Find Academic Supervisors

Rani MoranView profile

Lecturer

• Decision Making
• Reinforcement Learning
• Cognitive Maps
• +5 more

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 AzarView profile

Assistant Professor

• Hardware Security
• Verification Formal Methods
• Advanced VLSI Testing
• +3 more

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 LiView profile

Associate Professor

• Artificial Intelligence
• Spatial-Temporal Data Science
• Smart Cities
• +5 more

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 DubeyView profile

Assistant Professor

• Climate Change
• Computational Modeling
• Machine Learning
• +3 more

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-BrownView profile

Professor

• Artificial Intelligence
• Machine Learning
• Game Theory
• +5 more

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 RahimiView profile

Professor

• Computational Intelligence
• Machine Learning
• Artificial Intelligence
• +10 more

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 KwasinskiView profile

Professor

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

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 YanView profile

Assistant Professor

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +3 more

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 YangView profile

Professor

• Artificial Intelligence
• Robotics
• Control Systems
• +7 more

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 RamezaniView profile

Associate Professor

• Robotics
• Control Theory
• Nonlinear Systems
• +3 more

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.

Devin K. HarrisView profile

Professor

• Structural Health Monitoring
• Smart Cities
• Digital Twins
• +5 more

Devin K. Harris is a Professor and Chair of the Department of Civil and Environmental Engineering at the University of Virginia . His work focuses on large-scale infrastructure systems , combining image-based measurement techniques, simulation , visualization , and data analytics to advance structural health monitoring , smart cities , and digital twins . He also investigates reinforced/prestressed concrete behavior and innovative materials in civil infrastructure. Education : Ph.D., M.S., and B.S. in Civil Engineering from Virginia Tech (2007), Virginia Tech (2004), and University of Florida (1999). Research Interests Structures and Mechanics - Sustainable Infrastructure Systems Infrastructure Condition Assessment Structural Health Monitoring Smart Cities Digital Twins Applied Machine Learning Scientific Awards Delmar L. Bloem Distinguished Service Award (2021) IAspire Leadership Academy Fellow (2020–2022) ASCE Journal of Bridge Engineering Outstanding Reviewer (2013) UVA Teaching Resource Center Excellence in Diversity Fellowship (2012–2013) ACI Young Member Award for Professional Achievement (2011) Grants Principal Investigator for EAGER: Adaptive Digital Twinning: An Immersive Visualization Framework for Structural Cyber-Physical Systems (NSF #2136724) and Performance Characteristics of In-Service Bridges (Virginia Transportation Research Council, 2018–2020). Co-led NCHRP 23-16 on Machine Learning applications in transportation agencies. Labs & Teams Leads the Infrastructure Simulation, Sensing and Evaluation Lab (I-S2EE) , equipped with DIC systems , mobile GPR , thermal imaging , and cyber-physical simulation tools. Collaborates with the Omni-Reality & Cognition Lab for AR/VR integration in infrastructure evaluation.

Raju VatsavaiView profile

Associate Professor

• Geospatial Analytics
• Big Data Management
• Data Mining
• +5 more

Raju Vatsavai is an Associate Professor in the Department of Computer Science at North Carolina State University, affiliated with the Center for Geospatial Analytics. He joined NC State in 2014 as part of the Chancellor’s Faculty Excellence Program cluster hire in Geospatial Analytics. Education: PhD and MS in Computer Science from University of Minnesota Prior Roles: Lead Data Scientist at Oak Ridge National Lab, roles at University of Minnesota, IBM Research, AT&T Labs, and C-DAC (India) His research in geospatial analytics spans big data management , spatiotemporal data mining , deep learning for remote sensing , and high-performance computing , with applications in national security, climate change, and crop monitoring. Recent work includes deep learning frameworks for cloud imputation , multi-sensor satellite data harmonization , and transfer learning applications in crop classification . He has been a leading investigator on grants from the National Geospatial-Intelligence Agency, Department of Energy, and Department of Homeland Security. Labs: Associate Director of the Center for Geospatial Analytics Expertise: Spatial computing, Earth observation, nuclear proliferation detection via remote sensing

Ali KashaniView profile

Senior Lecturer

• Sustainable and low-carbon concrete
• Cementitious materials
• Robot-aided construction
• +8 more

Dr. Ali Kashani is a Senior Lecturer at the University of New South Wales (UNSW) within the School of Civil and Environmental Engineering. His research focuses on sustainable and low-carbon concrete materials, robot-aided construction (particularly 3D printing), and Circular Economy-aligned applications. Leadership in cementitious materials innovation Expertise in 3D printing for construction Advocate for waste valorisation and carbon capture Dr. Kashani has secured approximately $7 million in research funding and holds a patent in lightweight concrete foam. His work spans 70+ publications with 9,000+ citations, including media coverage in the Sydney Morning Herald and The Fifth Estate. He actively contributes to professional organizations such as MECLA, RILEM, and ASTM. Recent research trends include AI and optimization algorithms for sustainable concrete mix design, chloride diffusion modeling, and 3D printing performance analysis. His publications often address waste material integration, durability assessment, and eco-friendly construction practices. Scientific Awards: National and NSW Awards for 'Excellence in Concrete' (Technology and Innovation) from the Concrete Institute of Australia Churchill Fellowship for Digital Construction and 3D Printing sponsored by AVJennings Dr. Kashani serves as Co-Chair of the cement and concrete working group at MECLA and contributes to RILEM and ASTM committees. His email is ali.kashani@unsw.edu.au , and his office is located in the Civil Engineering Building (H20), Level 2, Room CE204, UNSW.

Annette R. GrilliView profile

Research Professor

• Ocean Renewable Energy
• Coastal Hazard Assessment
• Offshore Wind Farm Siting Optimization
• +4 more

Annette R. Grilli is a Research Professor in the Department of Ocean Engineering at the University of Rhode Island , focusing on ocean renewable energy and coastal hazard assessment. Her work integrates numerical modeling and statistical analysis to study extreme events like tsunamis and storms. Ph.D. in Climatology, University of Delaware (2000) M.S. in Oceanography, University of Liege (1984) B.S. in Geography & Education, University of Liege (1983) Her research spans offshore wind farm siting optimization , tsunami propagation modeling , and coastal erosion dynamics . Recent publications highlight applications of phase-resolving wave models and machine learning to coastal resilience and marine renewable energy systems. Grants include collaborations with NOAA , Department of Energy , and NSF , focusing on coastal hazard visualization , tsunami detection algorithms , and design elevation mapping under climate change scenarios. She contributes to digitalCommons@URI with over 100 publications in Ocean Engineering and Civil Engineering domains.

Istvan David McMasterView profile

Assistant Professor

• Digital Twins
• Model-Driven Engineering
• Sustainability
• +6 more

Istvan David is an Assistant Professor in the Department of Computing and Software at McMaster University , with research expertise spanning Digital Twins , Model-Driven Engineering , and Sustainability . His work bridges theoretical and applied domains, focusing on smart ecosystems , collaborative modeling , and AI-driven simulation . Key contributions include frameworks for digital twin evolution and interoperability in sustainable systems. Education : BSc, MSc, and PhD in Computer Engineering and Computer Science from Budapest University of Technology and Economics, and University of Antwerp. Research Areas : Digital Twins, Model-Driven Engineering, Reinforcement Learning, Smart Ecosystems, Sustainability, Collaborative Modeling, Cyber-Biophysical Systems, and Software Architecture. Recent Article Trends emphasize AI integration with digital twins, collaborative modeling in industrial contexts, and sustainable systems engineering . His work often combines machine learning with formal modeling to address challenges in technical sustainability and smart agriculture .