جست‌وجوی استاد و پژوهشگر

Kaiming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.

Jennifer Rossمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Biological Condensed Matter Physics
• +۵ مورد دیگر

Jennifer Ross is a Professor of Physics and Associate Dean for Creativity, Scholarship, and Research at the College of Arts & Sciences of Syracuse University . As a biophysicist, she investigates how cells organize their interiors through self-assembly and active matter principles, focusing on the microtubule cytoskeleton and enzyme-driven systems using single-molecule imaging . Her research bridges fundamental physics with biological organization . Education: Ph.D. in Physics, University of California, Santa Barbara (2004) B.A. in Physics and Mathematics, Wellesley College (2000) Research Focus: Self-organization of cytoskeletal networks Active matter dynamics in biological systems Motor protein interactions and cargo transport Programming circadian materials via biomolecular systems Microtubule severing mechanisms Recent Article Trends: 2025 studies explore kinesin-driven cytoskeletal composites, urease-DNA origami engineering, and crosslinker-regulated network mechanics 2024-2023 work examines ionic strength effects on microtubules, programmable circadian materials, and motor-cargo dynamics Earlier studies analyze actin-microtubule composites, liquid crystal phase control, and severing enzyme mechanisms Scientific Awards: Fellow of the American Physical Society (APS) and American Association for the Advancement of Science (AAAS) Cottrell Scholar (2025) and STAR Award Margaret Oakley Dayhoff Award (Biophysical Society) Grants: Leads multiple NSF, Sloan Foundation, and Research Corporation grants for projects like "Energy and Entropy Sculpting" and "Explorations: SUPER-Tech SHIP" . Teaching: Offers courses in experimental physics, microscopy, and biophysics, including a globally adopted hands-on microscope-building curriculum. Lab: Heads the Bio-Active Matter Lab , studying how cells harness noisy systems for autonomous organization.

Tianqi Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +۶ مورد دیگر

Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

Stephen Lichtمشاهده پروفایل

دانشیار

• Marine Robotics
• Biologically Inspired Propulsion
• Nonlinear Attitude Control
• +۵ مورد دیگر

Stephen Licht is an Associate Professor of Ocean Engineering and Graduate Director at the University of Rhode Island's College of Engineering, where he directs the Robotics Laboratory for Complex Underwater Environments (R-CUE). His research focuses on developing maritime robots capable of operating in dynamic and unpredictable environments through biologically inspired propulsion, distributed pressure sensing, model-based optimal control, and compliant underwater manipulation technologies. Ph.D. in Oceanographic and Mechanical Engineering from MIT/WHOI Joint Program (2008) B.S. in Mechanical Engineering from Yale University (1998) Former Senior Research Scientist at iRobot and Senior Robotics Engineer at Vecna Robotics Current Research Affiliate with MIT Department of Mechanical Engineering Former Visiting Faculty at Libera Università di Bolzano (2019-2020) Dr. Licht's research spans marine robotics with emphasis on biologically inspired propulsion systems that provide high authority and bandwidth thrust, nonlinear attitude control for maneuvering in dynamic conditions, compliant underwater manipulation technologies, and unmanned aerial monitoring of coastal structures. His work bridges mechanical engineering principles with oceanographic applications to create more capable underwater robotic systems that can operate in complex marine environments. His recent publications demonstrate a strong trend toward soft robotics applications for deep-sea exploration, with particular focus on jamming grippers and neutrally buoyant manipulation systems. The research also shows increasing integration of additive manufacturing techniques for field-deployable solutions and computational methods for autonomous systems operating in challenging marine environments. His work spans fundamental control theory, mechanical design, and practical field applications. Dr. Licht has secured significant research funding as both Principal Investigator and Co-Principal Investigator from major organizations including the Office of Naval Research, NOAA, NSF, and various university collaborations. His grants focus on advancing unmanned underwater vehicle technology, soft robotics for deep-sea applications, and coastal monitoring systems. Active mentor to numerous graduate and undergraduate students in Ocean Engineering Successful track record of student placements at organizations including Jaia Robotics, Scripps Institution of Oceanography, FORSSEA Robotics, and government research labs Collaborates with researchers at MIT, WHOI, University of Connecticut, University of Maine, and international institutions Licht leads the R-CUE lab which develops innovative solutions for underwater robotics challenges, with particular expertise in biomimetic propulsion, soft robotics for deep-sea applications, and autonomous systems for environmental monitoring. The lab maintains strong industry connections with OceanGate Inc. and FabNewport, and engages with local educational institutions through outreach programs with Roger Williams Middle School.

Arvind Parkheمشاهده پروفایل

استاد

• Global Supply Chains
• Strategic Alliances
• International Joint Ventures
• +۶ مورد دیگر

Arvind Parkhe is a Laura H. Carnell Professor of Strategy and International Business at Temple University’s Fox School of Business. He previously taught at Indiana University’s Kelley School of Business (1989–2003) and holds degrees from IIT Bombay (B.Tech.), Georgia State University (MBA), and Temple University (Ph.D.). His research focuses on strategic alliances, global supply chains, international joint ventures, and trust in cross-border partnerships. He has published in top journals like the Academy of Management Journal and Strategic Management Journal, earning awards such as the AIB Best Dissertation Award (1990) and JIBS Decade Award (2001). Teaching excellence spans all academic levels: recognized as outstanding faculty for Ph.D., MBA, and undergraduate programs. Service leadership includes Vice President of the Consortium for Undergraduate International Business Education (2014), Chair of Fox’s Strategic Management Department (2010–2020), and Managing Director of Fox MBA Programs (2005–2010). He received Temple’s Musser Outstanding Service Award (2012) and Faculty Service Excellence Award (2012). Key research contributions emphasize interorganizational networks, global competitive strategies, and trust mechanisms in alliances. His work bridges theory and practice, addressing challenges in global business operations and strategic collaboration.

Russell Epsteinمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Behavioral Neuroscience
• Spatial Navigation
• +۴ مورد دیگر

Russell Epstein is a Professor and Director of Graduate Studies in the Department of Psychology at the University of Pennsylvania. He is affiliated with the Center for Cognitive Neuroscience and Goddard Labs. His research focuses on neural mechanisms underlying visual scene perception, spatial navigation, and memory. Epstein holds a BA in Physics from the University of Chicago and a PhD in Applied Mathematics from Harvard University. Epstein’s research interests include high-level vision, spatial cognition, and the neural basis of environmental representations. His lab uses functional MRI and cognitive neuroscience techniques to study how scenes, objects, landmarks, and spaces are encoded in brain systems such as the parahippocampal place area and retrosplenial cortex. Recent work explores cognitive maps, grid-like neural representations, and the role of multisensory cues in navigation. His articles emphasize spatial navigation strategies, hierarchical cognitive maps, and the interplay between perception and memory. Notable contributions include investigations into hippocampal spatial metrics, olfactory navigation, and the neural underpinnings of environmental learning. Epstein teaches courses on cognitive neuroscience, including PSYC 149 and PSYC 600. He advises two graduate students in Psychology and has no listed scientific awards. His work is supported by grants (unspecified) and conducted within collaborative teams at the Center for Cognitive Neuroscience. Epstein’s research extends to labs focused on spatial cognition and neuroimaging, advancing understanding of how humans mentally map environments through visual and sensory integration.

• Political Science
• International Relations
• Comparative Politics
• +۳ مورد دیگر

Trajche Panov serves as a Teaching Fellow (academic rank: Lecturer) in the Department of Political Science at Trinity College Dublin, contributing to undergraduate and postgraduate instruction within the university's humanities and social sciences framework. His research spans core political science disciplines with emphasis on: International Relations Comparative Politics Political Theory Public Policy European Politics Political Methodology Dr. Panov maintains active scholarly engagement through his teaching role while advancing research in contemporary political systems and governance structures across European and global contexts.

Minh Q. Phanمشاهده پروفایل

دانشیار

• System Identification
• Control Theory
• Robotics
• +۴ مورد دیگر

Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.

Peichen Zhongمشاهده پروفایل

استادیار

• Materials Science
• Machine Learning
• Computational Modeling
• +۵ مورد دیگر

Dr. Peichen Zhong is an Assistant Professor in the Department of Materials Science and Engineering at the National University of Singapore (NUS). He leads the Applied Machine Learning and Materials Modeling (AM³) Group, focused on advancing computational methods for clean energy technologies. His research integrates machine learning with atomistic simulations to tackle challenges in battery materials, disordered materials, and sustainable energy systems. Education: B.S. in Physics from University of Science and Technology of China (2018); Ph.D. in Materials Science from UC Berkeley (2023, advised by Prof. Gerbrand Ceder); Postdoctoral training at Lawrence Berkeley National Lab and BIDMaP, co-advised by Persson, Cheng, and Krishnapriyan. Research Interests: Computational modeling of battery cathodes/electrolytes, AI-driven interatomic potentials, statistical mechanics in disordered materials, and generative models for scientific discovery. Key areas include Li/Na-ion batteries, solid-state reactions, and sustainable energy materials. Awards: BIDMaP Emerging Scholar Fellowship (UC Berkeley CDSS, 202?), 2023 Rising Stars in Materials Science (CMU/MIT/Stanford). Labs/Teams: The AM³ Group at NUS MSE focuses on interdisciplinary research combining theory, computation, and AI4Science. Current openings include PhD students and postdoctoral researchers.

Robert JOLIETمشاهده پروفایل

دانشیار

• Finance
• Portfolio Management
• SRI (Socially Responsible Investing)
• +۳ مورد دیگر

Robert JOLIET is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics and Management from HEC-ULg Management School (Belgium) and has held various academic leadership roles, including Director of Academic Development and Academic Director of the PGE (MSc in Management). His research focuses on finance, portfolio management, SRI (Socially Responsible Investing), and corporate finance, with a particular emphasis on international expansion, capital structure, and sustainable investment strategies. Education: 2008: Ph.D. in Economics and Management, HEC-ULg Management School, Belgium 2004: Master of Advanced Studies (DEA), Management Sciences (Finance), University of Liège, Belgium 2002: Master of Science in Business Engineering (Finance), University of Liège, Belgium Research interests span SRI fund dynamics, corporate social responsibility valuation, international debt policies, and market liquidity. Notable contributions include analyzing the impact of SRI mutual funds on corporate governance and environmental practices, and exploring the stability of sovereign debt structures in the Eurozone. Awards include the 2005 FORTIS Award (Third Prize) and the 2003 E.G. de Barsy Doctoral Fellowship. He has published extensively in journals like Economics Letters and Journal of Banking & Finance . Academic leadership roles include directing programs and departments at IÉSEG, alongside coordinating the Finance Track and managing academic development initiatives. His teaching focuses on financial data analysis, active portfolio management, and investment strategies in capital markets. He is a member of the LEM (Laboratoire d'Économie Moderne) research group and contributes to interdisciplinary research in finance and economics.

Nadia Figueroaمشاهده پروفایل

استادیار

• Robot Learning from Demonstration
• Human-Robot Collaboration
• Dynamical Systems
• +۵ مورد دیگر

Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science (CIS) and Electrical and Systems Engineering (ESE), and is a core faculty member at the GRASP Lab. Prior to Penn, she was a Postdoctoral Associate at MIT’s CSAIL and earned her PhD in Robotics from EPFL under Prof. Aude Billard. Her research focuses on physical and perceptual adaptive intelligence for robots, enabling fluid collaboration with humans in dynamic environments. Key applications include robot learning from demonstration , human-robot co-manipulation , safe navigation in human-centric spaces , and rehabilitation robotics . Her work integrates machine learning control theory artificial intelligence biomechanics psychology with guarantees of stability, safety, and robustness . Recent publications highlight advancements in reactive collision avoidance dynamical system learning intent estimation EEG-driven assistive control origami-based reconfigurable robots across platforms like autonomous vehicles and humanoid robots. She has authored a 2022 textbook on dynamical systems for robot control and received the Presidential Assistant Professorship at Penn.

Werner Wendelinمشاهده پروفایل

استاد

• Schramm-Loewner Evolution
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Wendelin Werner is a renowned mathematician and currently the Rouse Ball Professor of Mathematics at the University of Cambridge (since 2023). Previously, he held professorships at ETH Zürich (2013–2023) and the University of Paris-Sud in Orsay (1997–2013). His research focuses on probability theory, mathematical physics, and complex analysis, particularly Schramm-Loewner evolution and conformal invariance. He has received prestigious awards, including the Fields Medal (2006), SIAM George Pólya Prize (2006), and Fermat Prize (2001). His work bridges stochastic processes and geometric structures, with implications for statistical mechanics and critical phenomena. Werner studied at the École Normale Supérieure (1987–1991) and earned his PhD from Université Paris VI under J. F. Le Gall (1993). Early career positions included postdoctoral research at Cambridge (1993–1995) and roles at CNRS (1991–1993, 1995–1997). His academic contributions span stochastic geometry, critical systems, and random curves, with seminal papers on loop measures, percolation, and SLE. He has delivered major lectures globally, including at the International Congress of Mathematicians (IMC 2006) and the College de France's Cours Peccot. Werner’s honors include membership in the French Academy of Sciences (2008), the Berlin-Brandenburg Academy (BBAW), and the German National Academy Leopoldina. He is also an honorary fellow of Gonville and Caius College, Cambridge (2009). His research has advanced understanding of universal scaling limits in two-dimensional models, earning recognition as a leader in probability and mathematical physics.

Mark Steedmanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Cognitive Science
• Natural Language Processing
• +۹ مورد دیگر

Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.

Amitabha Bagchiمشاهده پروفایل

استاد

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +۷ مورد دیگر

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

Rob Timmermansمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Mechanics
• Quantum Field Theory
• +۳ مورد دیگر

Prof. Rob Timmermans is a Professor of Theoretical Physics and Vice-Dean for Education at the University of Groningen (UG). He is affiliated with the Faculty of Science and Engineering and the Precision Frontier — Van Swinderen Institute for Particle Physics and Gravity. His research focuses on theoretical particle physics, quantum mechanics, and precision measurements, particularly in electric dipole moment (EDM) searches using molecules like BaF. His work includes developing methods for molecular beam manipulation, phase-space analysis, and symmetry violation studies. He has contributed to collaborations such as NL-eEDM, advancing techniques for EDM detection and precision physics. Prof. Timmermans has received nominations for teaching awards, reflecting his commitment to education. Research highlights include studies on nucleon decay, antinucleon-nucleon interactions, and chiral effective field theory. His lab activities involve collaborations on laser-cooled molecules and trapping techniques. Prof. Timmermans’ articles often address fundamental physics questions, such as Lorentz violation in beta decay and parity violation in molecular systems. Awards: Nominated for Faculty Teaching Award 2014, Teacher of the Year 2014-15. Grants/Advising: Leads projects on EDM searches and particle physics, with active roles in international collaborations. Labs/Teams: Van Swinderen Institute, Precision Frontier group.