جستجوی استاد راهنمای دانشگاهی

Fei-Fei Li is the Sequoia Capital Professor in Computer Science at Stanford University and Founding Co-Director of the Stanford Institute for Human-Centered AI (HAI). She holds courtesy appointments in the Graduate School of Business and is a Senior Fellow at HAI. Her work bridges AI research with interdisciplinary applications in healthcare, robotics, and policy. Dr. Li pioneered the ImageNet dataset, instrumental in the AI revolution, and co-founded World Labs to advance spatial intelligence and generative AI. Education: B.A. in Physics, Princeton University (1999) Ph.D. in Electrical Engineering, Caltech (2005) Doctorate (Honorary), Harvey Mudd College (2022) Research Interests: AI ethics, computer vision, robotic learning, healthcare applications, and human-AI collaboration. Her teams developed frameworks like MOMA for activity recognition and BEHAVIOR for embodied AI benchmarks. She advocates for diversity in tech and co-founded AI4All to mentor underrepresented students. Key Contributions: ImageNet and ImageNet Challenge Stanford Vision and Learning Lab (SVL) Policy advisory roles for U.S. Senate, UN Secretary-General, and California Governor Labs & Initiatives: Leads the People, AI & Robots Group (PAIR), Partnership in AI-Assisted Care (PAC), and the Human-Centered AI Institute. Her work emphasizes ethical AI deployment and societal impact. Awards: VinFuture Prize (2024), IEEE Fellow, National Academy memberships (Engineering, Medicine, Arts & Sciences), and recognition as one of Time’s AI100 Influencers.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359

• Computer Vision
• Pattern Recognition
• Image and Video Processing
• +۴ مورد دیگر

Takeshi Ikenaga is a Professor at Waseda University’s School of Fundamental Science and Engineering and Graduate School of Information, Production and Systems . He earned his Ph.D. in Information & Computer Science from Waseda University in 2001, following B.E. and M.E. degrees in Electrical Engineering (1988–1990). His career spans roles at NTT LSI Laboratories (1990–2002), Kitakyushu Foundation for Advancement of Industry, Science and Technology (FAIS) (1999–2002), and visiting researcher at the University of Massachusetts (1999–2000). Research Interests : Application-specific SoCs for video/image processing, including compression (H.264/AVC, H.265/HEVC), filters (super-resolution, noise reduction), recognition systems (feature detection, object tracking), and communication (UWB, LDPC). He also works on many-core processor design, ultra-low-delay vision systems, and sports analytics (volleyball, figure skating) with real-time 3D pose estimation and ball tracking. Awards : Recipient of the Furukawa Sansui Award (Waseda University, 1988) IEICE Research Encouragement Award (1992) Multiple Best Paper/Presentation Awards (2006–2022) at conferences including DAC/ISSCC, LSI IP Design, ISOCC, ISPACS, and CVIT APSIPA Distinguished Lecturer Certificate (2015) Waseda University Presidential Teaching Award (2020)

• Video Processing
• Image Processing
• Signal Processing
• +۷ مورد دیگر

Hiroshi Ishikawa is a Professor in the Department of Computer Science and Engineering at Waseda University's Faculty of Science and Engineering. He also serves as a Visiting Professor at the National Institute of Informatics since 2016. Previously, he held positions at Nagoya City University from 2004-2010 as Assistant Professor, Associate Professor, and Professor. His academic journey includes being a JST PRESTO Researcher from 2009-2013 and an Associate Research Scientist at New York University's Courant Institute of Mathematical Sciences from 2000-2001. Ph.D. in Computer Science, New York University (2000) Master of Science, Kyoto University Bachelor's degree in Mathematics, Kyoto University Faculty of Science (1991) Hiroshi Ishikawa's research spans perceptual information processing, computer vision, artificial intelligence, deep learning, and discrete optimization. His work focuses on developing algorithms for image restoration, segmentation, and understanding, with significant contributions to energy minimization techniques in computer vision. He has pioneered approaches in sketch simplification, medical image segmentation, and higher-order graph cuts. His research bridges theoretical advances in mathematical optimization with practical applications in medical imaging, computer graphics, and consumer electronics. Ishikawa's recent publications demonstrate a strong focus on leveraging deep learning for image enhancement and understanding. His work spans super-resolution techniques, colorization methods, human avatar generation, and medical image analysis. A notable trend is the increasing integration of attention mechanisms and generative models to solve complex vision problems, with growing emphasis on real-world applications in medical imaging and computer graphics. His research group consistently produces high-impact work that appears in top-tier computer vision conferences. 75th Annual IEICE Best Paper Award (2019) Innovative Technologies 2016 Special Prize for Culture (Ministry of Economy, Trade and Industry) MIRU Nagao Award (Best Paper Award) (2009) Young Author Award (IEEE Computer Society Japan Chapter, 2006) MIRU2006 Excellent Paper Award (2006) Harold Grad Memorial Prize (Courant Institute of Mathematical Sciences, NYU, 2000) As a professor at Waseda University, Ishikawa has mentored numerous students who have become active researchers in computer vision, including Yuya Masuda, Edgar Simo-Serra, and Satoshi Iizuka. His research has been supported by various grants, including JST PRESTO funding from 2009-2013. He has served on editorial boards for prestigious journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and has held leadership roles in major computer vision conferences such as ICCV, CVPR, and ACCV. Ishikawa leads a vibrant research group at Waseda University focused on computer vision and image processing. His laboratory collaborates extensively with researchers at Nagoya City University, National Institute of Informatics, and international institutions. The group maintains strong connections with industry partners, particularly in medical imaging and consumer electronics sectors, translating theoretical advances into practical applications.

• Perceptual information processing
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Ryan M Hebert, MD, is an Assistant Professor of Neurosurgery at the Yale School of Medicine, affiliated with the Neurosurgery department. He specializes in neurovascular disorders, including open and endovascular treatments for intracranial aneurysms, arteriovenous malformations, carotid disease, and dural arteriovenous fistulas. He also focuses on interventions for ischemic and hemorrhagic strokes. His practice locations include Yale New Haven Hospital and Lawrence and Memorial Hospitals in Connecticut. Education & Training: Bachelor’s in Molecular, Cellular, and Developmental Biology from the University of Colorado – Boulder (Howard Hughes Undergraduate Research Grant recipient) MD from Yale School of Medicine (NIH-funded thesis on CCM3 in cerebral cavernous malformations) 7-year Neurosurgery Residency at Yale New Haven Hospital, including enfolded neuro-interventional training CAST-accredited fellowship in Endovascular Neurosurgery and Neurovascular Critical Care at Thomas Jefferson University Hospital (2016) Research Interests: Dr. Hebert’s work centers on emerging surgical and endovascular techniques for neurovascular disorders, risk stratification in carotid disease, vasospasm prediction/post-subarachnoid hemorrhage treatment, and outcomes of minimally invasive spine surgery. His studies often integrate clinical practice with translational research, emphasizing patient outcomes and procedural optimization. Key topics include flow diverters, cranial nerve palsies, hospital frailty risk scores, and immune mechanisms driving aneurysm rupture. Publications Trends: His recent articles emphasize clinical outcomes of endovascular procedures, stroke interventions, and immune mechanisms in aneurysm rupture. He frequently collaborates on meta-analyses and systematic reviews to evaluate treatment efficacy and complication rates, particularly involving cranial nerve recovery and thrombectomy volumes. Awards & Grants: He secured an NIH-funded thesis and a Howard Hughes Undergraduate research grant. No explicit scientific awards are listed, but his work has been recognized via peer-reviewed publications and contributions to clinical guidelines. Advising & Grants: While no students are listed, he collaborates with prominent researchers like Charles Matouk, Kevin Sheth, and Joseph Antonios on NIH-funded projects. His research addresses healthcare resource utilization and procedural volume impacts on mortality, indicating a focus on translational and clinical studies. Labs & Teams: Active in interdisciplinary teams at Yale School of Medicine, particularly in neurovascular critical care and endovascular surgery. Collaborates with the Stroke Center and Janeway Society for physician-scientist development.

• Neurovascular Surgery
• Cerebrovascular Disorders
• Stroke Intervention
• +۳ مورد دیگر

Kim Jae-ho serves as Associate Professor in the Department of Electronic Information and Communication Engineering at Sejong University since September 2020, concurrently directing the Metaverse Autonomous Twin Research Center (ITRC) under the Ministry of Science and ICT. His leadership extends to the National Smart City Committee and TTA Internet of Things/Smart City Platform PG, with research focusing on hyper-connected autonomous intelligence systems for smart city applications. His research program centers on three interconnected pillars: (1) On-Device/Edge/Cloud-based autonomous intelligence architectures enabling distributed decision-making, (2) Spatial/situational awareness systems for intelligent environments, and (3) Collaborative intelligence frameworks for unmanned vehicle networks. This work bridges theoretical AI with real-world deployment in IoT ecosystems and metaverse applications, emphasizing practical implementations for societal benefit. Recent publications (2023-2025) reveal a strategic shift toward metaverse-autonomous system integration, with 68% of articles addressing digital twin alignment, radar/vision sensor fusion, and multimodal AI for robotics. Key trends include UAV swarm coordination (23% of works), battery life prediction for industrial IoT (15%), and large language model integration for robotic perception (12%), demonstrating consistent focus on deployable autonomous intelligence solutions. His scientific recognition includes six major awards: Minister of Land, Infrastructure and Transport Award for Smart City contributions (2020) National Academy of Engineering of Korea's '100 Technologies Leading Korea 2025' (2017) Prime Minister's Commendation for Science/Technology Promotion (2016) Minister of Trade, Industry and Energy Technology Award (2016) KETI Person of the Year (2016) Minister of Science ICT Future Planning SW R&D Award (2014) Professor Kim actively mentors graduate researchers through doctoral and master's thesis supervision while managing $12.7M in active grants including the 7-year Metaverse Autonomous Twin ITRC (2021-2028) and Connected Intelligent Sensor Platform project (2022-2028), with recent funding targeting UAV safety interfaces and industrial IoT battery systems. He leads the Autonomous Intelligent Systems (AISL) Laboratory at Ocean AI Center 529, which integrates government-funded research with industry partnerships to develop deployable autonomous intelligence solutions for smart cities and metaverse applications.

• Autonomous Systems
• Artificial Intelligence
• Internet of Things (IoT)
• +۵ مورد دیگر

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
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

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)

• Computer Vision
• Machine Learning
• Visual Saliency Prediction
• +۵ مورد دیگر

Zion Zibly, MD, MBA is an Associate Professor in the Department of Neurosurgery at Yale School of Medicine . He holds multiple leadership roles including Director of the Center of Neuromodulation , Director of the Center of Neurosurgical Cancer Pain , and Head of Stereotactic & Functional Neurosurgery and the Focused Ultrasound Institute . Previously served as Chair of Neurosurgery at Sheba Medical Center after graduating from Technion’s Faculty of Medicine (MD) and Coller School of Management (MBA). Research Interests: Specializes in Neuromodulation for movement disorders (Parkinson’s, tremors, dystonia), Deep Brain Stimulation , Gene Therapy for pediatric neurodegenerative conditions, Oncological Neurosurgery , and Neurological Pain Management . Combines Functional Neurosurgery with Focused Ultrasound technology. Scientific Contributions: Participated in pioneering Alzheimer’s brain stimulator procedures and Gene Therapy applications. Active member of the North American Association of Functional Neurosurgery and Israeli Neurosurgical Society . Clinical Expertise: Implantation of electrostimulators for Parkinson’s and essential tremor, treatment of Benign/Malignant CNS Tumors , and management of Neurological Pain Conditions . Affiliated with Yale Cancer Center and Center for Brain & Mind Health .

• Neurosurgery
• Functional Neurosurgery
• Neuromodulation
• +۳ مورد دیگر

Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.

• Computer Science
• Audio Processing
• Image Processing
• +۳ مورد دیگر

Pranav Rajpurkar is an Associate Professor at Harvard University, co-founder of a2z Radiology AI, and lead of the Rajpurkar Lab. His work pioneers AI systems that emulate physician-level expertise in medical tasks, with a focus on multi-modal medical AI and radiology. He joined Harvard faculty at age 25 and became Associate Professor at 30 after completing his Stanford PhD at 19. Education: Bachelor of Science (CS), Stanford University, 2015 Master of Science (CS), Stanford University, 2018 PhD (CS), Stanford University, 2021 Rajpurkar's research centers on building AI that thinks and communicates like doctors, with breakthrough work in ECG arrhythmia detection and chest X-ray interpretation. His lab develops foundational datasets (ReXGradient-160K, RadRevise) and benchmarks for medical AI, spanning computer vision, NLP, and multimodal systems. Current work focuses on generative AI for clinical workflows, voice-guided emergency assessment, and rigorous evaluation frameworks for medical LLMs. His 2025 publications reveal three dominant trends: (1) Generative medical AI for radiology report generation and editing, (2) Voice-enabled AI agents for prehospital care, and (3) Multimodal clinical monitoring systems. Key advancements include universal biomedical foundation models (UniBiomed), 3D CT segmentation from text reports, and frameworks for AI-human role separation in radiology. Scientific Awards: Forbes 30 Under 30 in Science (2022) MIT Tech Review Innovator Under 35 (2023) Nature Medicine Early-career Researcher To Watch (2022) Rajpurkar has mentored over 84,000 students through Harvard courses and Coursera's AI for Medicine program. His lab secures major NIH and NSF grants for medical AI development, with recent funding focused on multimodal emergency care systems (MC-MED) and radiologist-AI collaboration. He directs the Harvard-Stanford Medical AI Bootcamp and co-hosts The AI Health Podcast. The Rajpurkar Lab operates as a cross-institutional hub with collaborators at Stanford, MIT, and major hospitals. It drives initiatives like the MAIDA framework for global medical imaging data sharing and develops open-source tools including RadGraph for radiology report analysis. Current projects focus on voice AI for stroke assessment and generative models for non-invasive cancer management.

• Medical AI
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.

• Spoken Language Processing
• Natural Language Processing (NLP)
• Paralinguistics
• +۶ مورد دیگر

Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.

• Multimedia Signal Processing
• Computer Networks
• Communication Engineering
• +۳ مورد دیگر

Jinjin Gu is a tenure-track Assistant Professor at Sofia University "St. Kliment Ohridski" 's INSAIT (Institute for Computer Science, Artificial Intelligence, and Technology), leading research on visual cognition and intelligence. Her work spans visual perception, processing, generation, and reasoning. Education: Ph.D. in Electrical and Computer Engineering (2024), University of Sydney B.Sc. in Computer Science and Engineering (2020), Chinese University of Hong Kong, Shenzhen Her research focuses on visual cognition , including agentic systems , diffusion models , GAN architectures , model interpretability , super-resolution , and multimodal vision-language systems . She has developed novel paradigms like HYPIR for diffusion-quality restoration at GAN speeds. Recent publications highlight advancements in image/video restoration , generative modeling , and visual reasoning . Her work addresses critical challenges in model generalization , causal interpretation , and real-world application robustness . Scientific Awards: Stanford University's World's Top 2% Scientists (2024) Yunfan Award at World Artificial Intelligence Conference (WAIC) (2023) She has advised students contributing to TPAMI, CVPR, and ICLR publications, and serves as Area Chair for ICLR 2026, NeurIPS 2025, and ICML 2025.

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۷ مورد دیگر

YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر