جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Ubiquitous Computing
• Explainable Artificial Intelligence
• +۴ مورد دیگر

Brian Y. Lim is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the NUS Ubicomp Lab. His research integrates Explainable AI (XAI) , Human-Computer Interaction (HCI) , and Ubiquitous Computing to address societal challenges in healthcare , wellness , and urban sustainability . He holds a Ph.D. and M.S. in Human-Computer Interaction from Carnegie Mellon University and a B.S. in Engineering Physics from Cornell University. Education: B.S., Engineering Physics, Cornell University M.S., Human-Computer Interaction, Carnegie Mellon University Ph.D., Human-Computer Interaction, Carnegie Mellon University His research focuses on designing human-centric AI systems that prioritize transparency and user trust. Key areas include explainable AI algorithms , context-aware computing , data visualization , and trustworthy machine learning . Projects like RexNet and Directed Diversity demonstrate his work in creating relatable AI explanations and enhancing crowd creativity through algorithmic prompting. Recent publications (2022–2025) highlight advancements in faithful visual explanations , user-centric XAI frameworks , and healthcare analytics . Notable awards include the CHI'22 Best Paper Award , IMWUT Distinguished Paper Award , and MOE Outstanding Mentor Award . He has advised numerous PhD and Masters students and leads the NUS Ubicomp Lab, which explores applications like TasteHealthy (food recommendation app) and Food Loves Fellowship (narrative data visualization).

Xingang Panمشاهده پروفایل

استادیار

• Computer Vision
• Generative Models
• 3D Vision
• +۳ مورد دیگر

Xingang Pan is an Assistant Professor in the College of Computing and Data Science at Nanyang Technological University (NTU), leading the MMLab@NTU. His research focuses on generative AI and visual content creation, particularly in generative models, 3D vision, computer graphics, and computer vision. Prior to NTU, he was a postdoc at the Max Planck Institute for Informatics and earned his Ph.D. from the Chinese University of Hong Kong (2021) and B.Sc. from Tsinghua University (2016). His work emphasizes generative intelligence, exploring long-term world simulation, diffusion models, and multi-scale 3D generation. Notable contributions include WORLDMEM (2025), Alias-free Latent Diffusion (2025), and SAR3D (2025). His research has been published in top venues like CVPR, ICCV, and SIGGRAPH. Xingang Pan oversees the MMLab@NTU, which actively recruits students globally without nationality constraints. The lab’s projects include GAN2Shape (unsupervised 3D reconstruction from 2D GANs) and LN3Diff (scalable 3D generation).

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

• Topological Data Analysis
• Data Visualization
• Computational Topology
• +۵ مورد دیگر

Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions

Haneen Farahمشاهده پروفایل

دانشیار

• road infrastructure design
• road user behavior
• traffic safety
• +۷ مورد دیگر

Dr. Haneen Farah is an Associate Professor in the Department of Transport & Planning at Delft University of Technology and co-director of the Traffic and Transportation Safety Lab. She also serves as head of the Traffic Systems Engineering section. Her research focuses on road infrastructure design, road user behavior, and traffic safety, integrating transportation engineering, human factors, and econometrics. Prior to TU Delft, she was a postdoc at KTH Royal Institute of Technology and earned her M.Sc. and Ph.D. in Transportation Engineering from the Technion-Israel Institute of Technology. Her work includes national/international projects like SAMEN (mixed automated/human traffic implications), AfroSAFE (road safety in Africa), and XCARCITY (sustainable city mobility). She teaches undergraduate and graduate courses on road design and traffic safety, including online programs for low/middle-income countries. Farah supervises multiple PhD and Master students in her research areas, contributing to over 50 peer-reviewed publications. Key research themes include infrastructure design for automated vehicles, driver behavior modeling, cyclist safety, and policy implementation of the Safe System approach. Her interdisciplinary approach bridges engineering and psychology to enhance traffic safety and efficiency through advanced analytics and simulation models.

• Computer Vision
• Machine Learning
• Data-Driven Learning
• +۵ مورد دیگر

Alexei A. Efros is the Howard Friesen Professor in the EECS Department at the University of California, Berkeley, and a core member of the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at Carnegie Mellon University’s Robotics Institute. His research focuses on data-driven computer vision, self-supervised learning, computational photography, and generative models. He has pioneered advancements in visual representation learning, including seminal work on neural radiance fields and generative adversarial networks. Education Background: Efros holds a PhD in Computer Science from MIT, though specific details of his academic journey are not explicitly provided in the text. His career includes postdoctoral research at the University of Oxford with Andrew Zisserman and collaborative work with Team WILLOW at INRIA Paris. Research Interests: Efros explores how vast uncurated visual data can be leveraged for understanding and synthesizing the visual world. Key areas include self-supervised learning, generative models, and applications in robotics and art. His lab has contributed influential techniques such as Style Transfer, GAN-based image synthesis, and neural scene representation learning. Recent work emphasizes real-time adaptation (Test-Time Training), 3D perception models, and ethical AI implications of generative systems. Publications: Over 150+ publications span topics like Generative Adversarial Networks (GANs), unsupervised learning, and visual-linguistic models. Notable works include Unpaired Image-to-Image Translation (CUT/GAU), Style Transfer , and Swapping Autoencoder . His research has significant industry impact, with techniques adopted in Adobe’s software and generative AI applications. Grants & Collaborations: Efros has secured major funding from NSF, DARPA, and industry partnerships (e.g., Adobe, NVIDIA). He co-leads projects on scalable vision models, ethical AI, and real-world perception systems. Current collaborations include work with MIT, NYU, and INRIA Paris. Labs & Teams: Leads the BAIR Vision Group at Berkeley, fostering interdisciplinary research between computer vision, graphics, and robotics. The group emphasizes Slow Science principles, prioritizing deep exploration over rapid publication.

Maria Gorlatovaمشاهده پروفایل

دانشیار

• Augmented Reality
• Virtual Reality
• Internet of Things
• +۷ مورد دیگر

Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.

• Computer Vision
• Machine Learning
• Human Perception Modeling
• +۵ مورد دیگر

Deva Ramanan is a Professor at the Robotics Institute of Carnegie Melllon University, where he leads research in computer vision and machine learning. His work focuses on modeling human visual perception, leveraging large-scale visual data, and developing systems for 3D understanding, neural rendering, and autonomous systems. He advises a large group of PhD students and has mentored numerous postdoctoral researchers now in leading roles across industry and academia. His research interests include computer vision, machine learning, human perception modeling, 3D scene understanding, neural rendering, autonomous driving, video understanding, and multimodal foundation models. These areas reflect his focus on both foundational models and their application to real-world problems in robotics and AI. The recent publications highlight a strong trend toward multimodal and 3D-aware models, with increasing use of diffusion models, neural fields, and large vision-language systems. Key themes include scene flow, 3D reconstruction from monocular video, autonomous driving perception, and robust evaluation of vision-language models. There is a clear emphasis on both methodological innovation and practical deployment in dynamic environments. Marr Prize, Honorable Mention (ICCV 2021) Best Paper, Honorable Mention (ECCV 2020) Best Paper Finalist (WACV 2024) Best Paper Award (WACV 2016) Best Industrial Paper, Honorable Mention (BMVC 2017) Marr Prize winner (ICCV 2009) Deva Ramanan has advised numerous PhD and master’s students, many of whom are now at top institutions and companies including Apple, Meta, Google, Nvidia, OpenAI, and Princeton. He has received substantial funding from IARPA, DARPA, NSF, Intel, Google, and Facebook for projects in video analytics, dispersed computing, visual cloud systems, and multi-task recognition. His group has developed influential datasets and benchmarks used widely in the community. He leads a vibrant research lab focused on advancing computer vision through deep learning and multimodal integration. His team works on core challenges in perception, including 3D reconstruction, motion modeling, object detection, and scene understanding, with applications in robotics and autonomous systems.

Sarita V Adveمشاهده پروفایل

استاد

• Computer Architecture
• Extended Reality Systems
• Heterogeneous Computing
• +۴ مورد دیگر

Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.

Rebecca Lawsonمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuroscience
• Psychiatry
• +۶ مورد دیگر

Professor Rebecca Lawson is a Professor of Neuroscience and Computational Psychiatry at the University of Cambridge, affiliated with the Department of Psychology and Bye-Fellow at Peterhouse College. Her research focuses on understanding how humans learn to make predictions under uncertainty, with applications to mental health conditions such as anxiety and depression. She leads the Prediction and Learning (PaL) Lab, which combines computational modeling, neuroimaging (e.g., 7T MRI), and behavioral experiments to study cognitive processes in typical and atypical populations. Key research interests include computational psychiatry, autism spectrum disorders, neuroimaging techniques, and the neurochemical basis of learning mechanisms. She has received significant funding, including a £4.3m Wellcome Mental Health Award and the Sir Henry Dale Fellowship. Notable contributions include advancing theories of neural gain and sensory expectations in autism, as well as studies on uncertainty processing in anxiety and depression. Professor Lawson holds academic awards such as the BNPA Lishmann Prize and the BAP Psychopharmacology Award. She actively contributes to public engagement, including initiatives like Knit-a-Neuron and involvement with PrideinSTEM. Her lab collaborates internationally, with projects like the CamRAA study investigating autism-anxiety overlaps and the Chemical and Brain Basis of Uncertainty (CBBU) study exploring pharmacological interventions. Education: PhD in Cognitive Neuroscience (University of Cambridge), BA (Hons) Psychology & Philosophy (University of Glasgow). Grants: £4.3m Wellcome Award, Parke-Davis Fellowship, Lister Institute Prize. Labs/Teams: Principal Investigator of the PaL Lab; collaborations with MRC Cognition and Brain Sciences Unit, Yale University, and Brown University.

Rana Hanockaمشاهده پروفایل

استادیار

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

Rana Hanocka is an Assistant Professor of Computer Science at the University of Chicago, leading the 3DL research group focused on AI-driven 3D geometry processing. She earned her Ph.D. in 2021 from Tel Aviv University under Professors Daniel Cohen-Or and Raja Giryes. Her work explores neural networks for unstructured 3D data, including mesh convolutional networks and self-priors for shape reconstruction. Key research areas include geometric deep learning, human-AI collaboration in 3D modeling, and interpretable neural networks. Notable contributions include MeshCNN (SIGGRAPH 2019) and Point2Mesh (NeurIPS 2020), advancing mesh analysis and point cloud processing. Awards include the 2023 Pazy Research Award and 2020 Rising Star in EECS. Education : Ph.D. Computer Science, Tel Aviv University (2021) Labs/Groups : 3DL Group at UChicago Grants : NSF Grant for AI-driven 3D modeling tools (2023) Research directions emphasize creative human-AI partnerships, unsupervised learning from shape collections, and explainable 3D neural networks. Ongoing projects include style-aware 3D synthesis, interactive segmentation, and multimodal shape interfaces.

Jieyu Zhaoمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• AI Ethics
• +۳ مورد دیگر

Jieyu Zhao is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), part of the Viterbi School of Engineering. She holds a Ph.D. from UCLA and was a NSF Computing Innovation Fellow at the University of Maryland. Her research focuses on fairness in machine learning and NLP, particularly addressing biases in AI systems and promoting ethical AI practices. Education: Ph.D. in Computer Science (UCLA, 2021), Postdoc at University of Maryland (2022-2023), and multiple research internships at Microsoft Research, Google Research, and AI2. She also completed her undergraduate studies at the University of Virginia. Research interests include bias detection and mitigation in NLP models, trustworthy AI, and social responsibility in technology. Her work has been recognized with awards such as the EMNLP Best Long Paper Award (2017), Microsoft PhD Fellowship (2020), and EECS Rising Star (2021). She has delivered talks at conferences like NeurIPS, EMNLP, and USC Law School, and serves on committees including AAAI workflow chair. Her lab explores topics like alignment of LLMs, fairness in sequential decision-making, and ethical model interventions. She actively participates in initiatives promoting diversity and inclusion in AI, such as the UN-WOMEN Beijing panel on gender equality. Awards: 100 Brilliant Women in AI Ethics (2025), Capital One Research Award (2024), Microsoft PhD Fellowship (2020) Grants: NSF Computing Innovation Fellowship, USC Viterbi funding Labs/Teams: Leads the USC Lab focused on trustworthy AI and ethical NLP

Lexing Xieمشاهده پروفایل

استاد

• Machine Learning
• Social Media Analysis
• Computer Vision
• +۷ مورد دیگر

Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.

• 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

Silvia VESCOمشاهده پروفایل

دانشیار

• Japanese Art History
• Edo Period Art
• Ukiyo-e Prints
• +۷ مورد دیگر

Dr. Silvia VESCO is an Associate Professor in the Department of Asian and North African Studies at Ca' Foscari University of Venice, where she specializes in Japanese art history, particularly Edo-period visual culture, ukiyo-e prints, illustrated books, and the works of Katsushika Hokusai. She is actively involved in research, teaching, and cultural curation, with a strong focus on Italian collections of Japanese art and cross-cultural artistic dialogues between Japan and the West. Education: PhD in History of Indian and East Asian Art, Ca' Foscari University of Venice (1999) Master in Art & Archaeology, SOAS, University of London (1995) Degree in Oriental Languages and Literatures, University of Venice (1993, with honors) Her research interests encompass Japanese aesthetics, museum studies, and the global reception of Japanese art. She has led major digitization projects for the Museum of Oriental Art in Venice and contributed to the development of its new museological framework. Her scholarly work reveals a consistent focus on Hokusai, Edo-period print culture, and the philosophical dimensions of Japanese visual forms. The recent publications highlight a deep engagement with Hokusai’s pedagogical works, the aesthetics of emptiness, and contemporary reflections on nature in post-Fukushima Japan. Her interdisciplinary approach bridges art history, cultural studies, and museum practice. Scientific Recognition: Finalist, Premio Nazionale per la divulgazione scientifica 'Giancarlo Dosi' (2020) Japan Foundation Fellowship (1997) Postgraduate scholarship from the University of Venice (1994) She has supervised numerous MA theses, served on PhD committees, and mentored students in Japanese art history. Her research has been supported by grants from the Japan Foundation and institutional collaborations with museums and cultural centers. She is a referee for VQR, Edizioni Ca' Foscari, and international journals, and serves on the scientific committees of several academic and museum publications. Labs and Teams: Scientific Committee, Museum of Oriental Art, Ca' Pesaro, Venice Scientific Committee, Edizioni Ca' Foscari Coordinator, Scientific Committee for Ca' Foscari Japanese Studies, Arts and Literature Member of the editorial board of 'Venezia Arti' and 'Quaderni del Polo museale del Veneto'