جست‌وجوی استاد و پژوهشگر

• Software Verification
• Formal Methods
• Programming Languages
• +۵ مورد دیگر

Jenna Wise DiVincenzo is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. She specializes in research areas such as software verification, formal methods, and programming languages, with a focus on gradual verification techniques that combine static and dynamic analysis. Her work emphasizes usability and scalability in verification tools, and she has contributed to projects like Gradual C0 and gradual null-pointer analysis. Dr. DiVincenzo earned her PhD in Software Engineering from Carnegie Mellon University (2023) and a BS in Mathematics and Computer Science from Youngstown State University (2017). She has interned at IBM Research, MIT Lincoln Laboratory, and the Software Engineering Research and Empirical Studies Lab at YSU. Her awards include the Google PhD Fellowship, NSF GRFP Fellowship, and 2022 Rising Star in EECS. Her research projects span theoretical advancements in gradual verification, empirical studies on usability, and practical tool development. She advises PhD students (e.g., Craig Liu, Conrad Zimmerman) and collaborates on initiatives like gradual verification for Rust and educational tools to teach verification concepts. Her work also explores leveraging large language models for specification generation and enhancing verification tool soundness through formal proofs.

Brenden Lakeمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۷ مورد دیگر

Brenden Lake is an Associate Professor of Computer Science and Psychology at Princeton University, starting Fall 2025. Previously, he was an Associate Professor of Psychology and Data Science at New York University. He is the principal investigator of the lab for Human & Machine Intelligence, which moved from NYU to Princeton in 2025 and is jointly affiliated with the Department of Computer Science and the Department of Psychology. His lab is located in Princeton's Peretsman Scully Hall, rooms 117, 120, and 121. Ph.D., Massachusetts Institute of Technology, 2014 Lake's research focuses on the intersection of human and machine intelligence, specifically examining human cognitive abilities that elude current AI systems. His work centers on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. He employs modern neural network modeling approaches including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos. His research aims to advance both psychology and computer science by exploring what makes human intelligence unique and using those insights to develop more powerful AI systems. Lake's recent publications demonstrate significant trends in grounded language acquisition through child perspectives, systematic generalization in neural networks, and the intersection of developmental psychology with AI. His work has appeared in top-tier venues including Science (2024) and Nature (2023), with multiple publications exploring how insights from human cognition can improve machine learning systems. His research shows how incorporating human cognitive ingredients can make AI systems more powerful and human-like while addressing longstanding debates about neural network capabilities. Science publication (2024) on Grounded language acquisition through the eyes and ears of a single child Nature publication (2023) on Human-like systematic generalization through a meta-learning neural network Multiple publications covered by major media outlets including New York Times and Washington Post Lake advises Ph.D. students in computer science, psychology, and related fields through his lab. His research is supported by publications in top venues across computer science and cognitive science. He teaches courses including Computational Cognitive Modeling and Advancing AI through Cognitive Science, bridging the theoretical and practical aspects of his research. Lake leads the lab for Human & Machine Intelligence, which studies the ingredients of intelligence in humans and machines. The lab investigates human cognitive abilities that current AI systems cannot replicate, with the dual goal of advancing psychological understanding of human intelligence while developing more capable artificial intelligence systems. Current research focuses on few-shot concept learning, learning through goal generation, and learning by asking questions.

Andreas Vlachosمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +۱۰ مورد دیگر

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +۷ مورد دیگر

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

Oliver Kroemerمشاهده پروفایل

دانشیار

• Robot Learning for Manipulation
• Motion Planning for Manipulation Tasks
• Learning and Classification Algorithms
• +۴ مورد دیگر

Oliver Kroemer is an Associate Professor at Carnegie Mellon University's Robotics Institute (RI), affiliated with the Intelligent Autonomous Manipulation (IAM) Lab. His research focuses on enabling robots to learn versatile manipulation skills through lifelong frameworks, with applications in elder care, environmental maintenance, and hazardous operations. Developed methods for robot learning via physical interaction and reinforcement learning Created representations for contact states and motor primitives to improve skill generalization Research Interests: Spanning robot learning, tactile sensing, force-velocity control, and lifelong skill acquisition. Projects include Agile and Dynamic Interactions for Mobile Manipulation and Integrated Planning and Learning (Pillar project). Scientific Awards: Finalist, Georges Giralt Ph.D. Award (2015) Education: Masters & Bachelors in Engineering, University of Cambridge (2008) Ph.D., Technische Universitaet Darmstadt (2014) Students & Affiliates: Current PhD: Mark Lee, Sarvesh Patil, Saumya Saxena, Yunus Seker, Zilin Si Past PhD: Alex LaGrassa, Tabitha Lee, Qiao Liang, Shivam Vats, Kevin Zhang

Noah D. Goodmanمشاهده پروفایل

دانشیار

• Computational models of cognition
• Probabilistic programming languages
• Natural language semantics and pragmatics
• +۶ مورد دیگر

Noah D. Goodman is Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy) at Stanford University. He directs the Computation & Cognition Lab (CoCoLab) at Stanford, where he leads research on computational models of cognition, integrating logic and probability. His work spans cognitive psychology, linguistics, and computer science. Primary Appointment: Psychology Department By Courtesy: Computer Science Department and Linguistics Department Director: Computation & Cognition Lab (CoCoLab) Goodman's research focuses on computational models of cognition, with particular interest in probabilistic approaches to understanding human thought. His work integrates logic and probability to model concepts, categorization, intuitive theories, causal learning and reasoning, social cognition (including reasoning about others' goals, beliefs, and actions), cognitive development (especially acquisition of abstract knowledge), and natural language semantics and pragmatics. He has made significant contributions to the development of probabilistic programming languages as tools for cognitive modeling. His recent publications demonstrate a strong trend toward integrating probabilistic modeling with linguistic theory and social cognition. The articles span computational cognitive science, natural language processing, and artificial intelligence, with a consistent theme of using probabilistic frameworks to understand complex cognitive phenomena. Many papers explore how humans make inferences under uncertainty across different domains. Goodman teaches several courses at Stanford including Language and Thought (Psych 132), Computation and Cognition: the Probabilistic Approach (Psych 204/CS 428), Foundations of Cognition (Psych 205), and Introduction to Cognitive Science. He has also led seminars on topics ranging from natural and artificial intelligence to the science of meditation.

• Machine Learning
• Adversarial Robustness
• Computer Vision
• +۴ مورد دیگر

Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration

Ram Rajagopalمشاهده پروفایل

دانشیار

• Smart Grids
• Renewable Energy Integration
• Power Systems Optimization
• +۳ مورد دیگر

Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.

Christopher Pottsمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Semantics
• +۵ مورد دیگر

Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.

• Psycholinguistics
• Cognitive Psychology
• Speech Prosody
• +۴ مورد دیگر

Andrés Buxó-Lugo serves as an Assistant Professor of Psychology at the University at Buffalo, where he directs the Language Processing and Computation Lab. His research investigates the cognitive mechanisms underlying language production, comprehension, and acquisition with a specialized focus on speech prosody—the rhythm, intonation, and intensity patterns in speech—and their role in human communication. His primary research interests include psycholinguistics, cognitive psychology, speech prosody, language production, language comprehension, language acquisition, and computational linguistics. He examines how listeners integrate diverse linguistic cues during speech processing, how individuals learn unfamiliar constructions like non-native pronunciations or novel prosodic patterns, and the cognitive basis of durational changes in speech. His work also explores how communicative context shapes prosodic production and how higher-level linguistic information aids prosodic structure parsing. Analysis of his 15 most recent publications (2019-2025) reveals consistent interdisciplinary work bridging cognitive science, linguistics, and computational modeling. Key trends include phonological representation studies, speech planning mechanisms, intonation adaptation across talkers, lexical representation structures, and the integration of input expectations in syntactic parsing. His research demonstrates significant methodological diversity, incorporating experimental paradigms, computational modeling, and acoustic analysis to unravel language processing complexities. As director of the Language Processing and Computation Lab at the University at Buffalo, Buxó-Lugo leads research initiatives focused on developing computational models of language processing while investigating the cognitive foundations of speech and prosody through empirical experimentation and theoretical innovation.

Sebastian Schererمشاهده پروفایل

استاد پژوهشی

• Robotics
• Unmanned Aerial Vehicles
• Autonomous Navigation
• +۱۳ مورد دیگر

Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.

Manolis Savvaمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Robotics
• +۴ مورد دیگر

Roles & Affiliations: Manolis Savva is an Associate Professor at Simon Fraser University's School of Computing Science and holds a Canada Research Chair in Computer Graphics. He leads research in 3D scene understanding, with applications in graphics, vision, and robotics. Previously, he was a researcher at Facebook AI and Princeton University. Education: Ph.D. in Computer Science (2016), Stanford University, advised by Pat Hanrahan B.A. in Physics and Computer Science (2009), Cornell University Research Interests: His work focuses on analyzing, organizing, and generating 3D content, particularly for holistic scene understanding. Key areas include articulated objects, embodied AI, and datasets like ScanNet , Matterport3D , and Habitat . His methods drive applications in robotics, autonomous agents, and virtual environments. Publications Trends: Recent work emphasizes generative models (e.g., SINGAPO for articulated object parts), embodied AI benchmarks (Habitat), and multimodal scene analysis. His papers often address challenges in scalability, realism, and cross-modal fusion for 3D environments. Awards: CHCCS Early Career Researcher Award (2022) ICLR 2023 Outstanding Paper Award ICCV 2019 Best Paper Nomination (Habitat) Advising & Grants: Supervised over 15 graduate students, many advancing to top PhD programs and tech companies. Active in grants for embodied AI, scene understanding, and robotics. Labs/Teams: Leads the 3DLG (3D Learning Group) and GrUVi (Graphics and Vision) groups at SFU. Collaborates extensively with industry (e.g., Meta, NVIDIA) on AI-driven 3D research.

• Computational Social Science
• Network Science
• Data Visualization
• +۷ مورد دیگر

Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh, and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber). She leads the Pitt Computational Social Dynamics Lab (PICSO Lab) and holds secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program. PhD in Computer Science from Arizona State University Postdoctoral research at Harvard University and Northeastern University Her research focuses on computational approaches for: Networked social dynamics High-dimensional social information summarization Trust and distrust propagation Misinformation detection Policy diffusion analysis Recent publications span 2014-2020, covering: Social media crisis response Graph visualization techniques Policy diffusion patterns Misinformation mitigation Temporal topic modeling Scientific support includes: National Science Foundation (NSF) awards Minerva/ONR funding AFOSR grant for distrust modeling DARPA Understanding Group Biases program As director of PICSO Lab, she leads interdisciplinary research teams on: Digital accountability Urban mobility patterns Health informatics via crowdsourcing Trust-influence dynamics

Catherine Laiمشاهده پروفایل

دانشیار

• Speech and Language Processing
• Computational Linguistics
• Prosody
• +۱۷ مورد دیگر

Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.

Roberto Berjón Gallinasمشاهده پروفایل

استاد

• Computer Science
• Semantic Technologies
• Internet of Things
• +۶ مورد دیگر

Roberto Berjón Gallinas is a Professor at the School of Computer Science, Deusto University. His research focuses on semantic technologies, internet of things, and data integration, with a strong emphasis on mobile applications and open data. He leads two research groups: ICS (Innovación en Ciencias Sociales) MARATON (Mobile applications, internet of things, data processing, semantic technologies, open data) His recent work includes developing smart tracking frameworks for IoT workflows and digital twins, event-driven architectures for IoT systems, and semantic integration solutions for university research. His projects often bridge technical innovation with practical applications in collaborative e-learning and spatial analytics. Key trends in his publications highlight advancements in IoT frameworks , semantic technologies for data integration, and real-time systems using BLE and open data. These works frequently intersect with collaborative e-learning , university transparency , and adaptive mobile platforms .