Shih-Fu Chang is the Dean of Columbia Engineering and holds the Morris A. and Alma Schapiro Professorship at Columbia University. His research focuses on computer vision, machine learning, and multimedia information retrieval. He is recognized as a foundational figure in the field of content-based visual search and has pioneered innovations in image/video search engines, crime prevention systems, and brain-machine interfaces. His leadership roles include Chair of Columbia's Electrical Engineering Department (2007-2010), Editor-in-Chief of the IEEE Signal Processing Magazine (2006-2008), and Senior Executive Vice Dean at Columbia Engineering, where he drives strategic planning and international collaboration. Dr. Chang has received prestigious awards including the ACM Multimedia Technical Achievement Award, IEEE Signal Processing Technical Achievement Award, and IEEE Kiyo Tomiyasu Award. He is a Fellow of AAAS, ACM, and IEEE, and an Academician of Academia Sinica. His recent work emphasizes multimodal reasoning, few-shot learning, and vision-language systems, with applications in healthcare diagnostics and multimedia benchmarking. His research spans cross-modal understanding, event extraction, and adaptive AI systems. Key contributions include systems like Ferret-v2 for multimodal grounding and RESIN for schema-guided event tracking. He has advised multiple startups and actively contributes to curriculum development in AI and engineering education.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۳۱-۴۵ از ۱٬۵۸۵ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Heather Culbertsonمشاهده پروفایل
استادیار
- Haptics
- Robotics
- Virtual Reality
- +۳ مورد دیگر
Dr. Heather Culbertson is an Assistant Professor in the Department of Computer Science at the University of Southern California, leading the HaRVI (Haptics Robotics and Virtual Interaction) Laboratory. Her research focuses on designing haptic hardware and algorithms to create intuitive human-robot interactions through touch, emphasizing realism in virtual environments and social connectedness. Key areas include mediated touch, wearable haptic systems, and multisensory integration in VR/AR applications. The lab's work spans robotics, human perception modeling, and cross-disciplinary solutions for healthcare and immersive technologies. Research interests revolve around tactile feedback systems, social robotics, and the psychological impact of haptic interfaces. Recent work explores real-time haptic texture rendering, affective communication via touch, and motion platforms for VR. Culbertson's projects often integrate human factors early in design to ensure usability and immersion. Publications highlight advancements in haptics coding standards, drone-human interaction, and emotion regulation systems. Her contributions bridge engineering and cognitive sciences, addressing challenges in wearable tech, surgical training simulations, and assistive robotics. The HaRVI Lab collaborates across disciplines to advance tactile interfaces for virtual reality, social robotics, and medical applications.
Yuchen Liuمشاهده پروفایل
استادیار
- Networking
- Machine Learning
- Cybersecurity
- +۶ مورد دیگر
Yuchen Liu is an Assistant Professor in the Department of Computer Science and Department of Electrical & Computer Engineering (by courtesy) at North Carolina State University. He earned his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology. His research spans networking, machine learning, and cybersecurity, focusing on wireless systems, digital twins, and networked agentic systems. Research areas: Networking (3D UAV networks, mmWave/THz communication, cybersecurity), Machine Learning (generative AI, LLMs, reinforcement learning), Digital Twins (synchronization optimization, edge caching), Software Development (differentiable simulators, open-source testbeds) His recent publications emphasize neurosymbolic AI, diffusion models for wireless systems, and multi-agent approaches to spectrum sensing. Articles highlight applications in UAV networks, vehicular security, satellite localization, and federated learning defenses. Honors include NSF CAREER (2025), NVIDIA Academic Grant (2025), NCSU Carla Savage Award (2025), and multiple IEEE/ACM Best Paper Awards. Current projects are supported by NSF CNS (#2312138), NSF SaTC (#2350075), and NSF NAIRR Pilot Demonstration (#2506757) grants.
Shang-Tse Chenمشاهده پروفایل
دانشیار
- Machine Learning
- Data Mining
- Cybersecurity
- +۵ مورد دیگر
Shang-Tse Chen is an Associate Professor at the Department of Computer Science and Information Engineering and Graduate Institute of Networking and Multimedia , National Taiwan University . He leads the NTU AI Security Lab , focusing on applied and theoretical machine learning with emphasis on cybersecurity, adversarial ML, and ML privacy/fairness. Education: PhD in Computer Science (Georgia Tech, 2019), BSc in CSIE (NTU, 2010) Awards: K. T. Li Young Researcher Award (2025), IBM PhD Fellowship (2018), KDD Best Student Paper Runner-Up (2016), NSF SaTC Grant (2017-2021) His research spans adversarial ML, certified defenses, model inversion attacks, and intersection with differential privacy/fairness. Recent work includes physical adversarial attacks on object detectors and practical defenses using JPEG compression. He teaches courses like Security and Privacy of Machine Learning and Introduction to Medical Informatics . Key publication trends show focus on adversarial robustness (ICML/NeurIPS/ICLR), cybersecurity applications (ACSAC), and ML fairness (ACL/EMNLP). Collaborations include industry partnerships with Intel Labs and Symantec. Scientific Awards: K. T. Li Young Researcher Award (2025) ACM TiiS Best Paper Honorable Mention (2020) IBM PhD Fellowship (2018) KDD Audience Appreciation Award Runner-Up (2018) Symantec Fellowship Runner-Up (2016) KDD Best Student Paper Runner-Up (2016) NSF Grant (2017) He advises 13 current students (PhD/MS/Undergrad) and has mentored alumni now at CMU/UC Berkeley. The lab actively recruits postdocs and students across levels.
- Computer Vision
- Deep Learning
- Machine Learning
- +۶ مورد دیگر
Oswald Lanz is a tenured full professor at the Faculty of Engineering of the Free University of Bozen-Bolzano , leading the Visual Computing Lab . He holds a Ph.D. in Computer Science and a Mathematics degree from the University of Trento. Prior to his current role, he was a researcher and head of research at FBK Trento. He is an endowed professor collaborating with Covision Lab , an AI hub in Bressanone, and coordinates the board of professors for the PhD in Computer Science program since 2025. His research focuses on Computer Vision, Deep Learning, and Video Analytics , with applications in sports technology, medical imaging, and industrial automation. Key achievements include the Amazon AWS Machine Learning Research Award (2020) , ACM Multimedia Best Paper (2015) , and Best Student Paper at ICIAP (2007) . He co-organized the ELLIS-VISMAC Winter School (2025) and chaired ICIAP 2019 . His work spans novel view synthesis, action recognition, and anomaly detection, supported by patents in video tracking and detection. He teaches courses like Deep Learning and Artificial Intelligence in undergraduate and graduate programs. Recent projects such as 5VREAL integrate 5G, edge computing, and AI for sports analysis. His collaborations bridge academia and industry, exemplified by his role in Covision Lab and multidisciplinary initiatives like DSS4LCO for food supply chains. Lanz’s publications emphasize spatiotemporal modeling, neural architecture search, and hybrid machine vision systems.
Habib Ullahمشاهده پروفایل
دانشیار
- Computer Vision
- Machine Learning
- Deep Learning
- +۷ مورد دیگر
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
David Salesinمشاهده پروفایل
عضو هیئت علمی
David Salesin is an Affiliate Professor in the Department of Computer Science & Engineering at the University of Washington and a Principal Scientist/Director at Google Research since 2019. He has held academic roles at Cornell University (Visiting Assistant Professor, 1991-92) and guest professorships at Zhejiang University. His career spans academia and industry, including leadership at Adobe's Creative Technologies Lab (2005-17) and Microsoft Research (1999-2005). PhD, Stanford University (1991) Sc.B., Brown University (1983) His research focuses on computer graphics, particularly non-photorealistic rendering, digital typography, color science, and adaptive document layout. He pioneered techniques in image-based rendering, pen-and-ink illustration, and facial animation, with applications in multimedia and user interface design. Article Trends : His work bridges procedural content generation, 3D visualization, and artistic computing, emphasizing user-driven tools for creative industries. Key subfields include texture advection, multiresolution modeling, and real-time camera control for virtual cinematography. Scientific Awards : ACM Fellow (2002) ACM SIGGRAPH Achievement Award (2000) Carnegie Foundation Professor of the Year (1998) NSF Presidential Faculty Fellow (1995-98) Alfred P. Sloan Research Fellowship (1995-97) Numerous industry grants and lab donations He has advised over 30 PhD and Master's students, including leaders at Microsoft, Pixar, and Google. His labs at UW and Adobe focused on graphics, imaging, and creativity tools.
Dr. Ian Stavness is a Professor and Department Head in the Department of Computer Science at the University of Saskatchewan. His research focuses on interdisciplinary applications of computer science, including deep learning in agriculture, biomedical computation, and 3D display technologies. He leads the Biological Imaging & Graphics (BIGLAB) laboratory, which develops tools for plant phenotyping and musculoskeletal modeling. Education: Ph.D. in Computer Engineering, University of British Columbia, 2010 M.A.Sc. in Computer Engineering, University of British Columbia, 2006 B.Sc. in Computer Science & B.Eng. in Electrical Engineering, University of Saskatchewan, 2004 Research Interests: Deep Learning for Plant Phenomics & Agriculture 3D Displays and VR/AR Technologies Musculoskeletal Biomechanical Simulation (OpenSim, ArtiSynth) Computer Vision and Image Analysis Awards: ACM CHI 2019 Honourable Mention ACM VRST 2018 Polyphony Digital Award Key Projects: Deep Plant Phenomics Platform Parametric Human Project (Digital Human Modeling) P2IRC (Plant Phenotyping & Imaging Research Center)
Devi Parikhمشاهده پروفایل
دانشیار
Devi Parikh is an Associate Professor at the School of Interactive Computing, Georgia Institute of Technology, and a Research Director at Meta’s FAIR lab. Her research focuses on generative models, AI for creativity, computer vision, and natural language processing. Education: B.S. in Electrical and Computer Engineering from Rowan University (2005), M.S. and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University (2007, 2009). Research interests include embodied AI, human-AI collaboration, and creative applications of AI. She has held visiting positions at Cornell, MIT, CMU, and others. Awards include NSF CAREER Award, IJCAI Computers and Thought Award, and multiple fellowships. Led development of Habitat , a platform for embodied AI research, and contributed to the Open Catalyst Project for renewable energy storage.
Aykut Erdemمشاهده پروفایل
دانشیار
Aykut Erdem is an Associate Professor of Computer Engineering at Koç University, affiliated with the KUIS AI Center. He earned his PhD, MSc, and BSc in Computer Engineering from Middle East Technical University (METU), with visiting researcher experiences at Virginia Tech (2004) and MIT (2007). His research focuses on learning-based approaches for visual data understanding, including image editing, visual saliency estimation, and vision-language integration. Research Interests: Vision and Graphics, Machine Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Generative Artificial Intelligence. Recent work includes text-guided image/video editing, diffusion models for object removal, and GAN-based frameworks for domain adaptation. Scientific Awards: Young Scientist Award (BAGEP 2021) by Science Academy in Computer Engineering Best Paper Award at 5th Multimodal Learning and Applications Workshop (2022) Collaborations and Funding: Principal investigator for TUBITAK 1001 project on generative AI for visual data (2021-2024), Adobe Research Gift (2023), and co-investigator for multiple TUBITAK grants. Serves as Associate Editor for IEEE Transactions on Image Processing (2022-present).
- Computer Vision
- Autonomous Driving
- Robotics
- +۷ مورد دیگر
Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools
Erkut Erdem is a Professor in the Department of Computer Engineering at Hacettepe University, where he leads the Computer Vision Laboratory (HUCVL). His research focuses on computer vision and machine learning, particularly on incorporating different kinds of context (spatial, temporal and cross-modal) into visual processing across all levels from low to high-level vision. He received his Ph.D. (2008), M.Sc. (2003), and B.Sc. (2001) from Middle East Technical University. Prior to joining Hacettepe University in 2010, he completed a post-doctoral fellowship at Ecole Nationale Supérieure des Télécommunications (2009-2010) and held visiting researcher positions at UCLA (2007) and Virginia Tech (2004). His current research interests include Visual Saliency Prediction, Automatic Image Description, Video/Photoset Summarization, Image Filtering, and Image Editing. Recent work has focused on multimodal learning with video-language models, diffusion-based image editing, and event-based vision for low-light conditions. His research has been published in top venues including NeurIPS, ICLR, ICCV, SIGGRAPH, and ACL. He has received significant recognition including The Young Researcher Award from Turkish Academy of Sciences and being named a 2022 Outstanding Associate Editor of IEEE Transactions on Multimedia. He has secured multiple research projects funded by TUBITAK and received gift funds from Adobe Research for text-guided image synthesis work. Current Teaching: BBM202: Algorithms, AIN434/BBM444: Fundamentals of Computational Photography Graduate Supervision: 6 current Ph.D. students, numerous recent graduates including Burak Ercan (2024) and Aysun Kocak (2023) Professional Affiliations: Co-affiliated with Koç University and İş Bank AI Center (KUIS AI)
Chuang Ganمشاهده پروفایل
استادیار
- Artificial Intelligence
- Robotics
- Computer Vision
- +۷ مورد دیگر
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
- Digital Marketing
- Machine Learning
- Computer Vision
- +۷ مورد دیگر
Prof. Jochen Hartmann holds the Digital Marketing professorship at the TUM School of Management (Munich). Previously, he was an assistant professor at the University of Groningen's School of Business and Economics and worked as a management consultant at McKinsey & Company. He earned his doctorate from the University of Hamburg and coordinated the DFG research group FOR 1452 (2019-2022). His research focuses on digital marketing and machine learning, particularly analyzing unstructured data (computer vision, NLP) and generative AI. Key themes include social media, algorithmic fairness, diversity in advertising, and human-machine interactions. Education: Ph.D. in Business Administration (University of Hamburg), Management Consulting experience at McKinsey & Company. Research interests combine cutting-edge AI techniques with marketing challenges. Recent work explores generative AI's impact on advertising, algorithmic bias in finance, and visual search innovations. His text/image mining studies rank among top-cited articles in marketing journals like the International Journal of Research in Marketing and Journal of Marketing Research. Awards include the EMAC-Sheth Sustainability Award, Lindau Nobel Laureate Meetings' Young Economist distinction, and multiple best dissertation awards. Grants: Led DFG-funded research group (2019-2022). Affiliated with Columbia Business School (visiting scholar) and Mannheim Business School (lecturer in machine learning). Labs/Teams: Active in interdisciplinary research groups focusing on AI applications in marketing and business analytics.