Find Academic Supervisors

Nirupam GuptaView profile

Assistant Professor

• Distributed Machine Learning
• Federated Learning
• Privacy-Preserving Algorithms
• +4 more

Nirupam Gupta is an Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen. His research focuses on robustness, privacy, and fairness in distributed machine learning systems, with particular emphasis on federated learning and Byzantine fault tolerance. He actively contributes to the design of secure, resilient, and privacy-preserving algorithms for collaborative AI systems. He teaches courses in Privacy in Machine Learning (PriMaL) Machine Learning B (MLB) both offered in hybrid formats with full remote participation support. His latest book, Robust Machine Learning: Distributed Methods for Safe AI , provides foundational insights into the field. Key research themes in his work include: Byzantine-Robust Optimization Differential Privacy in Federated Systems Personalization under Adversarial Conditions Secure Aggregation Techniques Contact: nigu@di.ku.dk

Brigitte PientkaView profile

Professor

• Programming Languages
• Verification
• Type Theory
• +6 more

Brigitte Pientka is a Professor at McGill University's School of Computer Science , where she leads the Computation and Logic group . She earned her PhD from Carnegie Mellon University in 2003 and previously studied at the University of Edinburgh and Technical University of Darmstadt. Education PhD in Computer Science, Carnegie Mellon University (2003) University of Edinburgh Technical University of Darmstadt Research Interests Her work focuses on the theoretical and practical foundations for building reliable software systems, combining logic, type theory, and verification with system-building. Key areas include: Type Theory and Dependent Types Logical Frameworks and Mechanized Metatheory Session-Typed Concurrency and Linear Logic Metaprogramming and Contextual Type Systems Theorem Proving and Formal Verification Functional Programming and Language-Based Security Professional Roles She has served as: PC Chair for ICFP'24 and CPP'24 General Chair for POPL'20 Executive Editor of Logical Methods in Computer Science Steering Committee Member for LICS, POPL, and ESOP Scientific Awards Dr. Pientka has received: Test of Time Award at PPDP'18 Humboldt Fellowship for research at MPI-SWS, Germany Labs & Teams She actively develops the Beluga programming language , a tool for mechanizing meta-theory proofs and type-driven program manipulation.

Drazen PrelecView profile

Professor

• Behavioral Economics
• Neuroeconomics
• Decision-Making Psychology
• +5 more

Drazen Prelec is a Professor of Management Science and Economics at the MIT Sloan School of Management , with appointments in the Department of Economics and Department of Brain and Cognitive Sciences . His work bridges psychology, neuroscience, and economics to study decision-making mechanisms. Education: AB in Applied Mathematics (Harvard College), PhD in Experimental Psychology (Harvard University) Prelec's research focuses on behavioral economics and neuroeconomics , particularly in areas like risky choice, time discounting, self-control, and consumer behavior. He develops normative decision theory while investigating its empirical limitations using behavioral and fMRI methods. Two major projects include: Self-Signaling : Examines non-causal motivation where individuals favor actions diagnostic of favorable outcomes over causal ones Bayesian Truth Serum : Creates scoring systems to reward honest judgments in domains without objective truth criteria His recent publications span topics in crowd wisdom , temporal discounting , and neuroeconomic modeling , with applications to consumer neuroscience, political forecasting, and behavioral ethics. Guggenheim Fellowship Junior Fellow, Harvard Society of Fellows Prelec contributes to understanding how credit card usage activates brain reward centers to increase spending, and explores honesty biases in social interactions. His work integrates computational models with empirical neuroscience to address foundational questions in decision theory.

Rafael J. PascualView profile

Lecturer

• Old English poetry
• Anglo-Saxon literature
• Medieval linguistics
• +5 more

Rafael J. Pascual is an Associate Faculty Member at the University of Oxford, holding roles as Stipendiary Lecturer in English at New College, Lecturer in Old and Middle English at Magdalen College, and Departmental Lecturer in English Language and Literature at the Faculty of English and Pembroke College. His research focuses on medieval English language and literature, particularly Old English poetry and its continental sources. He has been funded by the Spanish State Research Agency (AEI) through the Ramón y Cajal programme, receiving €120,000 for his project on Anglo-Saxon poetry. Education: PhD from the University of Granada (2014) on the dating and textual criticism of Beowulf, followed by a Postdoctoral Fellowship at Harvard University. He has also been a Postdoctoral Research Assistant at CLASP (ERC-funded project). Research interests include Old English metre, monsters in Beowulf, Old Norse influence, and the editing of poetical manuscripts. His work bridges historical linguistics and literary analysis, with contributions to journals like Medium Ævum and Studia Neophilologica. Major awards include the 2013–2014 Extraordinary Doctorate Award and the 2017 Excellence in Knowledge Award from Grupo Caja Rural. He co-edited Old English Philology: Studies in Honour of R.D. Fulk (2016) and Tradition and Innovation in Old English Metre (2022). Teaching includes courses on early medieval literature, the history of the English language, and material texts. His academic service includes roles at multiple Oxford colleges and contributions to the CLASP project.

Qi ZhangView profile

Assistant Professor

• Artificial Intelligence
• Reinforcement Learning
• Trustworthy AI Systems
• +4 more

Qi Zhang is an Assistant Professor in the Department of Computer Science and Engineering, AI Institute at the Molinaroli College of Engineering and Computing, University of South Carolina. His research focuses on developing safe, reliable, and trustworthy AI systems through advancements in reinforcement learning and decision-making algorithms for uncertain environments. He holds a Ph.D. from the University of Michigan (2020) and a B.E. from Shanghai Jiao Tong University (2015). Research interests include artificial intelligence, reinforcement learning, multi-agent systems, and decision-making under uncertainty. Key themes involve leveraging domain knowledge for robust AI solutions and ensuring ethical, transparent, and risk-aware system designs. His work spans applications in autonomous systems, healthcare, robotics, and materials science. Current projects emphasize improving algorithmic trustworthiness, safety, and adaptability across diverse contexts. He is affiliated with the AI Institute at USC and maintains an active research lab focused on these areas.

Bin HuView profile

Assistant Professor

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +5 more

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

Kimi Kondo-BrownView profile

Professor

• Japanese as a Heritage Language
• Curriculum & Assessment
• Second Language Studies
• +3 more

Dr. Kimi Kondo-Brown is a Professor and Associate Dean for Academic Affairs at the University of Hawaiʻi at Mānoa . She holds affiliations in the Department of East Asian Languages and Literatures (Japanese BA, MA, PhD programs) and serves as Cooperating Graduate Faculty in the Department of Second Language Studies (PhD in SLS). Her research focuses on Japanese as a Heritage Language , language assessment , and curriculum development . Education: PhD in Education (UHM), MA in Linguistics (University of Utah). She leads initiatives in heritage language education, publishing widely on language testing, curriculum design, and multilingual education. Key works include Japanese as a Heritage Language (Cambridge 2024) and Introduction to Assessment for Japanese Teachers (reprinted 2022). She directs assessment institutes and collaborates with international scholars like Emeritus Prof. James Brown. Research emphasizes heritage language maintenance and post-pandemic language testing . She co-edits peer-reviewed volumes on assessment modules and serves on editorial boards. Her work bridges theoretical linguistics with practical pedagogy, addressing needs of heritage speakers and global language learners. Grants and awards include contributions to the National Foreign Languages Resource Center. She advises on curriculum alignment and assessment frameworks, with a focus on equity in language education. Her legacy includes over 30 publications and 25 years of teaching Japanese and SLA methodologies.

Pavan PoudelView profile

Assistant Professor

• Parallel and Distributing Computing Algorithms
• Robotics
• Internet of Things
• +3 more

Dr. Pavan Poudel is an Assistant Professor of Computer Science at the University of Houston-Clear Lake (UHCL), part of the College of Science and Engineering. He joined UHCL in Fall 2024 after working as a Research Engineer in the software industry and completing a postdoctoral research position at Augusta University's Department of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Kent State University (2021) and a Bachelor's in Computer Engineering from Tribhuvan University, Nepal. His research focuses on Parallel and Distributed Computing, Robotics, and Fault-Tolerance, with expertise in optimizing scheduling algorithms and designing scalable systems for distributed environments. Dr. Poudel's research includes transactional memory systems, distributed robotics algorithms (e.g., gathering, scattering, and coverage), and fault-tolerant solutions for real-time applications like medical and disaster response. His work bridges theoretical computer science with practical applications, emphasizing efficiency and scalability. He has published extensively in venues like Theoretical Computer Science (TCS) and Information, addressing topics such as time-optimal robot navigation and adaptive versioning in transactional memory. His academic awards include the Best Presentation Award at Kent State’s Graduate Research Symposium (2021) and scholarships like the John Sechrist and Hine Scholarships (2019–2020). He teaches courses such as Computer Organization and Assembly Language and Computer Game Programming. His publications demonstrate a strong focus on distributed systems, robotics algorithms, and concurrency control, reflecting his commitment to advancing scalable and reliable computing solutions.

Justin Y. ShiView profile

Associate Professor

• Scalable Computing
• Blockchain Protocols
• Software Safety
• +5 more

Justin Y. Shi is an Associate Professor at Temple University's Computer and Information Science Department. His research focuses on scalable computing, blockchain protocols, software safety, and infinitely scalable systems. He holds a PhD from the University of Pennsylvania and has held roles including Interim Director of the Center for Advanced Computing and Communications (1992-1998) and CIS Department Chair (2007-2009). B.S. Computer Engineering, Shanghai Jiao-tong University (1977) M.S. Software Engineering, University of Pennsylvania (1983) PhD in Concurrent Programming, University of Pennsylvania (1984) His research explores decoupling programs and data from physical devices to achieve fault tolerance and scalability. Key contributions include the Statistic Multiplexed Computing (SMC) paradigm and foundational work on parallel algorithms and distributed systems. He has authored patents on scalable parallel computing and high-performance blockchain systems. Teaching highlights include courses on quantum computing, cybersecurity, and fullstack programming. He has advised over 15 students, many now in industry and academia. Founder & CEO of Parallel Computers Technology Inc. (2000-2017) Co-founder of SMC Labs (2022-present) Active in editorial roles for IEEE Blockchain Technology Briefs and contributor to NSF workshops on cloud computing. His work has been exhibited at Supercomputing Conferences (1991-2018).

Mohsen SaffariView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Spatiotemporal Data Analysis
• +3 more

Mohsen Saffari is an Assistant Professor of Computer Engineering at Purdue University Northwest (PNW), joining the Electrical and Computer Engineering department in Fall 2024. His research and teaching focus on machine learning, deep neural networks, and their applications in spatiotemporal data analysis, particularly in power systems and computer vision. He holds a Ph.D. and is actively involved in advancing interdisciplinary approaches to energy systems and intelligent technologies. His research emphasizes the theoretical and practical aspects of machine learning and deep learning models applied to spatiotemporal data. His work spans power systems optimization, including fault detection in power distribution networks and probabilistic optimal power flow analysis. In computer vision, he explores domain adaptation techniques for traffic scene understanding and masked face recognition. He also develops innovative methods like physics-informed graph neural networks and generative adversarial networks for solving complex engineering and healthcare challenges. Recent trends in his publications highlight advancements in graph neural networks for power systems, spatiotemporal data applications, and domain adaptation in computer vision. His contributions include energy disaggregation techniques, photovoltaic forecasting, and medical imaging through interdisciplinary approaches combining deep learning with physics-based models. Dr. Saffari’s teaching focuses on machine learning fundamentals, artificial neural networks, and software engineering. He advocates for engineers’ role in shaping societal advancements through critical thinking and innovation.

• Applied Mathematics
• Mathematical Physics
• Condensed Matter Physics
• +9 more

Henrik Jensen is a Senior Research Investigator in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. His work spans applied mathematics, mathematical physics, and interdisciplinary complexity science. He holds affiliations with the CFM-Imperial Institute of Quantitative Finance, Centre for Complexity Science, and the Georgina Mace Centre for the Living Planet. His research focuses on emergent phenomena in systems ranging from biological populations to financial networks. Notable contributions include studies on metapopulation dynamics, epigenetic regulation in birds, and information-theoretic approaches to complex systems. Dr. Jensen has presented globally at conferences like the Brazilian Meeting of Condensed Matter Physics and the Complex 2012 conference. His expertise bridges theoretical frameworks with empirical data, particularly in evolutionary ecology and climate modeling. His current work explores multi-generational fitness effects, genomic prediction in wild populations, and the interplay between environment and genetic architecture. He has pioneered methodologies for analyzing cosmological data using group entropies and developed computational tools for genetic assignment in metapopulations. His lab integrates mathematical modeling with field studies to address challenges in conservation biology and systems biology. While no formal students are listed, his collaborative approach has generated extensive interdisciplinary publications.

Elisa BaniassadView profile

Teaching Professor

• Software Engineering
• Educational Technology
• Aspect-Oriented Programming
• +3 more

Elisa Baniassad is a Teaching Professor in the Department of Computer Science at the University of British Columbia (UBC), within the Faculty of Science. She specializes in software engineering education and has received numerous teaching accolades including the UBC Killam Teaching Prize and CS-Can/INFO-CAN Excellence in Teaching Award. Her courses focus on software construction, engineering principles, and advanced software design. Dr. Baniassad has taught CPSC 310 (Introduction to Software Engineering), CPSC 210 (Software Construction), and CPSC 410 (Advanced Software Engineering) across multiple terms since 2000. Her research interests span educational methodologies in software engineering, aspect-oriented programming, and the design of effective learning tools. Notable contributions include studies on team dynamics in software development, automated assessment techniques, and pedagogical frameworks for large-scale programming courses. She has authored over 50 peer-reviewed articles on topics ranging from mutation analysis in student tests to the efficacy of online learning environments. Awards include recognition for teaching excellence at UBC and contributions to computer science education. Her work emphasizes practical applications of software engineering principles in academic settings, with a focus on fostering student mastery through innovative assessment strategies and feedback mechanisms.

Giovanni SansaviniView profile

Associate Professor

• Renewable Energy Systems
• Energy Infrastructure Resilience
• Power Systems Optimization
• +3 more

Prof. Giovanni Sansavini is an Associate Professor in the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on energy systems resilience, renewable energy integration, and sustainable infrastructure design. He leads projects related to grid optimization, energy policy, and risk engineering, with a particular emphasis on decarbonization strategies for Switzerland and Europe. His work bridges theoretical modeling and practical implementation, addressing challenges such as power transmission equity, distributed energy resource management, and hydrogen storage integration. Recent projects include developing data-driven frameworks for grid reliability assessment and analyzing the socio-technical impacts of energy transitions. Prof. Sansavini collaborates with industry and policymakers to ensure research informs actionable solutions. His publications highlight interdisciplinary approaches to energy system design, emphasizing the trilemma of affordability, sustainability, and security.

• Renewable Energy
• Fluid Dynamics
• Turbine Design
• +3 more

Christopher Vogel is a Research Fellow at New College and a Senior Research Associate in the Department of Engineering Science at the University of Oxford. He holds a first-class BE(Hons) in Engineering Science from the University of Auckland and completed his DPhil at Oxford under the Oxford Martin School’s Programme on Globalising Tidal Power Generation. His work focuses on advancing renewable energy technologies, particularly in tidal and wind energy systems. His research interests include fluid dynamics of tidal turbines, aerodynamic performance optimization, blade design under erosion, and large-scale renewable energy array modeling. He has contributed to projects like the Tidal Energy Research Group and the FastBlade facility for full-scale tidal blade testing. Vogel’s work integrates computational fluid dynamics (CFD), experimental testing, and multi-scale analytical models to address challenges in energy extraction efficiency, structural durability, and system reliability. Recent publications highlight advancements in actuator line methods for turbine wake prediction, uncertainty quantification in blade-element momentum theory, and dynamic loading analysis of tidal arrays. His studies often bridge theory and application, emphasizing practical solutions for marine and wind energy deployments. Collaborations include the Tidal Benchmarking Project and investigations into hybrid systems combining wave energy converters with breakwaters. Vogel’s interdisciplinary approach addresses both technical and environmental dimensions of sustainable energy systems.

Traci J. HessView profile

Professor

• Human-Computer Interaction
• Decision-Making Systems
• Electronic Commerce
• +3 more

Traci J. Hess is the Douglas & Diana Berthiaume Endowed Professor of Information Systems and Senior Associate Dean at the Isenberg School of Management, University of Massachusetts Amherst. She holds a PhD (1999), MA (1997) in Information Systems from Virginia Tech, and a BS in Accounting from the University of Virginia (1988). Her academic career includes roles at Washington State University and industry experience as Senior Vice President in banking and auditing roles at Ernst & Young. Her research focuses on Human-Computer Interaction , Decision Support Systems , and Trust in Digital Technologies . She explores topics like online review dynamics, privacy mechanisms in health communities, and algorithmic labor platforms. Hess has published extensively in journals like Journal of the Association for Information Systems and MIS Quarterly , and her work bridges cognitive psychology with technology design. Hess has received prestigious awards including the Isenberg Outstanding Research Award (2012) and Transactions on Human-Computer Interaction Best Paper Award (2011). Her teaching spans Business Intelligence , Database Management , and Research Methods . She has advised numerous graduate students (not listed explicitly in texts) and contributes to digital strategy research. Her recent work (2023–2024) examines reviewer badges’ impact on consumer trust and human agency in algorithmic labor systems. She emphasizes user-centric design principles to mitigate negative emotional responses to technology (e.g., technostress).