جست‌وجوی استاد و پژوهشگر

Mingyu Dingمشاهده پروفایل

استادیار

• Robotics
• Robot Learning
• Embodied AI
• +۴ مورد دیگر

Mingyu Ding is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges robotics, embodied AI, and computer vision, focusing on building agents that interact effectively with physical environments. PhD in Robotics, University of Hong Kong (2022), advised by Ping Luo Postdoctoral Fellow, BAIR@UC Berkeley (with Masayoshi Tomizuka) Visiting Scholar, CSAIL@MIT (with Joshua Tenenbaum) B.S. in Computer Science, Renmin University of China (under Zhiwu Lu) His work emphasizes robot learning through physical simulation, multimodal foundation models, and self-supervised methods. Key contributions include Embodied Concept Learner (ECL) and Sparse Diffusion Policy frameworks. Recent publications highlight trends in 3D vision, diffusion-based planning, and language-driven robotic behavior synthesis. Awards include ICRA Best Paper (2024), ME Rising Star (2023), and CVPR Doctoral Consortium (2023). Session Chair for ICRA 2025 Associate Editor for IROS 2025 Guest Editor for Robotics Special Issue: Embodied Intelligence

Nati Srebroمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Deep Learning
• +۴ مورد دیگر

Nati Srebro is a Professor at the Toyota Technological Institute at Chicago with a cross-appointment as a Part-Time Professor in the Department of Computer Science and Committee on Computational and Applied Mathematics at the University of Chicago. He earned his PhD from MIT in 2004 and has held previous positions including post-doctoral fellow at the University of Toronto, Visiting Scientist at IBM, and Associate Professor at the Technion. Professor Srebro's research focuses on methodological, statistical and computational aspects of Machine Learning and Optimization. His work spans foundational contributions to learning theory, matrix reconstruction, and optimization techniques. He is particularly known for introducing the use of nuclear norm for machine learning, work on wider Markov networks, and advancing our understanding of the relationship between learning and optimization. His current research interests include understanding deep learning through optimization, distributed and federated learning systems, algorithmic fairness, and practical adaptive data analysis. His publication record shows consistent contributions to core machine learning conferences and workshops, with recent work focusing on symmetric and asymmetric hashing techniques, matrix parameter learning, and optimization methods. The publications demonstrate a strong theoretical foundation with practical applications across various machine learning domains. Professor Srebro has been actively involved in several research programs including the Federated and Collaborative Learning program (Spring 2026, as Visiting Scientist and Program Organizer), Modern Paradigms in Generalization (Fall 2024), and multiple summer clusters on Deep Learning Theory and Fairness. His program participation reflects his leadership in emerging areas of machine learning research. Contact: nati@ttic.edu | (773) 834-7493 | Toyota Technological Institute at Chicago, 6045 S. Kenwood Ave., Chicago, IL 60637

George H. Chenمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +۳ مورد دیگر

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

Brandon Luciaمشاهده پروفایل

استاد

• computer architecture
• computer systems
• programming languages
• +۸ مورد دیگر

Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.

Tatsunori Hashimotoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

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.

• Computer Security
• Privacy
• Applied Cryptography
• +۶ مورد دیگر

Aggelos Kiayias FRSE is Chair in Cyber Security and Privacy and Director of the Blockchain Technology Laboratory at the University of Edinburgh. He is also Chief Scientist at blockchain technology company Input Output. His academic work spans over two decades with more than 200 publications in cryptography, blockchain, and security. Dr. Kiayias received his Ph.D. from the City University of New York and was an undergraduate at the University of Athens Mathematics department. His research focuses on computer security, privacy, applied cryptography, and foundations of cryptography with particular emphasis on blockchain technologies, distributed systems, e-voting, secure multiparty protocols, and identity management. His recent work demonstrates continued innovation across multiple dimensions of blockchain technology, with publications spanning theoretical foundations, practical implementations, economic modeling, and privacy-preserving techniques. The research output shows strong emphasis on security analysis, consensus protocols, transaction processing, and economic incentives within decentralized systems. His work bridges theoretical cryptography with real-world blockchain applications. Among his notable recognitions are an ERC Starting Grant, Marie Curie fellowship, NSF Career Award, Fulbright Fellowship, election as Fellow of the Royal Society of Edinburgh in 2021, and the BCS Lovelace Medal in 2024. He has served as program chair for major conferences including the Cryptographers' Track of RSA (2011), Financial Cryptography (2017), Real World Crypto Symposium (2020), and Public-Key Cryptography Conference (2020), and as general chair of Eurocrypt 2013. Professor Kiayias has supervised over 20 PhD students, many of whom have gone on to academic positions at institutions including Imperial College, Stanford University, Royal Holloway University London, University of Glasgow, University of Sydney, and Virginia Commonwealth University. His current advisees include Yu Shen, Amirreza Sarencheh, and Christina Ovezik with expected graduations between 2025-2026. He leads the Blockchain Technology Laboratory at the University of Edinburgh and is involved in several major blockchain research projects including Panoramix and Fentec. His research has received significant funding from the European Union (Horizon 2020, ERC), UK research councils (EPSRC), US agencies (NSF, DHS, NIST), and Greek research bodies.

• Machine Learning
• Deep Learning
• Foundation Models
• +۱۱ مورد دیگر

Bo Han is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University's Faculty of Science, where he leads the Trustworthy Machine Learning and Reasoning (TMLR) Group. He also holds a visiting scientist position at the RIKEN Center for Advanced Intelligence Project (RIKEN AIP) in Japan. His research focuses on developing trustworthy and efficient machine learning systems, particularly under imperfect data conditions such as noisy labels, out-of-distribution data, and weak supervision. Bo Han's research interests span Machine Learning , Deep Learning , Foundation Models , Causal Representation Learning , Weakly and Self-supervised Learning , Robustness and Security in Machine Learning , Federated Learning , and AI for Science . His work aims to build intelligent systems that can reliably learn and reason from complex, imperfect real-world data. His recent publications reveal a strong trend toward trustworthy foundation models , robust reasoning with large language models , out-of-distribution detection , privacy-preserving learning , and causal robustness . His research integrates theoretical foundations with practical applications, often published in top-tier venues like NeurIPS, ICML, ICLR, and TPAMI. Notable Awards and Honors: Outstanding Paper Award, NeurIPS Most Influential Paper, NeurIPS IEEE AI's 10 to Watch Award IJCAI Early Career Spotlight INNS Aharon Katzir Young Investigator Award Dean's Award for Outstanding Achievement RGC Early CAREER Scheme Bo Han has been actively involved in the academic community, serving as a Senior Area Chair and Area Chair for NeurIPS, ICML, and ICLR, and as an Associate Editor for IEEE TPAMI, MLJ, and JAIR. He has advised numerous PhD and research students and leads a globally distributed research group. His work is supported by major grants from RGC, NSFC, GDST, RIKEN, and industry partners including Microsoft, Alibaba, Tencent, and Baidu. He also leads research initiatives in Trustworthy Machine Learning , including projects on federated learning, model unlearning, privacy-preserving AI, and robust foundation models, often in collaboration with industry and international institutions.

• Computational Imaging
• Artificial Intelligence
• Inverse Problems
• +۷ مورد دیگر

Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email

Seddik M. Djouadiمشاهده پروفایل

استاد

• Robust Control
• Distributed Control Systems
• Wireless Communication
• +۶ مورد دیگر

Seddik M. Djouadi is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD from McGill University (1999), an M.A.Sc. from École Polytechnique (1992), and a B.Sc. from École Nationale Polytechnique (1989). His research focuses on robust control, distributed systems, wireless communication, model reduction, and power networks. He has authored over 100 publications and received awards including recognition as an outstanding reviewer by Automatica (2006-2007, 2003-2004) and the Tibbets Award (1999). Education: PhD in Electrical Engineering, McGill University, 1999 M.A.Sc., École Polytechnique, 1992 B.Sc., École Nationale Polytechnique, 1989 Research Interests: Robust and distributed control Wireless communication systems Model reduction for fluid flows Power network dynamics and renewable integration Biological systems modeling Cyber-physical systems security Awards: Outstanding Reviewer, Automatica (2006-2007, 2003-2004) Best Paper Award, Mediterranean Conference on Intelligent Systems (2008) Ralph E. Powe Junior Faculty Enhancement Award (2005) Tibbets Award (1999) Students and Advising: Djouadi has mentored over 20 graduate students and postdoctoral researchers, including current advisees Yichen Zhang and Ehsan Raoufat. Notable past students include Jin Dong (now at Oak Ridge National Laboratory) and Xiao Ma (Western Digital). Projects: Current research includes distributed control of large power networks, data-driven nonlinear model reduction, and stochastic modeling of wireless systems. Collaborations involve ORNL and TVA on energy systems and cybersecurity.

Madeline Taylorمشاهده پروفایل

دانشیار

• Energy Law
• Energy Policy
• Agricultural Policy
• +۷ مورد دیگر

Associate Professor Madeline Taylor is an Australian Research Council (ARC) Early Career Industry Fellow at Macquarie Law School and Co-Lead of the Energy, Communities, and Market Regulation Stream at the Transforming Energy Markets Research Centre. She specializes in socio-legal aspects of energy transition, focusing on regulatory frameworks, energy justice, and land-use conflicts between energy developers and communities. Her work bridges property law, commercial law, and comparative law to address challenges in renewable energy integration and resource governance. Affiliations: Honorary Associate at Sydney Environment Institute, Member of Sydney Institute of Agriculture Industry Partners: NSW Department of Primary Industries and Spark Renewables (FAARM Project) Research Interests: Energy law and policy, agricultural-legal synergies, renewable energy siting, and just transition principles. She co-edits the Oil, Gas and Energy Law Journal and contributes to the LexisNexis Energy and Resources Law in Australia . Recent Articles Highlight Key Themes: Offshore wind governance, agrivoltaics regulatory systems, hydrogen licensing, and energy justice frameworks. These works emphasize interdisciplinary approaches to balancing economic, environmental, and social dimensions of energy transitions. Awards: 2023 Lawyers Weekly Women in Law Academic/Researcher of the Year, 2024 AFR Higher Education Awards Finalist, Clean Energy Council Transformational Leadership Scholarship Teaching and Engagement: Embeds climate justice in commercial law education; advises governments and NGOs on energy policy. Active in RE-Alliance Management Committee and IUCN Climate Change Law initiatives.

• intelligent systems
• high performance distributed computing
• resource management
• +۸ مورد دیگر

Professor Omer Rana serves as Professor of Performance Engineering and International Dean for the Middle East at Cardiff University's School of Computer Science and Informatics. He also holds the prestigious position of Cross-Council Research Director for the UK National Edge AI Hub, demonstrating his leadership in national research initiatives. Previously, he led the Complex Systems research group and served as Dean of International for the Physical Sciences and Engineering College at Cardiff University. Professor Rana is a Fellow of both the Learned Society of Wales and the Higher Education Academy, and serves on the Advisory Board of the Welsh Ethnic Minority Professors Initiative (WEMPI). His research expertise centers on the intersection of intelligent systems and high performance distributed computing, with particular focus on applying intelligent techniques to resource management in distributed systems. His scholarly contributions span edge computing, cloud computing, Internet of Things (IoT), artificial intelligence, cybersecurity, federated learning, privacy-preserving systems, and sustainable computing. Professor Rana has published extensively in top-tier journals and conferences, with recent work emphasizing practical applications in industrial automation, smart buildings, and sustainable computing solutions. Professor Rana's publication record demonstrates consistent leadership in edge computing and distributed systems research, with a growing emphasis on practical implementations across diverse application domains. His work bridges theoretical computer science with real-world technological challenges, particularly in industrial automation, smart environments, and sustainable infrastructure. Fellow of the Learned Society of Wales Fellow of the Higher Education Academy Professor Rana actively supervises postgraduate students and has secured significant research funding for projects related to edge computing, IoT, and distributed systems. His international collaborations span Europe, Asia, and the Middle East, reflecting his global influence in the field. He serves as a sought-after keynote speaker, workshop chair, and panel moderator at major international conferences including IEEE/ACM Utility and Cloud Computing (UCC) and IEEE Edge Computing. He leads multiple research initiatives, most notably as Cross-Council Research Director for the UK National Edge AI Hub, where he shapes national research directions in edge computing and AI. His work has practical applications across industrial automation, smart building management, electric vehicle infrastructure, and sustainable computing solutions.

Tuuli Toivonenمشاهده پروفایل

استاد

• Geoinformatics
• Spatial Analytics
• Machine Learning
• +۷ مورد دیگر

Tuuli Toivonen is a Professor of Geoinformatics at the Department of Geosciences and Geography, University of Helsinki. She leads the transdisciplinary Digital Geography Lab , which addresses human-scale spatial analytics for sustainable societies. She serves as Vice-Director of Geography Degree Programs (post-2022) and previously held the Director role (2020-2022). After receiving the ERC Consolidator Grant in 2022, she continues advancing open science through active memberships in HELSUS and URBARIA . PhD in Geography (University of Turku, 2006) Specialist Vocational Qualification in Leadership (2023) Life Member, Clare Hall, University of Cambridge (since 2021) Her research focuses on Human-Place Interactions through accessibility/mobility lenses, combining Open Data , Machine Learning , and Spatial Analytics . Key application areas include Urban Geography , Conservation Science , and Governance Policy . Recent publications address Dynamic Cities (2018), Social Media for Conservation (2019), and Environmental Exposure During Travel (2021). Her lab produces datasets like the Helsinki Region Travel Time Matrix series (2014-2023). Scientific Awards: European Open Data Champion (2017) Open Science Price (2017) University of Helsinki Geography Award (2018) She supervises Master's thesis work and serves as Opponent for doctoral defenses across Europe. Her teaching portfolio includes Advanced Geoinformatics and Digital Geographies courses.

Andrew Riceمشاهده پروفایل

استاد

• Programming Languages
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)

Bruno Clerckxمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• MIMO Wireless Networks
• +۷ مورد دیگر

Prof. Bruno Clerckx is a Professor of Wireless Communications and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. He leads the Communications and Signal Processing Group and the Wireless Communications and Signal Processing Lab. Education: M.Sc. and Ph.D. in Electrical Engineering from Université Catholique de Louvain, Belgium Doctor of Science (DSc) from Imperial College London Research Interests: Focuses on wireless communications and signal processing for next-generation networks, including MIMO systems, reconfigurable intelligent surfaces (RIS), rate-splitting multiple access (RSMA), and integrated sensing and communications (ISAC). His work emphasizes 6G technologies, full-duplex systems, and energy-efficient architectures. Key contributions include pioneering research on beyond-diagonal RIS and RSMA prototyping. Awards: 2021 Blondel Medal (France) 2021 Adolphe Wetrems Prize (Royal Academy of Belgium) Fellowships from IEEE and IET IEEE Communications Society Distinguished Lecturer (2021-2023) Labs & Teams: Heads the Wireless Communications and Signal Processing Lab, and is affiliated with the IEEE Special Interest Groups on RSMA and BD-RIS. Collaborates with global institutions including Stanford University, Tsinghua University, and Samsung Electronics. Industry Experience: Former CTO of Silicon Austria Labs and contributor to 4G/5G standards at Samsung. Holds 80+ patents and authored two books on MIMO systems.

• Wireless Communications
• Radar Systems
• Antenna Arrays
• +۶ مورد دیگر

Professor Athanassios Manikas holds the Chair of Communications & Array Processing in the Department of Electrical & Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He is a Fellow of both the IET and IMA, and has held significant editorial roles including Associate Editor for IEEE Transactions on Aerospace and Electronic Systems. His research focuses on wireless communications, radar systems, antenna array processing, and applied mathematics, with over 50 supervised PhD students and 150+ Masters projects. He leads Imperial's research group in array processing and has extensive industry collaborations, including technical leadership of the University Defence Technology Centre in Signal Processing (2008-2013). Awards include the IEEE PIMRC 2022 Best Paper Award and recognition as an IEEE COMSOC Distinguished Lecturer (2016-2017). His work integrates differential geometry principles with array processing, as detailed in his monograph Differential Geometry in Array Processing . Professor Manikas has served as an expert witness in high-profile cases and contributes to academic governance roles such as the Royal Society's International Fellowship Committee. His research group is affiliated with the Space Lab at Imperial, focusing on innovative applications of array signal processing in aerospace and defense systems.