Find Academic Supervisors

Soumen ChakrabartiView profile

Professor

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +3 more

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

• Algorithmics
• Machine Learning
• Mathematical Modeling
• +2 more

Flavio Chierichetti is a Full Professor in the Department of Computer Science at Sapienza University of Rome, Italy. His research focuses on algorithmics, machine learning, and mathematical modeling, particularly in the contexts of social networks and the Web. His work integrates theoretical and applied approaches to address complex challenges in these domains. Research Interests : His primary areas of investigation include the design and analysis of algorithms, application of machine learning techniques, and the development of mathematical models to explore social network dynamics and web-based systems. These fields reflect his expertise in bridging computational theory with real-world data science problems. Scientific Awards : Google Focused Research Award Grants and Academic Engagement : PRIN project 20229BCXNW Additionally, he serves as an associate editor for ACM Transactions on Algorithms and has held leadership roles in program committees of major conferences like KDD, WWW, NeurIPS, and SODA. His teaching responsibilities include courses on Algorithms, Advanced Algorithms, and Algoritmi e Complessità.

Martin HeppView profile

Professor

• Web Science
• Internet of Things
• Big Data and Data Science
• +5 more

Martin Hepp is Professor of Web Science and Digitalization at the University of the German Federal Armed Forces Munich, where he leads the E-Business and Web Science Research Group. His academic career spans multiple prestigious institutions including the University of Innsbruck, Florida Gulf Coast University, and IBM Research. He holds a PhD in Business Information Systems from the University of Würzburg where he graduated Summa Cum Laude. His research focuses on shared data structures at Web scale, with particular expertise in Semantic Web technologies, ontology engineering, and data quality management. He is best known for developing the GoodRelations vocabulary for e-commerce, which since November 2012 has become the e-commerce core of schema.org, the official data markup standard adopted by major search engines including Google, Yahoo, Bing, and Yandex. His work is widely implemented by companies ranging from Google and Yahoo to BestBuy, Kmart, Volkswagen, and Renault, as well as tens of thousands of smaller businesses. His publication record shows a consistent focus on practical applications of Semantic Web technologies in business contexts, with recent work emphasizing Linked Data applications, product classification systems, and e-commerce standards. His research demonstrates strong industry impact, particularly through the adoption of GoodRelations as a foundational standard for web commerce data. Linked Data-a-thon Award (4th prize) at ISWC 2009 ACM Senior Member appointment (2009) FIT-IT outstanding research proposal award (2nd place, 2007) Dissertation Award from Alcatel SEL Foundation (2004) Dissertation Award from Unterfränkische Gedenkjahrstiftung (2004) Professor Hepp has served on numerous editorial boards including the International Journal on Semantic Web and Information Systems, and has been a program committee member for over sixty conferences including ECIS, EKAW, ESWC, ISWC, and WWW. He has organized more than fifteen workshops and conference tracks on conceptual modeling and Semantic Web topics. His work bridges academic research with practical business applications, particularly in the areas of e-commerce standards and semantic data interoperability.

Jiameng PuView profile

Researcher

• Cybersecurity
• Machine Learning
• Deep Learning
• +4 more

Jiameng Pu is a Researcher in the Department of Computer Science at Virginia Tech, part of the College of Engineering. Their research focuses on cybersecurity, deep learning, and adversarial machine learning, with notable contributions to deepfake detection and AI robustness. Pu holds a Ph.D. in Computer Science and Applications from Virginia Tech (2017–2022) and a B.E. in Computer Science and Technology from Wuhan University (2013–2017). Education : Ph.D. in Computer Science and Applications, Virginia Tech (2017–2022) B.E. in Computer Science and Technology, Wuhan University (2013–2017) Research Interests : Pu’s work addresses cutting-edge challenges in cybersecurity, including detecting deepfake content (text, images, and videos), defending against adversarial attacks on AI models, and improving robustness in medical imaging systems. Their research combines techniques from machine learning, computer vision, and data mining to enhance security and reliability in AI applications. Key Publications : Pu has authored influential papers on deepfake detection (IEEE S&P 2023, WWW 2021) and AI security (USENIX Security 2021, EuroS&P 2020). Their work spans theoretical advancements and practical tools like NoiseScope for blind deepfake image detection. Awards : Dr. Dennis Kafura Graduate Fellowship (2022) Nominated for 2021 PhD Fellowship Awards (Facebook Visa Research Scholarship) IEEE S&P 2020 Student Travel Grant NDSS 2019 Student Travel Grant National Endeavor Fellowship (Twice), Chinese Ministry of Education Pu’s professional experience includes software engineering at Facebook (2021) and data science roles at IBM and Wuhan University labs. They actively collaborate with industry and academia to advance AI security and ethical AI practices.

Hubert T.H. ChanView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +4 more

Professor Hubert T.H. Chan is an Associate Professor at the Department of Computer Science, University of Hong Kong, and serves as Programme Director for the BEng(CompSc) programme. He holds a PhD from Carnegie Mellon University (2007) and previously worked as a postdoc at Max-Planck-Institut für Informatik in Germany. His research focuses on algorithms, combinatorial optimization, discrete metric spaces, and security & privacy, with applications in graph theory, network analysis, and privacy-preserving mechanisms. Education: PhD in Computer Science, Carnegie Mellon University, 2007 BEng (Computer Science), details not specified in text Research interests emphasize algorithmic design for dynamic systems, privacy in data aggregation, and efficient optimization techniques. His work bridges theoretical computer science with practical applications in network security and distributed systems. Key grants include Hong Kong RGC-funded projects on oblivious data structures, spectral hypergraph analysis, and dynamic metric problems (2012–2018). His publications span top conferences like SODA, FOCS, WWW, and ICML, addressing challenges in clustering, sorting, privacy, and graph decomposition. He oversees research groups exploring hypergraph learning, secure computation, and algorithmic privacy. His lab's work often intersects with real-world systems, emphasizing both theoretical rigor and practical relevance.

Rajmohan RajaramanView profile

Professor

• Approximation algorithms
• Algorithmic game theory
• Distributed computing
• +3 more

Rajmohan Rajaraman is a Professor at the Khoury College of Computer Sciences at Northeastern University . He specializes in theoretical computer science with a focus on approximation algorithms, algorithmic game theory, distributed computing, and network optimization. His research group contributes to advancing algorithms for dynamic networks and sensor systems. He has advised numerous PhD students, including graduates now at institutions like Google, Raytheon, and IMDEA Madrid. Current advisees include Sushant Agarwal and Omer Wasim. He teaches advanced courses such as CS 7870: Modern Algorithms Toolkit and CS 3000: Algorithms & Data Structures . Prof. Rajaraman is actively involved in academic leadership: he serves on the Computing Community Consortium (CCC) Council, is an associate editor for Theory of Computing Systems and International Journal of Foundations of Computer Science , and has chaired major conferences like ACM SPAA 2011 and IEEE DCOSS 2010. He co-organizes the NU Theory Seminar and leads Northeastern's Algorithms & Theory Group . His professional contributions extend to editorial roles for SICOMP and TOCS special issues, and he has delivered tutorials on network algorithms at venues like the ISI-IMSc Summer School. His work bridges theoretical foundations with practical applications in distributed systems and networked environments.

Michael SchmittView profile

Professor

• Experimental Particle Physics
• Collider Physics
• Muon-to-Electron Conversion
• +3 more

Michael Schmitt is a Professor and Chair in the Department of Physics and Astronomy at Northwestern University. His research focuses on experimental particle physics, particularly collider experiments at the Large Hadron Collider (LHC) and the Mu2e experiment at Fermilab. He leads studies in electroweak physics, searches for new particles beyond the Standard Model, and precision measurements of Z boson decays. Current projects include the CMS experiment's WW/WWW cross-section measurements, rare Z boson decays, and the Mu2e collaboration's magnetic field mapping. Schmitt also contributes to advanced data analysis techniques, including Bayesian Blocks and random forest classifiers. He teaches statistical methods for physicists and has advised multiple graduate students and postdocs. Positions: Professor, Chair of Physics & Astronomy, CMS Collaboration member, Mu2e Collaboration member Education: PhD in Physics from Harvard University (1991) Labs/Teams: CMS and Mu2e experimental groups, Northwestern High Energy Physics group Recent Research Highlights: Precision electroweak measurements, high-granularity calorimeter timing studies, muon-to-electron conversion background analysis. Over 100 peer-reviewed publications in Phys. Rev. D, JHEP, and other top journals.

Philip ChangView profile

Assistant Professor

• Experimental Particle Physics
• Higgs Boson Research
• Multiboson Signatures
• +3 more

Philip Chang is an Assistant Professor in the Department of Physics at the University of Florida's College of Liberal Arts and Sciences. His research focuses on experimental particle physics, particularly the electroweak and Higgs sectors. He develops track-finding algorithms using AI for High-Luminosity LHC challenges. Chang's work analyzes multiboson signatures and quarkonium states at the LHC, with recent publications exploring CP violation, exotic particle searches, and vector boson scattering. His group utilizes machine learning for collision data reconstruction. Publications emphasize precision measurements of Higgs couplings, rare triboson events, and beyond-Standard-Model signatures using CMS detector data.

Antonietta LanzaView profile

Assistant Professor

• Man-Machine Interaction
• Machine Learning
• Knowledge Acquisition
• +4 more

Antonietta Lanza is an Assistant Professor of Computer Science at the Department of Informatics, University of Bari, Italy. She earned her degree in Information Science from the same university in 1978 and has been a faculty member since 1984. Her research spans man-machine interaction, machine learning, and knowledge acquisition with applications in technical document processing and geospatial data interpretation. Education : Degree in Information Science (University of Bari, 1978) Affiliations : Department of Informatics, University of Bari Her research focuses on integrating machine learning with geospatial analysis, particularly for wind speed forecasting, topographic map interpretation, and cadastral data processing. She has developed algorithms for spatio-temporal modeling and spatial clustering of structured data. Recent publications primarily address wind power prediction, spatio-temporal lazy learning, and cluster-based time series modeling. Applications span renewable energy, environmental planning, and maritime AIS data analysis. She actively contributes to academic associations AI*IA (Italian Association for Artificial Intelligence) IAPR-IC (International Association of Pattern Recognition - Italian Chapter) and has created software like PS_package , a tool for line-drawing segmentation.

Vangelis MarkakisView profile

Associate Professor

• Theoretical Computer Science
• Game Theory
• Mechanism Design
• +3 more

Vangelis Markakis is an Associate Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), where he has been a faculty member since 2009. He holds a PhD in Theoretical Computer Science from Georgia Institute of Technology (2005), an M.Sc. in Computer Science (Georgia Tech 2004), and a Diploma in Electrical and Computer Engineering from NTUA (2000). His academic career includes postdoctoral research at the University of Toronto and CWI (Amsterdam). Research Interests: Theoretical computer science, algorithm design, game theory, mechanism design, social networks, combinatorial optimization Awards: Recipient of the 2025 ACM SIGecom Test of Time Award for foundational work on fair allocations (2004) and the 2003 ACM EC Best Student Paper Award His work bridges algorithmic game theory, fair division, and computational economics, with notable contributions to approximation algorithms for Nash equilibria and auction mechanisms. He has supervised numerous PhD and MSc students, including prominent researchers now at institutions like University of Essex, NTU Singapore, and Yale. Scientific activities include extensive program committee memberships (ACM EC, AAAI, AAMAS, SAGT, WWW) and organizing conferences like ISCO 2012 and ACAC 2009. His teaching portfolio covers game theory, data structures, and advanced algorithms.

• Data Science
• Recommender Systems
• Behavioral Data Analytics
• +3 more

Christoph Trattner is a Professor and Director of MediaFutures: Research Centre for Responsible Media Technology and Innovation at the University of Bergen. He leads research in the Department of Information Science and Media Studies , focusing on the intersection of Behavioral Data Analytics and Recommender Systems with applications in health, media, and sustainability. His work spans Over 120 publications in top venues like NATURE , JASIST , and WWW Secured 54 million Euros in funding (30 million as PI) Founded SFI MediaFutures, a 26 million Euro research center Recent research trends include Cross-cultural food preference modeling via visual analytics Popularity bias mitigation in news recommenders Behavioral nudges for healthy eating Ethical AI in journalism and media Awards ACM Distinguished Speaker (2021) Best Paper Honorable Mention at The Web Conference (2017) Editorial board member for journals like Future Internet and AI & Ethics As a supervisor, he has guided over 35 MSc/PhD students. Currently exempt from teaching due to directorial duties, he welcomes thesis supervision inquiries via christoph.trattner@uib.no .

Rubén Cuevas RumínView profile

Associate Professor

• Online Advertising
• Web Transparency
• Data Privacy
• +5 more

Rubén Cuevas Rumín is an Associate Professor in the Department of Telematics Engineering at Universidad Carlos III de Madrid (UC3M), where he also serves as Deputy Director of the UC3M-Santander Big Data Institute (IBiDat). He holds a PhD and MSc in Telematics Engineering from UC3M (2010, 2007), an MSc in Telecommunications Engineering from UC3M (2005), and an MSc in Network Planning and Management from Aalborg University (2006). His research focuses on Online Advertising, Web Transparency, Privacy, Internet Measurements, and their applications in economics and sociology. Notable contributions include studies on Facebook's use of sensitive data, BitTorrent ecosystem analysis, and measurement of cultural aspects through large-scale data. Rubén has authored over 70 publications in top venues like ACM CoNEXT, WWW, Usenix Security, and IEEE/ACM Transactions. He leads or co-leads 16 research projects funded by EU H2020/FP7, Spanish government, and private entities. His work has been featured in major media including BBC, The Financial Times, and Nature. Awarded the Telefonica Foundation Prize for Best PhD Thesis (2010) and the 2018 Emilio Aced Research Prize for data protection research, he teaches courses in Telecommunications, Telematics, and Computer Science. Current projects include the EU-funded TESTABLE and SMOOTH initiatives, focusing on web security and GDPR compliance.

Dmitri LoguinovView profile

Professor

• Big-data computing
• Graph algorithms
• Internet measurement
• +8 more

Dmitri Loguinov is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the Dwight Look College of Engineering. His research focuses on big-data computing, graph algorithms, network measurement, and cybersecurity. He holds a PhD from the City University of New York (2002) and a B.S. from Moscow State University (1995). Education: Ph.D. Computer Science, City University of New York (2002) B.S. Computer Science (with honors), Moscow State University (1995) Research interests span stochastic analysis of networks, peer-to-peer systems, congestion control, and large-scale web crawling. His work includes the IRLbot project, scaling to 6 billion web pages, and influential contributions to OS fingerprinting (e.g., Faulds and Hershel systems). Key publications address triangle enumeration algorithms, distributed systems synchronization, and high-performance streaming frameworks like Vortex. He has received multiple awards including the Best Paper Award at IEEE P2P 2009 and WWW 2008. Advising over 40 graduate and undergraduate students, Loguinov has led NSF-funded projects on data streaming (CNS-1717982), web crawling (CNS-1017766), and P2P churn analysis (CNS-0720571). His labs focus on extreme-performance systems and scalable network measurement.

Xia HuView profile

Associate Professor

• Data Science
• Interpretable Machine Learning
• Automated Machine Learning
• +1 more

Xia Hu is an Associate Professor in the Department of Computer Science at Rice University. His research focuses on data science, interpretable machine learning, automated machine learning, and network analytics. He leads projects such as AutoKeras, an open-source automated deep learning system widely adopted in industry. His work integrates machine learning with real-world applications in healthcare and production systems like TensorFlow and Bing. Dr. Hu holds a PhD from Arizona State University (2015), and has received prestigious awards including the NSF CAREER Award and ACM SIGKDD Rising Star Award. Education: PhD, Computer Science, Arizona State University (2015) Master's Degree, Computer Science, Beihang University (2009) Bachelor's Degree, Computer Science, Beihang University (2006) Research Interests: Dr. Hu explores automated machine learning (AutoML) techniques, interpretable AI systems, and ethical considerations in algorithmic fairness. His work bridges theory and practice, addressing challenges in model efficiency, bias mitigation, and healthcare applications. Recent projects include improving LLM compression, fairness in graph neural networks, and leveraging AI for medical diagnosis prediction. Publications Trends: His recent articles emphasize large language model optimization (e.g., quantization, compression), fairness in AI systems, and healthcare applications like risk assessment in heart transplants. He also contributes to foundational research in explainable AI and data-centric methodologies. Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Multiple Best Paper nominations (ICDM 2019, WWW 2019) Labs & Collaborations: His group develops open-source tools like AutoKeras and collaborates with industry leaders (e.g., Apple, Bing). Research spans interdisciplinary projects combining AI with healthcare, cybersecurity, and network analysis.

Yujun YanView profile

Assistant Professor

• Graph-based Machine Learning
• Data Mining
• Network Science
• +3 more

Yujun Yan is an Assistant Professor in the Department of Computer Science at Dartmouth College, joining in January 2023. Her research focuses on the intersection of machine learning and network science, particularly developing expressive graph-based ML models and applying them to neuroscience, knowledge reasoning, and multi-agent systems. She earned her Ph.D. (2022) and M.S. (2020) from the University of Michigan-Ann Arbor, and B.S. from Southeast University, China. Education: Bachelor of Science, Southeast University, Nanjing, China Master of Science, University of Michigan-Ann Arbor Doctor of Philosophy, University of Michigan-Ann Arbor Research Interests: Designing generalizable graph neural networks Applications in neuroscience and program understanding Addressing challenges like heterophily and oversmoothing in GNNs Structural modeling in multi-agent systems and knowledge graphs Recent Research Trends: Her work emphasizes scalability, interpretability, and cross-domain applicability of graph-based models, with notable contributions to size generalization, medical time-series analysis, and agentic AI for scientific discovery. She actively explores ethical and fairness aspects of collaborative AI systems. Awards & Recognition: Co-PI for DARPA AIQ project (2025) NeurIPS 2025 Area Chair ICML 2024 Travel Award for student collaborators Advising & Grants: Advising PhD students (Xuyuan Liu) and multiple undergraduate interns (e.g., Ly Nguyen, Gaotang Li) Involvement in prestigious programs like the James O. Freedman Presidential Scholars Program Recipient of grants including Google Faculty Research Award (2020) Labs & Initiatives: Leads a research group focused on advancing graph ML and interdisciplinary applications. Co-organized workshops on Agentic AI for Science at ICLR and WWW 2025, fostering collaboration between AI and scientific domains.