Find Academic Supervisors

Sorin LernerView profile

Professor

• Programming Languages
• Program Verification
• Security and Privacy
• +3 more

Sorin Lerner is a Professor and Chair of the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). His research spans programming languages, program verification, security/privacy, and human-computer interaction (HCI). He actively seeks graduate students and postdocs to join his research group. Education: PhD in Computer Science, University of Washington (2006) Research Interests: Sorin’s work focuses on applying programming language techniques to program verification, security, and live programming environments. His projects include the Rango system for retrieval-augmented proving, Projection Boxes for live programming visualization, and Radar for concurrent program analysis. He also explores gamification in education through Proof Games and Code Spells. Awards: Recipient of the PLDI 2003 Best Paper Award ICSE 2025 Distinguished Paper Award for Rango Teaching: Sorin teaches courses on programming languages (CSE 130, CSE 230), compilers (CSE 231), and specialized topics in automated theorem proving (CSE 291). He emphasizes live programming and interactive tools to enhance learning. Lab & Collaborations: Leads the PL@UCSD research group, collaborating with institutions like Microsoft, Google, and The University of Texas at San Antonio. Projects often involve cross-disciplinary efforts in software reliability, formal methods, and educational technology.

• Automated Software Productivity
• Code Reliability and Quality
• Program Synthesis
• +5 more

Dr. Ali Shokri is a tenure-track Assistant Professor in the Department of Computer Science at the University of Houston, where he joined in May 2025. His research focuses on Automated Software Productivity (code synthesis, translation) and Code Reliability & Quality (bug detection, security assurance). Ph.D. in Computer Science (Rochester Institute of Technology, 2023) M.Sc. in Information Technology (Tarbiat Modares University, 2014) B.Sc. in Software Engineering (University of Tehran, 2007) His recent work explores program synthesis and formal verification applied to security and reliability. Publications in top venues like ICSE, OOPSLA, and ASE highlight his contributions to binary-level pointer analysis , taint-based call graphs , and interprocedural synthesis for security. Dr. Shokri has served on program committees for conferences including ICSE’25, USENIX’24, and ASE’22. He actively recruits PhD students aligned with his focus areas and teaches COSC 4315 - Programming Languages and Paradigms at UH. First-place award winner at ACM ASE’21 2018 Data61 research scholarship 2018 University of Melbourne PhD admission with fellowship He previously worked as a postdoctoral research associate at Virginia Tech’s Electrical and Computer Engineering department (2023) and was a research intern at Google and PARC during his PhD. Two patents emerged from his work on program synthesis and mathematical model derivation.

Reuven Avi-YonahView profile

Professor

• Corporate Taxation
• International Taxation
• Tax Competition
• +3 more

Reuven Avi-Yonah is the Irwin I. Cohn Professor of Law at the University of Michigan Law School and director of the International Tax LLM Program. He holds appointments in the University of Michigan’s Weiser Center for Europe and Eurasia. His expertise spans corporate and international taxation, with a focus on tax competition, cross-border tax policies, and global tax frameworks. He advises the U.S. Treasury and OECD, chairs the OECD’s International Network for Tax Research steering group, and is affiliated with Oxford University’s Centre for Business Taxation. Avi-Yonah earned a BA (summa cum laude) in History from Hebrew University (1983), followed by an AM (1984), PhD (1986) in History, and JD (magna cum laude, 1989) from Harvard University. Prior to academia, he practiced law at top firms in New York and Boston, and taught law at Harvard and history at Boston College. His research addresses modern tax challenges like digital services taxation, corporate tax avoidance, and global minimum tax systems. Recent work critiques retaliatory taxation, defends public access to multinational tax data, and advocates for coherent post-Moore v. U.S. tax reforms. He has authored over 150 publications, including seminal books like *Advanced Introduction to International Tax* (2015) and *International Tax as International Law* (2007). Avi-Yonah holds prestigious fellowships (American Bar Foundation, American College of Tax Counsel) and leadership roles in institutions like the American Law Institute. He advises governments on tax policy and frequently testifies before U.S. congressional committees.

• Accessibility
• Deaf Studies
• Human-Computer Interaction
• +4 more

Abraham Glasser serves as an Assistant Professor in the Department of Accessible Human-Centered Computing within Gallaudet University's School of Science, Technology, Accessibility, Mathematics, and Public Health. His teaching portfolio includes graduate courses such as AHC-604 Applied Information Accessibility, AHC-691 Thesis Accessibility Project Planning, and specialized topics in accessibility standards and data visualization, reflecting his commitment to advancing accessible technology education. Professor Glasser's research centers on dismantling digital barriers for deaf and hard of hearing communities through innovative human-computer interaction solutions. His work pioneers sign language interfaces for personal assistants, investigates captioning system optimization, and explores AI-driven accessibility enhancements. Methodologically, he combines empirical user studies with technical development to create genuinely inclusive technologies that address real-world communication challenges faced by deaf users in digital environments. Analysis of his recent publications reveals a strong trajectory toward integrating artificial intelligence with accessibility needs, particularly through large language models for caption customization and robust sign language recognition systems. His research consistently prioritizes deaf user perspectives in technology design, with increasing focus on ethical AI implementation and workplace accessibility solutions as evidenced by his 2025 publications on employment barriers and AI action planning. Professor Glasser actively mentors graduate students, currently advising five master's candidates in accessibility-focused research. His professional service includes leadership roles as Chair for the ASSETS 2023 Student Research Competition and Associate Chair for ACM CHI 2024, demonstrating significant contributions to the accessibility research community. He has secured internal funding through Gallaudet's Faculty Development Committee grant to advance his accessibility initiatives.

Yang ShiView profile

Assistant Professor

• Educational Data Mining
• Learning Analytics
• AI in Education
• +4 more

Yang Shi is an Assistant Professor of Computer Science at Utah State University, specializing in educational data mining, learning analytics, and AI-driven approaches to enhance computing education. He focuses on developing data-driven methods to improve intelligent tutoring systems and student modeling in programming education. His research spans programming language processing, software analysis, and deep learning applications in education. Shi actively contributes to interdisciplinary conferences and workshops, including EDM, LAK, and SIGCSE, and has organized the CSEDM workshop. His work addresses challenges in scaling educational tools, detecting academic integrity issues in code submissions, and leveraging large language models for programming instruction. He explores novel pedagogical strategies, such as comic-based learning toolkits and the impact of on-demand code examples on novice programmers. Shi's research integrates machine learning and human-AI collaboration to address barriers in computing education, such as anomaly detection in programming datasets and improving code tracing question generation. He investigates how AI feedback systems and generative models can enhance student engagement and learning outcomes. His multi-disciplinary approach bridges computer science and educational theory, emphasizing practical applications to improve STEM education accessibility and effectiveness.

Chandan ReddyView profile

Professor

• Machine Learning
• Natural Language Processing
• Healthcare Analytics
• +5 more

Chandan Reddy is a Professor in the Department of Computer Science at Virginia Tech. He holds a Ph.D. from Cornell University and an M.S. from Michigan State University. His research focuses on Machine Learning, Natural Language Processing, and their applications in Healthcare, Software, Transportation, and E-commerce. His work has been funded by NSF, NIH, DOE, DOT, and industries, resulting in over 200 peer-reviewed publications. Notable awards include the Best Application Paper at SIGKDD 2010 and the Franz Edelman Award finalist in 2011. Education: Ph.D., Computer Science, Cornell University M.S., Computer Science, Michigan State University Research Interests: Machine Learning and NLP applied to healthcare analytics, big data systems, and complex data challenges. His work emphasizes scalable algorithms, fairness in AI, and generative models. Recent projects include scientific equation discovery via LLMs and bias mitigation in language models. Publications Trends: Recent work explores LLM-driven reasoning, hyperbolic neural networks, and healthcare informatics. Key areas include data privacy (e.g., synthetic medical data), interpretable models (e.g., time-series clustering), and graph-based methods for product search. Awards: Best Application Paper Award at ACM SIGKDD 2010 Best Poster Award at IEEE VAST 2014 Best Student Paper Award at IEEE ICDM 2016 INFORMS Franz Edelman Award Competition Finalist 2011 Advising & Grants: Advises graduate students on machine learning research, with grants supporting interdisciplinary projects in healthcare, transportation, and AI ethics. Leads the GraphZoo toolkit for hyperbolic GNNs and collaborates on large-scale medical data initiatives. Labs/Teams: Directs research in Virginia Tech’s Data Analytics Lab, focusing on big data platforms, healthcare analytics, and explainable AI systems. Active in developing open-source tools like GraphZoo for graph neural networks.

• Conceptual Modelling
• Enterprise Modeling
• Requirements Engineering
• +3 more

Monique Snoeck is a full professor and coordinator of the Information Systems Engineering Research Group (LIRIS) at KU Leuven's Faculty of Economics and Business (FEB). She serves on the FEB Council and Campus Council while contributing to DigiSoc and Leuven.AI institutes as a member focused on digital society and AI initiatives. Her research pioneers conceptual modelling, enterprise modeling, requirements engineering, and model-driven engineering, notably through the MERODE methodology and MERLIN prototyping tool. Current work integrates AI with learning analytics for education and develops automatic UI generation from conceptual models, alongside capacity-building projects for Cuba's software industry using model-driven approaches. Recent publications reveal converging trends in LLM-enhanced conceptual modeling education, XAI applications for credit risk analysis, and digital process twins for business scenario simulation. Key themes include model validation techniques, IoT application patterning, and multi-model integration for domain-process alignment. She supervises PhD candidates Tiukhova, E. (credit risk/XAI) and Verbruggen, C. (multi-modelling), leading grants including 'Explanations in AI for Learning Analytics' (2025-2029), 'Personalized Feedback Generation' (2024-2028), and 'Cuba Software Industry Capacity Building' (2024-2029) as promotor. As LIRIS leader, she drives model-driven engineering research while collaborating with Leuven.AI on AI integration and DigiSoc on digital public service co-creation initiatives.

• Robotics
• Industry 4.0
• Predictive Maintenance
• +5 more

Prof. Hans Wernher van de Venn is a Professor at the ZHAW School of Engineering's Institute of Mechatronic Systems, Zurich, Switzerland. His work focuses on Industry 4.0, robotics, predictive maintenance, and human-robot collaboration. He leads and collaborates on projects such as SmartAssets, Cybathlon 2024, and Robo-Mate, aiming to enhance industrial automation and safety. Research Interests: His research spans robotics systems, digital twins, and sustainable manufacturing. Notable contributions include predictive maintenance algorithms using autoencoders and domain adaptation techniques, as well as advancements in human-robot interaction safety through deep learning. He has also pioneered wearable robotic exoskeletons for industrial applications. Projects & Awards: Over 20 projects highlight his leadership in Industry 4.0 innovation. Recent work includes testing frameworks for digital twins, generative SDK development, and applying ChatGPT for structured industrial data. His publications emphasize practical applications of AI in manufacturing and healthcare robotics. Grants & Labs: Active in collaborative grants focusing on smart factories, maintenance systems, and embedded systems. His team develops solutions at the intersection of mechatronics and software engineering, with a focus on real-world deployment.

Daniel ZingaroView profile

Professor

• Computer Science Education
• Educational Technology
• Programming Pedagogy
• +3 more

Daniel Zingaro is a Professor, Teaching Stream and Associate Chair (CSC) in the Department of Computer Science at the University of Toronto Mississauga. He holds a PhD from the Ontario Institute for Studies in Education (OISE) at the University of Toronto, focusing on Computer Science Education. His research emphasizes measuring and improving student learning in CS, particularly through concept inventories and Peer Instruction methodologies. Education: PhD in Computer Science Education, OISE, University of Toronto Research Interests: Zingaro's work centers on pedagogy in introductory CS courses, including the design of effective teaching strategies, the impact of Peer Instruction, and the development of assessment tools like the Basic Data Structures Inventory (BDSI). He also explores the role of generative AI in programming education and competitive programming pedagogy. Publications: His work spans textbooks (e.g., Algorithmic Thinking , Learn to Code by Solving Problems ) and peer-reviewed articles on topics like student performance prediction and educational technology. Recent trends focus on integrating AI tools like GitHub Copilot into teaching and assessing foundational CS concepts. Awards: Best Paper Award (Journal of Online Learning and Teaching, 2012) Teaching & Advising: Teaches courses ranging from introductory programming to advanced topics like Operating Systems and Algorithms. He actively develops curriculum materials and advocates for student-centered learning approaches. Current initiatives include exploring LLMs' impact on CS1 pedagogy and expanding accessible educational resources in multiple languages. Labs/Teams: Leads projects on AI-assisted programming education and collaborates with institutions globally to refine concept inventories and Peer Instruction techniques.

• Intelligent User Interfaces
• Computer Science Education
• Human-Robot Interaction
• +3 more

Tovi Grossman is an Associate Professor in the Department of Computer Science at the University of Toronto. He holds the NSERC/Autodesk Industrial Research Chair in Human-Computer Interaction (HCI) and has received prestigious awards like the Alfred P. Sloan Research Fellowship (2021) and the NSERC E.W.R. Steacie Memorial Fellowship (2020). His research focuses on intelligent user interfaces, mixed reality, and AI-driven interaction techniques. Education: Ph.D. in Human-Computer Interaction from the University of Toronto. Previously, he was a Distinguished Research Scientist at Autodesk Research. Research Interests: Grossman explores how emerging technologies like AI and mixed reality can enhance user experiences. His work spans intelligent UIs, human-robot interaction, and education technologies. Notable projects include Autodesk Sketchbook Motion (Apple iPad App of the Year 2016) and Geppetto (CHI 2019 Best Paper Award). Articles Trends: Recent work emphasizes AI-assisted creativity (e.g., generative design tools), mixed reality applications in education, and human-AI collaboration in programming and robotics. Over 180 peer-reviewed articles and 100+ patents highlight his contributions. Awards: 18+ best paper awards, Sloan Fellowship, NSERC Chairs. Grants: NSERC/Autodesk Chair (2019–2024), multiple industry collaborations. Team: Supervises a dynamic team of Ph.D./M.Sc. students and undergraduate researchers, focusing on HCI innovation. Collaborates with Toronto-area industry labs like Autodesk.

Ashique KhudaBukhshView profile

Assistant Professor

• Natural Language Processing (NLP)
• AI for Social Impact
• Computational Social Science
• +4 more

Ashique KhudaBukhsh is an Assistant Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He directs the Social Insight Lab and is affiliated with the ESL Global Cybersecurity Institute. His research focuses on NLP and AI for Social Impact, addressing challenges in multilingual social media analysis, political polarization, and auditing AI systems for unintended harms. His work has been featured in top AI conferences and media outlets like The New York Times and BBC. Education: PhD in Artificial Intelligence from Carnegie Mellon University (2017). He joined RIT in 2021. Notable awards include the RIT GGCIS Emerging Scholar Award (2023) and selection for the AAAI 2024 New Faculty Highlight program. Research Interests: Machine Learning and NLP applications for social issues Political polarization analysis through AI Audit of generative AI for harmful content Multilingual and noisy social media text processing Teaching: Offers courses like 'Tracking Political Sentiments Using Machine Learning' (co-taught with political scientists) and advanced data science seminars. Current students include Sujan Dutta, Arka Dutta, and Soumyajit Datta. Awards and Media: Over 100 media mentions including coverage on BBC and Wired. His team exposed toxic content in Google's PaLM2 model, leading to model improvements. Labs and Collaborations: Leads the Social Insight Lab, collaborating on projects like analyzing gender bias in Bollywood and AI-based responses to global crises.

• Machine Learning
• Data Science
• Artificial Intelligence
• +7 more

Jing Rui He is a Professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign, and Director of the MSIM Program. He holds affiliate positions in the Siebel School of Computing and Data Science and the Center for Digital Agriculture at the National Center for Supercomputing Applications (NCSA). His research focuses on machine learning, graph theory, and AI ethics, with emphasis on anomaly detection, federated learning, and trustworthy AI systems. He has received multiple accolades including AAAI Senior Member (2023), ACM Distinguished Member (2023), and the NSF CAREER Award (2019). Research interests include graph-based learning, temporal networks, and ethical AI applications. He leads initiatives in climate data modeling and digital agriculture through NCSA collaborations. Notable contributions include developing robust federated learning frameworks and graph anomaly detection algorithms. His work bridges theoretical advancements with practical applications in cybersecurity, recommendation systems, and climate science. Recent publications highlight innovations in large language model safety, multi-modal climate benchmarks, and fair anomaly detection. He actively participates in interdisciplinary projects, such as the TrustLOG workshop on trustworthy graph learning. His research is supported by grants from NSF and industry partnerships, emphasizing translational research with societal impact. Awards: AAAI Senior Member (2023), ACM Distinguished Member (2023), IEEE Senior Member (2020) Grants & Advising: NSF CAREER Award (2019), advises on interdisciplinary projects at NCSA and Siebel School Labs/Teams: Center for Digital Agriculture, TrustLOG workshop leadership, GLB (Graph Learning Benchmarks) initiatives

• Health Information Technology
• Older Adults Health
• Conversational Agents
• +3 more

Jessie Chin is an Assistant Professor at the University of Illinois at Urbana-Champaign, holding affiliations across multiple institutions including the School of Information Sciences, Educational Psychology, Coordinated Science Lab, Beckman Institute, and National Center for Supercomputing Applications (NCSA). Her research focuses on health information technology, human-computer interaction, and aging populations. She investigates how conversational agents, voice user interfaces, and smart devices can improve health literacy, behavior change, and well-being among older adults. Education: Not explicitly stated in provided texts. Her work integrates cognitive science principles with design methodologies to create inclusive technologies. Key areas include: Co-designing voice interfaces for older adults Evaluating smart speaker feasibility in behavioral interventions Examining social dynamics in health peer discussions Exploring motivational states via large language models Recent studies highlight metaphor-driven conversational agent design, privacy considerations in aging tech, and health belief modeling through social media analysis. She collaborates across disciplines, leveraging computational methods and mixed-methods research to address health inequities and technology adoption barriers. Affiliated with interdisciplinary centers like the Beckman Institute and NCSA, her work bridges health informatics, AI ethics, and aging research.

Víctor Ponce LópezView profile

Research Fellow

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Víctor Ponce López is a Senior Research Fellow in AI for Health at University College London's Department of Medical Physics and Biomedical Engineering and Research Department of Pathology. He holds affiliations at UCL Energy Institute's IslandsLab group, the European Laboratory for Learning and Intelligent Systems (ELLIS), and the University of Bristol's Department of Computer Science as an Honorary Research Associate. His research focuses on AI applications in healthcare, disaster response, and social computing, leveraging machine learning, computer vision, and deep learning. He has authored over 30 publications, including work on medical AI, disaster behavior analysis, and gesture recognition. He supervised multiple students and contributed to projects like SPHERE for home healthcare systems and ChaLearn LAP challenges. His educational background includes a Ph.D. (2016) and M.Sc. (2012) from the University of Barcelona and Polytechnic University of Catalonia. Education: Ph.D., Mathematics and Computer Science, University of Barcelona (2016) M.Sc., Artificial Intelligence, Polytechnic University of Catalonia (2012) B.S., Computer Science, University of Barcelona (2010) Research Interests: AI in healthcare, disaster response analysis, computer vision, deep learning, gesture/action recognition, and multimodal signal processing. Notable Projects: SPHERE: Smart home sensors for elderly care ChaLearn LAP challenges on personality analysis ReMeDi: Dementia assistive technology Recent Articles Overview Recent work includes applying large language models to medical question classification (2024), using search data for early cancer detection (2024), and analyzing social media behavior during disasters (2022). His research spans from geophysical deformation prediction (2021) to calorie estimation via silhouette analysis (2018). Early contributions include gesture recognition frameworks (2015–2011) and multimodal communication analysis in criminal mediation (2015). Grants & Students Supervised 7+ students including Yue Sun (B.Sc.), Xiang He (M.Sc.), and Baiyu Chen (Ph.D.). Collaborated on EU-funded projects like ReMeDi and ChaLearn LAP. His work bridges academia and industry via partnerships with Eurecat, GSMA, and Orange. Labs & Teams Active in UCL's VILab (Visual Information Lab) and the ChaLearn initiative. Part of the SPHERE consortium for home healthcare technology.

Minjia ZhangView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +9 more

Minjia Zhang is a tenure-track Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Department of Electrical and Computer Engineering (ECE) and the Center for Artificial Intelligence Innovation (CAII) at the National Center for Supercomputing Applications (NCSA). He leads the Supercomputing System and AI Lab (SSAIL) and previously served as a Principal Researcher at Microsoft Research Redmond from 2016 to 2023, contributing to projects like DeepSpeed and DeepCPU. Education: Ph.D. in Computer Science and Engineering from Ohio State University (2016), advised by Prof. Michael D. Bond. Prior to UIUC, he held visiting positions at Microsoft Research during his doctoral studies. His research focuses on efficient machine learning systems, including training/inference optimization on parallel/distributed/heterogeneous hardware, model compression, and large-scale AI applications such as agentic AI and vision-language models. Research Interests: Large-scale DL/AI applications (e.g., agentic AI, multimodal systems), effective efficiency algorithms (model compression, data efficiency), and high-performance ML systems. He emphasizes cross-disciplinary collaboration, integrating system design with AI advancements to tackle scalability challenges in scientific discovery and industrial deployment. He has been recognized with the NSF CAREER Award (2024), Honorable Mention of the ICLR 2024 Outstanding Paper Award, and OOPSLA 2015 Distinguished Paper and Artifact Awards. His work has been applied to Microsoft products like Bing and Azure, enhancing latency and cost efficiency. Advising and Grants: Actively mentoring students in PhD/Master's/Undergraduate programs, emphasizing systems and AI. Recipient of NSF grants and the Amazon PhD Fellowship (Xinyu Lian). His lab's VecFlow and MiniKV projects highlight contributions to vector databases and efficient LLM inference. Labs/Teams: SSAIL Lab, collaboration with Microsoft Research (DeepSpeed), BigScience consortium (BLOOM 176B model), and NCSA's CAII. His work bridges systems research and AI, targeting high-impact applications in science and industry.