جست‌وجوی استاد و پژوهشگر

Lin Geng Fooمشاهده پروفایل

پژوهشگر

• Video Analysis
• Activity Understanding
• Diffusion Models
• +۲ مورد دیگر

Lin Geng Foo is a postdoctoral researcher at the Max-Planck Institute for Informatics under the Visual Computing and Artificial Intelligence (VCAI) Department, supervised by Prof. Christian Theobalt. He earned his PhD and Bachelor's from the Singapore University of Technology and Design (SUTD) with the President's Graduate Fellowship and top honors. His research focuses on video analysis , activity understanding , diffusion models , and dynamic neural networks , particularly for 3D human generation/editing . His work includes groundbreaking papers on adversarial attacks, human mesh recovery, and pose estimation. Key trends in his recent publications include applications of diffusion models dynamic networks 3D human modeling video analysis action recognition neural rendering for tasks like mesh recovery, controllable video editing, and sign language translation. Scientific awards include PREMIA Best Student Paper 2023 Commendation Prize Keppel Awards of Excellence (2018, 2019) EY Business Analytics Award He serves as a reviewer for major conferences (CVPR, ICCV, NeurIPS, ICML) and has presented talks at ICME 2024 Metaverse Workshop and SDSC Industry & Demo Day 2024.

Stephan Günnemannمشاهده پروفایل

استاد

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +۴ مورد دیگر

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

• Artificial Intelligence
• Neuroscience
• Applied Mathematics
• +۳ مورد دیگر

Anastasia Borovykh is an Assistant Professor in the Department of Mathematics at Imperial College London, affiliated with the Faculty of Natural Sciences and the Imperial-X initiative. Her research focuses on developing computational models to enhance artificial intelligence systems, integrating insights from neuroscience, applied probability, signal processing, and mathematical modeling. Key application areas include healthcare (e.g., Parkinson’s disease biomarkers), neuroscience (brain connectivity analysis), and privacy preservation in neural networks. Her affiliations include Academic Women in Mathematics, the Applied Mathematics and Mathematical Physics group, and the Artificial Intelligence Network. She leads a research group emphasizing interdisciplinary approaches to AI, leveraging tools from statistical mechanics and advanced signal processing techniques. Recent publications highlight her work on diffusion models, privacy in machine learning, and reinforcement learning applications in chaotic systems control. Her research often bridges theoretical foundations with practical implementations in healthcare and cybersecurity domains. No scientific awards are explicitly listed in the provided information. Her advising and grants section remains unspecified, though her active research group suggests ongoing collaborative projects. She is part of Imperial-X, a platform for cross-disciplinary innovation.

Varun Chandrasekaranمشاهده پروفایل

استادیار

• Computer Security
• Privacy Engineering
• AI Ethics and Governance
• +۳ مورد دیگر

Varun Chandrasekaran is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois' Grainger College of Engineering. His research focuses on computer security, privacy, and information trust, with a growing emphasis on AI ethics and governance. He teaches courses including Computer Security I & II (CS 461/463) and Advanced Topics in Security, Privacy, and Machine Learning (CS 562). His work addresses critical challenges in generative AI security, adversarial machine learning, and privacy-preserving technologies. Dr. Chandrasekaran's research highlights include analyzing risks of AI models like OpenAI's Strawberry, advocating for transparent AI development, and proposing solutions like machine unlearning frameworks (e.g., Amun). His contributions span theoretical advancements and practical applications, such as secure code translation and watermarking defenses. He emphasizes peer-reviewed security practices over proprietary solutions, aligning with cryptographic principles for reliability. Recent projects explore multimodal data curation (MM-GEN), bias mitigation in LLMs, and automated benchmarking (Benchagents). His work bridges academic research with real-world cybersecurity needs, addressing threats from adversarial actors exploiting AI capabilities. Chandrasekaran frequently collaborates with industry and policy experts to shape ethical AI frameworks and standards.

• Artificial Intelligence and Law
• Algorithmic Decision-Making
• Copyright Law
• +۴ مورد دیگر

Amy Cyphert is an Associate Professor at the West Virginia University College of Law, where she has established herself as a leading scholar at the intersection of artificial intelligence and law. A Morgantown native, she graduated from Carnegie Mellon University in 2001 as a Truman Scholar and earned her J.D. cum laude from Harvard Law School in 2005. Her educational background includes: B.A., Carnegie Mellon University, 2001 (Truman Scholar) J.D., Harvard Law School, 2005 (cum laude) Professor Cyphert's research focuses on the legal and ethical implications of artificial intelligence, particularly in the areas of generative AI, algorithmic decision-making in criminal justice, and copyright law. She has developed and teaches courses on Artificial Intelligence and the Law, including specialized instruction on Regulating AI, Civil Procedure, and related subjects. Her work explores how AI systems affect legal practice, regulatory frameworks, and societal structures. Her scholarly output demonstrates a clear trajectory from foundational work on education law and criminal justice to increasingly sophisticated examinations of AI's impact across multiple legal domains. Recent publications reveal deep engagement with medical AI applications, patent law implications, and the fundamental question of how legal systems should respond to rapidly evolving AI technologies. Her interdisciplinary approach connects technical AI developments with concrete legal challenges. Her notable recognition includes: Privacy Papers for Policymakers Award from the Future of Privacy Forum (2021) Professor Cyphert has received significant scholarly impact through citations by multiple courts, including the U.S. Court of Appeals for the 9th Circuit, Eastern District of New York, and several state courts. Her work bridges theoretical scholarship with practical legal application, as evidenced by her frequent presentations at national conferences and media engagements. She has collaborated extensively with colleagues across disciplines, particularly on AI-related research with Professor Sean Tu and others. As Director of ASPIRE at WVU from 2010-2024, she demonstrated leadership in academic programming while maintaining an active research agenda. Her professional trajectory from law clerk for Judge Laura Taylor Swain in the Southern District of New York to litigation associate at WilmerHale before joining academia reflects a career grounded in practical legal experience that informs her scholarly work.

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

Qiang Qiu is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He actively contributes to the Vertically Integrated Projects (VIP) program, fostering interdisciplinary collaboration between faculty, students, and projects. His research spans machine learning, computer vision, robotics, and generative AI, with particular emphasis on diffusion models, transformer fine-tuning, and graph neural networks. Key themes include improving model generalizability through orthogonal low-rank embeddings, developing efficient parameter-tuning strategies, and advancing visuotactile manipulation in robotics. Recent publications reveal trends in attention control for text-to-image alignment, posterior sampling for diverse image generation, and federated learning security. His work addresses gradient conflicts in machine unlearning and explores multimodal sensing for robotic manipulation.

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۹ مورد دیگر

Gözde Ünal is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering, under the School of Computer and Informatics. She has held leadership roles including Deputy Dean and former Director of the Application and Research Center. She previously served as Associate and Assistant Professor at Sabancı University and taught at Georgia Institute of Technology. Education: Ph.D., Electrical and Computer Engineering, North Carolina State University (1998–2002) M.Sc., Electrical and Electronics Engineering, Bilkent University (1996–1998) B.Sc., Electrical and Electronics Engineering, Middle East Technical University (1992–1996) Her research focuses on medical image computing, computer vision, deep learning, augmented and virtual reality, and AI for healthcare . She specializes in image segmentation, point cloud processing, and surgical scene analysis. Her work bridges computer science with biomedical applications, particularly in MRI, ultrasound, and dermoscopic imaging. She leads interdisciplinary research in both medical and cultural heritage domains. The recent publications (2021–2025) highlight a strong trend in deep learning for medical image segmentation , adversarial attacks on point clouds , AI in surgical skill assessment , and cultural heritage digitization using 3D point clouds . Her work increasingly employs transformers, self-supervised learning, and multimodal fusion, with a focus on robustness and real-world deployment. Scientific Awards: Fakülte Yılın Seçkin Eğitimcisi Ödülü, ITU, 2018 Marie Curie Alumni Association Career Award, 2017 GENÇ BİLİM KADINI, L’Oréal Turkey, 2010 GEBİP, Turkish Academy of Sciences, 2010 She has supervised numerous students and research projects, securing grants from EU and TTO (Technology Transfer Office). Her projects include AI-based optimization in banking, AI for assisted reproduction, deep learning for quality control, and normalization of multimodal brain networks. She leads a dynamic research group working on cutting-edge AI applications. She is actively involved in professional societies: President of the Marie Curie Alumni Association Turkish Chapter, member of MICCAI, IEEE, and TMRD. Her lab focuses on AI-driven solutions in medical imaging and heritage preservation, with strong international collaborations, particularly in Europe.

• Software Engineering
• Artificial Intelligence
• Cybersecurity in AI
• +۵ مورد دیگر

Amin Alipour is an Associate Professor in the Department of Computer Science at the University of Houston, where he directs the Software Engineering Research Group. He holds a Ph.D. from Oregon State University and multiple advanced degrees from institutions in Iran and the U.S. His work focuses on improving safety and user experience of AI-assisted software engineering tools, with funding from NSF and IARPA. Prior to joining the U.S., he taught at the University of Kashan and Shiraz University in Iran. Education: Ph.D. in Computer Science, Oregon State University (Advisor: Alex Groce) M.S. in Computer Science, Michigan Technological University M.S. in Computer Engineering, Tarbiat Modares University B.S. in Computer Engineering, Petroleum University of Technology (Iran) Research interests include: Secure AI systems, particularly in code LLMs Trojan detection and mitigation in neural models Educational impacts of AI tools in programming education Formal methods for software verification Human-AI collaboration in programming tasks His recent articles explore cybersecurity in AI, student interactions with LLMs, and adversarial attacks on code models. He currently serves on program committees for ICER 2025 and ICSME 2025. His research group develops tools like FeatureExtractor and ProgramTransformer for analyzing neural code intelligence models.

• Computer Vision
• Computer Graphics
• Computational Photography
• +۶ مورد دیگر

Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA

Michał Woźniakمشاهده پروفایل

استاد

• machine learning
• pattern recognition
• classifier ensemble
• +۱۲ مورد دیگر

Michał Woźniak is a Professor at the Department of Systems and Computer Networks, Wrocław University of Science and Technology. He serves as Head of the Department and leads the Machine Learning Research Team. His research spans machine learning, pattern recognition, data stream mining, and imbalanced data classification. He actively supervises MSc theses and leads multiple research projects including those on continual learning, classifier ensembles, and fake news detection. Research Interests: Machine learning, particularly inductive and continual learning Pattern recognition and classifier ensembles Data stream mining under concept drift Imbalanced data classification Fake news and disinformation detection Cybersecurity and medical decision support His recent publications (2024–2025) focus on continual learning under concept drift, deep learning for image fusion and forgery detection, ensemble methods for imbalanced data, and AI applications in network optimization. These works reflect strong trends in adaptive machine learning, robust classification, and real-world AI deployment. Scientific Awards: BEST PAPER AWARD FOR CLVISION CVPR WORKSHOP 2024 He supervises numerous students and collaborates extensively on interdisciplinary projects. He has been involved in projects such as LM LDS (2021–2024), MOO (2020–2025), IDStream (2018–2022), and others. He is also a project manager and active in academic service, including membership in the Committee on Informatics of the Polish Academy of Sciences. His research group maintains a strong presence in AI and machine learning applications. Laboratory and Teams: He leads the Machine Learning Team and is involved in multiple research groups including the Advanced Data Analysis Methods Team and Metaheuristics Team . His lab focuses on developing robust, adaptive AI models for real-world challenges.

• Interdisciplinary Gender Research
• Science Fiction and the Posthuman
• Care Ethics
• +۷ مورد دیگر

Ingvil Førland Hellstrand is a Professor in Interdisciplinary Gender Research at the Department of Media and Social Sciences, Faculty of Social Sciences, University of Stavanger (UiS). She is actively engaged in transdisciplinary research at the intersection of gender, technology, and care. Her work is deeply rooted in feminist theory, speculative fiction, and ethics. Her research interests include storytelling practices, science fiction as method, posthuman ethics, welfare technologies, and caring futures. She explores how narratives shape knowledge and influence ethical frameworks in technologically mediated care. Her work critically examines the role of AI, care robots, and digital tools in reshaping care practices, especially in elderly care contexts. Her recent publications reveal a strong focus on feminist monster studies, affective knowledge, art-based research, and the ethical implications of AI and robotics in healthcare. Themes such as vulnerability, relationality, and speculative futures recur across her work, often bridging literary analysis with social science inquiry. Universitetsfondets formidlingspris (2023) She leads a work package in the Norwegian Research Council–funded project Caring Futures , which develops care ethics for technology-mediated care practices. She is a founding member of The Monster Network and a steering committee member of the Nordic Network Gender, Body, Health . At UiS, she contributes to Professional Relations and The Greenhouse , an environmental humanities initiative. She frequently collaborates with artists and researchers in public engagement and experimental research formats, including art exhibitions and interdisciplinary seminars.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Computer Science
• Computational Linguistics
• Environmental Science
• +۳ مورد دیگر

Weihong Zhong is an active researcher with dual expertise in Computer Science (particularly large language models and controllable text generation) and Environmental Science (focusing on biodegradation of industrial pollutants like phthalate esters and plastic waste). Their work includes methodological innovations in ACL and EMNLP venues, as well as biochemical studies in Applied and Environmental Microbiology and Synthetic and Systems Biotechnology . Key collaborators include Xiaocheng Feng , Lei Huang , and Bing Qin , with whom they co-author multiple papers across NLP and environmental research. Weihong Zhong’s computational research addresses LLM hallucinations (via frameworks like RHIO, Seal, and FRONT), context window extension (through positional encoding optimization), and controllable generation (using latent space probability density estimation). In environmental science, their work focuses on degrading phthalate esters (PAEs) and plastic pollutants using bacterial consortia and enzymatic engineering. Papers like the 2024 GroundBench study and 2023 MMHalSnowball framework highlight their contributions to multimodal hallucination mitigation. Scientific awards or honors are not explicitly documented in the provided materials. Their interdisciplinary approach bridges technical innovation in AI/ML with applied biological solutions for environmental remediation, as evidenced by publications in both top-tier computational conferences and environmental journals .

Nikos Komodakisمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Nikos Komodakis is a Professor in the Computer Science Department at the University of Crete, Greece, where he develops efficient, scalable and mathematically well-grounded algorithms for analyzing visual data including static natural images, video, and medical image data. His research spans deep learning, computer vision, machine learning, and artificial intelligence with significant contributions to self-supervised learning, few-shot learning, and knowledge distillation techniques. His work demonstrates a strong theoretical foundation combined with practical applications, particularly in medical imaging. Komodakis has published extensively in top-tier computer vision venues including CVPR, ICCV, ECCV, and IEEE Transactions on Image Processing. His recent publications (2022-2025) show a growing emphasis on medical image analysis applications while maintaining strong contributions to fundamental computer vision problems. Notable contributions include novel approaches for unsupervised representation learning that surpass state-of-the-art methods, effective techniques for knowledge distillation (such as the QUEST framework), and innovative frameworks for few-shot visual learning. Komodakis serves on the editorial boards of prestigious journals including the International Journal of Computer Vision, Computer Vision and Image Understanding Journal, and Computational Intelligence Journal. He has been a frequent area chair for major computer vision conferences including CVPR, ICCV, ECCV, and BMVC. Spyros Gidaris received the Ponts Foundation Best Thesis Prize and the University Paris-Est Best Thesis prize under Komodakis' supervision Sergey Zagoruyko received the AFRIF 2018 Thesis Prize for his PhD work supervised by Komodakis His research group has developed influential techniques including Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning, which surpassed previous state-of-the-art methods. The group maintains active GitHub repositories for many of their publications, demonstrating commitment to reproducible research. Current research directions include advancing medical image analysis through deep learning, improving self-supervised learning frameworks, and developing more efficient neural network architectures.

Sanjeev Aroraمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity
• Algorithm Design
• +۵ مورد دیگر

Sanjeev Arora is the Charles C. Fitzmorris Professor of Computer Science at Princeton University , where he has been since 1994. He earned a B.S. in Math with Computer Science from MIT in 1990 and a Ph.D. in Computer Science from UC Berkeley in 1994. Education MIT (B.S. 1990) UC Berkeley (Ph.D. 1994) His research spans theoretical computer science , computational complexity , and machine learning theory . Recent work focuses on mathematical frameworks for AI, including skill-based training, model interpretability, and synthetic data pipelines. Key article trends include automated theorem proving , LLM instruction tuning , topic modeling unlearning , and visual reasoning evaluation . Awards span the ACM-EATCS Gödel Prize , Simons Investigator Award , and ACM Infosys Foundation Award . He advises 18 Ph.D. students and directs the Princeton Language and Intelligence center.