Find Academic Supervisors

Prasad TadepalliView profile

Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

Gao HuijunView profile

Professor

• Networked Control Systems
• Signal Processing
• Image Processing
• +7 more

Prof. Gao Huijun is a Full Professor at the School of Aeronautics and Astronautics, Harbin Institute of Technology (HIT), where he also serves as Director of the Research Institute of Intelligent Control and Systems. He holds honorary positions at The University of Hong Kong and the University of Alberta. His research focuses on networked control systems, robotics, and signal processing, with over 380 SCI publications and an H-index of 120. Positions: Director of Interdisciplinary Science Research Center (2019–present), Dean of School of Science (2015–2019), and Honorary Professor at The University of Hong Kong (2009–present). Education: Ph.D. in Control Science and Engineering from HIT (2005), postdoc at University of Alberta (2005–2007). Research interests span networked control systems, multi-agent systems, and opto-mechanical integration. His work has led to 100+ patents and prestigious awards like the IEEE Fellow (2013) and National Natural Science Award (2014). Editorial roles include Co-Editor-in-Chief of IEEE Transactions on Industrial Electronics and Senior Editor roles in multiple journals. He has advised over 30 doctoral students, many of whom hold academic or industry leadership roles. Key Awards: National Natural Science Award (2014), China Youth May Fourth Medal (2011), Highly Cited Researcher (2014–2020). Grants: Chief Scientist of National Key R&D Programs, Director of China Postdoctoral Science Foundation. Labs/Teams: Leads the Research Institute of Intelligent Control and Systems, focusing on interdisciplinary projects like AOI systems and industrial robotics.

Rayna DimitrovaView profile

Assistant Professor

• Verification
• Reactive Systems Synthesis
• Control and Robotics
• +3 more

Rayna Dimitrova is a Tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Previously, she held positions as Lecturer (Assistant Professor) at the University of Sheffield and University of Leicester, and postdoctoral roles at the University of Texas at Austin and the Max Planck Institute for Software Systems. She earned her PhD from Saarland University. Her research focuses on formal methods, including verification and synthesis of reactive systems, applications to control and robotics, quantitative analysis of probabilistic systems, and information-flow security. Key interests include strategic synthesis under partial observability, probabilistic uncertainty, and continuous dynamics, with applications to autonomous systems. She has served in numerous prestigious roles, including PC co-chair for VMCAI 2024, HYPER 2023, and SMT 2018. Her awards include the RS3 Best Paper Award (VMCAI 2012) and nominations for ETAPS (TACAS 2015) and EMSOFT (2014) Best Paper Awards. Her research group includes PhD students Rafael Dewes, Philippe Heim, and Saleh Soudijani. She has taught courses on reactive synthesis, program analysis, and decision procedures at multiple institutions, including the University of Sheffield and TU Kaiserslautern.

• High-performance Computing
• Energy-efficient Computing
• Machine Learning
• +3 more

Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.

Michael Reichhardt HansenView profile

Associate Professor

• Formal Methods
• Embedded Systems
• Real-Time Systems
• +3 more

Michael Reichhardt Hansen is an Associate Professor at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. He holds a MSc in Engineering (1982) and a PhD in Computer Science (1987) from DTU. He has held postdoctoral and visiting roles, including a fellowship at IBM Research Center (1984–1985) and a guest professorship at Oldenburg University (1992–1993). His research focuses on formal methods, embedded systems, and real-time systems, emphasizing mathematically grounded software development. He has authored/co-authored three books and over 70 scientific articles. Research interests include model-based program construction, temporal logic, functional programming, and ontology alignment. He has led projects on ontology matching frameworks (e.g., MDMapper) and formal methods for real-time systems. Hansen has supervised PhD students in areas like data integration and genetic circuit synthesis. He has contributed to international conferences and serves as a reviewer/examiner for academic programs. His work bridges theoretical foundations with industrial applications, particularly in embedded systems and data management. He has collaborated internationally, with projects addressing ESG reporting standards and regulatory compliance through automated data traceability.

• Electric Vehicles
• Fuzzy Logic
• Blockchain
• +5 more

Shahid Hussain is an Associate Professor at King Abdullah University of Science and Technology (KAUST) in the Computer, Electrical and Mathematical Sciences and Engineering Division. His research spans multiple domains including electric vehicle infrastructure, blockchain technology, and intelligent systems for IoT applications, with a focus on practical implementations of computational intelligence techniques. Dr. Hussain's research interests include: Electric Vehicles and Smart Grid Integration Fuzzy Logic and Intelligent Decision Systems Blockchain Applications for Security and Privacy Machine Learning for IoT and Healthcare Applications Energy Management Systems Smart City Infrastructure Development His recent publications demonstrate a strong focus on applying hybrid computational approaches to solve complex engineering problems, particularly in transportation systems and healthcare applications. Dr. Hussain has published extensively in IEEE journals, with particular emphasis on innovative approaches to electric vehicle charging infrastructure, secure IoT systems, and blockchain-enabled solutions for real-world challenges. Dr. Hussain maintains active collaborations with researchers globally, particularly with Reyazur Rashid Irshad, Young-Chon Kim, and Subhasis Thakur. His work bridges theoretical advancements with practical implementations, as evidenced by his research on fuzzy integer linear programming for EV charging stations and blockchain-enabled security frameworks for medical IoT systems.

• Strategic Leadership
• Corporate Governance
• Organizational Search
• +3 more

Wei Shen is the Dean’s Council Distinguished Professor and Chair of the Department of Management and Entrepreneurship at Arizona State University’s W. P. Carey School of Business. He previously served as Associate Dean for W. P. Carey China Programs and Faculty Director of the China Executive MBA Program. His research focuses on CEOs, boards of directors, and organizational impacts, with publications in top-tier journals like Administrative Science Quarterly and Strategic Management Journal. Educated at Peking University (B.S. in Psychology) and Texas A&M University (Ph.D. in Business Administration), Shen has received awards including the Robert J. Litschert Best Doctoral Student Paper Award and the Mays Outstanding Ph.D. Alumni Award. He currently serves as President-Elect of the International Association for Chinese Management Research (IACMR). His research interests include strategic leadership, corporate governance, organizational search mechanisms, and research methodology. Key themes in his work address CEO accountability, board dynamics, and the interplay between organizational performance and governance structures. Shen’s publications span over two decades, consistently exploring governance mechanisms, strategic decision-making, and leadership succession. Recent work examines tiered status hierarchies in competitive actions, gender bias in CEO roles, and compliance with governmental mandates. His books include Empirical Methods in Organization and Management Research (4th ed.) and Organizational Management: A Perspective of Chinese Enterprises in the Age of Digital Intelligence . He has advised numerous academic programs and holds leadership roles in advancing Chinese management research. His courses include doctoral seminars, strategic management, and global corporate strategy, reflecting his commitment to educating future business leaders.

Glen ChouView profile

Assistant Professor

• Robotics
• Trustworthy Autonomy
• Machine Learning
• +4 more

Glen Chou is an Assistant Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds dual B.S. degrees from UC Berkeley (Electrical Engineering & Computer Science and Mechanical Engineering) and M.S./Ph.D. degrees from the University of Michigan (Electrical & Computer Engineering). Prior to Georgia Tech, he was a postdoctoral researcher at MIT CSAIL. Chou directs the Trustworthy Robotics Lab, focusing on algorithms for general-purpose robots and autonomous systems to ensure capability, safety, and resilience in real-world conditions. His research integrates control theory, machine learning, optimization, and formal methods. He emphasizes both theoretical foundations (e.g., proving algorithmic properties) and practical deployment on real robots. Key research areas include trustworthy autonomy, human-robot interaction, and constraint learning from demonstrations. His lab addresses challenges in perception, motion planning, and system reliability through interdisciplinary approaches. Chou has received notable awards including the Robotics: Science & Systems Pioneer (2022), NDSEG Fellowship (2019), and NSF Graduate Fellowship (2019). His work bridges foundational theory with real-world applications, with a focus on safety-critical systems.

William IdsardiView profile

Professor

• Neurolinguistics
• Phonology
• Psycholinguistics
• +3 more

William Idsardi is a Professor of Linguistics at the University of Maryland, affiliated with the Program in Neuroscience and Cognitive Science and the Maryland Language Science Center. His research focuses on neurolinguistics, phonology, and psycholinguistics, combining experimental, computational, and theoretical approaches. Key areas include phonological abstraction mechanisms, the neural basis of speech perception, and the computational foundations of phonological patterning. His work bridges disciplines, investigating how phonological categories are represented in the brain using neuroimaging techniques like MEG. Notable contributions address the role of categorical perception in fricative sounds, the phoneme hypothesis, and computational complexity differences between phonology and syntax. Collaborative projects explore dialect perception, infant lexical learning, and cross-linguistic vowel systems. Publications span phonological theory, neuroimaging studies, and computational linguistics, emphasizing interdisciplinary methods. His research has implications for understanding language acquisition, speech disorders, and the evolution of linguistic structure. Labs associated with his work include the EEG/ERP Lab and Maryland Neuroimaging Center.

Sam LindleyView profile

Associate Professor

• Programming Languages
• Formal Methods
• Type Systems
• +3 more

Sam Lindley is a Reader in Programming Language Design and Implementation at the School of Informatics, University of Edinburgh. He holds a UKRI Future Leaders Fellowship in Effect Handler Oriented Programming. His research focuses on foundational aspects of programming languages, including effect systems, session types, type theory, and functional programming. Key research interests span algebraic effects, concurrency models, type systems for resource management, and language implementation techniques. Lindley's work bridges theory and practice, addressing challenges in systems programming, web technologies (e.g., WebAssembly), and compiler design. Recent publications (2023–2025) emphasize effect handlers for low-level languages (e.g., C, WebAssembly), modal type systems for memory management, and formal semantics for concurrent systems. His work often explores how advanced type systems can improve expressivity and efficiency in programming models. Scientific Awards: UKRI Future Leaders Fellowship Labs/Teams: Laboratory for Foundations of Computer Science (LFCS) Grants: Active funding via UKRI Fellowship

Jonathan BalkindView profile

Assistant Professor

• Computer Architecture
• Operating Systems
• Programming Languages
• +3 more

Jonathan Balkind is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). His research focuses on the intersection of computer architecture, programming languages, and operating systems, with an emphasis on pragmatic system design and open-source hardware. He leads the ArchLab at UCSB and is affiliated with the OpenPiton project, an open-source manycore research framework. Education includes a PhD and MA in Computer Science from Princeton University (adviser: Prof. David Wentzlaff), an MSci in Computing Science from the University of Glasgow (advisers: Prof. Joseph Sventek and Dr. John O'Donnell), and exchange studies at UCSB. His work has been supported by awards such as the NSF Early CAREER Award (2023) and the Open Hardware Trailblazer Fellowship (2022). Research interests span heterogeneous computing, cache-coherent systems, FPGA integration, and domain-specific architectures. Notable projects include the 25-core Piton chip, the CIFER SoC with embedded FPGA, and the DECADES manycore processor. Recent publications address fused-kernel operating systems (Stramash), control logic synthesis, and hyperloop data-center architectures. His awards reflect contributions to open-source hardware and academic mentorship, including Siebel Scholarship (2018), Gordon Y.S. Wu Fellowship (2013–2017), and multiple teaching/research recognitions. He actively collaborates with industry (e.g., Microsoft Research, ARM) and advises on open-source projects.

Simon FosterView profile

Senior Lecturer

• Verification
• Theorem Proving (Isabelle/HOL, Agda)
• Formal Semantics
• +4 more

Simon Foster is a Senior Lecturer in the Department of Computer Science at the University of York. His research focuses on formal methods, theorem proving (using tools like Isabelle/HOL and Agda), and the verification of cyber-physical systems. He holds a PhD and MComp from the University of Sheffield. Research Interests: Foster specializes in formal semantics, unifying theories of programming, and functional programming. His work addresses challenges in verifying complex systems, including robotic control software and safety-critical applications. He has contributed to projects like CyPhyAssure and RoboCalc, emphasizing assurance case generation and probabilistic modeling. Recent Work Trends: His recent publications (2022–2025) emphasize scalable verification techniques for cyber-physical systems, probabilistic modeling, and formal verification of robotic systems using Isabelle/HOL. Key themes include hybrid systems theorem proving, assurance case automation, and the integration of formal methods with robotic state machines. Grants & Projects: He led the CyPhyAssure project (2018–2021) and contributed to the H2020 INTO-CPS initiative. His roles include Research Fellowships in safety-critical systems and model-driven architectures. Labs & Teams: Active in the High Integrity Systems group at York, focusing on formal methods for safety-critical systems and collaborative tool development for systems engineering.

Laura PozziView profile

Professor

• Embedded Systems
• Compiler Techniques
• Coarse-Grain Reconfigurable Architectures
• +3 more

Laura Pozzi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Switzerland, since 2015. She previously held positions as Associate Professor (2011–2015) and Assistant Professor (2005–2011) at USI. Prior to joining USI, she was a postdoctoral researcher at EPFL's Processor Architecture Laboratory (2001–2005), a research engineer at STMicroelectronics (2000), and an Industrial Visitor at UC Berkeley (2000). Education: MS and PhD in Computer Engineering from Politecnico di Milano, Italy (1996–2000). Her research focuses on the interaction between compiler and architecture design, particularly in embedded systems , with key areas including approximate computing , coarse-grained reconfigurable arrays (CGRAs) , high-level synthesis (HLS) , and fuzz testing . She has led projects on automated design space exploration, compiler optimizations for reconfigurable architectures, and error estimation in approximate circuits. Recent Publications span topics like SAT-based mapping for CGRAs , grammar-based fuzzing of shell interpreters , and approximate logic synthesis , reflecting her interdisciplinary work bridging hardware/software co-design and software verification. Scientific Awards: Credit Swiss Best Teaching Award IEEE DAC Best Paper Award Leadership Roles: Co-Program Chair, IEEE Symposium on Application Specific Processors (SASP) Editorial Board Member, IEEE Design and Test Students: Current: Rodrigo Otoni (Postdoc), Morteza Rezaalipour (PhD), Riccardo Felici (PhD), Cristian Tirelli (PhD) Alumni: Ilaria Scarabottolo (PhD/Postdoc), Lorenzo Ferretti (PhD/Postdoc), Georgios Zacharopoulos (PhD), Giovanni Ansaloni (PhD/Postdoc), Paolo Bonzini (PhD)

Yassine LakhnechView profile

Professor

• Computer Security
• Cryptography
• Formal Verification
• +5 more

Yassine Lakhnech is a Professor at the University Joseph Fourier (Grenoble 1), leading the 'Distributed and Complex Systems' research team within the VERIMAG laboratory. His work focuses on computer security, cryptography, formal verification, and programming language semantics. He has been actively involved in coordinating major interdisciplinary research initiatives like the PERSYVAL-lab, which addresses cyber-physical systems (CPS) challenges. His research bridges computational and formal approaches, with contributions to cryptographic protocol analysis, information flow control, and automated theorem proving. Key projects include the development of tools like HERMES for cryptographic protocol verification and involvement in ANR-funded projects such as Verso, PROSE, and AVOTE. He has also organized international conferences and workshops, including the Canada-France MITACS Workshop on Foundations & Practice of Security and the Eighth ACM/IEEE International Conference on Formal Methods and Models for Codesign. His publications span over 147 papers, with an h-index of 25 and g-index of 42, reflecting significant contributions to formal methods, security protocols, and hybrid systems. Despite his role as Vice President of Research, he maintains active scientific productivity, emphasizing the integration of theoretical and practical advancements in CPS and security.

Fred PopowichView profile

Professor

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +4 more

Fred Popowich is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He holds adjunct positions at Dalhousie University's Faculty of Graduate Studies and is an Associate Member of SFU's Department of Linguistics and Cognitive Science Program. His academic career began post-PhD (Cognitive Science/Artificial Intelligence, University of Edinburgh, 1989) and has spanned over three decades at SFU. Education: PhD in Cognitive Science/Artificial Intelligence (University of Edinburgh, 1989); M.Sc. and B.Sc. in Computing Science (Simon Fraser University and University of Alberta, 1985/1982). Research focuses on natural language processing (NLP), machine translation, intelligent systems, and big data applications. He directs SFU’s Big Data Initiative and leads the Natural Language Laboratory, supervising MSc/PhD students in computing science. His work includes developing systems for smart homes, toxic language detection in social media, and energy grid analysis. Industry roles include co-founding Axonwave Software (as CTO/President) and contributing to technology commercialization. Current projects address EV charging impacts, personalized learning systems, and real-time load monitoring. Publications span machine translation, sentiment analysis, and NLP applications in education and energy systems. His work bridges theoretical computer science with practical applications in healthcare, smart cities, and education.