جست‌وجوی استاد و پژوهشگر

Michael Kaessمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• SLAM (Simultaneous Localization and Mapping)
• +۶ مورد دیگر

Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

Manik Varmaمشاهده پروفایل

استاد مدعو

• Machine Learning
• Deep Learning
• Information Retrieval
• +۱۶ مورد دیگر

Manik Varma is a Distinguished Scientist and Vice President at Microsoft Research India, and an Adjunct Professor at the Indian Institute of Technology Delhi. He is a Fellow of the Indian Academies of Science (IASc, INSA, NASI), the Indian National Academy of Engineering (INAE), and the Association for Computing Machinery (ACM). He has received prestigious awards such as the Shanti Swarup Bhatnagar Prize and Microsoft Gold Star Award. Education : BSc in Physics from St. Stephen's College (David Raja Ram Prize) BA in Theoretical Physics from the University of Oxford (Rhodes Scholar) DPhil in Computer Vision and Machine Learning from the University of Oxford (University Scholar) Post-doctoral Fellow at the Mathematical Sciences Research Institute (MSRI), Berkeley Visiting Miller Professor at UC Berkeley His research focuses on Machine Learning (Extreme Classification, Resource-efficient ML, Supervised Learning), Information Retrieval (Computational Advertising, Dense Retrieval, Recommender Systems), and Computer Vision (Image Search, Object Recognition). Recent work includes graph-regularized encoders, label variance reduction, and multimodal classification frameworks. His publications span extreme classification algorithms like NGAME , SiameseXML , and DECAF , with applications in IoT, web search, and recommendation systems. He leads a research group at Microsoft Research India and advises PhD students at IIT Delhi. Scientific Awards : Shanti Swarup Bhatnagar Prize (Government of India) Microsoft Gold Star and Achievement Awards WSDM 2019 Best Paper Prize BuildSys 2019 Best Paper Runner-up Fellow of ACM, IASc, INSA, NASI, INAE He has supervised numerous PhD students, including Sonu Mehta and Suchith Prabhu, and collaborates with institutions like Microsoft Research India, IIT Delhi, and UC Berkeley. His research has led to scalable solutions for billion-label classification and resource-constrained IoT applications.

Shoudong Huangمشاهده پروفایل

استاد

• Mobile robot navigation
• Simultaneous localization and mapping (SLAM)
• Underwater robotics
• +۴ مورد دیگر

Shoudong Huang is a Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney, and Deputy Director of the UTS Robotics Institute. His research focuses on mobile robot navigation , SLAM , nonlinear state estimation , and surgical robotics . He has published over 200 papers and is recognized as one of the 100 Most Influential Scholars in Robotics (Aminer, 2018). PhD in Automatic Control, Northeastern University (China) Postdoctoral Research Fellow, University of Hong Kong (1998-2000) Research Fellow, Australian National University (2001-2003) Full-time academic roles at UTS since 2004 His work addresses challenges in robot localization across extreme environments (underwater, underground mining, surgical settings) and develops globally optimal SLAM algorithms with guaranteed performance. He has secured over $4 million AUD in external funding, including ARC Discovery grants and industry partnerships. Recent publications emphasize cross-modal calibration (camera-LiDAR), interval analysis for bounded noise , and template-based deformable surface reconstruction . These span applications in autonomous driving, surgical navigation, and UAV guidance. Chancellor’s Medal for Research Excellence (2020) Supervisor of the Year (2023) Best Paper Award (2016 ICARCV) Huang serves as Associate Editor for IEEE Transactions on Robotics and International Journal of Robotics Research , and has held leadership roles in top robotics conferences like IROS and RSS. His collaborations span MIT, USC, Zhejiang University, and industry partners including PMSW Research Pty Ltd and Multiplex Constructions Pty Ltd.

Aarti Singhمشاهده پروفایل

استاد

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +۷ مورد دیگر

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

• Electrochemical Engineering
• Catalysis Engineering
• Separations Engineering
• +۳ مورد دیگر

Raj Ganesh S. Pala serves as a Professor in the Department of Chemical Engineering at the Indian Institute of Technology Kanpur. His research focuses on electrochemical engineering with particular expertise in energy storage systems, catalysis, and materials science. Dr. Pala maintains an active research program investigating fundamental electrochemical processes and their applications in energy conversion and storage technologies. Dr. Pala earned his PhD in Physical Chemistry from the University of Utah (1999-2005), followed by a Master's in Science (Biophysical Chemistry) from the Indian Institute of Science and National Center for Biological Science (1997-1998), and a Bachelor's in Chemical and Electrochemical Technology from the Central Electro-Chemical Research Institute (1992-1996). His research interests span electrochemical engineering, catalysis, and separation processes with applications in energy storage, water treatment, and sustainable chemical production. Dr. Pala's work bridges fundamental electrochemistry with practical engineering applications, particularly in battery technology and electrocatalysis. His recent publications demonstrate a strong focus on energy storage materials, particularly lithium-ion battery cathodes, and electrocatalytic processes for sustainable chemical production. The research spans from fundamental materials characterization to practical device implementation, with particular emphasis on improving efficiency, selectivity, and longevity of electrochemical systems. Dr. Pala is based in Office NL-II 304 at IIT Kanpur, where he conducts his research and teaching activities in the Department of Chemical Engineering. His work contributes significantly to IIT Kanpur's research initiatives in sustainable energy and advanced materials.

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.

• Machine Learning
• Natural Language Processing
• Music Information Retrieval
• +۱۲ مورد دیگر

Colin Raffel , currently an Associate Professor at the University of Toronto and Associate Research Director at the Vector Institute , is a leading researcher in machine learning and natural language processing . His career spans roles at Hugging Face (Faculty Researcher), Google Brain (Senior Research Scientist), and UNC Chapel Hill (Assistant Professor). Education: PhD in Electrical Engineering (Columbia), MA in Music/Science (Stanford), BA in Mathematics (Oberlin) Key affiliations: Google Brain (2016-2020), Hugging Face (2021-present), Vector Institute (2023-present) His research focuses on language model development , attention mechanisms , efficient machine learning , and music information retrieval . Recent work explores model merging , parameter-efficient fine-tuning , and data-constrained language models . Teaching : Has instructed courses at University of Toronto and UNC Chapel Hill on Neural Networks , Deep Learning , and Information Theory . Academic service includes organizing ICLR workshops and serving as Senior Area Chair for NeurIPS and EMNLP . Notable awards : NSF CAREER (2022), Caspar Bowden Award (2023), NeurIPS Outstanding Paper (2023) Key contributions : Core developer of WT5 , Git-Theta , and mir_eval software

Cheryl Gaimonمشاهده پروفایل

استاد

• Innovation and Management of Technology
• Knowledge Management and Outsourcing
• New Product/Process Development
• +۲ مورد دیگر

Cheryl Gaimon holds the Esther and Edward J. Brown Chair and serves as a Regents' Professor at the Scheller College of Business, Georgia Institute of Technology. She is Faculty Director of the Management of Technology (MoT) Certificate Program, which she helped establish, and teaches across all academic levels including executive education. Professor Gaimon initiated the Operations Management Program and served as its first Area Coordinator for seven years. Her academic credentials include: Ph.D. in Operations Research from Carnegie Mellon University M.S. in Industrial Administration (Operations Management) from Carnegie Mellon University B.S. in Mathematics and Economics (Magna Cum Laude) from Brooklyn College, City University of New York Professor Gaimon's research centers on managing knowledge-based resources in technology-driven environments. Her work spans Innovation and Management of Technology , Knowledge Management and Outsourcing , New Product/Process Development , Alliances for Innovation , and Sustainability Research . She examines how firms navigate technological change through strategic resource allocation, knowledge transfer, and sustainable operations, with increasing emphasis on environmental strategies in recent years. Her publication trajectory reveals consistent focus on operations management within technological evolution—from foundational work on technology acquisition and capacity planning to contemporary studies on environmental strategies and multidisciplinary technology management. Key journals featuring her research include Management Science , Operations Research , and Production and Operations Management . Major recognitions include: Regents' Professor designation (2005) by the Georgia Board of Regents The 1999 Georgia Tech Research Award for doctoral student development Brady Family Award for Faculty Research Excellence (2014) Best Department Editor Award for POM (2024) POMS Fellow status and Distinguished Service Award (2009/2014) As an educator, she has mentored doctoral students and shaped curriculum through the MoT program. Her professional leadership includes serving as POMS President (2008-2009), founding co-President of the POMS College on Product Innovation and Technology Management, and editorial roles at top journals including Management Science and Production and Operations Management . Professor Gaimon directs the interdisciplinary Management of Technology Certificate Program, fostering cross-departmental collaboration for technology management research and education while advancing sustainability integration in operations.

Arman Cohanمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Arman Cohan is an Assistant Professor in the Department of Computer Science at Yale University, where he leads the Yale NLP Lab since its founding in January 2023. His research spans natural language processing and machine learning with emphasis on language modeling, representation learning, retrieval systems, and specialized domain applications including scientific discovery and AI for science. His primary research interests include: Natural Language Processing Machine Learning Large Language Models Information Retrieval AI for Science Scientific Problem-Solving Recent publications (2025) demonstrate intense focus on evaluating and advancing LLM capabilities across multimodal reasoning, scientific claim verification, financial domain applications, and biological modeling. The lab consistently produces high-impact work accepted at top-tier conferences including ACL, EMNLP, and ICLR, with 11 papers at ACL 2025 alone. Scientific awards include: Best Paper Award at AI4Research Workshop (IJCAI 2024) Outstanding Paper Award at EACL 2023 Best Paper Award at ACL 2024 for Olmo language model research Professor Cohan actively advises PhD students including Kaili Liu, Jacob Dunefsky, Alan Li, Yilun Zhao, and has graduated researchers such as Linyong Nan (now at Zoom) and Ansong Ni (now at Meta). The lab maintains strong industry partnerships and receives substantial research funding as evidenced by its prolific output and conference presence. The Yale NLP Lab hosts the annual New England NLP Workshop and regularly features speakers from leading institutions including Meta AI, Allen Institute for AI, and DeepMind, fostering a collaborative environment for advancing NLP research.

Furong Huangمشاهده پروفایل

دانشیار

• Machine Learning
• Trustworthy Machine Learning
• Generative AI
• +۷ مورد دیگر

Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.

Huan Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Formal Verification of Machine Learning
• AI Safety
• +۴ مورد دیگر

Huan Zhang serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Department of Computer Science and the Coordinated Science Laboratory. His research focuses on building trustworthy AI systems with formal verification techniques to provide provable guarantees for safety-critical applications, particularly in machine learning and neural networks. Dr. Zhang received his Ph.D. in Computer Science from UCLA in 2020, advised by Professor Cho-Jui Hsieh. His academic journey includes an M.S. in Computer Engineering from UC Davis (2014) and a Bachelor of Engineering from Zhejiang University (2012). Prior to joining UIUC, he completed a postdoctoral fellowship at Carnegie Mellon University (2021-2023) with Professor Zico Kolter. Huan Zhang's research program centers on formal verification of machine learning systems, with particular emphasis on neural network verification, AI safety, robustness, and reliability. He pioneered the linear bound propagation-based verification framework that enables formal verification for networks with millions of neurons. His work spans five major research categories: formal verification of machine learning, training trustworthy ML models, machine learning safety and adversarial attacks, reinforcement learning safety, and optimization for scalable machine learning. His CROWN framework (NeurIPS 2018) established a foundational approach for neural network verification through efficient linear bound propagation. His recent publications demonstrate a strategic expansion from foundational verification techniques toward increasingly complex systems including large language models, vision-language models, and robotic control systems. The research trajectory shows a clear progression from theoretical frameworks to practical implementations with real-world applications, particularly in safety-critical domains. His work increasingly bridges formal methods with practical AI deployment requirements. Winner of International Verification of Neural Networks Competition (VNN-COMP) as team leader (2021-2024) Schmidt Futures AI2050 Early Career Fellowship ($300,000 research grant) Adversarial Machine Learning (AdvML) Rising Star Award (2021) IBM PhD Fellowship (2018) Dr. Zhang leads the development of α,β-CROWN, a neural network verifier that has won VNN-COMP 2021-2023, and auto_LiRPA, a PyTorch-based library for perturbation analysis on general computational graphs. He has mentored numerous graduate students from CMU, UCLA, UIUC, and Columbia University. His research is supported by significant funding including the Schmidt Futures fellowship and industry collaborations. He teaches courses including ECE 120, ECE 484, ECE 584, and ECE 598 HZ on topics ranging from computing fundamentals to safe autonomy and machine learning. Dr. Zhang maintains active research collaborations across multiple institutions and is affiliated with UIUC's Coordinated Science Laboratory. His work has significant implications for safety-critical AI applications in autonomous systems, healthcare, and other mission-critical domains where reliability guarantees are essential. He regularly gives guest lectures at institutions including Yale, Stony Brook, and the University of Nebraska Lincoln on formal verification techniques.

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Luke Zettlemoyer is an Adjunct Professor of Computer Science and Engineering at the University of Washington, with affiliations to the Department of Linguistics. He focuses on machine learning, natural language processing, and multimodal systems, contributing to advancements in large language models, ethical AI, and scalable architectures. His research addresses challenges in model alignment, generalization, and cross-domain integration. Key research interests include multimodal reward models, efficient tokenization strategies, and model optimization techniques. He has explored topics such as neural trajectories for robot learning, content-adaptive image processing, and ethical mitigation of verbatim data reproduction. His publications span 2023–2025, emphasizing practical applications of AI in robotics, vision-language systems, and scalable retrieval-based models. While no formal awards are listed, his work reflects significant contributions to foundational AI research.

• Robotics
• Robot Learning
• Machine Learning
• +۷ مورد دیگر

Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.

Samuel J. Gershmanمشاهده پروفایل

استاد

• Learning
• Memory
• Decision Making
• +۶ مورد دیگر

Samuel J. Gershman is a Professor of Psychology at Harvard University, affiliated with both the Department of Psychology and the Center for Brain Science. He directs the Computational Cognitive Neuroscience Lab (CCNLab), where he investigates how the brain acquires richly structured knowledge about the environment and uses this knowledge to guide adaptive behavior. Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, followed by postdoctoral training in the Department of Brain and Cognitive Sciences at MIT (2013-2015). His research spans computational neuroscience, cognitive psychology, and machine learning. His primary research interests include learning, memory, decision making, and computational neuroscience. Gershman's work integrates behavioral, neuroimaging, and computational techniques to understand cognitive processes. He has made significant contributions to understanding memory systems, reinforcement learning, and the computational principles underlying human cognition. Analysis of Gershman's recent publications reveals a strong focus on computational approaches to understanding cognitive processes, with particular emphasis on memory systems, decision-making mechanisms, and the intersection of artificial intelligence with cognitive neuroscience. His work often bridges theoretical computational models with empirical neuroscience data, exploring how the brain implements efficient cognitive algorithms. Gershman actively mentors graduate students and postdoctoral researchers, with current advisees working on diverse projects spanning computational modeling, neuroimaging, and behavioral experiments. His lab investigates topics ranging from dopamine signaling to social cognition using a combination of theoretical and experimental approaches. The CCNLab, which Gershman directs, brings together researchers from psychology, neuroscience, computer science, and related fields to explore the computational principles of cognition. The lab utilizes a range of methodologies including behavioral experiments, neuroimaging, computational modeling, and theoretical analysis to address fundamental questions about how the mind works.