جستجوی استاد راهنمای دانشگاهی

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.

• Data Systems for AI
• Retrieval-Augmented Generation (RAG)
• Database Systems
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Hossein Fani is a Professor of Computer Science at the University of Windsor, affiliated with the Faculty of Science. He leads the Graph Neural Network Lab, focusing on AI-driven solutions for team recommendation, graph-structured data analysis, and online grooming detection. His work integrates machine learning, natural language processing, and social network analysis to address challenges in collaborative systems and cybersecurity. Research Interests: Graph Neural Networks Expert Team Formation Algorithms Query Refinement and Backtranslation Techniques Online Safety and Grooming Detection Latent Aspect Detection in Text Key Projects include the Graph Neural Network Lab, where his team applies neural networks to graph-structured data, and the development of tools like RePair and LADy for query refinement and aspect detection. He has advised students such as Christine Wong, Farinam Hemmatizadeh, and Soroush Ziaeinejad on projects involving customer feedback analytics and team recommendation systems. His work has contributed to NSERC-funded grants for research infrastructure in the Faculty of Science. Labs/Teams: Graph Neural Network Lab RePair Toolkit Development OpeNTF Benchmark Library

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Stergios Chatzikyriakidis is a Professor of Computational Linguistics at the University of Crete and an Associate Researcher at the Centre for Linguistic Theory and Studies in Probability (CLASP). He holds a BA in Greek Philology (specializing in Linguistics) from Aristotle University of Thessaloniki, an MSc and PhD in Computational Linguistics and Formal Grammar from King’s College London. His career includes roles at Royal Holloway (University of London), CNRS, the Open University of Cyprus, and the University of Gothenburg. From 2016–2021, he served as Associate Director of CLASP. His research spans the intersection of NLP and Theoretical Linguistics, focusing on Natural Language Inference, Dialogue Modelling, Semantic Parsing, and the integration of symbolic logic with Machine Learning. He also develops NLP resources for under-resourced languages and explores constructive type-theoretical semantics. Additionally, he is an accomplished novelist with four Greek-language novels and two short story collections (one in English). His work emphasizes interdisciplinary methods, combining formal grammatical frameworks with probabilistic and computational techniques. Key themes include metaphor detection, sentiment analysis, and the implementation of formal semantics in AI systems.

• Computational Linguistics
• Natural Language Processing (NLP)
• Theoretical Linguistics
• +۹ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر