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نمایش ۷۰۶-۷۲۰ از ۱۸٬۴۲۱ استاد راهنما
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دانشیار
Fei Song is an Associate Professor at the University of Guelph . Their research applies Machine Learning and Artificial Intelligence techniques to Natural Language Processing . Email: fsong@uoguelph.ca Research Interests include: Document Classification Information Retrieval Topic Modeling Sentiment Analysis Text Summarization Text Segmentation Temporal Analysis Scientific Awards :
Matteo Magnani is a Professor in the Division of Computing Science at the Department of Information Technology, Uppsala University. He leads the Uppsala University Information Laboratory and is a founding member of the Uppsala University Computational Social Science Lab. His research spans network science, artificial intelligence, data science, and computational social science, with a focus on social data mining and multilayer networks. PhD in Computer Science, University of Bologna, 2006 Graduated with honours in Information Sciences, University of Bologna, 2002 Studies in Computer Science at University of Marne la Vallée and Imperial College London Matteo Magnani's research interests include social network analysis, multilayer and probabilistic networks, community detection, visual analytics, and the application of AI to digital media and climate communication. His work bridges computer science and social sciences, particularly in analyzing online discourse and digital intermediaries. He has contributed significantly to the understanding of network structures, uncertainty in networks, and the ethical dimensions of algorithmic analysis. His recent publications highlight trends in fairness in community detection, visual saliency in network layouts, emotional reactions to climate visuals online, and deep learning applications in social media. Topics frequently involve YouTube, Twitter, and online public debates, using advanced network and machine learning methods. Rotary Prize for best student of the Science Faculty Best Paper Award Funniest Presentation Award Best Poster Award Pedagogical Prize from UTN Distinguished University Teacher (Sweden) Docent title (Sweden) Magnani has supervised numerous students and collaborated widely, particularly with Luca Rossi, Alexandra Segerberg, and Davide Vega. He has secured funding from major sources including VR, H2020, STINT, and MIUR. He leads active research labs focused on information systems and computational social science, fostering interdisciplinary collaboration and innovation in network-based research.
Lindsey Kent is a Professor at the University of St Andrews' School of Medicine, where she serves as Deputy Head and Director of Admissions. Her research focuses on the biological foundations of psychiatric disorders, particularly ADHD and developmental language disorders. Education: Medicine (Aberdeen University, 1989), PhD (University of Birmingham, 1998) Academic Appointments: Wellcome Trust Fellow (1999), University Lecturer (University of Cambridge, 2003), Reader (University of St Andrews, 2007) Research Trends: Dr. Kent's work explores psychiatric genetics, neurodevelopmental disorders, and molecular mechanisms through genome-wide association studies, exome sequencing, and genetic epidemiology. Key Collaborations: Psychiatric Genomics Consortium, EAGLE Consortium, 23andMe Technical Focus: ATP2C2 gene variants, TNR gene associations, polygenic risk architectures Awards: Fellow of the Higher Education Academy (2000) Academic Contributions: She has directed two Wellcome Trust-funded projects on ADHD genetics and co-authored over 91 peer-reviewed publications, including significant studies in Nature Communications and Human Molecular Genetics . Her editorial roles include serving on the American Journal of Medical Genetics board (2007–2014).
پژوهشگر
Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
Nicholas Mattei is an Associate Professor of Computer Science at Tulane University and Co-Director of the Tulane Center for Community Engaged AI. He holds a Ph.D. from the University of Kentucky (2012) and researches artificial intelligence, machine learning, and decision-making systems. His work combines theory, data, and experiments to develop algorithms supporting individual and group decision-making. Dr. Mattei's research spans AI ethics, fairness in algorithms, computational social choice, and preference learning. He has published over 100 academic articles and received multiple grants from organizations including Google, IBM, and the National Science Foundation, including a 2024 NSF CAREER Award. He co-authored 'Computing and Technology Ethics: Engaging Through Science Fiction' from MIT Press. Prior to joining Tulane, he held research positions at IBM Research, Data61/CSIRO, and NASA Ames Research Center. His teaching portfolio includes courses on Discrete Mathematics, Data Science, Artificial Intelligence, and Multi-agent Systems.
YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.
Jeanna Matthews is a Full Professor of Computer Science at Clarkson University and an affiliate at Data and Society. She holds a PhD from UC Berkeley (1999) and teaches courses ranging from operating systems to cybersecurity. Her research focuses on algorithmic transparency, AI ethics, and societal impacts of automated systems. Matthews is a prominent ACM leader, serving on multiple committees including the Technology Policy Subcommittee on AI Accountability. She has pioneered work on forensic software analysis in criminal justice systems and delivered DEF CON presentations on virtualization security and adversarial testing. Awards include ACM Distinguished Speaker and Fulbright Specialist roles. Her work emphasizes open-source tools and critical thinking in education, extending to global service learning programs in the Dominican Republic and Brazil. Education: PhD in Computer Science (UC Berkeley, 1999), B.S. in Math/Computer Science (Ohio State, 1994), B.A. in Spanish (SUNY Potsdam, 2016). Research Interests: Cybersecurity vulnerabilities, algorithmic accountability frameworks, automated decision systems in justice contexts, and ethical AI design. Recent projects include investigating bias in DNA forensic software through a Brown Institute grant and analyzing political polarization on social platforms. Awards & Recognition: ACM Distinguished Speaker (2018-present) Fulbright Specialist (2018-present) 2018-2019 Brown Institute Magic Grant ACM SIGOPS Chair (2011-2015) ACM Special Interest Group Governing Board Chair (2016-2018) Teaching & Outreach: Designed courses integrating open-source tools and critical inquiry, including abroad programs in Mexico, Brazil, and the Dominican Republic. Advocates for lifelong learning strategies and questioning underlying assumptions in computing systems. Key Projects: Forensic Software Accountability: Examining discrepancies in DNA analysis tools Algorithmic Transparency: Frameworks for auditing automated systems Cybersecurity Education: Adversarial testing methodologies for justice software
استادیار
Dr. John D. Osborne is an Assistant Professor in the Department of Computer Science at the University of Alabama at Birmingham (UAB), with a secondary appointment in the same department. He specializes in Natural Language Processing (NLP), particularly in biomedical text analysis and clinical phenotyping. His work focuses on applications such as drug repurposing and identifying patient cohorts for research and clinical care. Dr. Osborne’s research interests include the relationship between genotype and phenotype, microbial bioinformatics, and developing NLP systems to support healthcare. He previously contributed to the Disease Ontology at Northwestern University and worked at the CDC and UAB’s Viral Bioinformatics Resource Center. His lab at UAB develops integrated registry systems to identify patients with conditions like reportable cancers, multiple myeloma, delirium, and opiate use disorder. No scientific awards or specific grants are mentioned in the provided text.
استادیار
Mahdi S. Hosseini is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and a faculty member of the Applied AI Institute. He holds a PhD from the University of Toronto (2016) and completed a postdoctoral fellowship at UofT, supported by MITACS-Elevate and NSERC fellowships. His research focuses on advancing deep learning and computer vision for computational pathology and healthcare technologies, aiming to develop AI tools for clinical diagnosis. He currently supervises graduate students and has published over 30 papers and two patents. Education: PhD in Electrical and Computer Engineering from the University of Toronto (2016), postdoctoral training at UofT collaborating with Huron Digital Pathology Inc. (Waterloo, Ontario). Research interests include deep learning, computer vision, computational pathology, medical imaging, and AI ethics (P4AI project). His work emphasizes developing explainable AI systems for clinical pathology, biomarker discovery, and efficient learning algorithms. Professional service includes serving as Area Chair for NeurIPS 2023, CVPR 2023-2024, and ECCV 2024. He reviews grants for CIHR, NSERC, and serves on program committees for key conferences (ICCV, CVPR, NeurIPS). Teaching includes courses on applied AI, machine learning, and deep learning for computational pathology at both graduate and undergraduate levels. Awards: MITACS-Elevate Fellowship (postdoc), NSERC Research Funding (2016-2017). His work has led to patents in diagnostic systems and has collaborated with hospitals and pathologists to advance clinical applications. Labs/Teams: Active collaborations with the Applied AI Institute at Concordia, Huron Digital Pathology, and healthcare institutions. Research emphasizes interdisciplinary approaches between computer science and clinical medicine.
استادیار
Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.
استادیار
Smita Ghosh is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University, part of the College of Arts and Sciences. Her research focuses on social network analysis, algorithms for information diffusion, and applications in cybersecurity, disaster management, and machine learning. She holds a B.Tech. from the West Bengal University of Technology, India, and an M.S. and Ph.D. from the University of Texas, Dallas. Her work addresses challenges in rumor containment, clickbait detection, and optimizing network models for social media content analysis. Recent publications include studies on hypergraph-based solutions for rumor blocking and stochastic models for emergency response in social networks. She also explores cross-modal topic modeling for enhancing content detection algorithms. Notable contributions include developing data-driven strategies for identifying hate speech spreaders and improving wildfire severity predictions using environmental features. Her research bridges theoretical computer science with real-world applications in public health, education, and disaster management. Her academic contributions include organizing conference proceedings like the 18th International Conference on Algorithmic Aspects in Information and Management (AAIM 2024). She actively contributes to educational initiatives such as the Classroute project, creating multilingual educational content for Punjabi and Urdu speakers.
Anshumali Shrivastava is an Associate Professor of Computer Science, Electrical and Computer Engineering, and Statistics at Rice University, affiliated with the George R. Brown School of Engineering. His research focuses on large-scale machine learning, randomized algorithms for big data, and graph mining. He holds a PhD from Cornell University (2015) and an MSc from the Indian Institute of Technology Kharagpur (2008). His research interests span scalable deep learning, efficient neural network inference, and probabilistic algorithms. He has pioneered techniques in compressed learning, hashing-based search, and distributed optimization for handling massive datasets. Notable contributions include methods for accelerating LLM inference, memory-efficient quantization, and graph processing algorithms. Teaching: Probabilistic Algorithms, Large-Scale ML, and Machine Learning Seminars Awards: Charles W. Duncan Jr. Achievement Award (2023), Young Faculty Research Award (2021), NSF CAREER Award (2017), and multiple best paper awards His work bridges algorithm design with practical applications in recommendation systems, genomics, and edge computing. Current efforts focus on sustainable AI, hardware-aware compression, and efficient training/inference pipelines for large models.
Adam Jatowt is a Professor and Head of the Data Science group at the Department of Computer Science, University of Innsbruck. He also serves as Deputy Head of the Digital Science Center and Research Center for Digital Humanities. His academic career spans roles at Kyoto University (2010-2020), National Institute of Advanced Industrial Science and Technology (AIST), and visiting positions at Karlsruhe Institute of Technology, University of La Rochelle, and University of California Berkeley. Research interests focus on temporal aspects of NLP/IR, computational history, large language models, and future forecasting. He leads projects combining digital humanities with advanced AI techniques, including temporal validity assessment and hint generation systems. Recent publications (2025) emphasize LLM applications in temporal analysis, QA systems, and energy sector digitalization. His work has been recognized through awards like the Friedrich Wilhelm Bessel Research Award (2024) and top-cited paper distinction in Information Sciences. He actively organizes conferences like ECIR 2026 and Text2Story workshops. Key contributions include developing WikiHint dataset, PlausibleQA framework, and tools like Rankify. His research also addresses societal challenges through ESG rating prediction and medical LLM applications.
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