Find Academic Supervisors

• Natural Language Processing
• Artificial Intelligence
• Information Retrieval
• +7 more

Dr. Rohini K. Srihari is a full Professor and Associate Chair in the Department of Computer Science and Engineering at the University at Buffalo, The State University of New York , and also serves as an Adjunct Professor in the Department of Linguistics within the School of Engineering and Applied Sciences . Her expertise lies at the intersection of natural language processing , artificial intelligence , information retrieval , and AI for social good . Education: PhD in Computer Science, University at Buffalo, 1992 B.Math, University of Waterloo, Canada Research Interests: Dr. Srihari’s research spans a wide range of topics including conversational AI systems (especially empathetic and socially-aware chatbots), disinformation detection and attribution , multilingual text mining for low-resource languages, and predictive analytics for social unrest using heterogeneous data sources. She also explores multimedia information retrieval and AI-driven early warning systems for social and economic disruption. Recent Research Trends: Her recent publications reflect a strong focus on conversational AI and AI for social good , with applications in mental health support, misinformation mitigation, and empathetic dialogue systems. She has led interdisciplinary teams in competitions like the Alexa Prize Socialbot Grand Challenge , where her team won the bronze medal in 2021. Scientific Awards & Recognition: Bronze Prize, Alexa Prize Socialbot Grand Challenge 4 (2021) NSF Grant: Purposeful Conversational Agents based on Hierarchical Knowledge Graphs Holds two US patents in multilingual text mining and face recognition Advising & Funding: Dr. Srihari has supervised numerous PhD and Master's students across departments and has led research funded by NSF , DARPA , IARPA , and the US Government . She has also served as Chief Data Scientist at PeaceTech Lab, leading AI initiatives for conflict prevention. Research Labs & Teams: She directs the Conversational AI for Social Good research group, which focuses on building trustworthy, empathetic, and socially responsible AI systems. The group integrates deep learning, knowledge graphs, and symbolic AI to address real-world societal challenges.

Tejas GokhaleView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +6 more

Tejas Gokhale is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), where he directs the Cognitive Vision Group and serves as Affiliate Faculty at the UMBC Center for AI. His research focuses on perception, learning, reasoning, and communication, with particular emphasis on conceptual characterization of visual scenes. Ph.D. from Arizona State University M.S. from Carnegie Mellon University B.E. from BITS Pilani Dr. Gokhale's research explores interpretation of visual data with incomplete information, recognizing and adapting to novelty and variations, leveraging external knowledge for generalization across contexts, and communicating visual knowledge to machines and humans. His work spans computer vision, machine learning, and artificial intelligence with applications in text-to-image generation, spatial reasoning, and domain generalization. His recent publications demonstrate a strong focus on improving spatial understanding in text-to-image models, concept learning in diffusion models, and enhancing robustness in vision systems. His research has been recognized with a Best Paper Award at CVPR 2024 and featured in AI Magazine. Best Paper Award at VDU Workshop @ CVPR 2024 AI Magazine Article summarizing his work (2024) Tutorial Chair for ICCV 2025 Co-PI for DARPA SciFy program grant (2024) Dr. Gokhale actively mentors students, currently supervising seven Ph.D. students and one M.S. student at UMBC, while also collaborating with students at Arizona State University and other institutions. He has received multiple funding awards including CIDER funding, Cybersecurity Institute funding, START funding, and SURFF funding from UMBC. His teaching includes courses on Computer Vision, Neural Networks, and Robust Machine Learning. He leads the Perception, Prediction, and Reasoning Seminar at UMBC and has organized tutorials at major vision conferences including ECCV 2024 and WACV 2024. His lab, the Cognitive Vision Group, focuses on developing reliable and robust computer vision systems that can understand and reason about visual content.

Zakaria KurdiView profile

Associate Professor

• Text & Data Mining
• Intelligent Tutoring Systems
• Social Network Analysis
• +5 more

Dr. Zakaria Kurdi is an Associate Professor of Computer Science at the University of Lynchburg. He joined the institution in 2016 and specializes in interdisciplinary research at the intersection of artificial intelligence, computational linguistics, and data science. His work spans text and data mining, intelligent tutoring systems, social network analysis, and cognitive modeling of spoken language. Education: PhD in Computer Science, Joseph Fourier University, France MS in Software Engineering, École Nationale Supérieure des Mines de Saint Étienne, France MS in Cognitive Science, Institut National Polytechnique de Grenoble, France BA in Linguistics, Université Lumière, France Research Interests: Dr. Kurdi’s research focuses on applying computational methods to linguistic and healthcare challenges. Key areas include analyzing spoken language disfluencies, developing adaptive educational tools for ESL learners, and leveraging machine learning for dementia detection through language analysis. He also explores bioinformatics applications, such as studying bacterial protein distributions, and social network analysis using datasets like Enron’s emails. Recent Research Trends: His publications (2024–2017) highlight a strong focus on interdisciplinary AI applications, with recent emphasis on medical diagnostics via natural language processing and computational biology. Earlier work includes foundational contributions to spoken language understanding systems and text complexity classification. Labs/Teams: While no specific lab is named, his affiliations imply involvement in interdisciplinary teams within the University of Lynchburg’s Computer Science department, collaborating on projects blending linguistics, AI, and biomedical data analysis.

• Low-Resource Language Processing
• Natural Language Processing (NLP)
• Machine Translation
• +7 more

Dr. Ignatius Ezeani is a Research Fellow in the Department of Computing and Communications at Lancaster University, specializing in Low-Resource Natural Language Processing (LowResNLP). His work focuses on developing tools and methods for under-resourced languages, particularly African languages such as Igbo, and minority languages like Welsh. He leads projects such as the Igbo-English Machine Translation initiative and the Welsh Summary Creator, advancing computational linguistics in these domains. His research interests include machine translation, part-of-speech tagging, named entity recognition, and corpus-based approaches to language processing. He is affiliated with the University Centre for Computer Corpus Research on Language (UCREL), contributing to data science and linguistic resource development. Ezeani supervises PhD student Chiamaka Chukwuneke and collaborates on initiatives like the National Corpus of Contemporary Welsh (CorCenCC). His projects emphasize participatory research, ensuring technologies address real-world linguistic needs in underrepresented communities. Key contributions include the IgboAPI Dataset, IgboBERT models, and frameworks for spatial narrative analysis. Ezeani’s work bridges computational linguistics with societal impact, fostering multilingualism through technology.

• Database Systems
• Natural Language Processing
• Data Mining
• +4 more

Hongrae Lee is a researcher specializing in database systems, natural language processing, and data mining. His work bridges structured data management with language models, focusing on tasks like natural language to SQL translation, similarity joins, and efficient data processing. His research interests include: Database query optimization and similarity search Language model applications for text generation and hallucination correction Web data curation and structured data ecosystems Cloud storage optimization and distributed systems Hongrae Lee's recent publications (2022-2023) highlight trends in large language models (LLMs) for dialogue applications, attributed text generation, and acronym disambiguation with weak supervision. Earlier work (2016-2007) established foundational techniques in database scalability, LSH-based similarity estimation, and geographical data thinning. He has collaborated extensively with researchers at institutions like Google, Seoul National University, and University of British Columbia on projects such as WebTables, LaMDA, and Google Fusion Tables. His contributions span both theoretical advancements (e.g., variance-aware query optimization) and practical systems (e.g., CloudRAMSort, T5-based disambiguation).

• Database Systems
• Data Mining
• Natural Language Processing
• +2 more

Alessandro Fiori serves as an External Lecturer and Teaching Assistant at Politecnico di Torino's Department of Control and Computer Engineering (DAUIN), affiliated with the College of Computer, Film, and Mechatronics Engineering. His instructional responsibilities span database systems, data science, and data visualization courses across undergraduate and graduate programs. His research focuses on: Database system design and optimization Natural language processing for structured data Data mining techniques for complex datasets Multimodal data representation Automated content generation pipelines As a member of the Database and Data Mining Group (DBDM), he contributes to projects involving data modeling tools and NLP-driven data transformation. Recent publications (2023-2025) demonstrate a strong focus on applying natural language processing to database systems, including LLMs for schema interpretation, automated documentation generation from structured data, and multimedia content synthesis. His earlier work includes clustering algorithms for transportation data analysis.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +7 more

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.

Amit KumarView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Approximation Algorithms
• +7 more

Amit Kumar is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Delhi . He teaches several core and advanced courses including Mathematical Programming (COL 756) , Advanced Algorithms (COL 758) , Approximation Algorithms (COL 754) , Introduction to Automata and Theory of Computation (COL 352) , Design and Analysis of Algorithms (COL 351) , Data Structures (COL 106) , Discrete Mathematics (COL 202) , and Numerical Analysis and Scientific Computing (COL 726) . His office is located in Room 417, Bharti Building, and he uses email amitk@cse.iitd.ac.in for communication. His research lies primarily in combinatorial optimization and online algorithms , with recent work focusing on clustering problems with side constraints, understanding biases in evaluation processes, and online allocation with additional constraints. He has made significant contributions to approximation algorithms, stochastic optimization, and fairness in algorithmic systems. His research interests span theoretical foundations and practical applications in algorithm design. The recent publications (2023–2025) reflect a strong trend in fairness-aware algorithms , learning-augmented online algorithms , and advanced clustering techniques . The work spans top-tier venues such as STOC, FOCS, SODA, ICML, and NeurIPS, indicating high impact and interdisciplinary reach. Topics include bias in evaluation, fair service allocation, consensus clustering, and robust sorting, showing a shift toward socially aware and data-driven algorithmic solutions. Scientific Awards: Best paper award at International Symposium on Algorithms and Computation (ISAAC), 2023 Advising and Grants: While specific students and grants are not listed in the provided texts, Amit Kumar has supervised or co-supervised numerous publications with junior collaborators, suggesting active mentoring. His extensive collaboration network (e.g., with Anupam Gupta, Debmalya Panigrahi, Ragesh Jaiswal) indicates leadership in research projects and likely involvement in funded grants, though specific grant details are not mentioned. Labs and Teams: No specific lab or research group name is mentioned in the provided materials. However, his frequent collaborations and supervision of research projects suggest he is part of or leads a research team in theoretical computer science and algorithms at IIT Delhi.

• Multimodal Machine Learning
• Natural Language Processing
• Computer Vision
• +3 more

Rui Cao is a Researcher at the Department of Computer Science and Technology at the University of Cambridge, located in the William Gates Building. Their work focuses on multimodal AI systems, particularly addressing challenges in misinformation detection, hate speech moderation, and the analysis of internet memes. Rui's research integrates computer vision, natural language processing, and large language models to tackle real-world societal issues such as online toxicity and factual verification. Key research emphases include: Multimodal fact-checking systems (e.g., AVerImaTeC dataset) LLM-based hallucination mitigation in vision-language models Automated hate speech detection in memes and live video streams Zero-shot and few-shot learning for visual question answering Tools for meme analysis (e.g., Matk Toolkit) Recent work has explored adversarial data augmentation (HateGAN), modular neural architectures for hateful meme detection (Pro-CAP), and frameworks for resolving conflicting evidence in automated fact-checking. Their contributions span both technical advancements and ethical considerations in AI deployment. Research has been published in top-tier venues, with a focus on applications to social media content moderation and misinformation mitigation. No awards or grants are explicitly listed in the provided materials.

Prem DevanbuView profile

Research Professor

• Empirical Software Engineering
• Naturalness of Software
• Software Engineering education
• +3 more

Prem Devanbu is a Research Professor of Computer Science at the University of California, Davis, where he has been a faculty member since transitioning from his industrial R&D position at Bell Labs in New Jersey. He holds a distinguished position in the Department of Computer Science within the College of Engineering, focusing on cutting-edge research at the intersection of software engineering and artificial intelligence. Dr. Devanbu earned his B.Tech from the Indian Institute of Technology (IIT) Madras and completed his Ph.D at Rutgers University under the supervision of Alex Borgida. His career path from industry to academia has shaped his practical yet research-oriented approach to software engineering problems. Devanbu's research primarily centers on Empirical Software Engineering , the Naturalness of Software , and Software Engineering education . His groundbreaking work on the naturalness hypothesis—that software exhibits statistical properties similar to natural language—has profoundly influenced the field. This research has expanded to explore bimodality in software (its dual nature as both machine-executable code and human-readable text), opening new avenues for analysis and tool development. His recent work heavily focuses on the application of Large Language Models to software engineering tasks, particularly in code summarization, program repair, and type inference. Analysis of Dr. Devanbu's recent publications reveals a clear trend toward leveraging Large Language Models for software engineering tasks. His research demonstrates how statistical properties of code can be exploited to improve software development processes, with particular emphasis on program understanding, documentation generation, and automated repair. The work bridges theoretical insights about code naturalness with practical applications that address real-world software maintenance challenges. Dr. Devanbu has received numerous prestigious awards recognizing his contributions to the field: ACM SIGSOFT Outstanding Research Award (2021) - "for profoundly changing the way researchers think about software by exploring connections between source code and natural language" Alexander von Humboldt Research Award (2022) IEEE Computer Society Harlan Mills Award (2024) ACM Fellow Six "test-of-time" or "10 year most influential paper" awards (MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011, ICSE 2012) Throughout his career, Dr. Devanbu has been actively involved in mentoring the next generation of software engineering researchers, serving on doctoral committees, and participating in New Faculty Symposia to support early-career academics. His research has been supported by significant grants that have enabled his team to explore innovative approaches at the intersection of empirical methods and software tool development. At UC Davis, he has contributed to building a strong software engineering research group that bridges theoretical insights with practical applications. Dr. Devanbu leads research efforts focused on understanding the statistical properties of software and leveraging these insights to build practical tools. His work on the naturalness and bimodality of code has established a framework that continues to influence how researchers approach program analysis and software development. His current team is at the forefront of exploring how Large Language Models can be effectively applied to software engineering tasks while accounting for the unique characteristics of code as a specialized form of human communication.

Steve WilsonView profile

Assistant Professor

• Natural Language Processing
• Computational Social Science
• Information Literacy
• +8 more

Steve Wilson is an Assistant Professor in the Department of Computer Science, Engineering, and Physics at the University of Michigan-Flint, within the College of Innovation and Technology. He holds a PhD in Computer Science & Engineering from the University of Michigan and has previously served as an Assistant Professor at Oakland University and held postdoctoral positions at the University of Edinburgh and the University of Michigan. PhD | 2019 | University of Michigan | Computer Science & Engineering M.S. | 2015 | University of Michigan | Computer Science & Engineering B.Sc. | 2013 | Taylor University | Computer Science/Systems His research focuses on understanding online communication through Natural Language Processing, with emphasis on social context, information literacy, social media narratives, and educational applications. His work bridges computer science, psychology, and social informatics to analyze human behavior in digital environments. The most recent publications highlight a strong trend in using NLP for social good, including detecting misinformation, analyzing educational discourse, improving AI ethics, and understanding social dynamics such as sarcasm, offensive language, and user profiling. His work frequently appears in top venues like ACL, EMNLP, ICWSM, and SemEval, often in collaboration with leading researchers such as Rada Mihalcea and Walid Magdy. MWIN Faculty Innovation Fellow | 2025 | UM-Flint’s Office of Economic Development and EDA University Center for Community and Economic Development UM-Flint Proposal Academy Awardee | 2025 | University of Michigan-Flint Office of Sponsored Research Projects Steve Wilson is the Principal Investigator (PI) on multiple funded research projects, including a National Science Foundation CRII grant on social media framing and an internal UM-Flint grant on AI in STEM education. He co-investigates an NSF REU site on Home Health Technology. He leads the CoCoA Lab at UM-Flint, which focuses on computational analysis of communication and context. He mentors graduate students and is actively involved in developing new courses, including a recent offering in Natural Language Processing.

Matthew TurkView profile

Professor

• Computer Vision
• Human-Computer Interaction
• Augmented Reality
• +5 more

Matthew Turk is the President of the Toyota Technological Institute at Chicago (TTIC) and a Professor Emeritus at the University of California, Santa Barbara (UCSB). He holds dual appointments in Computer Science and the Media Arts and Technology Program at UCSB. His research focuses on computer vision, human-computer interaction, augmented reality, and AI ethics. He earned a BS from Virginia Tech, MS from Carnegie Mellon University, and PhD from MIT. Affiliations: President, TTIC (since 2019) Professor Emeritus, UCSB Department of Computer Science Former Chair, UCSB Computer Science Department and MAT Program Research Interests: Dr. Turk pioneers vision-based interfaces, mobile computing, and ethical AI governance. His work bridges technical innovation with societal impact, addressing bias in AI systems and legal implications of emerging technologies. He co-founded Caugnate (acquired by PTC/Vuforia), advancing AR collaboration. Awards & Recognition: ACM Fellow, IEEE Fellow, IAPR Fellow 2011-2012 Fulbright-Nokia Distinguished Chair Best Paper Awards at CVPR, ICCV, and ICMI Grants & Leadership: Led NSF-funded projects in computer vision and served as Program Chair for major conferences (e.g., CVPR, ACM Multimedia). Active in policy initiatives through the Computing Community Consortium. Labs & Teams: Directed the UCSB Four Eyes Lab, focusing on imaging, interaction, and innovative interfaces.

Richard YuView profile

Professor

• Wireless Networks
• Edge Computing
• Federated Learning
• +7 more

Richard Yu is a Professor at the Carleton School of Information Technology, Carleton University. His research focuses on cross-layer design in wireless systems, security in wireless networks, green information technologies, and multimedia over wireless networks. He holds a Ph.D. from the University of British Columbia and is a Professional Engineer (P.Eng). His work integrates machine learning, edge computing, and reinforcement learning to address challenges in vehicular networks, autonomous systems, and IoT. Education Ph.D., University of British Columbia Research Interests Professor Yu’s research spans wireless communication systems , edge computing , and AI-driven optimization . He explores novel paradigms such as federated learning for industrial IoT, secure UAV-assisted satellite networks, and generative AI for video understanding. His work emphasizes practical applications in autonomous vehicles, smart infrastructure, and industry 4.0 systems. Recent Trends in Publications His recent articles highlight advancements in reinforcement learning frameworks for resource allocation, safety-critical AI in video generation, and cross-modal fusion for embodied agents. He also investigates hybrid NOMA/OMA systems and quantum-inspired algorithms for next-generation networks. Advising & Grants No specific advisees are listed, but his research is supported by grants focusing on edge intelligence, vehicular networks, and secure computing. Labs & Teams Associated with Carleton’s School of Information Technology research groups, focusing on wireless systems and AI for industrial applications.

Panagiotis StamatopoulosView profile

Assistant Professor

• Artificial Intelligence
• Constraint Programming
• Logic Programming
• +9 more

Panagiotis Stamatopoulos is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been employed since 1993. He holds a PhD in Computer Science (1988) and a Diploma in Physics (1982) from the University of Athens. His research spans artificial intelligence, constraint programming, natural language processing, machine learning, and optimization. Specific interests include: Hybrid approaches combining constraint programming with operations research Natural language understanding for database access Parallel processing and distributed constraint solving Multi-agent systems and web intelligence applications His publications show consistent focus on constraint satisfaction algorithms, text summarization techniques, educational timetabling systems, and AI applications in diverse domains like sports analytics and robotics. Recent works demonstrate increased attention to NLP evaluation metrics and multimodal learning. He has supervised numerous diploma theses and led projects funded by the European Union (EDS, APPLAUSE, PARACHUTE, PARROT), University of Athens, and Olympic Airways. Stamatopoulos teaches undergraduate courses in Introduction to Programming and Logic Programming, plus postgraduate courses in Advanced Artificial Intelligence. He previously taught Artificial Intelligence, System Programming, and Expert Systems.

• Big Data Analytics
• Text Mining
• Machine Learning
• +6 more

Dr. Wei Zhang is a Senior Lecturer and Associate Head of People and Culture at the University of Adelaide's School of Computer and Mathematical Sciences, with a secondary appointment as Honorary Lecturer at Macquarie University. She holds a PhD in Computer Science from the University of Adelaide (2017) and leads research in big data analytics, text mining, machine learning, and IoT. Her work has resulted in over 100 peer-reviewed publications, including top-tier journals like ACM Computing Surveys and IEEE Communications Surveys. Dr. Zhang currently holds an ARC Early Career Industry Fellowship (IE230100119) and leads an ARC Discovery Project (DP240103070), with total funding exceeding AUD $3M from ARC, Google, and DST over five years. Her research focuses on text summarization, AIoT systems, adversarial attacks on neural networks, and medical information extraction. Professional roles include Program Coordinator, Course Coordinator, and outreach contact at her school. She actively contributes to academic governance as Associate Head of People & Culture and serves on conference committees (ACL, KDD, WWW) and journal editorial boards. Industrial collaborations span six years with roles in project leadership and technical advisory roles across multiple sectors. Dr. Zhang is a member of ACM, IEEE, and ACS, and her work has been recognized by prestigious awards such as top 100 global researchers in Network Security (SciVal). Her lab develops AI-driven solutions for IoT-enabled agriculture (e.g., mushroom cultivation monitoring) and smart construction systems, with applications in energy optimization, healthcare, and environmental monitoring.