Find Academic Supervisors

Jinghui ChengView profile

Associate Professor

• Human-Computer Interaction
• Software Engineering
• User-Centered Design
• +3 more

Jinghui Cheng is an Associate Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . He holds a PhD in Computer Science from DePaul University (2017), preceded by an MSE and BSE from Xi’an Jiaotong University, China. His research uniquely bridges Human-Computer Interaction (HCI) with Software Engineering , focusing on technologies that support domain experts with specialized information needs. Awards : Canada Research Chair Tier 2 in User Experience Design of Data-Driven Systems His recent work examines playful AI interactions (e.g., ChatGPT), designer-developer collaboration , and privacy motivation in UI/UX. He actively supervises PhD and Master’s students in the HCD Lab , with over 20 completed theses. Collaborations span institutions like the École Polytechnique and partnerships with researchers such as Jin L. C. Guo and Bram Adams . Cheng’s 15 most recent publications (2023-2025) reflect trends in AI-assisted design , usability in open-source communities , and ethical technology development . His grants include the AUDACE grant (2020) co-led with Dr. Gabrielle Pagé, focusing on healthcare applications.

Donna SlonimView profile

Professor

• Data Science
• Biological Networks
• Gene and Pathway Regulation in Human Development
• +10 more

Professor Donna Slonim is a leading computational biologist at Tufts University with dual appointments in the Department of Computer Science and Department of Immunology , and membership in the Genetics, Molecular and Cellular Biology program. She holds a Ph.D. from MIT (1996) and focuses on integrating genomic data with algorithmic approaches to advance disease diagnosis and treatment. Education Ph.D., MIT, 1996 M.S., University of California, Berkeley, 1991 B.S., Yale University, 1990 Research Interests include: Algorithm development for biological network analysis Precision medicine applications in human development Pharmacogenomics and drug discovery Temporal gene expression modeling Machine learning for biomedical data Scientific Awards : Best Student-Led Paper Award at ACM-BCB 2022 Academic Leadership : Teaches courses like Computational Biology , Statistical Bioinformatics , and Biological Networks Co-leads the BCB Group at Tufts On sabbatical during 2025-26 but remains active in research

• FinTech
• Machine Learning
• Credit Risk Modeling
• +8 more

Alessandro Bitetto is an Assistant Professor in Statistics at the Department of Economics and Management, University of Pavia. He holds an MSc in Applied Mathematics and has professional experience as a data scientist in banking/insurance sectors, focusing on econometric models and machine learning applications. His research interests span FinTech applications, network theory, credit risk modeling, and deep learning techniques like Graph Neural Networks. He collaborates externally with the University of Milan’s Mathematics and Logic department and teaches courses in Big Data Analysis, Python programming, and Advanced Statistics at the University of Pavia. Education: MSc in Applied Mathematics Research Interests: Alessandro's work bridges financial analytics and machine learning, with a focus on credit risk modeling, cryptocurrency ESG integration, and medical imaging applications. His recent projects include developing data-driven financial indexes using network theory and applying explainable AI to public health risk assessment. Publications Trends: His articles emphasize machine learning applications in credit risk, ESG factors in blockchain finance, and time-series analysis for financial stability. Notable contributions include studies on ICO underpricing mitigation and deep learning in echocardiogram analysis. Awards/Grants: No specific awards listed, but his collaborations suggest active grant-funded projects in interdisciplinary domains. Labs/Teams: Involved in cross-institutional collaborations, including work on pandemic data analysis via GitHub repositories like 'Computer_Vision_Tutorial' for medical imaging research.

Chad G. RoseView profile

Assistant Professor

• Robotics
• Human-Robot Interaction
• Robotic Rehabilitation
• +3 more

Chad G. Rose is an Assistant Professor of Mechanical Engineering at Auburn University's College of Engineering. He holds a Ph.D. and M.S. from Rice University and a B.S. from Auburn University. His research focuses on robotics, particularly in human-robot interaction, rehabilitation robotics, and wearable devices. He actively collaborates on projects like robotic rehabilitation systems and virtual reality empathy training for healthcare professionals. Dr. Rose has received internal research funding from Auburn University and contributes to multidisciplinary initiatives with Toyota and European institutions. Education : Ph.D. Mechanical Engineering, Rice University M.S. Mechanical Engineering, Rice University B.S. Mechanical Engineering, Auburn University His research interests emphasize robotic rehabilitation , including exoskeleton design for functional tasks and neuromechanical modeling of injuries. He also explores virtual reality applications to enhance empathy in medical training and collaborative assembly frameworks for industrial robotics. His work bridges biomechanics, control systems, and human-centered design. Recent publications span topics like soft hand exoskeleton control, human-robot collaborative assembly, and predictive haptic feedback for autonomous systems. His research has been highlighted in interdisciplinary collaborations and competitions like the NASA Robotic Mining Challenge. Awards : Recipient of Auburn University's internal research funding (2023) Dr. Rose advises projects in robotic systems development and has contributed to Auburn's Robotic Mining Team. His lab focuses on wearable robotics, sensorimotor interfaces, and human-centered automation. He collaborates with industry partners like Toyota and international institutions to advance safety and accessibility in robotics.

Matthew LeybourneView profile

Associate Professor

• Geochemistry
• Environmental geochemistry
• Earth and related environmental sciences
• +3 more

Matthew Leybourne is an Associate Professor in the Department of Geological Science and Geological Engineering at Queen's University, affiliated with the Faculty of Arts and Science. He holds a cross-appointment at the Arthur B. McDonald Canadian Astroparticle Physics Institute. His research focuses on geochemical and isotopic investigations of ancient and modern rocks, particularly developing analytical methods for ultra-low trace element concentrations. He leads projects at the Queen’s Facility for Isotope Research, including low-level trace material determinations for astroparticle physics applications. Education: PhD (1998) in Earth Sciences from the University of Ottawa; M.Sc. (1988) in Geology from Acadia University; B.Sc. (1985) in Geology from the University of Waikato. Research interests include ore deposit geochemistry, environmental geochemistry, and quality assurance in analytical methods. Key projects involve fluid geochemistry of ore deposits, marine hydrothermal systems, and isotopic tracers for astrophysics. His work bridges geology and physics, addressing questions about Earth’s early evolution and宇宙起源. Advising includes postdoctoral fellows focused on geochemical analysis and mineral exploration. He collaborates with the McDonald Institute on interdisciplinary projects combining geology with astroparticle detection. Affiliations include the Journal of Tethys, Canadian Geosciences Education Network, and the Society of Economic Geologists.

• Computer Vision
• Extended Reality (XR)
• Artificial Intelligence
• +7 more

Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.

Lesley ShannonView profile

Associate Professor

• Computer Engineering
• Reconfigurable Computing
• FPGA CAD Tools
• +6 more

Lesley Shannon is an Associate Professor in the School of Engineering Science at Simon Fraser University (SFU), specializing in computing system design and reconfigurable computing. She leads the Reconfigurable Computing Lab (RCL), focusing on FPGA-based architectures, heterogeneous multicore systems, and CAD tools. Her academic journey includes a B.Sc. from the University of New Brunswick (1999), M.A.Sc. and Ph.D. from the University of Toronto (2001 and 2006). Her research spans FPGA CAD algorithms, Networks-on-Chip (NoCs), dynamic partial reconfiguration (DPR), and application-specific architectures. Key projects include FUSE (OS abstraction for hardware accelerators), PolyBlaze (multicore system emulation), and uROAMs (microfluidic reconfigurable devices). She has secured funding from NSERC, Xilinx, and Altera, among others. Education: B.Sc., Electrical Engineering (Computer Option), University of New Brunswick, 1999 M.A.Sc., Applied Science, University of Toronto, 2001 Ph.D., Applied Science, University of Toronto, 2006 Roles: NSERC Chair for Women in Science and Engineering (BC/Yukon) Faculty Advisor, SFU WEST (Women in Engineering, Science, and Technology) Her lab employs 5 current students and has graduated 7, including work on FPGA CAD, microfluidics, and embedded systems. Courses taught include Advanced Digital Systems Design and Real-Time/Embedded Systems. Her research emphasizes reducing design time through on-chip profiling and CAD tools, with applications in biomedical imaging and quantum computing. Awards & Grants: NSERC Discovery Grant NSERC Strategic Grant (Lead PI, 2009; Co-PI, 2010) SFU Startup Grant and President’s Research Grant Lab & Collaborations: The RCL develops silicon and non-silicon architectures, including quantum computing and microfluidic systems. Partnerships include industry collaborations and open-source tools like Odin II synthesis framework.

Charles E. LeisersonView profile

Professor

• Parallel Computing
• Performance Engineering
• Algorithms
• +6 more

Charles E. Leiserson is a Professor of Computer Science and Engineering at MIT, holding the Edwin Sibley Webster Professorship in Electrical Engineering and Computer Science. He leads the Supertech Research Group and is Faculty Director of the MIT-Air Force AI Accelerator. His work focuses on parallel computing, performance engineering, and algorithms. Leiserson is renowned for co-authoring the foundational textbook Introduction to Algorithms , widely used in computer science education globally. He has pioneered technologies like the Cilk multithreaded programming language and contributed to supercomputing architectures such as the Connection Machine CM-5. His research bridges theoretical computer science with practical applications, emphasizing cache-oblivious algorithms and compiler optimizations. Leiserson has received multiple awards for his academic contributions and educational impact, including the ACM-IEEE Ken Kennedy Award and Margaret MacVicar Fellow distinction at MIT. Education: B.S., Yale University, 1975 Ph.D., Carnegie Mellon University, 1981 Research Interests: Leiserson’s work addresses performance engineering challenges in post-Moore’s Law computing. His group develops algorithms, software systems, and hardware strategies for scalable parallelism. Key areas include parallel programming frameworks (e.g., OpenCilk), cache-aware algorithms, and compiler optimizations. He emphasizes making parallel computing accessible to mainstream programmers through tools like Cilk and educational initiatives such as MIT’s Software Performance Engineering course. Projects & Leadership: Leiserson leads the Supertech Research Group and contributed to the Cilk Arts Inc. venture, acquired by Intel. He chairs the MIT Undergraduate Practice Opportunities Program (UPOP) and teaches courses on algorithms and discrete mathematics. His leadership workshops for faculty have educated hundreds worldwide on team management in academia. Awards & Recognition: 2014 ACM-IEEE Ken Kennedy Award IEEE Taylor L. Booth Education Award ACM Paris Kanellakis Theory and Practice Award Member of the National Academy of Engineering Labs & Teams: Active in MIT’s CSAIL, Leiserson collaborates through the Supertech Group and Theory of Computation communities. His current projects include Tapir compiler infrastructure, graph neural network applications for anti-money laundering, and deterministic parallel scheduling algorithms.

• Computational social science
• Intergroup relations
• Immigration
• +3 more

Friedolin Merhout is an Assistant Professor in the Department of Sociology at the University of Copenhagen, holding a tenured-track position. He serves as Director of Graduate Studies for the interdisciplinary MSc in Social Data Science and coordinates the Welfare, Inequality, and Mobility research group. His affiliations include the Copenhagen Center for Social Data Science (SODAS). Education: B.A. from Freie Universität Berlin (Germany), Ph.D. from Duke University (USA) Roles: Director of Graduate Studies, Research Group Coordinator, Faculty Member His research focuses on computational and experimental methods to study intergroup relations using digital trace, text, survey, and administrative data. Key themes include political polarization, radicalization, and the impact of digital media. Recent work combines Google search data with military records to explore discrimination-radicalization links and uses online experiments to address polarization. His publications span computational sociology, political behavior, and research methodology, with notable contributions on Russian IRA influence on U.S. voters and the efficacy of mobile platforms to reduce polarization. He leads the Crowdsourced Replication Initiative, examining variability in social science research outcomes. Office hours are Tuesdays 15:00–16:00 in room 16.1.49.

James R. GreenView profile

Professor

• Machine Learning
• Biomedical Informatics
• Computational Biology
• +9 more

James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications

• Housing Market
• Real Estate Economics
• Tax Policy
• +5 more

Are Oust is a Professor of Financial Economics at the Norwegian University of Science and Technology (NTNU School of Economics) and a Professor II at the Norwegian School of Economics. He specializes in housing market dynamics, real estate economics, and tax policy. His research focuses on housing bubbles, property valuation, and the impact of regulation on real estate markets. Affiliations: NTNU School of Economics (Professor) Norwegian School of Economics (Professor II) Deputy Head of Research at NTNU School of Economics (2021–present) Deputy Director of NTNU Center for Housing and Environmental Economics (2016–present) Education: PhD in Economics, NTNU (2013) Master’s in Accounting and Auditing, Economics, and Business Administration from NHH (Norwegian School of Economics) Bachelor of Science in Economics, NTNU Research Interests: Dr. Oust’s work emphasizes automated valuation models, housing market regulation, energy labeling in real estate, and the interplay between taxation and home ownership. His research has been published in journals such as Quantitative Finance , Journal of Real Estate Research , and Energy Policy . Key Contributions: His studies on housing bubbles, rental market dynamics, and the application of AI in real estate valuation have shaped policy debates. Recent work explores the role of adverse selection in iBuyer models and the predictive power of dwelling conditions in automated valuations. Grants & Leadership: He advises on real estate policy and serves on multiple boards, including the NTNU Center for Housing and Environmental Economics, and private real estate firms like Strinda Eiendom AS. His teaching focuses on personal finance, investment strategies, and tax planning.

• Artificial Intelligence
• Robotics
• Data Science
• +4 more

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

Qiang ZhuView profile

Professor

• Data Science
• Data Management
• Machine Learning
• +3 more

Qiang Zhu is a Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, holding the William E. Stirton Professorship (2017–2024). He founded the Data Science/Management Research Laboratory and is affiliated with the Michigan Institute for Data Science (MIDAS). His research spans data science, data management, and machine learning. Ph.D., University of Waterloo M.S., McMaster University M.Eng., Southeast University B.S., Southeast University Research focuses on advanced data indexing, query optimization, and AI-driven data management, with applications in genomics, network systems, and education. His work integrates machine learning with database systems for scalable solutions. Recent publications include topics in federated learning fairness, digital twin middleware, project-based CS education, and genome data indexing. Scientific contributions recognized through awards like the Wilkes Award (2008), ACM Distinguished Scientist (2013), and Springer Nature Editor of Distinction (2025). 2013–2018: Department Chair NSF, IBM, and Ford grants Over 250 conference committee roles He directs the Data Science/Management Research Lab, focusing on collaborative projects in genome analytics and smart computing infrastructures.

Jia XueView profile

Associate Professor

• Intimate Partner Violence
• Sexual Assault
• AI Ethics
• +6 more

Jia Xue is an Associate Professor at the Factor-Inwentash Faculty of Social Work, University of Toronto, with a joint appointment in the Faculty of Information. She joined U of T in 2018 as an Assistant Professor and was promoted in 2025. Her research focuses on computational approaches to social justice issues, including intimate partner violence, rape myth culture, and AI ethics. She holds a Ph.D. from the University of Pennsylvania, a law degree from Tsinghua University, and postdoctoral training at Harvard University. Her research lab, the Artificial Intelligence for Justice (AIJ) Lab, develops AI tools to study social ills like image-based sexual abuse and school bullying. Key projects include AI-powered chatbots for sexual violence victims and analyzing biases in algorithmic systems. Funded by grants from Connaught, SSHRC, and CIHR, her work bridges social work, computer science, and policy analysis. Jia has authored over 50 peer-reviewed articles in journals like Journal of Interpersonal Violence and Child Abuse & Neglect . Awards include the Ontario Early Researcher Award (2025) and Deborah K. Padgett Early Career Achievement Award (2025). She leads initiatives at the Schwartz Reisman Institute for Technology and Society, focusing on tech’s societal impacts. Teaching emphasizes ethical big data use in social justice contexts. Recent work includes pandemic-related social media analysis and developing interventions for marginalized populations. Her interdisciplinary approach combines machine learning, policy analysis, and qualitative research to address systemic inequities.

Runar Hilleren LieView profile

Research Fellow

• International Investment Law
• Computational Legal Studies
• International Economic Law
• +7 more

Runar Hilleren Lie is a Postdoctoral Research Fellow at the Department of Public and International Law, Faculty of Law, University of Oslo. He is actively engaged in interdisciplinary research at the intersection of law, technology, and international relations, contributing to major projects such as COPIID, NoRDASIL, and CLEANUP. His research interests span International Investment Law , Computational Legal Studies , International Economic Law , Energy Law , and Legal Technology . He employs data-driven and computational methodologies to analyze legal texts, arbitrator behavior, treaty development, and institutional dynamics in international dispute settlement. The most recent publications reveal a strong trend toward empirical and computational analysis of international investment law, particularly focusing on influence networks, authorship prediction, compliance politics, and the evolving role of legal actors in arbitration. His work bridges traditional legal scholarship with cutting-edge data science techniques. He teaches JUS5080 – Programming for Lawyers and JUS5671 – Legal Technology: Artificial Intelligence and Law , reflecting his commitment to integrating technological literacy into legal education. Email: r.h.lie@jus.uio.no, rhlie@jus.uio.no Phone: +47 22859431 Visiting Address: Domus Juridica, 7th floor, Kristian Augusts gate 17, 0164 Oslo Postal Address: Postboks 6706 St. Olavs plass, 0130 Oslo He is affiliated with the Law and Technology (JOT) research group and the Research Group on International Law . His current research projects include: COPIID : Compliance Politics and International Investment Disputes NoRDASIL : Advancing Data Science in Migration Law (NORDFORSK) CLEANUP : Machine Learning for the Anonymisation of Unstructured Personal Data (Research Council of Norway, 2020–2023)