جست‌وجوی استاد و پژوهشگر

Bo Daiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Machine Learning
• Data-Driven Decision Making
• +۳ مورد دیگر

Bo Dai is an Assistant Professor at the School of Computational Science and Engineering, Georgia Institute of Technology, and a Staff Research Scientist at Google DeepMind. His research focuses on Agent AI, Generative Models, and Representation Learning, aiming to create decision-making agents through world modeling. He holds a Ph.D. from Georgia Tech (2013–2018) and previously worked at Google Brain. Dai has authored numerous influential papers in top conferences like NeurIPS, ICML, and ICLR, and received the AISTATS Best Paper Award (2016). Education: Ph.D., School of Computational Science and Engineering, Georgia Tech (2013–2018) Research Interests: Reinforcement Learning and Representation Learning for decision-making agents Generative Models and their integration with Agent AI Provable and scalable algorithms for real-world applications His work bridges theory and practice, emphasizing spectral representations and provable guarantees in complex systems. Key Article Trends: His recent work emphasizes scalable spectral methods for multi-agent systems, diffusion policies, and representation-based techniques in reinforcement learning. He also explores LLM alignment and sim-to-real transfer learning. Awards: AISTATS Best Paper Award (2016) NeurIPS Workshop Best Paper (2017) Ross Fellowship (2011–2012) Advising & Grants: Supervises 6 current Ph.D. and M.S. students. Active in organizing workshops on reinforcement learning and serves as an Area Chair for top conferences. Labs & Software: Leads development of Representation-based Reinforcement Learning and Repr-Control toolboxes for nonlinear control and stochastic systems.

Peter Bühlmannمشاهده پروفایل

استاد

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

Ram Vasudevanمشاهده پروفایل

دانشیار

• Optimization and control of nonlinear systems
• Legged robot locomotion
• Autonomous systems safety
• +۴ مورد دیگر

Ram Vasudevan is an Associate Professor and Associate Chair of Graduate Studies in the Department of Robotics at the University of Michigan. His research focuses on developing tools for safe and robust deployment of robotic systems, emphasizing optimization, nonlinear control, and real-world applications. Key areas include legged robot locomotion, shared control systems, and safety-critical autonomous systems. Research Interests: Optimization and control of nonlinear systems, locomotion of legged robots, shared control active safety systems, and automation of diagnostic/rehabilitative tasks. His ROAHM Lab prioritizes mathematical guarantees for robotic performance, with applications in medical robotics, autonomous vehicles, and soft robotics. Recent work emphasizes trajectory optimization, sensor fusion, and safety-aware control strategies. He has contributed to benchmarks for autonomous vehicle perception and novel methods in thermal image restoration using neural radiance fields. Awards: None explicitly listed in provided text. Labs/Teams: Directs the ROAHM Lab, collaborating on projects like robotic tail mechanics, real-time motion planning, and sensor data analysis. Active in academic conferences including RSS and ICRA.

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +۵ مورد دیگر

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Henrik Sandbergمشاهده پروفایل

استاد

• Cyber-Physical Systems Security
• Power Systems
• Model Reduction
• +۶ مورد دیگر

Henrik Sandberg is a Professor at the Division of Decision and Control Systems , KTH Royal Institute of Technology , Stockholm, Sweden. He holds the title of Deputy Head of Division and is affiliated with the School of Electrical Engineering and Computer Science . Education: MSc in Engineering Physics (1999) PhD in Automatic Control (2004) from Lund University Postdoctoral position at Caltech (pre-2007) Research Interests: Focus on cyber-physical systems security , power systems , model reduction , and fundamental limitations of control systems . Key sub-areas include attack detection , networked control , privacy-preserving estimation , and resilient control architectures . Publications: Over 150 papers across IEEE Transactions and Automatica , covering topics like stealthy attacks , distributed control , LQG optimization , and thermodynamic costs in filtering . Recent work includes LWE-based encrypted control and Bayesian deception mechanisms . Scientific Awards: Best Student Paper Award Finalist at IEEE CASE 2014; Best Student-Paper Award at IEEE CDC 2004. Grants & Projects: Leads the DYNACON project (WASP Cybersec cluster) and collaborates on CERCES (critical infrastructure resilience). Serves as examiner for multiple advanced courses in cybersecurity and control systems. Contact: Email: hsan@kth.se Phone: +46 (0)8 790 7294 Room: A:607, Malvinas Väg 10, Stockholm

Sarah Deanمشاهده پروفایل

استادیار

• Machine Learning
• Data-Driven Control
• Decision-Making Systems
• +۶ مورد دیگر

Sarah Dean is an Assistant Professor in the Computer Science Department at Cornell University, affiliated with the College of Engineering. Her research focuses on the interplay of machine learning, optimization, and dynamics in real-world systems, particularly in control theory, recommendation systems, and ethical AI. Education: PhD in EECS, University of California, Berkeley (2021) Postdoctoral Research, University of Washington (2021-2022) Research Interests: Data-driven control systems, reinforcement learning, recommendation systems, user dynamics, algorithmic fairness, and the societal impacts of AI. She emphasizes foundational understanding of how learning systems interact with human and social processes. Recent Work Trends: Her articles explore topics like bilinear system identification, user participation dynamics in recommendation platforms, and ethical considerations in AI development. Recent work includes harm mitigation strategies and mathematical modeling of AI-human feedback loops. Awards: AI2050 Early Career Fellow (2024) Best Paper at ICML 2018 (Delayed Impact of Fair Machine Learning) Best Student Paper in Imaging Systems (OSA Congress 2018) Advising & Labs: Advises over 15 graduate and undergraduate students. Leads research on interactive ML systems, with contributions to projects like the 'MSGD' repository for streaming data learning. Active in the GEESE group, promoting socially responsible computing.

Pieter van Goorمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Control Theory
• Equivariant Systems Theory
• +۵ مورد دیگر

Pieter van Goor is a Research Fellow at the Australian National University (ANU), affiliated with the School of Engineering and the Systems Theory and Robotics (STR) group. He holds a PhD in Control Theory (completed 2022) and dual bachelor's degrees (BEng/BSc, 2018). His research focuses on equivariant systems theory, state estimation, and robotics applications. Key contributions include equivariant observer design, Lie group-based control, and geometric data fusion. Education: Bachelor of Engineering (Research & Development) (Honours) in Mechatronics (ANU, 2018) Bachelor of Science in Mathematics (ANU, 2018) PhD in Control Theory (ANU, 2022) Research Interests: Equivariant systems theory, nonlinear control, robotics applications, state estimation on Lie groups, sensor fusion, and geometric control methods. His work emphasizes symmetry exploitation in filter design and observer construction for systems with inherent geometric structures. Grants & Collaborations: Active collaborations include work with Robert Mahony and institutions like the IEEE. Research spans theoretical frameworks (e.g., equivariant filters) and applied systems (e.g., ArduPilot autopilot, event cameras). Labs/Teams: Member of the Systems Theory and Robotics (STR) group at ANU, focusing on advanced control theory and robotics.

David Meansمشاهده پروفایل

دانشیار مهمان

• Creative Writing
• American Fiction
• Short Story
• +۵ مورد دیگر

David Means is a Visiting Associate Professor of English at Vassar College, where he has taught since 2001. He holds a BA from The College of Wooster and an MFA in poetry from Columbia University. His academic work centers on creative writing, modern and contemporary fiction and poetry, and American culture. Education: BA, The College of Wooster MFA, Columbia University in the City of New York Means is a celebrated author of short fiction and novels, known for his lyrical prose, experimental narrative structures, and deep exploration of trauma, memory, and redemption. His work frequently appears in The New Yorker , Harper’s , The Paris Review , and other prestigious literary journals. His writing often returns to themes of violence, loss, and the quiet grace that emerges in their aftermath, drawing comparisons to Flannery O’Connor and Denis Johnson. His stories are marked by a nonlinear sense of time and a profound empathy for marginalized or broken characters. His recent publications include the story collection Instructions for a Funeral (2019) and Two Nurses, Smoking , with his novel Hystopia longlisted for the Man Booker Prize. His forthcoming non-fiction book, Pivot: Ruminations in the Shadow of Death , reflects on mortality and personal reflection. Selected Awards and Honors: Guggenheim Fellowship (2013) Three O. Henry Prizes Three Pushcart Prizes Los Angeles Book Prize for Fiction Finalist, National Book Critics Circle Award Finalist, Frank O’Connor International Short Story Prize Longlisted, Man Booker Prize Means has advised and influenced many emerging writers through his teaching and public readings. While specific students are not named in the sources, his role as a mentor is evident in his sustained presence in creative writing programs. He has no known research grants or lab affiliations, as his work is primarily literary and individual. He continues to publish and engage in public discourse on the nature of storytelling, including critiques of AI-generated fiction.

Joseph Bentsmanمشاهده پروفایل

استاد

• Control of nonlinear systems
• Control of distributed parameter systems
• Nonlinear oscillations
• +۷ مورد دیگر

Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.

• Probabilistic Verification
• Quantitative Model Checking
• Formal Methods
• +۴ مورد دیگر

Marta Kwiatkowska is a Professor of Computing Systems at the University of Oxford and a Fellow of Trinity College. Her research focuses on probabilistic verification , quantitative model checking , and formal methods for complex systems including autonomous robots, medical devices, and biological systems. She leads the development of the PRISM and PRISM-games probabilistic model checkers. Key research areas: Probabilistic systems, formal verification, autonomous robotics, medical device analysis, systems biology Grants: ERC Advanced Grant VERIWARE, EPSRC Programme Grant Mobile Autonomy Awards: 2024 ETAPS Test-of-Time Tool Award for PRISM Students: Current and former advisees in topics spanning formal methods, robotics, and quantitative verification The PRISM-games extension enables verification of stochastic multi-player games with applications in network protocols, autonomous systems, and game theory. Her work bridges theory, algorithms, and practical implementation, with real-world applications in ubiquitous computing and nanotechnology.

Baris Fidanمشاهده پروفایل

استاد

• Control Systems
• Autonomous Vehicles
• Robotics
• +۵ مورد دیگر

Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.

Kostas Bekrisمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

Kostas Bekris is a Professor in the Department of Computer Science at Rutgers University, specializing in Robotics and Artificial Intelligence. His research focuses on motion planning, autonomous manipulation, and robot control, with notable contributions to tensegrity robotics, perception-driven systems, and large-scale package handling. He leads a team conducting groundbreaking work in robotics, supported by grants from NSF, NASA, and industry collaborators like ExxonMobil. His group emphasizes interdisciplinary approaches, combining machine learning, topological methods, and differentiable physics modeling to advance robot capabilities in complex environments. Education details are not explicitly stated in the provided texts, but his academic career has included significant mentorship of PhD students and postdoctoral researchers. Key projects involve vision-driven manipulation pipelines, obstacle detection systems (PROBE), and resilient robot designs inspired by biological structures. He has been recognized for his work through prestigious awards including the NASA Early Career Grant and multiple NSF grants, as well as team achievements in robotics competitions like the Amazon Picking Challenge. Research interests span robotics subfields such as: Autonomous manipulation in cluttered environments Learning-based control for dynamic systems Topological data analysis for motion reasoning Tensegrity and soft robotics architectures Sim-to-real transfer in robotic tasks His team's work has produced open-source software tools and datasets, advancing benchmarks in manipulation and perception. Recent articles emphasize scalable solutions for industrial automation and robust navigation strategies in unstructured settings. Scientific achievements include: Development of PROBE for proprioceptive obstacle detection Advances in differentiable physics engines for tensegrity systems NSF-funded projects on robotic rearrangement and modular morphologies Advising contributions span over a decade, with current advisees focusing on topics like non-prehensile manipulation and large-scale storage optimization. Collaborations with industry (e.g., ExxonMobil) and academic partners (Yale University) reflect his commitment to applied robotics research. Labs and teams under his leadership include the Rutgers CS Robotics Group, contributing to projects like the ARIAC challenge platform and packing/industrial automation systems. Future work targets improved robot resilience in disaster scenarios and enhanced human-robot collaboration paradigms.

• Automatic parameter estimation in electric machines
• Multiphase drives
• Sensorless control
• +۷ مورد دیگر

Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.

John Baillieulمشاهده پروفایل

استاد

• Robotics
• Nonlinear control theory
• Mechanical systems control
• +۶ مورد دیگر

John Baillieul is Distinguished Professor at Boston University with joint appointments in Mechanical Engineering, Systems Engineering and Electrical & Computer Engineering. He directs experimental laboratories for real-time control of lightweight robotic systems and applies nonlinear control theory to complex multi-body, networked, and bio-inspired systems. Education: Ph.D., Harvard University Research Interests: Baillieul’s work spans robotics, nonlinear control, and networked systems. Early contributions resolved motion-planning for kinematically redundant manipulators; current themes include neuromimetic learning, vision-based navigation, and resilience of infrastructure networks such as power grids. His group couples rigorous geometric control with real-time hardware to create lightweight, high-performance robots and to uncover fundamental information limits in feedback systems. Recent Publication Trends (2021-2025): Over the past five years his output has concentrated on three synergistic directions: (i) neuromimetic and Koopman-based data-driven methods for estimating and controlling nonlinear systems, (ii) vision-based guidance and sparse optical-flow primitives for agile autonomous flight, and (iii) network-theoretic decomposition and information-rate studies for resilient operation of power grids and collective dynamics. Honors & Awards: IEEE Fellow 2025 Roger W. Brockett Control Systems Award Former Editor-in-Chief, IEEE Transactions on Automatic Control Affiliations & Service: He is a member of Boston University’s Center for Information and Systems Engineering (CISE), has served as Editor-in-Chief of IEEE Transactions on Automatic Control, and remains active in editorial and organizational roles across the IEEE control systems community.

Joanna Millsteinمشاهده پروفایل

پژوهشگر ارشد

• Fracture mechanics
• Ice dynamics
• Remote sensing
• +۵ مورد دیگر

Joanna Millstein is a Post-doctoral Fellow in Geophysics at the Colorado School of Mines. She earned her Ph.D. in Geophysics from the Massachusetts Institute of Technology in 2023 as part of the MIT-WHOI Joint Program in Oceanography and Engineering, where her dissertation focused on The Flow and Fracture of Antarctic Ice Shelves . She also holds an A.B. in Earth Sciences from Dartmouth College (2017). Her research centers on the deformation and fracture of glacier ice, working at the intersection of fracture mechanics, remote sensing (particularly SAR and InSAR processing), statistical mechanics, and stochastic models. Millstein uses observational data from satellites and field measurements to derive mechanical and statistical models for glacier ice processes, with particular focus on reconciling observations of ice fracture and iceberg calving with theoretical models. Her work aims to resolve the nonlinear physics of glacier ice to better understand future global climate change impacts. Millstein's publication record shows a strong focus on Antarctic ice dynamics, with recent work applying extreme value theory to analyze 47 years of iceberg calving events. Her research demonstrates consistent attention to both theoretical modeling and practical applications for understanding climate change impacts on polar regions. She has developed computational tools including CryoCloud, reflecting her commitment to open science and cloud-based infrastructure for cryosphere research. Her scientific contributions span ice shelf mechanics, fracture prediction, rheology, and statistical modeling of glacial processes. Millstein maintains an active research presence with publications extending to 2025, demonstrating ongoing contributions to the field of glaciology and climate science. She is affiliated with the glaciology research center at Colorado School of Mines and maintains an active GitHub presence with climate-related code repositories. Her work bridges theoretical geophysics with practical climate change impact assessment, particularly regarding ice sheet stability and sea level rise projections.