳ مورد دیگر

Shaowu Pan is an Assistant Professor of Aerospace Engineering at Rensselaer Polytechnic Institute (RPI), affiliated with the Future of Computing Institute (FOCI) and the Scientific Computation Research Center (SCOREC). He holds a Ph.D. in Aerospace Engineering and Scientific Computing from the University of Michigan and completed a postdoctoral fellowship at the University of Washington’s AI Institute in Dynamic Systems. Education: Ph.D., University of Michigan, 2021 M.S., University of Michigan, 2015 B.E. & B.S., Beihang University, 2013 Research Interests: His work focuses on the intersection of computational fluid dynamics, data-driven modeling, and scientific machine learning. Key areas include operator-theoretic modeling of fluid flows, generative AI for physical systems, and physics-informed neural networks. He develops novel algorithms for reduced-order modeling and stability-preserving surrogate models, with applications in turbulence, plasma physics, and aerodynamics. Key Contributions: Developed PyKoopman , an open-source Python package for Koopman operator approximation. Pioneered mesh-agnostic representation methods like Neural Implicit Flow for spatio-temporal data. Advanced physics-informed neural networks for solving Grad-Shafranov equations and plasma equilibrium problems. Awards & Recognition: John Tichy Junior Faculty Travel Grant (2024) Chinese Outstanding Student Abroad Award (2021) Richard and Eleanor Towner Prize Nominee (2019) Teaching & Mentorship: He teaches courses like MANE 2110: Numerical Methods and Programming for Engineers and mentors multiple Ph.D., master’s, and undergraduate students. His doctoral committee involvement spans interdisciplinary projects in fluid dynamics and AI. Grants & Software: Lead PI for NSF-funded projects on neural representation learning for turbulent flows. Developed software tools like spKDMD and Warp-DG for dynamics analysis and CFD simulations. Labs & Collaborations: Collaborates with institutions like Los Alamos National Laboratory and actively participates in conferences (e.g., AIAA SciTech, SIAM). His research bridges computational science, machine learning, and fluid dynamics to address complex nonlinear systems.

Arash Joorabchiمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۴ مورد دیگر

Arash Joorabchi is an Assistant Professor at the Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of machine learning, educational technology, and digital library systems, with particular emphasis on automated assessment, text mining, and knowledge organization techniques. Research Trends: Analysis of his publications reveals sustained contributions to automated short-answer grading, Arabic text classification, and semantic integration of Wikipedia with academic resources. Key methodologies include sentence transformers, hybrid text representation models, and citation-based indexing techniques. Technical Domains: His work spans natural language processing, educational data mining, metadata management, and semantic web technologies. Specific applications include Q&A platform analysis, library resource discovery, and curriculum development systems.

Athanasios Orphanidesمشاهده پروفایل

استاد

• Central Banking
• Monetary Policy
• Macroeconomics
• +۳ مورد دیگر

Athanasios Orphanides is a Professor of the Practice of Global Economics and Management at the MIT Sloan School of Management. He holds additional roles as Honorary Advisor to the Bank of Japan’s Institute for Monetary and Economic Studies, member of the Shadow Open Market Committee, and Research Fellow at the Centre for Economic Policy Research. His research focuses on central banking, monetary policy frameworks, and political economy. Orphanides earned undergraduate degrees in mathematics and economics, as well as a PhD in economics, all from MIT. Before joining MIT, he served as Governor of the Central Bank of Cyprus (2007–2012), a member of the ECB Governing Council, and Senior Advisor at the U.S. Federal Reserve. His work emphasizes robust monetary policy rules, such as natural growth targeting, to enhance economic stability. Recent publications address fiscal-monetary policy interactions, inflation targeting, and systemic risk management. Orphanides frequently advises policymakers on central bank strategies and crisis response mechanisms. His research highlights the importance of clear policy frameworks to avoid inflationary mistakes, drawing on historical analyses of U.S. and European central bank decisions. He critiques discretionary policies that prioritize employment over price stability, advocating instead for rules-based approaches constrained by inflation targets. Orphanides has contributed to global debates on central bank independence, crisis management, and the design of monetary instruments. His executive education courses at MIT Sloan focus on macroeconomic principles for business decision-making.