جست‌وجوی استاد و پژوهشگر

Carl Vondrickمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Carl Vondrick is an Associate Professor of Computer Science at Columbia University. He leads the Perception and Robotics research group focused on creating intelligent systems that understand and interact with the physical world through vision and robotics. His work emphasizes interpretable models, multimodal learning, and robustness. Education: PhD in Computer Science from MIT (2017), advised by Antonio Torralba BS in Computer Science from UC Irvine (2011), advised by Deva Ramanan Research Interests: Generative models for physical intelligence Interpretable machine learning Robotics and embodied AI Large-scale video understanding AI for scientific discovery Article Trends: Recent work focuses on neuro-symbolic methods , generative AI safety , and robot learning . Notable contributions include DIFFusion for subtle visual learning, DiSciPLE for scientific discovery, and differentiable robot rendering frameworks. Awards: 2024 PAMI Young Researcher Award 2021 NSF CAREER Award Advising & Grants: Current PhD students (9) and former advisees (4). Active grants from NSF, DARPA, Toyota Research Institute, and Amazon. Program chairs for ICLR 2025/2026. Labs/Teams: Leads Columbia's Perception Lab and collaborates with industry partners like Google, Cruise, and TRI.

Derek Nowrouzezahraiمشاهده پروفایل

استاد

• Computer Graphics
• Machine Learning
• Physics-based Simulation
• +۶ مورد دیگر

Derek Nowrouzezahrai is a Full Professor at McGill University's Faculty of Engineering, holding positions in the Department of Electrical & Computer Engineering, Desautels Faculty of Management (Associate), and School of Computer Science (Associate). He holds prestigious chairs including the CIFAR AI Chair and Mila-Ubisoft AI Chair. His research focuses on simulating physics for machine learning, Monte Carlo methods, light transport editing, and fluid simulation. Recent work includes neural implicit models, differentiable rendering, and reinforcement learning applications. He has supervised over 40 students across PhD, MSc, and undergraduate levels. Awards include the Best Paper Honourable Mention and CGF Cover Image Competition wins. His courses cover numerical methods, realistic image synthesis, and advanced computer graphics. Research contributions span 150+ publications with a focus on rendering, simulation, and AI integration. Research Interests: Physics-Based Simulation : Fluid dynamics, light transport, and material modeling Machine Learning Integration : Neural rendering, inverse problems, and generative models Real-Time Graphics : Efficient algorithms for global illumination and interactive visualization Multi-Agent Systems : Reinforcement learning for coordinated behavior Scientific Achievements: Developed scalable rendering techniques like Virtual Ray Lights and gradient-domain photon estimation Pioneered differentiable simulation frameworks for system identification Contributed to open-source datasets like Kubric for computer vision research Lab & Team: Leads a research group focusing on visual computing, with collaborations across computer science, engineering, and AI disciplines. Current focus areas include neural implicit representations and AI-driven physics simulations.

Raymond A Yehمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Raymond A. Yeh is an Assistant Professor in the Department of Computer Science at Purdue University since Fall 2022. Previously, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC) and completed his PhD in Electrical Engineering at the University of Illinois at Urbana-Champaign (UIUC) in 2021. His research focuses on machine learning and computer vision, particularly in developing algorithms for effective and explainable models across audio, vision, language, and multi-agent systems. Education: Ph.D., Electrical Engineering, UIUC (2021) M.S., Electrical Engineering, UIUC (2016) B.S., Electrical Engineering, UIUC (2014) Research Interests: His work bridges machine learning and computer vision, emphasizing equivariance in neural networks, robustness, and scalable algorithms. Key areas include: Generative models (diffusion models, inpainting) Equivariant deep learning architectures Multi-modal reasoning (vision-language) 3D reconstruction and simulation Recent Contributions: Recent work includes model immunization techniques, scale-equivariant networks, and novel datasets like Tree-D Fusion. His publications span top venues (CVPR, NeurIPS, ECCV) with 4,551 total citations (h-index 18 as of 2025). Awards: Google PhD Fellowship (2018) Best Paper Runner-up at CVPR Workshop (2024) National Science Foundation (NSF) Grant (2024) Purdue Seed for Success Award (2024) Teaching: Courses include Introduction to AI, Computer Vision with Deep Learning, and Foundations of Deep Learning. Student evaluations consistently score above 4.5/5.0. Labs: Leads the Purdue Vision and Learning Lab, focusing on advancing AI through robust, interpretable models with practical real-world applications.

• Computing Education
• Program Tracing
• Educational Pedagogy
• +۳ مورد دیگر

Greg L. Nelson is a Professor at the University of Maine, specializing in computing education research and visualization. His work focuses on program tracing, educational pedagogy, and assessment methods in computer science education. Previously affiliated with the University of Washington, his research spans topics like adaptive learning systems, prerequisite skill analysis, and the integration of AI in education. Research Interests: His primary areas include improving introductory programming instruction, developing diagnostic assessments, and exploring how prerequisite skills affect advanced learning. He also investigates visualization design constraints and the role of agency in self-directed learning environments. Publications: Nelson has authored/co-authored over 15 papers in venues like ICER, SIGCSE, and IEEE TVCG, emphasizing practical pedagogical strategies and empirical studies in computing education. Notable works include frameworks for program tracing pedagogy and the application of generative AI in creative media courses. Awards: He has received prestigious recognitions including the Herbrand Award and John Henry Award for contributions to computing education theory and practice. Contributions: Collaborates with institutions globally, focusing on curriculum development and educational technology innovation. His work bridges theoretical frameworks with real-world classroom applications, particularly in fostering equitable and effective learning environments.

Simone Paolo Ponzettoمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Knowledge Graphs
• +۷ مورد دیگر

Prof. Simone Paolo Ponzetto is the Chair of Information Systems III (Enterprise Data Analysis) at the University of Mannheim, leading the Natural Language Processing and Information Retrieval (NLP and IR) group within the Data and Web Science Group since 2013. His research bridges computational methods with applications in Social Sciences and Humanities. Full Professor (W3) since February 2016 Based at School of Business Informatics and Mathematics Contact: simone@informatik.uni-mannheim.de | ponzetto@uni-mannheim.de His work spans knowledge acquisition , multimodal NLP , and LLM applications in: GUI prototyping, scientific text analysis, political discourse modeling, and social science data integration. Recent projects explore zero-shot synthesis , cross-lingual knowledge editing , and ethical analysis of language models . Key research trends in his publications include LLM-driven interface design , multimodal summarization , and robust cross-lingual methods for NLP tasks. His group's work combines symbolic approaches with deep learning , leveraging knowledge graphs and distributional semantics across disciplines. Research grants include DFG-funded projects JOIN-T 2 , SFB 884 , and UNCOVER , alongside MWFK Baden-Württemberg programs for junior professors and part-time master's education in Data Science. The Data and Web Science Group under his leadership focuses on empirical research in computational social science and digital humanities, with applications in political text analysis, survey data integration, and surgical language modeling through projects like SurgicBERTa and FrameASt .

Seth Hutchinsonمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Artificial Intelligence
• +۳ مورد دیگر

Seth Hutchinson is a Professor at Northeastern University with joint appointments in the Khoury College of Computer Sciences and the Department of Electrical and Computer Engineering. His expertise spans robotics, control systems, and artificial intelligence, with a focus on robotics planning, sensing, control, and multi-robot systems. He holds a PhD in Electrical Engineering from Purdue University (1988) and is an IEEE Fellow. His research emphasizes bio-inspired robotics, human-robot interaction, and autonomous systems. He leads initiatives in the Institute for Experiential Robotics, advancing robotics applications in art, environmental monitoring, and human-centric systems. Key contributions include frameworks for robust control, task allocation in heterogeneous multi-robot teams, and sim-to-real transfer in autonomous systems. His work bridges theoretical control with practical applications, such as artistic brush painting with cable robots and emotion-aware bipedal navigation. Hutchinson advocates for diversity and inclusion in robotics through IEEE RAS initiatives and has contributed to foundational texts like Robot Modeling and Control . His recent projects address challenges in autonomous surface vehicle control, quadruped locomotion, and safety-aware task planning using large language models. Notable awards include the IEEE Fellow distinction, recognizing his impact on robotics research. He collaborates across disciplines, integrating vision-based localization, reinforcement learning, and sensor fusion into robotic systems. Current efforts include advancing resilient control policies and exploring the ethical dimensions of AI in academia and industry.

Russ Tedrakeمشاهده پروفایل

استاد

• Robotics
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Russ Tedrake is the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT. He directs the Center for Robotics at CSAIL and leads MIT's DARPA Robotics Challenge team. Additionally, he serves as Senior Vice President of Robotics Research at Toyota Research Institute (TRI). His work focuses on control solutions for complex dynamical systems, integrating mechanics, optimization, and machine learning for robotic manipulation. Education: B.S.E. in Computer Engineering, University of Michigan (1999) Ph.D. in Electrical Engineering and Computer Science, MIT (2004) Postdoctoral Associate, MIT Brain and Cognitive Sciences Department Research Interests: Tedrake’s research emphasizes elegant control strategies for underactuated and stochastic systems. Key areas include robust control design using non-smooth mechanics, merging systems theory with machine learning, and developing tools like the Drake software framework for simulation and analysis. His team explores topics such as contact-rich manipulation, trajectory optimization, and real-time motion planning. Notable Contributions: Tedrake’s group develops algorithms for dexterous robotics, including tactile sensing with GelSight sensors and high-speed manipulation strategies. The Drake toolbox is widely used for robotics simulation and control. Awards: 2024 MIT School of Engineering Distinguished Educator Award NSF CAREER Award Multiple teaching awards, including the 2021 Jamieson Teaching Award Labs & Projects: Leads the Robot Locomotion Group at CSAIL, which studies agile robotics. Active in TRI’s research on autonomous vehicles and industrial robotics.

Daniel Beßlerمشاهده پروفایل

پژوهشگر

• Knowledge Representation
• Cognitive Robotics
• Hybrid Reasoning
• +۴ مورد دیگر

Dr. Daniel Beßler is a Researcher at the University of Bremen specializing in knowledge representation and cognitive robotics. Affiliated with the Institute for Artificial Intelligence, he contributes to the Collaborative Research Center EASE (Extracting and Applying Structure from Everyday Activities) and develops frameworks bridging theoretical AI with practical robotic applications. His educational background includes a PhD in Computer Science from the University of Bremen (2022), with dissertation research on ontological representation of activity context for flexible robot task execution. This foundational work underpins his contributions to semantic reasoning in dynamic environments. Beßler's research centers on hybrid symbolic-subsymbolic reasoning systems, with leadership in developing KnowRob 2.0 – a knowledge processing framework enabling cognition-enabled robotic agents. His work extends to semantic scene representation, activity recognition in virtual reality, and game technology applications for robotics simulation. Key innovations include the SOMA ontology for socio-physical activity modeling and FailRecOnt for failure recovery in robotic planning. Recent publications (2021-2024) reveal strong trends in ontological modeling for robot introspection, user preference representation, and universal scene description translation. His work consistently addresses challenges in activity parsing, metacognition, and knowledge-driven manipulation in cluttered environments. Notable scientific recognitions include: Finalist for Best Robotics Paper Award at AAMAS 2018 Selection as one of the most important publications by IEEE Technical Committee on Cognitive Robotics Beßler has over four years of teaching experience in AI and robotics courses, and six years supervising student projects including RoboCup@Home competitions and Unreal Engine-based human modeling. He has secured research funding through CRC EASE and international collaborations focused on knowledge-driven robotics. As co-author of IEEE 1872.2 standard ontology and lead developer of openEASE cloud platform, he actively shapes robotics standardization while maintaining lab affiliations with CRC EASE and virtual reality research groups exploring GPU programming for interactive AI applications.

Antonio Torralbaمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Human Visual Perception
• +۴ مورد دیگر

Antonio Torralba is the Delta Electronics Professor of Electrical Engineering and Computer Science at MIT, leading the Artificial Intelligence + Decision-Making (AI+D) faculty within the EECS department. He is affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and has held roles such as director of the MIT Quest for Intelligence and MIT IBM Watson AI Lab. His research focuses on computer vision, machine learning, and human visual perception, with interests in multimodal systems, neural networks, and computational photography. Torralba has advised numerous PhD and master’s students, including notable alumni such as Tongzhou Wang, Carl Vondrick, and Bolei Zhou. He received a doctoral degree from the Universitat Politècnica de Catalunya (UPC) in 1994 and earned an Honoris Causa from UPC in 2020. His work spans foundational contributions to vision algorithms, generative models, and interpretable AI, with over 300 publications and co-authorship of the textbook Foundations of Computer Vision . Education: Doctorate in Electrical Engineering (UPC, 1994) Key Roles: Director of MIT Quest for Intelligence (2018–2020), Head of AI+D Faculty (2020–present) Research Labs: CSAIL, MIT-IBM Watson AI Lab His research emphasizes building systems that mimic human perception across modalities (vision, sound, touch) and explores topics like adversarial robustness, multimodal reasoning, and synthetic data generation. Notable projects include the ADE20K dataset, the SUN database, and contributions to neural scene representation learning. Torralba’s work bridges AI theory and practical applications, influencing both academia and industry. Scientific achievements include pioneering work on context-aware object detection, scene understanding, and generative models. His lab’s contributions span visual question answering, audio-visual reasoning, and embodied AI, with datasets like HACS and CLEVRER advancing benchmarking in action prediction and physical reasoning. Advising highlights include mentoring over 50 students and postdocs, many of whom have become leading researchers in academia and tech companies. Key grants and initiatives include the MIT Quest for Intelligence, which unites machine learning and decision-making research across EECS. His artistic projects, such as Average World and Visual Dictionary , visualize large-scale image patterns, reflecting his interest in the intersection of computation and art.

• Human-Computer Interaction
• Data Visualization
• Ubiquitous Information Experiences
• +۳ مورد دیگر

Matthew Brehmer (he/him) is an Assistant Professor in the School of Computer Science at the University of Waterloo, where he joined in Fall 2024. He leads the Ubiquitous Information Experiences (UBIX) research group, focusing on human-computer interaction and data visualization. Prior to Waterloo, Brehmer was a lead researcher at Tableau and a member of the EPIC HCI research group at Microsoft Research. Brehmer's research spans data visualization, human-computer interaction, and ubiquitous information experiences , with particular emphasis on spatial computing, multimodal interaction, and data storytelling. His work explores innovative ways to communicate and collaborate around data through techniques like gesture recognition, augmented reality video presentations, and novel timeline visualization approaches. He has pioneered techniques such as Diatoms for generative design inspiration and TimeSplines for flexible timeline authoring. His recent publications reveal a growing focus on spatial computing interfaces and multimodal data communication , particularly through projects like Tableau for visionOS and Tableau Gestures. His research increasingly bridges the gap between physical and digital spaces for data interaction, with applications in remote collaboration, presentation techniques, and expressive data storytelling. Scientific Awards: IEEE VIS Test of Time Award (2023) for 'A Multi-Level Typology of Abstract Visualization Tasks' IEEE VIS Best Paper Award (2023) for TimeSplines ACM UIST Best Paper Honorable Mention (2022) IEEE VIS Best Paper Honorable Mention (2018) for Charticulator Brehmer actively contributes to the visualization community through service roles including the IEEE VIS Executive Committee (2022-2026), Maryland Institute College of Art Data Analytics and Visualization MPS Advisory Panel (2023-2026), and as Publications Chair for the IEEE Computer Society Visualization and Graphics Technical Community Executive Committee (2022-2025). He has served on program committees for IEEE VIS, ACM CHI, Computation + Journalism, and Information+ conferences. Brehmer directs the UBIX research group (ubixgroup.ca), which focuses on ubiquitous information experiences through projects exploring spatial computing, gesture recognition, and novel interaction techniques for data visualization. The group recently developed Tableau for visionOS and RemixTape, a presentation authoring tool for narratives about metrics/KPIs.

Andrew Marmadukeمشاهده پروفایل

پژوهشگر

• Programming Language Theory
• Logic
• Formal Methods
• +۳ مورد دیگر

Andrew Marmaduke is a Postdoctoral Researcher at the University of Iowa , affiliated with the Department of Computer Science under the College of Liberal Arts and Sciences. Working in the Computational Logic Center under the guidance of Dr. Aaron Stump, his research focuses on programming language theory, logic, and formalization of mathematics through the Cedille language. Research Highlights Explores Cedille's capabilities in capturing advanced language features like large eliminations and induction-induction Investigates dependent intersections, equality of untyped terms, and erased function spaces Develops formal systems for mathematical constructions in programming languages Publication Trends : His work centers on type theory foundations in functional programming, with recurring themes in formal verification and programming language design. Key contributions include impredicative encodings (2023), simulation of large eliminations (2021), constructor subtyping optimizations (2020), and quotient type implementations (2019). Awards Best Student Paper Award at Trends in Functional Programming (TFP 2023) Technical Contributions : Co-developed olette , an optimal lambda evaluation tool for graphical reduction exploration. Previously authored a master thesis on carbon nanotube forest modeling at The University of Akron (2015).

• Human-Robot Interaction
• Haptics
• Virtual Reality
• +۶ مورد دیگر

Matthew Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University and a core member of the Ingenuity Labs Research Institute. He leads the Machine Intelligence and Human-Robot Interaction Laboratory (MItHRIL), focusing on human-robot interaction, haptics, and virtual/augmented reality technologies for practical applications in assistive robotics and entertainment. His educational background includes: PhD and MASc in Mechanical Engineering, University of British Columbia BASc in Mechatronics Engineering, University of Waterloo Dr. Pan's research centers on human-robot interaction , haptics , and virtual/augmented reality , with applications in assistive technologies and human augmentation. His work explores affective computing , psychophysiological responses , and haptic feedback systems to enhance natural human-robot collaboration. Key projects include robotic handover systems, mixed reality interfaces, and social robotics for Disney Parks. Analysis of his 15 most recent publications reveals a strong focus on robotic handovers , human search/approach , and haptic proxies in virtual reality . His work integrates machine learning for adaptive robot behaviors and studies social cues (gaze, touch, gestures) to improve interaction fluency. Trends indicate growing emphasis on real-world deployment of social robotics in assistive and entertainment contexts. Dr. Pan actively advises graduate students in the MItHRIL laboratory and has led industry projects at Disney Research, developing animatronic technologies and interactive guest experiences for Walt Disney Imagineering and Disney Parks. The MItHRIL laboratory serves as an interdisciplinary hub for robotics research, combining expertise in mechanical engineering, computer science, and human factors to advance intelligent systems for human augmentation and collaborative robotics.

Yu Wangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Machine Learning
• +۷ مورد دیگر

Yu Wang serves as an Assistant Professor in the Department of Computer and Information Science at the University of Oregon's School of Computer and Data Sciences, where he leads research in graph-based machine learning and AI systems. Joining the faculty in September 2024 after completing his PhD at Vanderbilt University, he directs the Graph Machine Learning Lab and actively recruits PhD students for cutting-edge projects at the intersection of data mining, network analysis, and trustworthy AI. His educational background includes a Ph.D. in Computer Science from Vanderbilt University (2019-2024) and a B.S. from Harbin Institute of Technology (2015-2019). During his doctoral studies, he completed research internships at Adobe Research and The Home Depot, focusing on knowledge graph applications for information retrieval. Wang's research centers on graph machine learning, with emphasis on neural-symbolic learning, LLM integration with structured knowledge, and trustworthy AI systems. His work addresses critical challenges in imbalanced/biased graph learning, generative graph models, and social network analysis, driving applications in cyber-security, biochemistry, and infrastructure systems. Current projects explore agentic social simulations and spatial-temporal machine learning for real-world impact. His publication trajectory shows increasing focus on LLM-graph integration and trustworthy AI, with recent work spanning generative graph models, personalized LLMs, and robust networking systems. Key trends include bridging symbolic reasoning with neural approaches, advancing evaluation benchmarks for social network analysis, and developing privacy-preserving techniques for graph-based retrieval systems. Outstanding Doctoral Student Award (Vanderbilt, 2023-2024) Best Paper Award at NeurIPS GLFrontiers Workshop (2023) Vanderbilt Graduate Leadership Anchor Award for Research (2023) KDD Outstanding Dissertation Award Honorable Mention (2025) NSF IIS-III Core Program Grant as Lead PI (2025) Top-10 Most Influential Papers at CIKM'22 and WWW'23 Wang mentors PhD students including Yongjia Lei (SDM Doctoral Forum Honorable Mention recipient) and Riya (Pulse Research Fellow), with research funded through NSF grants and industrial collaborations with Adobe, Visa, and Home Depot. His lab provides substantial computational resources including L40S GPUs and OpenAI API access, supporting both theoretical innovation and real-world deployments through industry internships. Current advising focuses on generative graph models, LLM-agent collaboration, and trustworthy AI frameworks for societal applications. The Graph Machine Learning Lab maintains strong ties with industrial research groups, facilitating student internships at Adobe Research and other tech companies while developing open-source tools like ChemicalX for drug discovery. Ongoing projects include GraphRAG frameworks for security applications and social simulation environments for studying network dynamics.

Kristin C. Bennettمشاهده پروفایل

استادیار

• Technical and Professional Communication
• Rhetoric and Composition
• Disability Rhetoric
• +۴ مورد دیگر

Kristin C. Bennett is an Assistant Professor of Technical and Scientific Writing and Director of Technical Writing in the Department of English at the University of Oklahoma. Her research critically interrogates normative technical and professional communication practices through disability justice frameworks to advance accessibility and equity across workplace, educational, and organizational contexts. Her educational credentials include: B.A. in English Language and Literature from The College of New Jersey (2009) M.A. in English Language and Literature from The College of New Jersey (2011) Ph.D. in Writing, Rhetorics and Literacies from Arizona State University (2022) Professor Bennett's scholarship spans technical and professional communication, disability rhetorics, health/medical rhetorics, digital rhetorics, and user-experience design. She challenges ableist norms in professional discourse by applying disability justice principles to documentation, design, and pedagogy. Her work emphasizes coalitional approaches that center marginalized experiences while transforming institutional practices toward radical inclusion. Analysis of her 2021-2024 publications reveals consistent focus on dismantling systemic ableism through interdisciplinary lenses—particularly at intersections of disability justice, technical communication, and user-experience design. Key themes include accessible digital pedagogy, equitable recruitment practices, medical communication reform, and critical analysis of institutional rhetoric across university career centers, writing programs, and medical contexts. Her notable recognition includes: Conference on College Composition and Communication 2024 award for Best Article Reporting Qualitative or Quantitative Research in Technical or Scientific Communication for "Transforming the Rights-Based Encounter: Disability Rights, Disability Justice, and the Ethics of Access" As Book Review Editor for Communication Design Quarterly , she shapes scholarly discourse in the field. No information regarding student advising, grant funding, or laboratory teams is provided in available materials.

Edwin Jakobsمشاهده پروفایل

مدرس

• Computer Graphics
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Edwin Jakobs serves as a Lecturer teaching Coding in the Master Non Linear Narrative program at the Royal Academy of Art, The Hague (KABK), while maintaining his primary role as lead software engineer and partner in RNDR. His dual expertise bridges computer science and visual arts. His formal education includes: MSc in Computer Science from Technical University in Delft BA in Visual Arts from Willem de Kooning Academy His research focuses on Computer Graphics, Computer Vision, and Machine Learning with strong applications in Creative Coding and Human-Computer Interaction . As the main developer of the OPENRNDR framework, he pioneers real-time graphics programming for artistic expression, integrating technologies like OpenGL and Microsoft Kinect SDK to explore the intersection of algorithmic processes and visual artistry. Prior professional experience includes lead software engineering at LUSTlab and programming at Treparel where he specialized in patent document visualization and classification systems. His technical proficiency spans Java, C++, Python, and JavaScript across domains including Natural Language Processing, SOLR, and Cassandra, demonstrating consistent innovation at the art-technology frontier.