جستجوی استاد راهنمای دانشگاهی

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Manik Varma is a Distinguished Scientist and Vice President at Microsoft Research India, and an Adjunct Professor at the Indian Institute of Technology Delhi. He is a Fellow of the Indian Academies of Science (IASc, INSA, NASI), the Indian National Academy of Engineering (INAE), and the Association for Computing Machinery (ACM). He has received prestigious awards such as the Shanti Swarup Bhatnagar Prize and Microsoft Gold Star Award. Education : BSc in Physics from St. Stephen's College (David Raja Ram Prize) BA in Theoretical Physics from the University of Oxford (Rhodes Scholar) DPhil in Computer Vision and Machine Learning from the University of Oxford (University Scholar) Post-doctoral Fellow at the Mathematical Sciences Research Institute (MSRI), Berkeley Visiting Miller Professor at UC Berkeley His research focuses on Machine Learning (Extreme Classification, Resource-efficient ML, Supervised Learning), Information Retrieval (Computational Advertising, Dense Retrieval, Recommender Systems), and Computer Vision (Image Search, Object Recognition). Recent work includes graph-regularized encoders, label variance reduction, and multimodal classification frameworks. His publications span extreme classification algorithms like NGAME , SiameseXML , and DECAF , with applications in IoT, web search, and recommendation systems. He leads a research group at Microsoft Research India and advises PhD students at IIT Delhi. Scientific Awards : Shanti Swarup Bhatnagar Prize (Government of India) Microsoft Gold Star and Achievement Awards WSDM 2019 Best Paper Prize BuildSys 2019 Best Paper Runner-up Fellow of ACM, IASc, INSA, NASI, INAE He has supervised numerous PhD students, including Sonu Mehta and Suchith Prabhu, and collaborates with institutions like Microsoft Research India, IIT Delhi, and UC Berkeley. His research has led to scalable solutions for billion-label classification and resource-constrained IoT applications.

• Machine Learning
• Deep Learning
• Information Retrieval
• +۱۶ مورد دیگر

Ruth Fong is a Teaching Professor at the Department of Computer Science, Princeton University , where she teaches foundational and advanced AI/ML courses (COS324, COS126) while leading the Looking Glass Lab in explainable AI research. She collaborates closely with the Visual AI Lab and Professor Olga Russakovsky . Education: PhD in Visual Geometry Group, University of Oxford (advised by Andrea Vedaldi , funded by Rhodes Trust and Open Philanthropy ) MSc in Neuroscience, University of Oxford (with Rafal Bogacz , Ben Willmore , and Nicol Harper ) AB in Computer Science, Harvard University (with David Cox and Walter Scheirer ) Research Focus: Pioneering Explainable AI and ML Fairness , with emphasis on post-hoc model understanding, interpretable-by-design architectures, and human-AI interaction frameworks. Her work spans computer vision, self-supervised learning, and neuroscience-inspired methodologies. Publication Trends: Recent papers (2023-2025) analyze interactive explanations , concept salience , and gender artifacts in vision datasets . Earlier work (2017-2020) established foundational techniques in extremal perturbations , backpropagation saliency , and neural network interpretability . Scientific Awards: Princeton Engineering Council Teaching Award (2025) Keller Center Summer Course Development Grant (2025) CHI Honorable Mention Paper Award (2023) Open Philanthropy AI Fellowship (2018) Rhodes Scholarship (2015) Advising: Directly mentored 10 Princeton undergraduates on IW/senior theses projects spanning generative AI , medical imaging fairness , and interactive visualization tools . Grants include Princeton SEAS and Open Philanthropy funding for the Looking Glass Lab. Lab & Team: Leads the Looking Glass Lab with 6 graduate/postgraduate members including Rawand Aziz , Matthew Barrett , and Ben Wachspress . Collaborates with faculty across Princeton and Oxford.

• Computer Vision
• Machine Learning
• Explainable AI
• +۳ مورد دیگر

Serge Suanez is a Professor of Physical Geography specializing in coastal geomorphology at the University of Western Brittany, affiliated with both the Department of Geography within the Faculty of Letters & Human Sciences and the LETG UMR 6554 CNRS research unit at the European University Institute of the Sea. His primary research focuses on coastal sediment dynamics, morphological responses to extreme events, and coastal management strategies at the nature-society interface. His research interests encompass coastal geomorphology with emphasis on sediment budgets, soft and rocky coastline responses to climatic and anthropogenic forcing, storm impacts on coastal systems, and gravel spit dynamics. Notable research programs include ANR GEOPRAS (2022-2025), SEA-EU-search (2021-2022), SeaLex (2020-2023), and long-term monitoring of the Sillon de Talbert and Vougot Beach sites. Analysis of his recent publications reveals strong focus on extreme storm impacts on high-energy coastlines, long-term morphological monitoring of gravel barriers, and coastal flooding risk assessment. His work spans both field measurements and modeling approaches, with particular emphasis on Brittany's coastline and Icelandic volcanic coasts. He has served in significant administrative roles including co-coordinator of the EUR ISblue research theme 'Coastal System Sustainability' since 2023, member of the Scientific Council of the European University Institute of the Sea since 2021, and former deputy director of the LETG research unit (2017-2020). His expertise extends to coastal management applications through numerous monitoring programs including annual morphosedimentary surveys of the Talbert channel (since 2007), Natura 2000 dune monitoring at Vougot Beach (2004-2023), and coastal rehabilitation projects across Brittany.

• Coastal Geomorphology
• Physical Geography
• Coastal Sediment Budget
• +۷ مورد دیگر

Tanumoy Mandal is an Assistant Professor in the School of Physics at the Indian Institute of Science Education and Research (IISER) Thiruvananthapuram, where he has been serving since February 2020. Previously, he held research positions at Uppsala University in Sweden (as Researcher and Guest Researcher), University of Delhi (as DST-INSPIRE Fellow), and Harish-Chandra Research Institute. His academic journey includes a Ph.D. from the Institute of Mathematical Sciences, Chennai, and an M.Sc. from the same institution. Dr. Mandal's research focuses on Beyond the Standard Model theories , Collider phenomenology , and Matter under extreme conditions , with particular interest in neutron stars and color superconductivity. His work bridges theoretical particle physics with experimental high-energy physics, especially focusing on LHC data analysis and interpretation. He has made significant contributions to understanding leptoquarks, vectorlike quarks, neutrino physics, and various extensions of the Standard Model. His publication record shows a clear trend toward addressing current experimental anomalies (such as muon g-2, W-mass, and B-physics anomalies) through novel theoretical frameworks including leptoquark models, U(1) extensions, and grand unification scenarios. Recent work increasingly incorporates machine learning techniques for enhanced collider searches and explores connections between high-energy physics and extreme astrophysical environments. As a research advisor, Dr. Mandal leads the PPP Group at IISER-TVM with current members including Arijit Das, Rachit Sharma, Aniket, Anirudhan, Kartik, Nayan, and Kunika. His past advisee Aniket Masaye completed his work during 2021-2022. His research has been supported through various fellowships including the DST-INSPIRE Fellowship and postdoctoral positions at prestigious institutions. Dr. Mandal maintains an active research program with 39 journal publications, 2 communicated papers, 2 arXiv preprints, and 9 conference proceedings to his credit. His work has accumulated 1231 total citations with an h-HEP index of 22, reflecting significant impact in the particle physics community. He leads the Particle Physics and Phenomenology Group (PPPG) at IISER-TVM, which focuses on theoretical and phenomenological aspects of high-energy physics and their connection to experimental data.

• Beyond the Standard Model theories
• Collider phenomenology
• Matter under extreme conditions
• +۲ مورد دیگر

T. Beyrouthy is a prominent researcher in high-energy physics with 313 publications, an h-index of 25, and over 3,000 citations. As a key contributor to the CMS experiment at CERN's Large Hadron Collider, his work spans Higgs boson characterization, supersymmetry searches, heavy ion collisions, and detector development. His research primarily focuses on experimental validation of the Standard Model and exploration of physics beyond it through proton-proton and Pb-Pb collision data. His research interests include: Particle detection methodologies for collider experiments Quantum chromodynamics in extreme energy regimes Searches for new particles and phenomena beyond the Standard Model Precision measurements of electroweak parameters Development of advanced detector systems for high-radiation environments Analysis of his recent publications reveals sustained contributions to CMS experimental programs across multiple frontiers: Higgs physics (width measurements, off-shell production), heavy flavor studies (J/ψ spectroscopy), and searches for right-handed currents. His work consistently appears in high-impact journals including Nature, Physical Review Letters, and Journal of High Energy Physics. Beyrouthy operates within the CMS collaboration's international framework, evidenced by extensive co-authorship networks with leading institutions worldwide. His technical contributions to detector development for LHC Run 3 highlight expertise in experimental infrastructure. The absence of student listings or teaching-focused publications suggests a primary research orientation within large-scale experimental collaborations rather than traditional academic advising roles.

• Particle Physics
• High Energy Physics
• Experimental Physics
• +۳ مورد دیگر

Paul Seymour is a distinguished mathematician and Albert Baldwin Dod Professor at Princeton University, holding positions in both the Department of Mathematics and the Program in Applied and Computational Math. He earned his doctorate from Oxford University and has held roles at institutions including the University of Waterloo, Ohio State University, and Bellcore. His work focuses on discrete mathematics, graph theory, and optimization, with notable contributions to the four-color theorem, the strong perfect graph theorem, and Hadwiger's conjecture. Education: PhD in Mathematics from Oxford University (1975), thesis on 'Matroids, hypergraphs and the max-flow min-cut theorem' under Aubrey William Ingleton. Research Interests: Graph Theory: Structural properties, induced subgraphs, Erdős-Hajnal conjecture, chi-boundedness, and algorithmic approaches. Discrete Mathematics: Matroid theory, optimization, and combinatorial algorithms. Theoretical Contributions: Robertson–Seymour theorem, four-color theorem simplification, and strong perfect graph theorem. Recent Research Trends: Recent articles explore induced subgraph density, polynomial chromatic bounds, and pure pairs in graph structures. His work often bridges theoretical insights with algorithmic applications, emphasizing structural graph theory. Awards: Fulkerson Prize (1979, 1994, 2006, 2009) George Pólya Prize (1983, 2004) Ostrowski Prize (2003) Honorary doctorates from Waterloo (2008) and Technical University of Denmark (2013) Collaborations & Roles: Editor-in-chief of the Journal of Graph Theory (with Carsten Thomassen). Active in collaborative efforts with Neil Robertson, Maria Chudnovsky, and others, producing foundational results like the Graph Minors series. Labs & Teams: Leads research groups at Princeton focusing on structural graph theory and discrete mathematics. Collaborations include the annual Barbados Graph Theory Workshops.

• Discrete Mathematics
• Graph Theory
• Optimization
• +۵ مورد دیگر

Victor Dubach is a doctoral student and lecturer at the University of Lorraine, affiliated with the Faculty of Science and Technology and the Institut Élie Cartan de Lorraine (IECL) . He has been a teaching assistant and lecturer for courses such as 'Programming and Data Structures', 'Linear Algebra', and 'Probability and Statistics' since 2022. His research focuses on combinatorial probability, random permutations, and probabilistic structures like permutons. Research Interests : Combinatorial probability, random permutations, permutons, locally uniform models, and increasing subsequences. Teaching Roles : Lecturer for advanced mathematics and computer science courses at Mines de Nancy, FST, and IUT Nancy-Charlemagne. Conference Participation : Presented talks at AofA 2024, MAS days, TU Graz, and CIRM workshops.

• Combinatorial Probability
• Random Permutations
• Random Trees and Graphs
• +۲ مورد دیگر

Sarra Ghaddab is a Lecturer and PhD student in Finance at the University of Claude Bernard Lyon 1 (France) and the University of Sousse (Tunisia) as part of a joint thesis. Her research explores the intersection of Internet Search Volumes and financial portfolios management , with broader interests in financial markets , quantitative finance , investor attention , portfolio optimization , non-life insurance , and extreme value theory . Contact: sarraghaddab@gmail.com Education : Master's degree in Finance and Actuarial Science (2020) from the Institute of High Commercial Studies of Sousse, awarded with honors . Research Overview : Her work examines how digital behavioral indicators, such as internet search volumes, can enhance quantitative finance models and portfolio optimization strategies. She also investigates applications of extreme value theory in non-life insurance risk assessment.

• Financial markets
• Quantitative finance
• Investor attention
• +۳ مورد دیگر