جست‌وجوی استاد و پژوهشگر

Ming-Hsuan Yangمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۳ مورد دیگر

Ming-Hsuan Yang is a Professor in the Department of Computer Science & Engineering at the University of California, Merced , where he also serves as the Graduate Chair for the Electrical Engineering and Computer Science (EECS) graduate group. His research spans computer vision , machine learning , and pattern recognition , with a focus on image and video restoration, object tracking, and 3D scene understanding. Ph.D., University of Illinois at Urbana-Champaign (2000) M.S., University of Texas at Austin (1994) M.S., University of Southern California (1992) B.S., National Tsing-Hua University, Taiwan (1991) His research interests include computer vision (object tracking, image deblurring, saliency detection), machine learning (transfer learning, sparse representation), and 3D reconstruction (Gaussian splatting, scene generation). He has pioneered methods in diffusion models , transformer architectures , and multi-modal vision-language systems . Recent publication trends show leadership in 3D mesh generation (ICCV 2025), video diffusion (CVPR 2025), and image restoration (PAMI 2025), with interdisciplinary applications in medical imaging (TMI 2024) and human motion analysis (WACV 2025). Scientific awards include Nvidia Fellowships and EECS Rising Stars recognitions for advisees, with Meta , Google DeepMind , and Adobe alumni placements. He has advised 18 PhD students and 13 MS students since 2009, with notable fellowships including Chancellor's Graduate Fellowship and GSOP Fellowship . His Visual Tracking and Learning Lab produces high-impact work in object tracking , image enhancement , and semantic segmentation , supported by NSF grants and industry collaborations . Lab alumni now lead R&D at top tech companies like Stability AI and Meta .

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

• Biomedical Signal Processing
• Respiratory Monitoring
• Sleep Disorders
• +۴ مورد دیگر

Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

Rishidev Chaudhuriمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Neural Dynamics
• Cognitive Neuroscience
• +۳ مورد دیگر

Rishidev Chaudhuri is an Associate Professor at the University of California, Davis in the Department of Neurobiology, Physiology and Behavior within the College of Biological Sciences. His research focuses on computational neuroscience and neural dynamics, employing mathematical models to investigate how neural circuits generate cognitive processes such as memory, perception, and decision-making. His work explores neural dynamics through models of memory systems, attentional mechanisms, and probabilistic inference. Recent publications highlight advances in understanding hippocampal memory scaffolds, parietal-frontal interactions, and neuromorphic computing inspired by brain architecture. Education: BA in Physics (Amherst College), PhD in Applied Mathematics (Yale University) Centers: Center for Neuroscience; affiliated with Applied Mathematics and Neuroscience Graduate Groups Scientific awards and honors are not explicitly mentioned in the provided materials.

Lifeng Zhouمشاهده پروفایل

استادیار

• Robotics
• Algorithms
• Combinatorial Optimization
• +۴ مورد دیگر

Lifeng Zhou is an Assistant Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Zhou Lab focused on advancing robustness and reliability in multi-robot systems through integration of foundation models. His research addresses real-world challenges in environmental monitoring, disaster response, and urban mobility. Education PhD, Electrical and Computer Engineering, Virginia Tech, 2020 MS, Control Science and Engineering, Shanghai Jiao Tong University, 2016 BS, Automation, Huazhong University of Science and Technology, 2013 Research Focus Dr. Zhou's work integrates robotics, algorithms, game theory and machine learning to develop secure and scalable autonomous systems. Primary research thrusts include: Resilient multi-robot coordination in adversarial environments Large language model integration for robotic decision-making Game-theoretic resource allocation strategies Risk-aware planning for autonomous vehicles Publication Trends Recent work (2024-2025) demonstrates strong focus on large language model applications in multi-robot systems, with 12/15 articles exploring LLM integration for flocking, scene segmentation, and decision-making. Additional emphasis includes adversarial robustness in target tracking (5 articles) and autonomous driving applications (4 articles). Awards and Recognition Best Paper Award, WACV 2025 LLVM-AD Workshop Professional Service Associate Editor, ICRA Conference Editorial Board Laboratory Focus The Zhou Lab develops foundational algorithms for secure and scalable multi-robot systems, with current projects spanning environmental monitoring drones, disaster response coordination, and autonomous vehicle perception systems.

Siwei Lyuمشاهده پروفایل

استاد

• Digital Media Forensics
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Siwei Lyu is a SUNY Distinguished Professor and SUNY Empire Innovation Professor in the Department of Computer Science and Engineering at the University at Buffalo. He serves as Co-Director of the Center for Information Integrity (CII) and Director of the UB Media Forensic Lab (UB MDFL). His research focuses on digital media forensics, computer vision, and machine learning, with significant contributions to counter-deepfake technologies. Education includes a PhD in Computer Science from Dartmouth College (2005), MS from Peking University (2000), and BS in Information Science from Peking University (1997). He has held academic positions at the University at Albany and New York University. His work spans media forensics, adversarial machine learning, and AI security. Notable achievements include developing the Celeb-DF dataset, leading NSF-funded projects, and testifying before U.S. and NYS legislative bodies on disinformation threats. Over $11.3M in grants have supported his research on AI-generated media detection, including a $5M NSF Convergence Accelerator grant. Key awards include IEEE and IAPR Fellowships, Google Faculty Award, and SUNY Chancellor's Research Award. He has authored 230+ papers, 4 patents, and serves on editorial boards of top journals and conferences (e.g., CVPR, ICCV).

Lynn Wuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Enterprise Social Media
• Innovation
• +۶ مورد دیگر

Lynn Wu is an Associate Professor at the Wharton School of the University of Pennsylvania, focusing on the intersection of artificial intelligence, analytics, and innovation. She teaches MBA, undergraduate, and doctoral courses on emerging technologies' transformative impact on business and society. Education: B.S. in Finance and Computer Science, MIT M.S. in Computer Science, MIT Ph.D. in Management Science, MIT Sloan Her research explores how AI and digital platforms reshape productivity, workforce dynamics, and innovation strategies, with applications in antitrust policy and startup ecosystems. Key trends in her publications include AI's role in post-IPO innovation, robotics' impact on managerial roles, and social media's ability to mitigate funding disparities. Scientific Awards: Kauffman Best Paper Award (2019) Sandy Slaughter Early Career Award (2019) AIS Early Career Award (2018) ISR Best Published Paper (2014) Best Paper Awards at ICIS (2009), HICSS (2013), etc. She has collaborated with IBM, Google, Meta, and advised the U.S. Department of Justice and World Bank, with her work cited by The New York Times , The Economist , and Harvard Business Review .

Anna Marie Munsterمشاهده پروفایل

استاد

• software studies
• technologies and embodiment
• multichannel and interactive installation work
• +۱۹ مورد دیگر

Professor Anna Marie Munster is a distinguished academic and artist at UNSW Art and Design (College of Fine Arts), where she has held a full-time tenured position since 2001. Her work bridges theoretical scholarship with creative practice, establishing her as a leading figure in digital media art and critical theory. University: University of New South Wales School: College of Fine Arts (UNSW Art and Design) Position: Professor Qualifications: PhD from UNSW (2002), BA (Class 1 Hons with University Medal) from University of Sydney (1984) Professor Munster's research explores the intersections of art, technology, and philosophy, with particular focus on statistical visuality, radical empiricism, the politics and aesthetics of machine learning, and more-than-human perception. Her work integrates process philosophy with contemporary media practices, examining how time, movement, and sonicity shape our experience of digital environments. She approaches these topics through both theoretical scholarship and creative practice, often collaborating with artist Michele Barker on multi-channel audiovisual installations. Her publications reveal a consistent trajectory exploring the relationship between embodiment and digital media, with recent work focusing on AI and machine learning in cultural contexts. Munster's creative output demonstrates sophisticated integration of technical innovation with philosophical inquiry, particularly in how her installations challenge conventional perceptions of time and space. ARC, UNSW Student Council Award for Excellence in Postgraduate Supervision (2017) Dean's Award for Excellence in Postgraduate Supervision (2015) Highly commended for Materializing New Media at Prix Ars Electronica (2008) Winner of National Digital Art Awards 'The Harries' (2006) University Press of New England Publishing Award (2005) Professor Munster has completed over 20 PhD and Masters supervisions at UNSW and been involved in another 10 secondary supervisions. She currently supervises five PhD candidates working on diverse topics including coding materialities, critical bio-textiles, schizophrenia and XR technologies, sound and resonance, and affective drawing. Her ARC-funded research projects demonstrate sustained commitment to interdisciplinary work, particularly at the intersection of art, science, and technology. Her creative practice with Michele Barker has resulted in numerous commissioned installations exploring perception, embodiment, and the relationship between humans and technology.

• Computer Graphics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at Durham University, UK. He holds editorial roles as Associate Editor of Frontiers in Education (Digital Education) and Editorial Board Member of Virtual Reality & Intelligent Hardware. His research focuses on Computer Graphics, Machine Learning, Geometric Modelling, Collaborative Virtual Environments, Visual Aesthetics, and Educational Technologies. He earned his B.A. (Hons) and M.Phil. from The Hong Kong Polytechnic University and his Ph.D. in Computer Graphics from City University of Hong Kong. Prior roles include Assistant Professor at HK PolyU and project manager of a Hong Kong Government ITF-funded project. **Education**: B.A. (Computing Studies) and M.Phil. from HK PolyU; Ph.D. in Computer Graphics (CityU Hong Kong). **Research Interests**: His work spans mesh saliency detection, human-object interaction recognition, cloud modeling, face beautification, and educational technology. Recent achievements include awards for papers (e.g., Best Paper at ITiCSE 2014) and recognition such as EPSRC Peer Review College membership. He leads Durham's Undergraduate Board of Examiners and has been an external examiner at Northumbria University. **Awards**: Best Paper (ACM ITiCSE 2014), Outstanding Paper (ICALT 2013), EPSRC Peer Review College (2024), Outstanding BMVC 2024 Reviewer. **Grants & Labs**: His research is supported by grants from EPSRC and others. He collaborates with the Centre for Vision and Visual Cognition, VIViD, and AIHS group at Durham.

Mahmoud El-Sakkaمشاهده پروفایل

دانشیار

• Image Compression
• Medical Imaging
• Image Segmentation
• +۵ مورد دیگر

Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.

Achilleas Psyllidisمشاهده پروفایل

استادیار

• Accessibility Measures
• Sustainable Urban Mobility
• Land-use and Transport Interaction
• +۸ مورد دیگر

Achilleas Psyllidis is an Assistant Professor of Urban Mobility and Director of the Urban Analytics Lab at TU Delft. He also leads the Social Urban Data Lab at Amsterdam Institute for Advanced Metropolitan Solutions and is affiliated with the LDE Centre for BOLD Cities. His roles include membership in TU Delft's Transport & Mobility Institute, the Mobility Futures Vision Team, and serving on the Executive Board of CUPUM. Education: PhD in Spatial Data Science (TU Delft, Faculty of Architecture and the Built Environment) Master of Science in Spatial Planning (National Technical University of Athens) Engineering Diploma in Architectural Engineering (National Technical University of Athens) Research Interests: Focuses on accessibility, walkability, land-use dynamics, and travel behavior. Develops computational methods for analyzing access equity, spatial segregation, and human mobility. Leads projects on sustainable urban mobility, environmental exposures, and the 15-minute city concept. Awards: CTwalk Map: Best Demo Award (ICT.Open 2024) ROUTE Ontology of Urban Transportation Entities (2015) Grants & Projects: Involved in initiatives like PERISCOPE (Social Resilience Design), Horizon2020 'Equal-Life' (Environmental Health), and SocialGlass (Urban Analytics Dashboard). Active in research collaborations across Europe and Asia. Labs & Teams: Directs Urban Analytics Lab and Social Urban Data Lab, focusing on data-driven urban solutions. Engages in interdisciplinary teams addressing mobility futures, urban health, and sustainable design.

John Nassourمشاهده پروفایل

پژوهشگر

• Computational Cognitive Systems
• Robotics
• Wearable Robotics
• +۵ مورد دیگر

John Nassour is a Researcher at the Technical University of Munich's School of Computation, Information and Technology, affiliated with the Chair of Cognitive Systems. He holds engineering degrees from Tishreen University (electronics), a Master's in intelligent systems from University of Cergy-Pontoise/École Nationale Supérieure de l'Électronique, and a joint PhD from University of Versailles/TUM. His interdisciplinary research focuses on computational cognitive systems applied to robotics, including wearable devices, humanoid robots, soft robotics, and robot learning for locomotion/manipulation. Before joining TUM in 2020, he was a lecturer/researcher at Chemnitz University of Technology. He teaches courses in cognitive systems, neuro-inspired engineering, and soft robotics.

Jonathan Cookمشاهده پروفایل

پژوهشگر ارشد

• Malaria Eradication
• Emerging Infectious Diseases
• Structure-Based Vaccine Design
• +۳ مورد دیگر

Dr Jonathan Cook is a Research Fellow at Jesus College, University of Oxford, affiliated with the Medical Sciences division. He holds a primary medical qualification from Queen’s University (Canada, 2021) and a PhD investigating viral immune evasion mechanisms under Prof Jeffrey Lee. His work was supported by the Vanier Canada Graduate Scholarship and recognized with the Stuart Alan Hoffman Memorial Prize. As an incoming Banting Fellow at the Jenner Institute, he focuses on vaccine design for infectious diseases. His research interests include malaria eradication, emerging pathogens, and structure-based vaccine strategies. **Education**: - PhD (Investigation of viral entry glycoproteins), University of Toronto, supervised by Prof Jeffrey Lee - Primary Medical Qualification, Queen’s University, Canada **Research Interests**: - Malaria Eradication : Developing vaccines targeting Plasmodium species. - Emerging Infectious Diseases : Designing rapid-response strategies for pathogens like SARS-CoV-2. - Structure-Based Vaccine Design : Leveraging protein structural data to enhance immunogenicity. **Awards**: - Vanier Canada Graduate Scholarships Programme (2017–2020) - Stuart Alan Hoffman Memorial Prize (2021) - Banting Fellowship (incoming) **Grants/Advising**: Works on Jenner Institute-funded projects. No listed advisees yet. **Labs/Teams**: Collaborates with Prof Lee's lab at the Jenner Institute and the University of Toronto's Medical Microbiology program.

Mahesh Tripunitaraمشاهده پروفایل

استاد

• Information Security
• Authorization and Access Control
• Cryptographic Key Transport
• +۷ مورد دیگر

Dr. Mahesh Tripunitara is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Associate Chair for Undergraduate Studies. He holds a PhD (2005) and Master's (1995) in Computer Science from Purdue University, along with a BSc (1993) in Computer Science from Dalhousie University. His research focuses on information security, authorization mechanisms, cryptographic key management, and hardware security, with industry experience at Motorola's R&D labs and Silicon Valley. His work spans theoretical advancements like access control policy analysis and practical applications such as secure payments systems and IoT device reliability. Notable awards include the Best Student Paper at Usenix Security 2013 and Best Paper at ACM SACMAT 2013. He actively serves on program committees for major security conferences including CCS, CODASPY, and SACMAT. Recent publications highlight innovations in cellular security (SUCI-Catchers defense), role-mining optimization, and blockchain smart contract auditing. Teaching includes advanced algorithm design courses (ECE 406/606) and digital computation (BME 121). His research emphasizes balancing security rigor with usability in authorization systems and hardware protection mechanisms.