جست‌وجوی استاد و پژوهشگر

Nori Francoمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quantum Information
• Quantum Optics
• +۶ مورد دیگر

Nori Franco serves as Professor in the Department of Physics at the University of Michigan and Chief Scientist at RIKEN's Theoretical Quantum Physics Laboratory in Japan. His dual appointments reflect his significant contributions to both American and Japanese academic communities, with continuous service at Michigan since 1990 and at RIKEN since 2002. His research spans quantum information, condensed matter physics, and quantum optics, with particular focus on light-matter interactions, superconducting qubits, optomechanics, and quantum open systems. Franco's work bridges theoretical foundations with experimental implementations, especially in circuit quantum electrodynamics and quantum computing applications. Analysis of his recent publications reveals a strong emphasis on non-Hermitian quantum systems, quantum control techniques, and applications of quantum information science to fundamental physics problems. His research group consistently produces highly cited work, with publications appearing in top journals across quantum physics and condensed matter disciplines. Scientific Awards: Charles Hard Townes Medal (2024) - sole recipient for fundamental contributions to quantum optics and quantum information processing Research Doctorate Honoris Causa from University of Messina (2024) Highly Cited Researcher for eight consecutive years (2017-2024) Member of Academia Europaea (2023) Willis E. Lamb Medal (2023) for quantum electronics research Throughout his career, Franco has secured significant research funding and mentored numerous students and postdoctoral researchers. His work has received international recognition through invitations to deliver prestigious lectures including the Stanislav Ulam Lecture and Sir Nevill Mott Lecture in 2024. His research group maintains strong collaborations across multiple continents, reflecting his global impact on quantum physics. At RIKEN, Franco leads the Quantum Information Physics Theory Research Team within the Quantum Computing Center, directing cutting-edge theoretical work that complements experimental efforts in quantum computing hardware development.

• Medical Informatics
• Artificial Intelligence in Healthcare
• Healthcare Data Integration
• +۷ مورد دیگر

Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.

Flavio P. Calmonمشاهده پروفایل

دانشیار

• Information Theory
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Flavio P. Calmon is the Thomas D. Cabot Associate Professor of Electrical Engineering at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He holds a Ph.D. in Electrical Engineering and Computer Science from MIT, an M.Sc. from the Universidade Estadual de Campinas (Brazil), and a B.Sc. from the Universidade de Brasília (Brazil). His research focuses on the intersection of information theory, machine learning, and statistics, with applications to privacy, fairness, and trustworthy AI systems. He has received prestigious awards, including the NSF CAREER Award (2018) and the James L. Massey Award (2024). Research interests include developing theoretical foundations for fair and private machine learning, understanding algorithmic bias, and designing systems with provable guarantees. His work spans information-theoretic tools for responsible AI, distributed privacy mechanisms, and understanding the limits of fairness interventions. He has advised numerous students, including Hao Wang, Hsiang Hsu, and Lucas Monteiro Paes, many of whom now hold prominent roles in academia and industry. Calmon's research is supported by grants from NSF, Amazon, Google, and IBM. He has organized workshops on AI in Brazil and leads initiatives to broaden participation in STEM from underrepresented groups. His lab collaborates with institutions globally and emphasizes both foundational theory and practical applications of machine learning.

Hongtu Zhuمشاهده پروفایل

استاد

• Biostatistics
• Medical Image Analysis
• Precision Medicine
• +۴ مورد دیگر

Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Gillings School of Global Public Health and leads the Biostatistics and Imaging Genomics Analysis Lab. His expertise spans statistical learning, medical imaging, AI, and big data integration, with a focus on precision medicine and biomedicine. Dr. Zhu earned his PhD in Statistics from The Chinese University of Hong Kong (2000) and has held prior roles including DiDi Fellow/Chief Scientist (2018-2020) and Bao-Shan Jing Endowed Professor at MD Anderson Cancer Center (2016-2018). He has published over 345 peer-reviewed articles in top-tier journals like Nature, Science, and JASA, and actively contributes to editorial roles including Coordinating Editor of JASA. His research interests include neuroimaging analysis, knowledge graphs, and AI applications in healthcare. Notable awards include the COPSS Snedecor Award (2025), IEEE Fellowship (2025), and IMS Medallion (2027). He has mentored over 80 PhD students/postdoctoral fellows and serves on NIH grant review panels and professional organizations like the ASA's Section on Statistics in Imaging. Key Contributions: Imaging genomics, brain connectivity studies, ridesharing market optimization, medical AI frameworks Lab Innovations: Brain Imaging Genetics Knowledge Portal, Biomedical Knowledge Graph Interface Teaching: Advanced biostatistics courses (Generalized Linear Models, Deep Learning in Biomedicine) Recent work explores causal inference in healthcare, X chromosome's role in neurobiology, and AI ethics in medical vision-language models. His interdisciplinary projects bridge statistics, computer science, and clinical practice to address complex biomedical challenges.

Vishal Ahujaمشاهده پروفایل

دانشیار

• Healthcare Operations
• Operations Management
• Service Operations
• +۷ مورد دیگر

Vishal Ahuja is an Associate Professor and Corrigan Research Professor at Southern Methodist University's Cox School of Business, with adjunct faculty status at University of Texas Southwestern Medical Center. He focuses on decision analytic tools for healthcare improvement through operations management. PhD, University of Chicago Booth School of Business MBA, University of Chicago Booth School of Business His research interests span healthcare operations, service optimization, and AI applications in clinical decision-making. He collaborates with the Department of Veterans Affairs, Parkland Hospital, and pediatric institutions to address care quality and delivery efficiency. Recent publications emphasize predictive modeling for chronic disease management, adaptive clinical trial design, and regulatory healthcare policy. Awards include the Dlin/Fischer Clinical Research Award (2021), INFORMS Pierskalla Award (2012), and 2025 AI75 recognition for Dallas-Fort Worth AI leadership. D CEO Excellence in Healthcare Award - Outstanding Healthcare Innovator (2023) NSF Game Changer Academies for Advancing Research Innovation (2022) C. Jackson Grayson Faculty Innovation Award (2022-23) At SMU, he teaches graduate courses in operations, supply chain, and service management, integrating corporate sector experience from chemical and consumer goods industries. His work has been cited by the FDA in safety labeling changes workshops.

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

• Information Technology Governance
• Cybersecurity
• Blockchain
• +۸ مورد دیگر

Dalibor Radovanović is a researcher at Singidunum University , affiliated with the Faculty of Business Informatics . His work spans cybersecurity, blockchain technologies, and their applications in business and IoT systems. Education: Doctoral Dissertation (2016, Singidunum University) Master's & Basic Studies (Faculty of Business Informatics) Secondary Education: ETŠ Nikola Tesla Research Interests include: Security frameworks for IoT and blockchain integration Smart card and wireless network vulnerabilities E-governance and corporate IT audit methodologies Machine learning applications in cybersecurity Environmental performance optimization in agribusiness Publication Trends reveal a focus on blockchain (2022), cybersecurity (2009-2022), and IT governance (2010-2017). His work bridges theoretical analysis with practical implementations in Serbia's digital economy. Collaborations with scholars like Marko Šarac and Saša Adamović highlight interdisciplinary approaches to securing financial systems, educational institutions, and industrial IoT applications.

Daniel Wolpertمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Sensorimotor Control
• +۴ مورد دیگر

Daniel Wolpert is a Professor of Neuroscience at Columbia University , where he is also Vice-Chair of the Department of Neuroscience and a key member of the Zuckerman Mind Brain and Behavior Institute . Additionally, he holds a part-time position as Director of Research at the Department of Engineering, University of Cambridge, and is a Fellow of the Royal Society and the Academy of Medical Sciences . Education: Medical Doctor (1989), D.Phil. in Physiology from the University of Oxford (1992) Previous Positions: Lecturer at Sobell Department of Motor Neuroscience (Institute of Neurology), Professor of Engineering at University of Cambridge (2005–2018) Wolpert is a world leader in sensorimotor control , combining computational neuroscience , Bayesian inference , and robotic/virtual reality technologies to reverse-engineer how the brain generates movements. His work emphasizes the brain's role in reducing sensorimotor uncertainty through predictive modeling and has implications for understanding disorders like autism and Parkinson’s disease. His awards include: Royal Society Ferrier Medal (2020) Minerva Foundation Golden Brain Award (2010) Royal Society Francis Crick Prize Lecture (2005) Daniel Wolpert actively contributes to public science communication, including a 2011 TED Talk on the computational role of the brain in movement, and leads the Wolpert Lab at Columbia, which investigates the neural basis of decision-making , motor learning , and reinforcement learning in both healthy and clinical populations.

Manish Vermaمشاهده پروفایل

استاد

• Multimodal transportation
• Dangerous goods transportation
• Risk assessment
• +۵ مورد دیگر

Manish Verma is Professor of Operations Management and Associate Dean, Graduate Studies at the DeGroote School of Business, McMaster University. His academic journey began with an MBA and PhD in Business Administration with Operations Management/Management Science specialization from Desautels Faculty of Management at McGill University. Dr. Verma's research focuses on multimodal transportation of dangerous goods, risk assessment, network design and planning in transportation, humanitarian logistics, green supply chain management, and disruption/resilience in transportation systems. His current research engagements center on safety and security issues in freight transportation and humanitarian logistics, funded by NSERC and SSHRC grants. He has been frequently approached by media to comment on railroad accidents involving dangerous goods. An analysis of his recent publications reveals a strong emphasis on hazardous materials transportation risk management, with significant contributions to rail-truck intermodal systems, hazmat risk modeling using value-at-risk methodologies, and emergency response planning for transportation networks. His work bridges theoretical operations research with practical transportation safety applications. $245K research grant for rail safety research from Government of Canada As an educator, Dr. Verma has taught courses including Predictive Analytics for Managers, Network Design Issues in Freight Transportation, and Management Science Research Issues. His scholarly impact is evidenced by publications in leading journals such as Transportation Research Part E, European Journal of Operational Research, and Safety Science. He actively contributes to real-world transportation safety through media commentary and research that informs policy decisions regarding dangerous goods transportation.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Wenpeng Yin is an Assistant Professor in Computer Science and Engineering, specializing in Natural Language Processing and Machine Learning. His research focuses on advancing Large Language Models (LLMs) and their applications in scientific, societal, and interdisciplinary domains. Research Interests : LLMs, medical QA, financial AI, model consistency, and instruction-following frameworks. Recent Work : Investigates low-resource NLP tasks, bias evaluation (Gptbias), and adaptive trading systems using LLMs. Current trends in his publications highlight innovations in multimodal learning, symbolic reasoning, and ethical AI, with a strong emphasis on practical implementations across diverse fields.

Kim Jae-hoمشاهده پروفایل

دانشیار

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

Vijay Kumarمشاهده پروفایل

استاد

• Robotics
• Multi-Robot Systems
• Micro Aerial Vehicles
• +۶ مورد دیگر

Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering at the University of Pennsylvania, with faculty appointments in the Departments of Mechanical Engineering, Computer and Information Science, and Electrical and Systems Engineering. He is a leading figure in robotics and computer architecture research. Research Interests include robotics, particularly multi-robot systems and micro aerial vehicles (MAVs), as well as computer architecture innovations for machine learning, GPU acceleration, and datacenter efficiency. His work spans theoretical foundations and practical applications in autonomous systems and hardware optimization. Scientific Awards include: 1991 NSF Presidential Young Investigator Award 1996 Lindback Award for Distinguished Teaching 2012 ASME Mechanisms and Robotics Award 2014 Engelberger Robotics Award 2017 IEEE George Saridis Leadership Award Multiple best paper awards at DARS, ICRA, and RSS conferences Editorial Leadership includes serving as Editor of the ASME Journal of Mechanisms and Robotics and Advisory Board Member of AAAS Science Robotics Journal . His GRASP Lab team developed foundational frameworks for micro UAV testbeds and swarm robotics.

• Algorithmic Game Theory
• Concurrent Systems
• Formal Verification
• +۳ مورد دیگر

Dr. Rasmus Ibsen-Jensen is a Lecturer in Computer Science at the University of Liverpool. Previously, he held a Postdoctoral position at IST Austria under Krishnendu Chatterjee and completed his PhD under Peter Bro Miltersen. Research Focus: Algorithmic game theory, strategy complexity in two-player zero-sum games, control flow graph algorithms, edit distance for automata, and theoretical biology applications. Teaching: Module Coordinator for second-year courses in database development (COMP207), C++ programming (COMP282), and industrial placement (COMP299). His work bridges computational game theory and formal verification, with recent publications exploring memory constraints in partial-information games, algebraic path properties in concurrent systems, and evolutionary spatial dynamics. While no scientific awards are explicitly mentioned in the provided text, his contributions to algorithmic complexity and interdisciplinary research (e.g., theoretical biology) highlight his academic impact.

Tobi Delbruckمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Event-Based Vision Systems
• Silicon Retinas
• +۶ مورد دیگر

Tobi Delbruck is a titular professor of physics and electrical engineering at ETH Zurich, where he leads the Sensors Group at the Institute for Neuroinformatics (INI) in Zurich, Switzerland. He collaborates closely with Shih-Chii Liu and Giacomo Indiveri as part of the 'hardware groups' at INI. Delbruck has also served as visiting faculty at Caltech and is a Fellow of the IEEE. His work focuses on bio-inspired and neuromorphic event-based sensory processing systems. Professor Delbruck's research spans multiple areas of neuromorphic engineering, with particular emphasis on event-based vision systems and low-power analog VLSI circuits. His work has significantly advanced the field of Dynamic Vision Sensors (DVS), which mimic the human retina's response to changes in brightness rather than capturing full frames. This approach enables extremely low-latency vision processing with minimal power consumption, making it ideal for high-speed applications and robotics. His research has applications in robotics, autonomous systems, and low-power embedded vision. Delbruck is an active contributor to the neuromorphic engineering community, co-organizing the annual Telluride Workshop on Neuromorphic Engineering and serving in leadership roles with IEEE. He has authored numerous influential publications and co-authored books including "Event-Based Neuromorphic Systems" and "Analog VLSI: Circuits and Principles." His jAER (Java Address-Event Representation) project provides open-source tools for real-time event-based sensory processing. Analysis of his recent publications shows a clear trend toward integrating event-based vision with deep learning techniques and applying these systems to practical robotics problems. His scientific achievements have been recognized with multiple awards including: IEEE Fellow Winner of Best Live Demonstration award at ISCAS 2012 Honorable Mention Award from Sensory Systems Technical Committee at ISCAS 2012 Overall Best Student Paper Award and Best Paper Award from Sensory Systems Technical Committee at ISCAS 2010 Winner of the 2006 ISSCC Jan Van Vessem Outstanding European Paper Award Professor Delbruck actively mentors students and has supervised numerous PhD and Master's theses in the areas of neuromorphic engineering and event-based vision systems. His group has secured significant research funding from various sources to support their innovative work in bio-inspired sensory processing. He teaches courses on "Electronics for Physicists II (Digital)" and "Neuromorphic Engineering," helping to train the next generation of researchers in this field. The Sensors Group at INI, which Delbruck leads, operates state-of-the-art facilities for designing and testing neuromorphic vision systems. The group maintains close collaborations with researchers worldwide and has developed several important open-source resources including the jAER project and bias generator design kits. Their work continues to push the boundaries of what's possible with event-based sensory processing, with applications ranging from high-speed robotics to low-power embedded vision systems.

Michael U. Gutmannمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bayesian Inference
• Likelihood-Free Inference
• +۹ مورد دیگر

Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University