جست‌وجوی استاد و پژوهشگر

Oya Beyanمشاهده پروفایل

استاد

• Biomedical Informatics
• Semantic Web Technologies
• Clinical Decision Support
• +۵ مورد دیگر

Prof. Dr. Oya Beyan is a Professor at the University of Cologne's Institute for Biomedical Informatics and a Core Scientist at the Center for Data and Simulation Science. Her research focuses on enabling FAIR (Findable, Accessible, Interoperable, Reusable) data management, distributed analytics on sensitive medical data, and data-driven innovations in healthcare. She leads projects like the PADME platform for federated machine learning and privacy-preserving analytics. Key areas include biomedical informatics, semantic web technologies, clinical decision support systems, and ethical challenges in data science. Research Interests: FAIR Data Principles & Infrastructure Privacy-Preserving Distributed Learning Explainable AI in Healthcare Semantic Interoperability Medical Data Integration Ethical & Social Implications of Data Use Notable Contributions: Development of the Personal Health Train framework for decentralized medical data analysis Leadership in EU-funded initiatives like NFDI4Health and Medical Informatics Collaborations Pioneering work on federated learning applications in oncology and rare disease research Lab & Affiliations: Prof. Beyan's work is anchored in the Institute for Biomedical Informatics and the Center for Data and Simulation Science, fostering interdisciplinary collaboration between computational science and medical research.

• Power System Protection
• Power Electronics
• High Voltage Engineering
• +۴ مورد دیگر

Michael Kleemann is an Associate Professor at the Faculty of Engineering Technology within KU Leuven , affiliated with the Department of Electrical Engineering (ESAT) . His research focuses on Power System Protection , Wireless Power Transfer , and Renewable Energy Integration , with a particular emphasis on inverter-based grid dynamics and fault analysis. Key Research Areas : Power system protection algorithms, capacitive wireless power transfer, fault location methods in medium voltage cables, and grid stability with high renewable penetration. Notable Projects : Lead projects on Protection of Future Distribution Grids (2021-2025), Capacitive Wireless Power Transfer (2020-2024), and Flux 50 ICON (2024-2026) for low-voltage DC grid protection. Publication Trends : Recent work explores capacitive wireless power transfer materials and control systems (2024-2025), fault detection algorithms for inverter-dominated grids (2023-2025), and machine learning applications in voltage regulation for photovoltaic-rich networks (2024). Teaching : Courses include Power System Protection (JPI322), Power Electronics (JPI0L8/JPI318), and Capacitive Wireless Transfer topics in graduate seminars.

Rani Moranمشاهده پروفایل

مدرس

• Decision Making
• Reinforcement Learning
• Cognitive Maps
• +۵ مورد دیگر

Dr. Rani Moran is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences. She is affiliated with the Centre for Brain and Behaviour. Her research focuses on decision-making, memory, and learning mechanisms, employing methods like computational modeling, neuroimaging, and pharmacological interventions. Key interests include reinforcement learning, cognitive maps, and exploration-exploitation dilemmas. Her work integrates experimental designs with advanced statistical analysis to understand flexible behavioral control. Recent studies explore model-based vs. model-free learning, credit assignment mechanisms, and disinformation's impact on learning biases. She has published in top journals such as Psychological Review and Nature Communications . Dr. Moran collaborates on projects involving neurocomputational models of confidence, meta-cognition, and social learning. Her research aims to develop interventions for optimizing cognitive processes, with applications in mental health and decision-making contexts.

Kimia Zamiri Azarمشاهده پروفایل

استادیار

• Hardware Security
• Verification Formal Methods
• Advanced VLSI Testing
• +۳ مورد دیگر

Kimia Zamiri Azar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, focusing on hardware security and verification methodologies. Her research bridges theoretical formal methods with practical security implementations in semiconductor design and testing. Her educational background includes: Ph.D. in Electrical and Computer Engineering, George Mason University (2021) Postdoctoral Research, University of Florida Dr. Azar's research spans hardware security with emphasis on system-level verification, VLSI design-for-trust, and advanced IC testing. She pioneers techniques in logic locking, secure heterogeneous integration, and IC supply chain security, developing frameworks for authenticated encryption in Systems-in-Package and runtime security monitoring. Her work integrates formal verification with innovative testing methodologies to address hardware trust challenges across the semiconductor lifecycle. Analysis of her recent publications reveals two dominant trends: (1) Application of large language models (LLMs) to hardware design tasks including high-level synthesis code generation and RTL optimization, and (2) Advancement of secure heterogeneous integration techniques for System-in-Package architectures with focus on counterfeit prevention and split-test security protocols. These directions address critical gaps in hardware trustworthiness amid increasingly complex semiconductor supply chains. Her scientific contributions have earned significant recognition: Best Paper Award at ICCAD 2019 Best Paper Award at ISVLSI 2020 Best Paper Award at ICCAD 2020 Best Paper Award at IEEE DCAS 2020 Best Paper Award at HOST 2022 Best Paper Award at DATE 2023 Dr. Azar secures substantial research funding from premier agencies including NSF, SRC, DARPA, AFRL, DoD (NG), and Microsemi. Her grants support projects spanning hardware security validation frameworks, secure heterogeneous integration, and AI-augmented verification methodologies. She actively mentors students in her research group, guiding publications in top venues like IEEE D&T, IEEE TC, and DAC while fostering industry-academic collaborations. Her work directly impacts semiconductor security standards through patented innovations and open-source verification tools. As an active IEEE and ACM member, she contributes to community advancement through conference organization (HOST, DATE), journal editorial roles, and workshop leadership on hardware security standards. Her research group collaborates with semiconductor industry leaders to translate theoretical security frameworks into practical design-for-trust methodologies for next-generation integrated circuits.

Yanhua Liمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Spatial-Temporal Data Science
• Smart Cities
• +۵ مورد دیگر

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI), where he has served since 2021 (previously as Assistant Professor from 2015-2021). He is also an affiliated researcher at UMass Transportation Center (UMTC). His educational background includes dual PhDs: Computer Science from University of Minnesota, Twin Cities (2013) and Electrical Engineering from Beijing University of Posts and Telecommunications (2009), along with an MS (2006) and BS (2003) in Electrical Engineering from Sichuan University. Dr. Li's research focuses on artificial intelligence and spatial-temporal data science with applications in smart cities and urban intelligence. His work particularly emphasizes imitation learning and meta learning in AI for understanding and influencing urban human agents' decision-making strategies, such as taxi drivers' passenger-seeking behaviors and urban travelers' transit choices. His laboratory develops advanced computational methods for urban transportation systems, traffic prediction, and spatial-temporal data analytics. His publication record shows a strong trajectory in top AI and data science venues, with recent work bridging foundation models with urban computing, enhancing robustness in spatial-temporal representation learning, and applying generative models to urban traffic estimation. His research spans computer vision, reinforcement learning, generative modeling, and spatio-temporal data analysis with applications in transportation, environmental monitoring, and urban planning. Best Applied Data Science Paper Award at SDM 2019 NSF CAREER Award (2020) Runner-up for the 10-Year Impact Award for SIGSPATIAL Conference (2024, for 2014 paper) Dr. Li has secured significant research funding including an NSF CAREER award ($529k), multiple NSF grants totaling over $2 million, and industry collaborations with DiDi Chuxing Research. He has advised numerous PhD students who have gone on to faculty positions at institutions like San Diego State University and SUNY Binghamton University. His research group maintains active collaborations with industry partners including DiDi Chuxing, Pitney Bowes Inc., and NVIDIA. He leads several research initiatives including the CityLines project for urban transportation systems and has contributed to foundational work in spatial-temporal imitation learning. His laboratory continues to expand into new areas including applying large language models to urban dynamics prediction and developing advanced methods for environmental monitoring.

Rachit Dubeyمشاهده پروفایل

استادیار

• Climate Change
• Computational Modeling
• Machine Learning
• +۳ مورد دیگر

Rachit Dubey is an Assistant Professor in the Department of Communication at UCLA, affiliated with the College of Letters & Science. He holds a PhD in Computer Science from Princeton University, an M.S. in Education from UC Berkeley, and a BEng in Computer Science from Nanyang Technological University. His research bridges computational modeling, behavioral experiments, and policy design to address climate change challenges, focusing on human motivation and policy impact. Key research areas include using AI-generated visualizations to enhance climate communication, understanding cognitive underpinnings of climate inaction, and designing interventions to reduce polarization. His work has been featured in Nature Human Behavior , Nature Sustainability , and PNAS . Notable contributions include demonstrating that binary climate data visuals significantly amplify perceived urgency compared to gradual trends, and developing computational models of grief using reinforcement learning. He received the 2024 SfNC Dissertation Award for his doctoral work. Dr. Dubey collaborates closely with policymakers to bridge research and practice, and is recruiting PhD students in cognitive science, climate policy, and machine learning. His lab, the Computational Cognitive Policy Lab, emphasizes quantitative rigor and interdisciplinary collaboration.

Kevin Leyton-Brownمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۵ مورد دیگر

Kevin Leyton-Brown is a Professor of Computer Science at the University of British Columbia (UBC), holding a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). He is also an Associate Member of the Vancouver School of Economics and a Fellow of the Royal Society of Canada, ACM, and AAAI. His research focuses on AI, machine learning, computational economics, and game theory, with notable contributions to algorithmic market design, heuristic algorithms, and large language models. He co-authored influential textbooks on multiagent systems and game theory, and his work has been recognized with prestigious awards including the INFORMS Franz Edelman Award and the Killam Teaching Prize. Education: PhD (Computer Science), Stanford University; MSc (Computer Science), Stanford University; BSc (Computer Science), McMaster University. Research Interests: Artificial Intelligence, Machine Learning, Game Theory, Computational Economics, Algorithmic Game Theory, Market Design, and Large Language Models. He has developed impactful tools like SATzilla, AutoWEKA, and Mechanical TA, and contributed to high-stakes projects such as spectrum auction design and Ugandan agricultural market platforms. Awards & Recognition: Royal Society of Canada Fellow (2023), ACM SIG-KDD Research Track Test of Time Award (2023), INFORMS Franz Edelman Award (2018), ACM Fellow (2020), AAAI Fellow (2018), Killam Teaching Prize (UBC), and numerous paper awards from top conferences like AAAI, ICML, and ACM-EC. Leadership & Affiliations: Director of UBC’s CAIDA and AIM-SI research clusters, former Chair of ACM SIG-Ecom, and advisor to companies like AI21 Labs and Auctionomics. He has held visiting roles at institutions including MIT, Harvard, and the Simons Institute.

Shahram Rahimiمشاهده پروفایل

استاد

• Computational Intelligence
• Machine Learning
• Artificial Intelligence
• +۱۰ مورد دیگر

Shahram Rahimi is a Professor and Department Head in the Department of Computer Science at the University of Alabama, College of Engineering. He concurrently holds an Adjunct Professor position at Mississippi State University. His research spans computational intelligence, machine learning, healthcare AI, cybersecurity, and quantum computing. He leads the PATENT Lab, focusing on predictive analytics, decision support systems, and AI-driven healthcare solutions. His educational background includes a Ph.D. in Computer Science. Key research areas include multi-agent systems, generative models, and predictive maintenance. He has served as an editor for journals like Scalable Computing: Practice and Experience and Informatica . Rahimi’s recent work emphasizes secure MLOps, quantum algorithms, and patient-centric medical systems. His publications address challenges in explainable AI, anomaly detection, and healthcare informatics. He actively contributes to conferences and journals in AI, cybersecurity, and computational intelligence. Editorial Roles: Scalable Computing, Engineering Letters, Informatica Labs: Predictive Analytics & Technology Integration (PATENT) Lab Key Focus Areas: Healthcare AI, Quantum Computing, Cybersecurity, Explainable Machine Learning

Andres Kwasinskiمشاهده پروفایل

استاد

• Cognitive Radios and Networks
• Machine Learning for Wireless Communications
• 5G/6G/NextG Networks
• +۵ مورد دیگر

Andres Kwasinski is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), part of the Kate Gleason College of Engineering. He serves as Graduate Program Director for the Ph.D. in Electrical and Computer Engineering and M.Sc. in Computer Engineering. He co-directs the Networking and Information Processing (NetIP) Lab and holds editorial roles with IEEE publications, including Chief Editor of the IEEE Signal Processing Repository and Associate Editor of IEEE Signal Processing Magazine. Education: Ph.D. and M.Sc. in Electrical and Computer Engineering from the University of Maryland, College Park (2004 and 2000), and B.Sc. in Electrical Engineering from the Buenos Aires Institute of Technology (1992). Prior to RIT, he worked at Texas Instruments, Lucent Technologies, and the University of Maryland. Research Interests: Cognitive radios, machine learning for dynamic spectrum access, 5G/6G networks, VR communications, cross-layer resource allocation, smart infrastructures, and signal processing. His work emphasizes sustainable and resilient communication systems, integrating renewable energy and AI-driven solutions. Notable Contributions: Authored/co-authored books on cooperative communications and 3D visual communications. Over 70 peer-reviewed publications, including works on energy-efficient wireless networks, microgrid integration for base stations, and deep reinforcement learning in cognitive radio. His research is funded by the NSF, Harris Corporation, and the Air Force Research Laboratory. Grants & Awards: Supported by grants from NSF and industry partners. Recognized for contributions to IEEE standards and technical leadership in signal processing and communications. Labs & Teams: Co-director of the NetIP Lab, focusing on networking, signal processing, and smart infrastructure. Collaborates on interdisciplinary projects in robotics, warehouse automation, and 5G/B5G systems.

Bing Yanمشاهده پروفایل

استادیار

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +۳ مورد دیگر

Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.

Simon Yangمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Control Systems
• +۷ مورد دیگر

Simon Yang is a Professor in the School of Engineering at the University of Guelph, part of the College of Engineering and Physical Sciences. His research focuses on artificial intelligence, robotics, sensors, control systems, and bio-inspired intelligence. He has contributed to advanced robotics applications, including mobile robot navigation, underwater vehicle control, and agricultural automation. Dr. Yang holds editorial roles for journals such as the International Journal of Robotics and Automation and IEEE Transactions on Cybernetics . His work bridges theoretical advancements with practical implementations in areas like sensor networks, machine learning, and multi-agent systems. Recent projects include developing robust control frameworks for autonomous systems, digital twin applications, and bio-inspired neural network algorithms. His research emphasizes real-world challenges in robotics, environmental monitoring, and precision agriculture, with a focus on integrating AI-driven solutions for enhanced decision-making and system reliability. Professional contributions include advisory roles in multiple journals and conference committees, reflecting his leadership in the field.

Alireza Ramezaniمشاهده پروفایل

دانشیار

• Robotics
• Control Theory
• Nonlinear Systems
• +۳ مورد دیگر

Alireza Ramezani is an Associate Professor of Electrical and Computer Engineering at Northeastern University, leading the SiliconSynapse Lab. He focuses on bio-inspired robotics, nonlinear systems, and robot locomotion, with a particular emphasis on morphological design and control inspired by biological systems. His work integrates control theory and experimental robotics to develop robots capable of navigating confined spaces, such as caves and ducts, using mechanisms derived from bat movements. Education : PhD, Mechanical Engineering, University of Michigan (2014) MS, Mechanical Engineering, ETH Zurich (2010) BSc, Mechanical Engineering, Iran University of Science and Technology (2007) Research Interests : Design of robots with non-traditional morphologies Nonlinear feedback control systems Legged and fluidic-based locomotion Bio-inspired robotics and biology-driven engineering Awards : 2024 ASME Rising Star Award 2024 NSF CAREER Award 2022 NASA Game Changing Program Award Science Magazine Top 5% Research Output (2020) Advising & Labs : Ramezani mentors students in projects like the NASA-funded “Crater Observing Bio-inspired Rolling Articulator” and oversees the SiliconSynapse Lab, which develops robots for space exploration and confined environments. Notable advisees include Henry Noyes, a NASA Space Technology Fellow. Labs/Teams : His lab collaborates with institutions like NASA’s Jet Propulsion Lab (JPL) on projects such as the Mars Multi-modal Morphing (M4) Rover and bio-inspired snake robots for lunar crater exploration.

Nils Wildeمشاهده پروفایل

استادیار

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +۱۶ مورد دیگر

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

• Machine Learning
• Fairness in Artificial Intelligence
• Federated Learning
• +۷ مورد دیگر

Yang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at the Baskin School of Engineering, University of California, Santa Cruz. Previously, they were affiliated with Harvard University and earned their PhD in 2015 from the Department of EECS at the University of Michigan, Ann Arbor. Their research lies at the intersection of machine learning, fairness, and trustworthy AI, with a strong focus on large language models, federated learning, and causal reasoning. Their research interests include: Machine Learning and Fairness Federated and Privacy-Preserving Learning Large Language Model Safety and Unlearning Causal Inference and Counterfactual Reasoning Anomaly Detection and Robust Forecasting Human-AI Interaction and Ethical AI Recent publications (2024–2025) demonstrate a strong trend in developing methods for machine unlearning, fairness in LLMs, and robustness under label noise and distribution shifts. Their work frequently appears in top-tier venues such as NeurIPS, ICLR, ICML, AAAI, and KDD, often in collaboration with researchers like Zhaowei Zhu, Mingyan Liu, Jiaheng Wei, and Kun Zhang. Themes include algorithmic fairness, model accountability, and human-aligned AI systems. Scientific contributions include: Frameworks for LLM unlearning and model editing Methods for fair classification and recourse Robust time series forecasting under anomalies Test-time adaptation in multimodal models Causal approaches to debiasing and policy learning While no formal advising list is provided, the depth and volume of collaborative work suggest active mentorship of graduate students and postdocs. Their research program is highly active, with numerous ongoing projects in trustworthy and socially responsible AI.

Huadong Moمشاهده پروفایل

مدرس ارشد

• Power and energy systems
• Cyber-physical systems
• Manufacturing systems
• +۶ مورد دیگر

Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.