جست‌وجوی استاد و پژوهشگر

Minh Q. Phanمشاهده پروفایل

دانشیار

• System Identification
• Control Theory
• Robotics
• +۴ مورد دیگر

Minh Q. Phan is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering. His expertise spans system identification, iterative learning control, model predictive control, robotic swarm control, and intelligent control systems. He holds a BS from the University of California, Berkeley, and MS/M.Phil/PhD degrees from Columbia University in Mechanical Engineering. Dr. Phan has contributed to over 50 peer-reviewed publications and serves as an Associate Editor for the Journal of Guidance, Control, and Dynamics. His research focuses on advancing control theory applications in robotics, structural health monitoring, and sustainable construction materials. Key contributions include the development of OKID (Observer/Kalman Filter Identification) methods and bilinear system identification frameworks. Education History: Bachelor of Science in Mechanical Engineering, UC Berkeley, 1985 Master of Science in Mechanical Engineering, Columbia University, 1986 Master of Philosophy in Mechanical Engineering, Columbia University, 1988 Doctor of Philosophy in Mechanical Engineering, Columbia University, 1989 Research Interests: Advanced control methodologies for dynamic systems Model-based predictive control strategies Applications in robotics and aerospace engineering Structural health monitoring via system identification Machine learning for materials science Teaching Responsibilities include courses like ENGG 149 (Systems Identification), ENGS 145 (Modern Control Theory), and ENGG 148 (Structural Mechanics). His work bridges theoretical control systems with practical industrial applications, including automation in food processing and sustainable construction practices. Dr. Phan has collaborated on projects addressing viral epidemiology in Vietnam and coastal erosion mitigation strategies.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Amir Zamir is a tenure-track Assistant Professor of Computer Science at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Computer & Communication Sciences. Previously, he worked at UC Berkeley, Stanford, and UCF with prominent researchers including Silvio Savarese, Jitendra Malik, Mubarak Shah, Rahul Sukthankar, and Leonidas Guibas. He currently leads the Visual Intelligence & Learning Lab at EPFL and serves as chief scientist of Duranta, having previously been the CVML chief scientist of Aurora Solar (a Forbes AI 50 company valued at $4B in 2022) from 2015 to 2022. Dr. Zamir's research spans computer vision, machine learning, and artificial intelligence, with a focus on developing general multi-modal/multi-task vision systems that operate as active agents in the real world. His work emphasizes slow science principles, seeking fundamental understanding over quick publications. Key research areas include embodied vision, multimodal foundation models, computational imaging, and vision-language integration. His notable projects include 4M, Taskonomy, Gibson Environment, Omnidata, and MultiMAE, which have significantly influenced the field of computer vision and embodied AI. Zamir has made substantial contributions to the computer vision community through numerous high-impact publications and leadership roles. His work demonstrates a consistent focus on creating vision systems that go beyond narrow and passive methods toward more general, active, and embodied approaches. The trajectory of his research shows increasing sophistication in handling multiple modalities and tasks within unified frameworks, culminating in recent work on multimodal foundation models that can handle diverse vision tasks. Dr. Zamir has received numerous prestigious awards including the Young Researcher Award 2022 from ECCV, the PAMI Mark Everingham Prize 2022, SIGGRAPH 2022 Best Paper Award for CLIPasso, CVPR 2018 Best Paper Award for Taskonomy, and CVPR 2016 Best Student Paper Award. He is also an ELLIS Faculty Scholar and received the NVIDIA Pioneering Research Award in 2018 for the Gibson Environment. As an advisor, Dr. Zamir has mentored numerous PhD students including Roman Bachmann, Andrei Atanov, Rishubh Singh, Jason Toskov, Kunal Pratap Singh, Zhitong Gao, Mingqiao Ye, and Muhammad Uzair Khattak. His former PhD students include Oguzhan Kar (now at Apple), Alexander Sasha Sax (co-advised with Jitendra Malik, now at Meta FAIR), and Teresa Yeo (now at MIT-Singapore Alliance). Dr. Zamir teaches several advanced courses including CS-503 Visual Intelligence, CS-500 AI Product Management, COM-304 Intelligent Systems, and ENG-615 Topics in Autonomous Robotics. Dr. Zamir leads the Visual Intelligence & Learning Lab at EPFL, which focuses on developing fundamental methods for visual intelligence that can operate effectively in real-world environments. The lab takes an interdisciplinary approach combining computer vision, machine learning, robotics, and cognitive science to create systems that can perceive, understand, and interact with the world. Current research directions include multimodal foundation models, computational imaging, embodied vision, and personalization of generative models.

Yuki M. Asanoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Yuki M. Asano is a full Professor at the University of Technology Nuremberg , leading the Fundamental AI (FunAI) Lab . Previously, he led the QUVA Lab at the University of Amsterdam and earned his PhD at the Visual Geometry Group (VGG) of the University of Oxford under Andrea Vedaldi and Christian Rupprecht. University of Technology Nuremberg (2024–present) University of Amsterdam (prior to 2024) University of Oxford (PhD, 2020) His research spans Artificial Intelligence , Machine Learning , and Computer Vision , with a focus on Causal Representation Learning , Self-Supervised Learning , and Efficient Model Adaptation . He pioneered techniques like BISCUIT (causal variable identification) and VeRA (parameter-efficient fine-tuning). His work extends to Medical Imaging and Environmental Monitoring through applications in fetal ultrasound analysis and marine debris detection. Recent publications (2023–2025) highlight advancements in Self-Supervised Learning , Vision-Language Models , and 3D Understanding . Notable papers include TWIST & SCOUT (multimodal LLM grounding), SIGMA (masked video modeling), and GeneralAD (anomaly detection). His ICCV 2023 work on Self-Ordering Point Clouds and MoSiC (optimal-transport motion trajectories) underscores his interdisciplinary approach. He received the JUPITER compute grant (2025) and an Outstanding Paper Award at ICLR 2024 . His collaborations span institutions like MIT-IBM Watson AI Lab, Qualcomm AI Research, and University of Amsterdam.

Yingyan Linمشاهده پروفایل

دانشیار

• Efficient Machine Learning
• Cross-Layer Innovations
• Green AI
• +۷ مورد دیگر

Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.

Roland Büchiمشاهده پروفایل

استاد

• Control Systems Engineering
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +۲ مورد دیگر

Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.

Adrian Wellerمشاهده پروفایل

استاد پژوهشی

• Artificial Intelligence
• Machine Learning
• Explainability
• +۹ مورد دیگر

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Kenji Kawaguchiمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

• Telecommunication Networks
• Beyond 5G/6G Mobile Networks
• Edge/Cloud Computing
• +۷ مورد دیگر

Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.

Tien Tsin Wongمشاهده پروفایل

استاد

• Generative AI
• Computer Graphics
• Computer Vision
• +۴ مورد دیگر

Tien Tsin Wong is a Professor in the Department of Data Science & AI at Monash University, Australia. Previously, he served as a Professor at the Chinese University of Hong Kong (1999–2024) and held a Visiting Assistant Professor position at the Hong Kong University of Science and Technology (1998–1999). His research focuses on Generative AI, Computer Graphics, Computer Vision, and Computational Manga, with significant contributions to GPU techniques, image-based rendering, and multimedia compression. Education: He earned a B.Sc. (1992), MPhil (1994), and PhD (1998) in Computer Science from the Chinese University of Hong Kong. Research Interests: His work bridges computational techniques with artistic applications, particularly in manga and animation. Notable areas include generative models, diffusion-based video synthesis, and physically plausible scene generation. His research aligns with UN Sustainable Development Goals through innovations in education and digital accessibility. Awards : He has received the 2004 Young Researcher Award, 2005 IEEE Transactions on Multimedia Prize Paper Award, and two international invention medals (Geneva 2018, Asia Hong Kong 2019). Editorial Roles : He serves as an Associate Editor for Computer Graphics Forum , IEEE Transactions on Visualization and Computer Graphics , and Computational Visual Media . His editorial work underscores his influence in advancing visualization and graphics research. Labs/Teams : While not explicitly named, his collaborations span global institutions, focusing on computational manga, generative AI, and GPU-optimized techniques. His work often involves interdisciplinary teams addressing challenges in digital media and AI.

David Noyceمشاهده پروفایل

استاد

• Transportation Safety
• Traffic Operations
• Vehicle-to-Infrastructure Communication
• +۵ مورد دیگر

David Noyce is a Professor and Executive Director of the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison's Department of Civil & Environmental Engineering. He also serves as Associate Director of the Safety Research Using Simulation (SaferSIM) Center, a University Transportation Center. His work focuses on transportation safety, traffic operations, and automated/connected vehicle systems. Key roles include leading over $35M in research since 2003 through the TOPS Lab and advancing technologies like V2V/V2I communication, winter maintenance strategies, and work zone safety. Education: PhD (Texas A&M, 1999), MS (Civil Engineering, UW-Madison, 1995), MBA (UW-Whitewater, 1994), BS (UW-Madison, 1984). Research interests span traffic control devices, driver behavior analysis, crash reconstruction, and multi-modal transportation systems. He pioneered the national implementation of flashing yellow arrow signals and developed the Wisconsin Connected and Automated Transportation Consortium. Current projects include C-V2X localization frameworks, automated vehicle safety testing, and winter maintenance efficiency. Recognized with 20+ awards, including ITE and ASCE Fellowships and the Dr. Arthur F. Hawnn Professorship. His labs offer full-scale driving simulators and field-testing capabilities for real-world validation of innovations.

Niki Kilbertusمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

Vineeth N Balasubramanianمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Computer Vision
• +۸ مورد دیگر

Vineeth N Balasubramanian is a Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad, with affiliate faculty status in the Department of Artificial Intelligence. His research focuses on the intersection of deep learning, machine learning, and computer vision, emphasizing explainability, robustness, and real-world applications. He leads Lab 1055, which investigates problems such as Explainable and robust AI/ML systems Lifelong learning in evolving environments Multimodal vision-language models Applications in agriculture, autonomous navigation, and human behavior analysis His recent work includes causal reasoning in transformers, vision-language model capabilities, and drone-based object detection. Funded by organizations like Google, Microsoft, Intel, and DST, he has received multiple awards including the World's Top 2% Scientists (2022-23), INSA/INAE Fellowships, and Best Paper recognitions. Lab 1055 collaborates with institutions like CMU, UBC, and Monash University, contributing to cutting-edge advancements in AI.

Chris Donahueمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Music Technology
• +۵ مورد دیگر

Chris Donahue is an Assistant Professor in the Computer Science Department at Carnegie Mellon University . He also serves as a part-time Research Scientist at Google DeepMind on the Magenta team. His work focuses on leveraging generative AI to enhance human creativity, particularly in music. Education: PhD in Computer Science (UC San Diego), Postdoctoral Scholar (Stanford University) His research spans controllable generative modeling of music and audio , with a focus on real-time interactive systems. Projects like Piano Genie , Beat Sage , and Copilot Arena demonstrate his commitment to real-world deployment. His Generative Creativity Lab (G-CLef) explores AI applications beyond music, including programming and natural language. Recent publications highlight advancements in multimodal music evaluation , real-time adaptation , and AI-driven sound morphing . He co-developed Magenta RealTime , an open-weight real-time music generation model, and MusicFX DJ Mode . Scientific Awards: Best Paper Award (top 1) at NAACL Student Research Workshop 2025 Best Paper Award (top 1% of submissions) at CHI 2025 Best Paper Runner-up at ISMIR 2021 He co-advises PhD students like Wayne Chi (NDSEG Fellow) and mentors Irmak Bukey . His lab receives support from the AIxArts incubator fund at CMU .

Kyros Kutulakosمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• 3D Sensing
• +۹ مورد دیگر

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.