جست‌وجوی استاد و پژوهشگر

Amalia Fokaمشاهده پروفایل

استادیار

• Artificial Intelligence Applications in Arts
• Computer Science for Creative Practices
• Digital Interaction
• +۴ مورد دیگر

Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.

Ion Androutsopoulosمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Ion Androutsopoulos is a Professor of Artificial Intelligence in the Department of Informatics at Athens University of Economics and Business (AUEB), where he also serves as Head of Department. He is founder and co-director of AUEB's Natural Language Processing Group and an Adjunct Researcher at the Digital Curation Unit and "Archimedes" Research Unit of the Research Centre "Athena". His research spans multiple dimensions of Artificial Intelligence with a focus on Natural Language Processing. Key interests include: Machine learning in NLP, particularly deep learning and large language models Question answering and retrieval augmented generation for document collections Dialog systems for new languages and knowledge domains Sentiment analysis and emotion recognition from text and speech Detecting toxic posts and disinformation online Image-to-text generation for medical diagnostics NLP applications in biomedical, legal, and financial domains His recent publications demonstrate strong activity across medical AI (particularly ImageCLEFmed Caption competitions where his group consistently ranks 1st-2nd), legal NLP (LexGLUE benchmark), financial NLP (EDGAR-CRAWLER), and multilingual challenges. His work shows increasing emphasis on large language models, explainability, and practical applications. Notable awards include: Top 2% scientist worldwide (Stanford University database, 2023) Multiple AUEB Excellent Teaching Awards (2017-18, 2021-22, 2023-24) Three consecutive BioASQ awards (2018-2020) Multiple 1st/2nd place rankings in ImageCLEFmed Caption competitions (2021-2025) He actively organizes major events including the Athens Natural Language Processing Summer School (AthNLP) and SemEval tasks. His group maintains strong industry and research collaborations, particularly in medical AI applications where they've developed systems that generate diagnostic captions from medical images with state-of-the-art performance.

Christos Makrisمشاهده پروفایل

دانشیار

• Data Structures
• Information Retrieval
• Data Mining
• +۵ مورد دیگر

Christos Makris is an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece. His academic career spans over two decades with significant contributions to computer science, particularly in data structures, algorithms, and information systems. He maintains active research collaborations and supervises graduate students in his areas of expertise. Dr. Makris's research spans several key areas in computer science with a strong focus on efficient data organization and processing. His work encompasses Data Structures , Information Retrieval , Data Mining , String Management and Processing Algorithms , Computational Geometry , Internet Technologies , Bioinformatics , and Multimedia Databases . His interdisciplinary approach bridges theoretical computer science with practical applications across various domains including web technologies, bioinformatics, and emergency response systems. Analysis of Dr. Makris's publication record reveals a consistent research trajectory focused on efficient algorithms for information management. His work demonstrates evolution from foundational data structure research in the 1990s to more applied work in web technologies, social media analysis, and machine learning applications in recent years. A notable pattern is his ability to adapt core algorithmic techniques to emerging application domains while maintaining theoretical rigor. Dr. Makris maintains an impressive scholarly record with over 3,000 citations, an h-index of 29, and an i10-index of 71 according to Google Scholar metrics. These indicators reflect the significant impact of his research within the computer science community. As an active faculty member, Dr. Makris maintains regular office hours on Tuesdays from 18:00-20:00 and Thursdays from 12:00-14:00. He is accessible via email at makri@ceid.upatras.gr or makri@upatras.gr for academic inquiries and student supervision.

Sarah E. Chasinsمشاهده پروفایل

استادیار

• Programming Languages
• Human-Computer Interaction
• Program Synthesis
• +۳ مورد دیگر

Sarah E. Chasins is an Assistant Professor in the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley, with research spanning programming languages and human-computer interaction. She leads the PLAIT Lab (Programming Languages for Approachable and Inclusive Tools) and serves as a faculty affiliate at the Berkeley Institute for Data Science (BIDS). Her work bridges computer science with social sciences, climate reporting, legal systems, and biological research through extensive collaborations. Dr. Chasins' research focuses on democratizing programming for non-traditional programmers including scientists, social scientists, journalists, and data scientists. Her work emphasizes creating approachable programming tools that help practitioners reach correct programs while understanding, extending, and trusting those programs. She specifically investigates program synthesis, programming tools for scientific domains, and human-centered programming language design. Her research mission aims to make programming a path to a more informed and evidence-driven society rather than just a way to get wrong answers faster. Her recent publications demonstrate a clear trajectory toward making programming more accessible and understandable. The work spans code search techniques, program synthesis approaches, refactoring tools, and studies of how domain experts interact with programming languages. A consistent theme across her research is placing human needs at the center of programming language design and implementation. Dr. Chasins actively mentors PhD students including Justin Lubin, Eric Rawn, Parker Ziegler, Sarah "Slim" Lim, Hellina Hailu Nigatu, David Minh-Duy Cao, and Marlena Preigh. She has previously advised numerous master's and undergraduate students who have gone on to work in both academia and industry. Her teaching includes core courses like CS164 (Programming Languages and Compilers) and specialized courses like CS294-184 (Building User-Centered Programming Tools) and CS39-001 (Technology, Society, and Power). She maintains an active service record in the programming languages community, having served on program committees for major conferences including PLDI, POPL, OOPSLA, and SPLASH. Notably, she co-chaired the Student Research Competition at PLDI 2020 and 2021 and has been involved in organizing workshops focused on programming languages and human-computer interaction.

Alvin Cheungمشاهده پروفایل

دانشیار

• Data Management
• Programming Languages
• Software Systems
• +۴ مورد دیگر

Alvin Cheung is an Associate Professor in the Computer Science Division at UC Berkeley's EECS department. He is affiliated with the Data Systems and Foundations group, Programming Systems group, Sky Lab, and SLICE Lab, and serves as a faculty affiliate at the Berkeley Institute for Data Science. He advises the Data Science Discovery Program and provides technical guidance to industry partners. His research spans data management, programming languages, and scalable software systems, with emphasis on helping users process large datasets efficiently. Key innovations include verified lifting (applying formal methods and ML to infer program properties) and systems for optimizing database-backed applications and geospatial analytics. Recent work explores LLM-driven code optimization and transpilation techniques. His publications (2023-2025) show strong trends in ML-enhanced systems, verified compilation, and data management tools. Articles frequently integrate formal methods, program synthesis, and hardware-aware optimizations across domains like databases, distributed computing, and HCI. Scientific Awards: ACSIC Rock Star Award (2025) Dahl-Nygaard Junior Prize (2024) VLDB Early Career Research Contribution Award (2023) IEEE TCDE Rising Star Award (2020) Sloan Fellowship (2019) NSF CAREER Award (2017) 20+ additional honors Advising & Grants: He mentors PhD/MS students (e.g., Lily Liu at OpenAI, Chenglong Wang at Microsoft Research). Research is funded by: NSF DOE ONR ARO Intel Notable grants include ONR Young Investigator Award and ARO Early Career Program Award. Labs & Teams: Leads projects in Berkeley's Data Systems/Programming Systems groups and collaborates with Sky Lab/SLICE Lab. Manages labs focused on verified compilation (e.g., Tenspiler) and data infrastructure (e.g., Spatialyze).

• Text mining
• Intelligent information retrieval
• Natural language processing
• +۵ مورد دیگر

Efstathios Stamatatos is a Professor in the Department of Information and Communication Systems Engineering at the University of the Aegean, Greece. He has been a faculty member at the university since 2004, following previous research positions at the Wire Communications Lab of the University of Patras, the Polytechnic University of Madrid, the Austrian Research Institute for Artificial Intelligence, and the TEI of Ionian Islands. His primary research interests include Text mining, Intelligent information retrieval, Natural language processing, Machine learning, and Computer music, with a particular focus on authorship attribution, plagiarism detection, and web genre identification. His work bridges computational linguistics, digital forensics, and digital humanities, applying machine learning techniques to analyze textual patterns and stylistic features. Dr. Stamatatos has authored numerous publications on authorship attribution, plagiarism detection, and text classification, with a strong emphasis on developing robust methodologies that work across different domains and languages. His research has evolved from traditional n-gram approaches to more sophisticated techniques involving machine learning ensembles, topic modeling, and recently, addressing challenges posed by generative AI systems. He has served as Associate Editor for the International Journal of Digital Crime and Forensics, and as Editorial Board Member for Language Resources and Evaluation and Information Processing and Management. He has also been Guest Editor for a Special Issue on Plagiarism and Authorship Analysis in Language Resources and Evaluation. Dr. Stamatatos has been instrumental in organizing the PAN (Uncovering Plagiarism, Authorship, and Social Software Misuse) workshop series, serving as co-organizer and contributing numerous overview papers on authorship verification, plagiarism detection, and digital text forensics. He has also served on program committees for major conferences including ACL, SIGIR, ECIR, and AAAI.

• Inorganic Chemistry
• Materials Science
• Metal-Organic Frameworks (MOFs)
• +۷ مورد دیگر

Professor Manos J. Manos is a distinguished faculty member in the Department of Chemistry at the University of Ioannina, where he was promoted to full Professor in April 2024. Previously, he served as Associate Professor (2019-2024), Assistant Professor (2014-2019), and Lecturer (2012-2014). His academic journey began with a PhD in Chemistry from the University of Ioannina in 2003, followed by postdoctoral research at Northwestern University, Michigan State University, and the University of Cyprus. Dr. Manos's research focuses on the synthesis and characterization of advanced inorganic and metal-organic materials, particularly metal-organic frameworks (MOFs) and chalcogenides, with applications in environmental remediation, water purification, and sensing technologies. His work has led to significant breakthroughs in heavy metal ion removal, uranium capture from seawater, and the development of luminescent materials for pollutant detection. Analysis of his 15 most recent publications reveals a strong emphasis on practical environmental applications, particularly the development of MOF-based materials for water treatment. His research demonstrates a clear progression toward creating functional, scalable materials for real-world environmental challenges, with increasing focus on cotton fabric composites, continuous flow systems, and multifunctional sorbents that address multiple contaminants simultaneously. h-index of 38 with over 7,000 citations 7 patents related to heavy metal removal and water treatment technologies Principal Investigator for multiple significant research projects including HFRI Project 348 and HEAVYMOFs (Project code: TAEDK-06193) Extensive service as reviewer for top chemistry journals including Journal of American Chemical Society, Nature Nanotechnology, and Angewandte Chemie Professor Manos has successfully supervised numerous graduate students and postdoctoral researchers, with his group producing high-impact research in materials chemistry. His laboratory focuses on the synthesis of functional materials with practical applications in environmental remediation, particularly water purification technologies. Current projects include the development of MOF-cotton composites for oil spill cleanup, superhydrophobic materials for selective pollutant removal, and advanced sorbents for critical metal recovery.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.

Gail E. Kaiserمشاهده پروفایل

استاد

• Software Engineering
• Program Analysis
• Software Testing
• +۳ مورد دیگر

Gail E. Kaiser is a Professor of Computer Science at Columbia University, where she conducts research in software engineering and security from a systems perspective. Her work spans program analysis, software testing, and pioneering applications of AI to software engineering (AI4SE) and vice versa (SE4AI), with significant contributions dating back to the 1980s. Education: PhD from Carnegie Mellon University ScB from Massachusetts Institute of Technology Research Focus: Professor Kaiser's work centers on static and dynamic program analysis , software testing , and software security . She pioneered applying software engineering testing techniques (particularly metamorphic testing) to machine learning software (SE4AI) during her 2005-2006 sabbatical at Columbia's Center for Computational Learning Systems. Her historical contributions include semantics-focused language-based editors (1980s-1990s, precursors to modern IDEs) and self-adaptation techniques for cloud computing (late 1990s-2000s). Publication Trends: Recent publications (2020-2024) reveal intense focus on AI-software engineering intersections: developing code generation/refinement systems (CYCLE), cross-lingual code search models (REINFOREST), execution-aware pre-training (TRACED), and specialized testing for deep learning systems. Her work consistently bridges traditional program analysis with cutting-edge AI methodologies to solve complex software quality challenges. Academic Service: Kaiser maintains active leadership in the research community through program committee roles at top conferences including PLDI, ICSE, ESEC/FSE, ASE, and SPLASH from 2013-2025. Her GitHub profile (gailkaiser) hosts course materials like COMS W4156 Advanced Software Engineering, demonstrating ongoing educational impact.

Demetris P. Gerogiannisمشاهده پروفایل

پژوهشگر

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +۷ مورد دیگر

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.

Moumoutzis Nektariosمشاهده پروفایل

مدرس

• Digital Storytelling
• E-Learning
• Technology-Enhanced Learning
• +۵ مورد دیگر

Moumoutzis Nektarios is a Lecturer at the School of Electronic & Computer Engineering at the Technical University of Crete , affiliated with the Distributed Information Systems and Applications Laboratory (TUC/MUSIC) . His work bridges the gap between technology, education, and the arts. Email: nmoumoutzis@tuc.gr Office: 141B-65, Science Building/IMMY (L), 1st Floor Phone: 282103 7395 His research focuses on technology-enhanced learning , digital storytelling , and serious games , with a strong emphasis on interdisciplinary approaches that combine design thinking , digital technologies , and arts to create innovative educational experiences. His projects often target diverse audiences, including K-12 students , young children , and children with ADHD . In recent years, his work has explored block-based programming tools , gamification of learning , and collaborative learning environments . Notable contributions include the development of eShadow+ for mixed-reality storytelling, Block-C for teaching C programming, and ViSTPro for spatiotemporal process visualization in education. He has also contributed to frameworks for digital competency , interoperability of e-learning systems , and personalized learning experiences . His work aligns with the European and National Curricula and emphasizes project-based learning and community-driven education .

Işıl Dilligمشاهده پروفایل

دانشیار

• Program analysis
• Program synthesis
• Program verification
• +۴ مورد دیگر

Işıl Dillig is an Associate Professor of Computer Science at the University of Texas at Austin, where she leads the UToPiA research group. Her academic career spans over a decade of significant contributions to programming languages research, particularly in program analysis, verification, and synthesis. Dr. Dillig received all her academic degrees (BS, MS, and PhD) from Stanford University before joining the faculty at UT Austin. Her educational background established the foundation for her innovative research approach that bridges theoretical computer science with practical applications. Her research focuses on developing techniques to make software systems more reliable, secure, and easier to build through advanced program analysis, verification, and synthesis methods. She has pioneered approaches that combine symbolic reasoning with machine learning to tackle complex software engineering challenges across multiple domains including security, databases, and programming language theory. Her work demonstrates exceptional depth in creating practical tools that address real-world software development problems while maintaining strong theoretical foundations. Analysis of Dr. Dillig's publication record reveals a consistent trajectory of innovation in program synthesis, with recent work expanding into neurosymbolic approaches that bridge neural networks with formal methods. Her research shows strong connections between theoretical foundations and practical applications, particularly in security-critical systems, database technologies, and blockchain applications. The evolution of her work demonstrates increasing sophistication in handling complex program structures while maintaining practical usability. Dr. Dillig has received prestigious recognition for her research contributions: Sloan Fellowship NSF CAREER award As a dedicated educator and research leader, Dr. Dillig has served in significant roles including Program Chair for PLDI 2022 and Steering Committee member for PLDI. She has mentored numerous students through her UToPiA research group, guiding research in program synthesis, verification, and analysis. Her work has been supported by substantial research grants that have enabled innovative projects at the intersection of programming languages and security. Dr. Dillig leads the UToPiA (UT Austin Programming, Languages, and Analysis) research group, which focuses on developing novel techniques for program analysis, verification, and synthesis. The group maintains strong collaborations with industry partners and academic institutions worldwide, translating theoretical advances into practical tools that address real software engineering challenges.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.