جستجوی استاد راهنمای دانشگاهی

Wenqi Shiمشاهده پروفایل

استادیار

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +۳ مورد دیگر

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.

• Data Quality
• Database Systems
• Data Profiling
• +۷ مورد دیگر

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

Gady Agamمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۸ مورد دیگر

Dr. Gady Agam is an Associate Professor in the Department of Computer Science at Illinois Institute of Technology (Illinois Tech), affiliated with the College of Computing. His primary research focuses on Computer Vision, Machine Learning, and Artificial Intelligence, with applications in medical imaging, remote sensing, and security systems. He leads the Visual Computing Lab, which explores topics such as deep learning, geometric modeling, and computational methods for data analysis. Dr. Agam teaches courses including CS584 (Machine Learning), CS512 (Computer Vision), and CS577 (Deep Learning). His research has resulted in over 100 peer-reviewed publications and collaborations with organizations like SPIE. He has held leadership roles in conferences such as Document Recognition and Retrieval and has contributed to industry-relevant projects like the MuscleX software. His academic contributions include advancements in image registration, feature detection, and automated medical diagnostics. He actively supervises graduate students in his lab and maintains partnerships with academic and industrial entities to drive innovation in visual computing technologies.

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +۲ مورد دیگر

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .

Tsz-Yan Milly Loمشاهده پروفایل

دانشیار

• Pediatric Critical Care
• Data Informatics
• Neurocritical Care
• +۳ مورد دیگر

Dr. Tsz-Yan Milly Lo is an Honorary Reader at the University of Edinburgh’s Usher Institute and a Consultant Paediatric Intensivist at the Royal Hospital for Children and Young People in Edinburgh. She leads the Research Programme in Paediatric Critical Care Medicine and holds the NHS Research Scotland Career Research Clinician Award. Her work focuses on leveraging data informatics to improve outcomes for critically ill children, particularly in neurocritical care and brain trauma. She leads international initiatives like KidsBrainIT (a EU-funded pediatric brain trauma data initiative) and Window in the Brain (developing seizure detection tools). Dr. Lo’s education includes clinical training across Edinburgh, Birmingham, and Melbourne, complemented by a PhD and post-doctoral fellowships in Edinburgh and Toronto. Her research spans clinical informatics, multi-disciplinary data integration, and translational medicine. Key collaborations include academic, clinical, and industry partnerships (e.g., with BrainsView Ltd and engineers at the University of Edinburgh). Her scientific impact includes over 20 peer-reviewed publications, with recent work emphasizing intracranial pressure monitoring, EEG-based seizure detection, and pediatric critical care quality improvement. Her grants total over €1M, including EU NEURON funding and MRC support. She supervises a dynamic research team focusing on innovation in critical care informatics and clinical excellence. Grants: £90,953 (G-WiB), £298,740 (WiB-2), £103,164 (WiB-1), €621,834 (KidsBrainIT). Public Engagement: Launched Scotland’s first PPIE group for pediatric critical care (Intensive-Share). Her lab, IMPACT-ACE, emphasizes clinician-scientist collaboration to drive evidence-based improvements in pediatric critical care. Current projects include global seizure detection tools and pediatric brain trauma big-data analytics.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

J. Ian Munroمشاهده پروفایل

استاد

• Data Structures
• Space Efficient Data Structures
• Algorithm Design and Analysis
• +۷ مورد دیگر

J. Ian Munro is a University Professor and Canada Research Chair in Algorithm Design at the Cheriton School of Computer Science, University of Waterloo. He has been a faculty member at Waterloo since completing his PhD at the University of Toronto in 1971 and was appointed University Professor in 2006, one of the highest honors the university bestows on its faculty. Dr. Munro's research focuses on the design, analysis, and implementation of efficient algorithms and data structures, with particular emphasis on space-efficient solutions. His work spans several key areas including succinct data structures, tree algorithms, text search methods, priority queues, and memory management for data structures. His research has had significant impact on how we understand the theoretical limits of data representation while maintaining efficient query performance. The trajectory of his recent publications (2020-2024) reveals a continued focus on pushing the boundaries of space efficiency in data structures, particularly for graphs and trees. His work increasingly addresses dynamic scenarios where data structures must be updated efficiently while maintaining query performance. The research shows strong connections between information theory, combinatorics, and practical algorithm design, with applications spanning database systems, computational geometry, and text processing. Fellow of the Association for Computing Machinery (FACM) Elected Fellow of the Royal Society of Canada (FRSC) in 2003 Canada Research Chair in Algorithm Design Dr. Munro has supervised over 24 PhD students throughout his career and has been actively involved in teaching advanced courses on data structures and algorithms. His research has been consistently funded through competitive grants, including his Canada Research Chair position. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network across numerous publications. As a member of both the Algorithms and Complexity Group and the Data Systems Group at Waterloo, Dr. Munro contributes to interdisciplinary research that bridges theoretical computer science with practical systems challenges. His work continues to influence both theoretical understanding and practical implementations of efficient data management solutions.

Katerina Sotirakiمشاهده پروفایل

استادیار

• Theoretical Cryptography
• Post-Quantum Cryptography
• Complexity Theory
• +۴ مورد دیگر

Katerina Sotiraki is an Assistant Professor in the Department of Computer Science at Yale University, associated with the Archimedes AI research group. Her work focuses on theoretical cryptography, post-quantum cryptography, complexity theory, and secure computation. She holds a Ph.D. (2020) and M.S. (2016) in Computer Science from MIT, and a B.S. in Applied Mathematics (2013) from the National Technical University of Athens. Prior to Yale, she was a postdoc at UC Berkeley working with Raluca Ada Popa and Alessandro Chiesa, under the guidance of Vinod Vaikuntanathan at MIT. Research Interests : Design and analysis of cryptographic protocols resilient to quantum computing Efficient zero-knowledge proofs and succinct arguments Algorithmic game theory and fair division problems Lattice-based cryptography and post-quantum security Awards & Honors : Winner of 7th iDASH Competition (Track 3) - 2018 Chateaubriand Fellowship - 2018 Paris C. Kanellakis Fellowship (MIT EECS) - 2013 Key Contributions : Developed HOLMES - a system for secure collaborative learning Pioneered lattice-based succinct arguments with polylogarithmic verification Advanced understanding of modulo-p arguments through topological methods Labs & Teams : Active member of the Archimedes AI group at Yale, focusing on secure computation and cryptographic protocols.

Gerhard Weikumمشاهده پروفایل

پژوهشگر

• Computer Science
• Database Systems
• Information Retrieval
• +۳ مورد دیگر

Gerhard Weikum is a renowned computer scientist and Director at the Max Planck Institute for Informatics in Saarbrücken, Germany. He holds the title of Scientific Member of the Max Planck Society. Previously, he served as a Professor at ETH Zurich (1990–1994) and Saarland University (1994–2003). His career includes roles as a Research Associate and Postdoc at TU Darmstadt, MCC in Austin, and ETH Zurich. Born in 1957, he earned his doctoral degree in computer science from TU Darmstadt in 1986. Education: Bachelor/Master studies in Computer Science at TU Darmstadt Doctoral Dissertation: TU Darmstadt (1986) His research interests span foundational areas of computer science, including database systems, information retrieval, and distributed computing. While specific publications are not listed here, his work aligns with the institute’s focus on cutting-edge informatics research. Grants and Advising: Details on grants, advising, or lab affiliations are not explicitly mentioned in the provided text. Labs/Teams: As a Director at the Max Planck Institute for Informatics, he likely contributes to interdisciplinary teams and research groups within the institute’s computational science initiatives.

• Data Science
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Xiuzhen Jenny Zhang is a Professor of Data Science at RMIT University , affiliated with the School of Computing Technologies. Her research bridges artificial intelligence, machine learning, and social media analysis, with a focus on text mining and trustworthy data science. Her recent publications highlight expertise in point-of-interest recommendation , misinformation detection , multi-task learning , and transformer-based NLP . Key trends include applications of large language models for social good, fairness in recommendation systems , and adversarial learning for robustness. Best Paper Award at TrustCom’12 Best Short Paper Award at ADCS’2009 She leads the Text And LanguagE (TALE) research group and has supervised over 20 PhD students. Research grants include Australian Research Council and Victoria state government funding.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

• Digital Forensics
• Image Processing
• Multimedia Analysis
• +۴ مورد دیگر

Giulia Boato is an Associate Professor at the University of Trento’s Department of Information Engineering and Computer Science (DISI). She teaches courses in Probability and Multimedia Data Security. Her expertise spans Cyber Security, Digital Forensics, and Multimedia Analysis, focusing on image and signal processing for data protection, forensics, and anti-forensics. She collaborates internationally with institutions like Tampere University of Technology and Dartmouth College, co-advising PhD students and contributing to European projects like LIVINGKNOWLEDGE and GLOCAL. Education: PhD in Information and Communication Technology (2005), M.Sc. in Mathematics (2002), Scientific Lyceum (1998) with bilingual Italian-German certification. Past roles include Assistant Professor at DISI (2006–2018) and visiting researcher at the University of Vigo (2006) and University of Innsbruck (2018). Research interests include multimedia data protection, image forensics (tampering detection, computer vs. natural data discrimination), and intelligent data management. She leads projects on social media forensics, event-based retrieval, and synthetic media detection. Awards include Best Paper at IEEE WIFS 2012 and Top 10% Paper at MMSP 2012. Professional contributions include roles as co-chair of workshops, Technical Program Committee member for ICIP and ICC, and reviewer for journals like IEEE Transactions on Information Forensics and Security. She has advised PhD theses and contributed to datasets like TrueFace and WILD for synthetic media analysis.

Kevin Garyمشاهده پروفایل

دانشیار

• Software Architecture and Design
• Open Source Software
• Agile Methods
• +۳ مورد دیگر

Kevin Gary is an Associate Professor in the School of Computing and Augmented Intelligence (SCAI) within the Ira A. Fulton Schools of Engineering at Arizona State University (ASU). He joined ASU in 2004 after prior industry experience and faculty work at the Catholic University of America. His research focuses on software agility, open source software, and applications in healthcare and e-learning. He has contributed to mHealth platforms addressing pediatric chronic conditions and adaptive e-learning systems. Education: Ph.D. in Computer Science from Arizona State University (1999). Research Interests: Software Architecture, Agile Methods, Open Source Software, Healthcare Informatics, and Educational Technology. His recent work explores agile impact on regression testing and lean metrics in open source software. He has developed mobile health apps for asthma, epilepsy, and anxiety, leveraging agile principles and AI. Teaching & Innovation: Created the Software Enterprise program, an industry-aligned pedagogy integrated into ASU’s software engineering curriculum. This initiative earned the President’s Award for Innovation in 2011. He has taught courses in software engineering, web applications, and secure software systems. Grants & Projects: Led projects funded by NSF, industry partners (e.g., UNICON, GEORGETOWN UNIV MED CTR), and foundations (Children’s National Medical). Notable projects include the Image-Guided Surgical Toolkit and the ATIC-funded Software Enterprise pedagogy model. Service: Reviewed for journals/conferences, served as Associate Chair of computing programs, and contributed to professional organizations (IEEE, ACM, ASEE).

Wayne G. Luttersمشاهده پروفایل

استاد

• Computer-Supported Cooperative Work (CSCW)
• Social Informatics
• Social Computing
• +۹ مورد دیگر

Wayne G. Lutters is a Professor and Associate Dean for Faculty in the College of Information Studies (INFO) at the University of Maryland. He holds office in Patuxent 1109C and can be reached at lutters@umd.edu. His research focuses on sociotechnical systems, computer-supported cooperative work (CSCW), and social informatics, with emphasis on IT-mediated infrastructural work and cybersecurity advocacy. Current projects include studies on scientific cyberinfrastructure, usable privacy and security, and moderation in social media. Dr. Lutters advises the Dean’s Student Advisory Council and explores topics like data privacy, sociotechnical cybersecurity, and the future of work. His recent work addresses challenges in security awareness programs, code set redundancy in healthcare, and collaborative systems design. He is actively involved in developing tools like the Value Set Hub to improve healthcare data curation practices. His research consistently bridges theory and practice, emphasizing ethical technology design and interdisciplinary collaboration. Notable contributions include frameworks for cybersecurity advocacy roles and methodologies for synthesizing scholarly knowledge. While currently not accepting new PhD students, he remains a key figure in shaping academic and infrastructural innovation at UMD.