جست‌وجوی استاد و پژوهشگر

Manon Revelمشاهده پروفایل

پژوهشگر ارشد

• AI alignment
• Collective intelligence
• Computational social choice
• +۳ مورد دیگر

Manon Revel is a Postdoctoral Research Scientist at Meta FAIR and a former Postdoctoral Employee Fellow at Harvard's Berkman Klein Center (2023–2024). She holds a PhD in Social and Engineering Systems and Statistics from MIT, alongside degrees from Ecole Centrale Paris and MIT's Technology and Policy program. Her research focuses on computational social choice, collective intelligence, and AI alignment, blending mathematics, political theory, and technology to design democratic decision-making systems. She has pioneered work on liquid democracy, governance frameworks for social media, and ethical AI alignment metrics. Revel has taught at MIT, Notre Dame, and the Kroc Institute for International Peace Studies, and led initiatives like the Future of Social Media reading group at the Institute for Rebooting Social Media. Her interdisciplinary approach addresses challenges in democratic innovation and AI ethics, with notable contributions to Management Science, AAAI, and AAMAS. Education: PhD in Social and Engineering Systems & Statistics, MIT (2023) M.S. in Technology and Policy, MIT B.S./Diplôme d'Ingénieur in Engineering & Applied Mathematics, Ecole Centrale Paris Research Interests: Designing mathematical models for collective decision-making in human and AI systems AI alignment through reward model evaluation and systematic error analysis Governance frameworks for social media and democratic institutions Liquid democracy's epistemic performance and scalability Key Contributions: Revel's work bridges theory and practice, including the SEAL framework for AI alignment, the Democracy Levels framework for ethical AI governance, and empirical studies on liquid democracy's truth-tracking capabilities. Her publications analyze social media regulation, interdisciplinary dialogue in academia, and representative body selection using axiomatic models. Awards & Affiliations: Doctoral Democracy Fellow, Harvard Kennedy School's Ash Center (2021–2023) Adjunct Professor, University of Notre Dame (past) Lead of AI & Democracy research program at Berkman Klein Oral presentation at AAAI-25 for SEAL: Systematic Error Analysis for Value Alignment Labs & Collaborations: Revel collaborates with institutions like the MIT Media Lab, the Responsible AI Institute, and the Center for Strategic and International Studies, focusing on interdisciplinary projects at the intersection of technology, democracy, and ethics.

Sandjai Bhulaiمشاهده پروفایل

استاد

• Machine Learning
• Operations Research
• Telecommunications
• +۳ مورد دیگر

Prof. Sandjai Bhulai is a Full Professor at Vrije Universiteit Amsterdam's Faculty of Science (Department of Mathematics) and holds affiliations with the Network Institute. He specializes in machine learning, operations research, and their applications in telecommunications, healthcare, and optimization. His research addresses real-world challenges such as suicide prevention through AI-driven systems, signal processing in underwater acoustics, and dynamic dispatch algorithms for logistics. Key research interests include machine learning algorithms for classification and prediction, reinforcement learning for optimization problems, and data-driven decision-making in healthcare and transportation. He has pioneered methods like the 'Dutch Draw' baseline for binary classification and contributed to ambulance dispatch systems and power grid topology optimization. Prof. Bhulai has published over 137 research outputs, including high-impact articles in International Journal of Medical Informatics , Ocean Engineering , and Transportation Science . He received the Best Paper Award (2018) and led projects such as the AI-BIPTO initiative for integrated production optimization. He advises on technical and ethical committees, including roles at NWO and the Mondriaanfonds. His teaching spans advanced courses in machine learning, algebraic geometry, and linear programming.

• Mechanical Engineering Asset Management
• System Reliability Engineering
• Condition-Based Maintenance
• +۸ مورد دیگر

Tieling Zhang is an Associate Professor at the University of Wollongong's School of Mechanical, Materials, Mechatronic and Biomedical Engineering. He holds a PhD from Tokyo University of Marine Science and Technology. His research focuses on system reliability engineering, machine learning applications, energy storage systems, and pipeline integrity management. He has published over 100 peer-reviewed articles and secured over AUD 7 million in research funding. Research Interests: Statistical Data Modelling, Big Data Analytics, Bayesian Inference, System Reliability Modelling, Degradation Modelling, Wind Turbine Maintenance, Battery Management, Smart Grids. Awards: Recipient of three Best Paper Awards from international conferences. He leads the Engineering Asset Management and Systems Engineering Research Group and serves on editorial boards of journals like Information and Machines . Teaching: Contributes to Master's programs in Engineering Asset Management and Engineering Management. Supervises PhD/Master’s students on topics like battery diagnostics, UAV wildfire management, and human-robot collaboration safety. Funding Highlights: Includes grants for battery health estimation, pipeline integrity, and railway asset management. Collaborates with industry partners like the Australasian Centre for Rail Innovation and Energy Pipeline Cooperative Research Centre.

• Machine Learning
• Intelligent Agents
• Computer Vision
• +۴ مورد دیگر

Konstantinos Blekas is a Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He is affiliated with the Polytechnic School and teaches advanced courses such as 'Machine Learning' (MYE002) and 'Probability and Statistics' (MYY304). His research focuses on Machine Learning, Intelligent Agents, Computer Vision, and Bioinformatics, with particular expertise in Reinforcement Learning, Deep Learning, and their applications in autonomous systems, traffic management, and aerospace engineering. Education: Ph.D. in Electrical and Computer Engineering, National Technical University of Athens (1997) Diploma in Electrical Engineering, National Technical University of Athens (1993) Research Interests: Dr. Blekas explores cutting-edge topics in machine learning, including generative adversarial networks (GANs), multi-agent systems, and reinforcement learning for autonomous navigation. His work spans domains such as unmanned surface vehicles, air traffic management, and medical informatics. Notable contributions include advanced frameworks for flight trajectory modeling, urban traffic optimization, and brain functional network analysis. Awards and Recognition: While specific awards are not explicitly listed, his extensive publications and contributions to AI and robotics reflect significant academic impact. Advising and Grants: He supervises research in machine learning applications, though specific student names or grant details are not provided in the texts. His courses emphasize practical implementation, with resources available on e-learning platforms like e-course.uoi.gr. Labs and Teams: Engaged in collaborative projects involving robotics and AI, though no specific lab names are mentioned. His work integrates interdisciplinary approaches across computer science, engineering, and biomedical fields.

Lihong Zhangمشاهده پروفایل

استاد

• VLSI Design Automation
• MEMS Design and Optimization
• Analog and Mixed-Signal Circuit Design
• +۷ مورد دیگر

Dr. Lihong Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds a PhD in Electrical Engineering from Otto-von-Guericke University (Germany) and has held post-doctoral positions at Concordia University, Dalhousie University, and the University of Washington. His research focuses on VLSI design automation, analog/mixed-signal circuits, MEMS, energy harvesting, and biomedical instrumentation. Dr. Zhang leads the CADLAMS lab, supported by NSERC, CFI, and industry partners. Education: B.E. (H.U.S.T. Wuhan), M.Sc. (Eng.) (H.U.S.T. Wuhan), Ph.D. (Otto-von-Guericke University, Germany). Research Interests: VLSI design automation, MEMS design, renewable energy harvesting, microfluidics, wireless sensor networks, and EDA tools. His work addresses challenges in nanometer-scale technologies, including layout-dependent effects and manufacturability. Awards: 2008 CFI Leaders Opportunity Fund, 2016 Memorial University Research Excellence Award. Grants include funding from NSERC, CFI, and industry collaborations. Students and Collaborations: Supervises over 50 graduate students and collaborates with industry partners like Cadence, Synopsys, and IBM. Active in editorial roles for IEEE journals and conference organization. Lab Infrastructure: CADLAMS lab includes advanced computing resources, EDA software, and lab equipment for circuit prototyping and testing.

Alexandra Psarrouمشاهده پروفایل

دانشیار

• Deep learning methods for Image Quality Performance
• Explainable learning models
• Reinforcement learning
• +۳ مورد دیگر

Alexandra Psarrou is a Reader in the School of Computer Science and Engineering at the University of Westminster, serving as Head of School from 2002-2019. She holds a BSc and MSc from Queen Mary University of London and a PhD from Queen Mary, London (1996). Her research focuses on dynamic vision, explainable AI, hyperspectral analysis, and image quality metrics. Education: BSc (1987), MSc (1988), PhD (1996) Roles: Reader, former Head of School, member of multiple University committees Research interests include deep learning, reinforcement learning, and applications in cultural heritage. She founded the Computational Vision Research Group (CVRG) in 1997, advancing techniques like recurrent neural networks and kernel PCA models. Awards include Best Paper Awards at BMVC (1999), IMECS (2008), and IS&T (2020). She led major grants like the NOESIS project (€709k) and diARTgnosis (€518k). Supervised 10+ PhD students in areas like gesture recognition and medical imaging. Labs/Teams: Computational Vision and Imaging Technologies Research Group Grants: NOESIS, diARTgnosis, HISTORIA, HEFCE NFF

Bedřich Sousedíkمشاهده پروفایل

دانشیار

• Applied and Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +۵ مورد دیگر

Bedřich Sousedík is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). He joined UMBC in 2014, transitioning from roles as a Research Associate at the University of Southern California and University of Maryland, College Park. His academic journey includes a Ph.D. in Applied Mathematics (2010, University of Colorado Denver) and a Ph.D. in Civil Engineering (2008, Czech Technical University in Prague). Education: Ph.D. in Applied Mathematics, University of Colorado Denver (2010) Ph.D. in Civil Engineering, Czech Technical University (2008) M.Eng. in Mechanical Engineering, Czech Technical University (2001) Research Interests: Focuses on applied and computational mathematics, including numerical analysis, scientific computing, uncertainty quantification, stochastic finite element methods, and domain decomposition techniques. His work emphasizes scalable algorithms for complex systems with parametric uncertainty. Professional Contributions: Author of ~43 refereed publications (24 journals, 12 conferences) with an h-index of 15 (Google Scholar) Principal Investigator on NSF grants totaling $219,999 for multilevel methods research Recipient of prestigious awards including the 2010 Prof. Babuška Prize and 2020 Early Career Excellence Award Advising & Teaching: Supervised 8 Ph.D. and Master’s students since 2014 Mentored undergraduate research projects in computational fluid dynamics and epidemiological modeling

Nicolo Colomboمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Computer Science
• Physics
• +۳ مورد دیگر

Nicolo Colombo is a Senior Lecturer in Computer Science at the Department of Computer Science, Royal Holloway, University of London. He is affiliated with the Centre for Intelligent Systems and Centre for Reliable Machine Learning. His research focuses on machine learning, statistics, physics, and their applications in areas like gravitational wave detection, uncertainty quantification, and conformal prediction. Colombo collaborates internationally on projects involving astrophysical signal analysis and algorithm development. He leads or co-leads two research projects: 'Revealing the secrets of neutron-star interiors with AI and the SKA' (Medical Research Council MRC-funded, 2024-2028) and the Turing Network Development Award (Alan Turing Institute, 2022). His work contributes to UN Sustainable Development Goals through technological advancements in machine learning and data science. Education: Ph.D. in relevant field (details not specified in text) Colombo's research interests span neural networks, probabilistic modeling, and their applications in astrophysics and energy systems. His recent work emphasizes conformal prediction for improving algorithm reliability in gravitational wave detection and route planning. His articles explore cutting-edge techniques like normalizing flows, graph autoencoders, and uncertainty calibration. Notable collaborations include work with institutions like the Alan Turing Institute and international researchers in physics and computer science. He has secured grants from major funding bodies and actively participates in academic networks. His labs focus on advancing AI methodologies for real-world challenges in science and engineering.

Michel Ballingsمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Marketing Science
• +۲ مورد دیگر

Dr. Michel Ballings is an Associate Professor and James and Joanne Ford Faculty Research Fellow at the University of Tennessee's Haslam College of Business, where he directs the JTV Center Intelligence Lab. His research develops machine learning systems for business outcomes using large-scale social and marketing data. Core research areas: Predictive modeling for entertainment and retail Social media analytics and digital engagement Recommender systems in financial services Emotion analytics in commerce Recent publications demonstrate applications in livestream retail analytics, movie success prediction, and B2B customer acquisition. His industry collaborations include Amazon's Visiting Academics program.

• Reinforcement Learning
• Robust Optimization
• Machine Learning
• +۱۰ مورد دیگر

Fei Miao is a Pratt & Whitney Associate Professor at the School of Computing, University of Connecticut, and a courtesy faculty member of the Department of Electrical & Computer Engineering. She serves as Director of the Miao Embodied AI Lab and is affiliated with the Institute for Advanced Systems Engineering. Previously, she was a postdoc researcher at the GRASP Lab and PRECISE Lab with Professors George J. Pappas and Daniel D. Lee at the University of Pennsylvania. Dr. Miao received her PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2016, where she also earned a dual Master's degree in Statistics from the Wharton School. She completed her undergraduate studies at Shanghai Jiao Tong University, earning a Bachelor's degree in Automation with a minor in Finance in 2010. Her research focuses on developing the foundations for the science of Embodied AI, with emphasis on assuring safety, efficiency, robustness, and security of cyber-physical systems through the integration of learning, optimization, and control. Her technical expertise spans multi-agent reinforcement learning, robust optimization, uncertainty quantification, control theory, and game theory. These methods are applied to connected and autonomous vehicles, intelligent transportation systems, transportation decarbonization, smart cities, and power networks. Her work involves both theoretical development and practical implementation, including system modeling, theoretical analysis, algorithmic design, and experimental validation using real urban transportation data, simulators, and small-scale autonomous vehicles. Dr. Miao's publication record reveals a strong focus on robustness in AI systems for transportation applications, with recent work emphasizing uncertainty quantification, safety guarantees, and multi-agent coordination. Her research demonstrates a clear trajectory from foundational theoretical work to practical implementations in real-world transportation systems. Her notable awards include the prestigious NSF CAREER Award (2021) for "Distributionally Robust Learning, Control, and Benefits Analysis of Information Sharing for Connected and Autonomous Vehicles," a Best Paper Award at ICCPS'21 for "DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services," and the "Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation" during her PhD studies. Dr. Miao has secured significant research funding, including a $509,573 NSF CAREER Award (2021-2026) and a $2.3 million NSF collaborative grant as PI of UConn (2020-2023). She has also received multiple NSF grants for projects related to electric vehicle fleets, vehicular sensing, and control for smart city systems. She actively collaborates with researchers across institutions and has given talks at leading universities and industry research labs including CMU, Microsoft Research, Northeastern, Caltech, UCLA, USC, UCSD, Facebook FAIR, Lawrence Berkeley National Lab, UC Berkeley, Nvidia, Stanford, Princeton University, Columbia University, Waymo, and New York University.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

• Topology Optimization
• Additive Manufacturing
• Structural Engineering
• +۳ مورد دیگر

Il Yong Kim is an Adjunct Associate Professor in the Department of Mathematics and Statistics at McMaster University . His research focuses on advanced structural optimization techniques, particularly topology optimization applied to additive manufacturing and aerospace engineering. Key areas include multi-material design, composite materials, and computational methods for lightweight structures. Research interests emphasize topology optimization , additive manufacturing , and multidisciplinary design , with applications in automotive, aerospace, and biomedical fields. Recent work addresses challenges in fiber-reinforced materials, build orientation optimization for 3D printing, and structural-acoustic performance. Publications from 2023–2025 highlight contributions to urban air mobility systems , lattice structure optimization , and machine learning-driven design . Over 160 scholarly outputs include conference papers (e.g., AIAA, SAE) and journal articles in Optimization and Engineering , Engineering Optimization , and Structural and Multidisciplinary Optimization . Collaborations span academia and industry, addressing real-world applications in aircraft components, automotive chassis, and biomedical devices. No explicit awards are listed, but his extensive publication record reflects significant contributions to computational mechanics and design optimization.

Leonidas Doumasمشاهده پروفایل

مدرس ارشد

• Analogy
• Relational Reasoning
• Mental Representation
• +۳ مورد دیگر

Leonidas Doumas is a Senior Lecturer in Psychology at the University of Edinburgh’s School of Philosophy, Psychology and Language Sciences. He holds a PhD in Cognitive and Developmental Psychology from UCLA and has held academic positions at Indiana University and the University of Hawaii before joining Edinburgh in 2013. His research focuses on relational reasoning, analogy, cognitive development, and computational modelling, particularly exploring how distributed systems like neural networks represent and generalize relational concepts. He teaches courses in developmental science, cognitive science, and statistics, and actively supervises PhD students in these areas. Dr. Doumas’s work combines empirical studies with computational models to investigate topics such as cross-domain generalization, relational learning in children and adults, and the role of structured representations in cognition. His research also employs neuroscientific techniques like EEG and TMS to understand cognitive processes. He is a core member of the Edinburgh Cognitive and Neuroethology Lab (CNE) and leads initiatives in immersive virtual reality (VR) for neuropsychological assessment, such as the Virtual Reality Everyday Assessment Lab (VR-EAL). His educational background includes a PhD under John Hummel, Keith Holyoak, and Cathy Sandhofer at UCLA, followed by postdoctoral training in Linda Smith’s lab at Indiana University. His current projects emphasize the intersection of symbolic and connectionist approaches to cognition, with a focus on how structured representations emerge through experience. Research Interests: Relational learning, analogy, cognitive development, computational modelling, neural networks, and VR applications in cognitive science Teaching: Year 1/2 Psychology courses, Cognitive Science, and advanced options on neural networks and induction Advising: Open to PhD/MSc students interested in relational cognition, computational models, or VR applications Publications span topics from neural network limitations in language processing to mechanisms of hierarchical linguistic structure, with a focus on bridging computational and empirical cognitive science.

Jack Umenbergerمشاهده پروفایل

پژوهشگر ارشد

• Control Theory
• Optimization
• Machine Learning
• +۳ مورد دیگر

Jack Umenberger is a Senior Research Fellow at the University of Oxford’s Department of Engineering Science and the ZERO Institute for zero-carbon energy research. He joined Oxford in 2023 as a Departmental Lecturer within the Control Group. Previously, he held postdoctoral positions at MIT (2019-2022) and Uppsala University, Sweden (2017-2019), and earned his PhD from the University of Sydney in 2018 for work on data-driven modeling of dynamical systems. His research focuses on the intersection of control theory, optimization, and machine learning, with applications to energy systems (e.g., battery management with Eatron Technologies) and robotics (e.g., motion planning with Dexai Robotics). Key areas of interest include data-driven system identification, model predictive control, reinforcement learning, and stability guarantees in dynamical systems. Notable projects include work on shortest paths in graphs of convex sets for robotic motion planning, convergence analysis of direct policy search in reinforcement learning, and system identification with stability guarantees. His contributions emphasize blending discrete and continuous decision-making frameworks in optimization and control problems. Umenberger collaborates with industry partners like Eatron Technologies and Dexai Robotics, advancing applications in energy and robotics. His work often bridges theoretical advancements with practical implementations, reflecting a commitment to impactful engineering solutions.

Silun Zhangمشاهده پروفایل

استادیار

• Nonlinear Control
• Large-Scale Complex Systems
• Privacy-Preserving Algorithms
• +۳ مورد دیگر

Silun Zhang is an Assistant Professor in the Department of Mathematics at KTH Royal Institute of Technology, affiliated with WASP (Wallenberg AI, Autonomous Systems and Software Program) and the Digital Futures Faculty. He holds a Ph.D. in Optimization & Systems Theory from KTH (2019), an M.S. and B.S. in Automation from Harbin Institute of Technology, China. Prior to KTH, he was a Postdoctoral Associate at MIT's Laboratory for Information & Decision Systems (LIDS) and EECS Department (2019–2023), where he collaborated with Prof. Munther Dahleh and contributed to the MIT MicroMasters Program in Statistics and Data Science. His research focuses on nonlinear control , large-scale complex systems , privacy-preserving algorithms , and risk-averse reinforcement learning . Key areas include control on manifolds (e.g., rigid-body attitude coordination), privacy in federated learning, and moment-based modeling for collective systems. He leads projects such as 'Analysis and Synergy of Hyper-networked Autonomy' and 'Designing Interaction-Aware Multi-Robot Systems.' Current team members include Georgios Vasileiou (Ph.D.), Lantian Zhang (Postdoc), and Malintha Fernando Chakravarthige (Postdoc). His work is supported by the Wallenberg Foundations and Digital Futures. Open positions are available for motivated Ph.D. students and postdocs.