Find Academic Supervisors

Haewoon KwakView profile

Associate Professor

• Network Science
• Machine Learning
• Computational Social Science
• +5 more

Dr. Haewoon Kwak is an Associate Professor at the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. He co-directs the Soda Lab research group with Prof. An, focusing on investigating social phenomena through large-scale data and computational tools to address significant societal challenges. His research spans multiple interdisciplinary fields including network science, machine learning, and computational social science. His work examines online social networks, social media dynamics, game analytics, computational journalism, and human-computer interaction. His research has been widely recognized, most notably for the highly cited paper 'What is Twitter, a social network or news media?' (WWW 2010) which has received over 9,500 citations. Dr. Kwak's research portfolio demonstrates consistent innovation across multiple domains. His recent publications show a strong focus on computational journalism, deep learning applications for media analysis, and understanding toxic behavior in online spaces. His work often bridges theoretical insights with practical applications for understanding information flow and social dynamics in digital environments. Outstanding PhD thesis award in Computer Science Department, KAIST Best paper award at IMC '07 for 'I Tube, You Tube, Everybody Tubes' Best paper award at SocInfo 2019 for 'Gender and Racial Diversity in Commercial Brands' Advertising Images on Social Media' Highly cited paper 'What is Twitter, a social network or news media?' (WWW 2010) with over 9,500 citations Dr. Kwak has served on program committees for major computer science and computational social science conferences. His work has been featured in prominent media outlets including Nature News, ACM Tech News, BBC, The Times, The Economist, Slate, and Scientific American. His Soda Lab at Indiana University continues to produce influential research at the intersection of computing and social science.

Laurent HascoetView profile

Researcher

• Computer Science
• Automatic Differentiation
• Program Analysis and Transformation
• +4 more

Laurent Hascoët is a permanent INRIA Researcher at Sophia-Antipolis center in France since 1986. He leads the ECUADOR team , previously known as TROPICS team, focusing on Automatic Differentiation (AD) since 1998. Research Interests: Specializing in Program Analysis and Transformation Parallelization Algorithms Compiler Techniques for AD Scientific Computing Applications Memory Optimization in Reverse Mode AD His work includes foundational research on data-flow analyses for adjoint programs, formalization of SPMD parallelization methods for mesh-based computations, and development of the TAPENADE Automatic Differentiation tool. The tool has been applied to complex systems like ocean circulation models (OPA) and CFD simulations. Scientific Work: He has contributed to Formalizing AD data-flow equations Extending TAPENADE to C language Optimizing checkpointing strategies Advancing tangent-on-reverse second derivative computation Reducing memory usage through address reversal techniques

Amitava DattaView profile

Professor

• Bioinformatics
• Computer Graphics
• Mobile and Wireless Networks
• +7 more

Amitava Datta is a Professor in the Department of Computer Science and Software Engineering at The University of Western Australia. His research focuses on areas such as bioinformatics, computer graphics, mobile networks, optical computing, and medical imaging. He holds a BSc from Calcutta University, an MSc from Kanpur University, and an MTech and PhD from IIT Madras. His expertise includes developing algorithms for medical image segmentation (e.g., pancreas and tumor detection), misinformation detection using BERT models, and social network analysis. He has contributed to projects like the BeeDAS data acquisition system for beehive monitoring and collaborated on cancer research targeting glioblastoma and osteosarcoma. His work aligns with UN Sustainable Development Goals, particularly in health and environmental sustainability. Awarded the Best Demo Paper Award at the 2017 WWW Conference, his research spans grants from organizations like the CRC for Honey Bee Products and NHMRC. He has supervised 37 research works, focusing on AI applications in healthcare, cybersecurity, and computational biology. His lab explores innovations in deep learning, medical informatics, and smart systems for environmental monitoring.

• Database Systems
• Data Stream Processing
• Uncertain Data Management
• +4 more

Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms

• Constitutional Law
• Comparative Constitutional Law
• Democratic Backsliding
• +5 more

Aziz Z. Huq is the Frank and Bernice J. Greenberg Professor of Law at the University of Chicago Law School, with a focus on US and comparative constitutional law. He holds a JD from Columbia Law School (2001, summa cum laude) and a BA in international studies and French from the University of North Carolina at Chapel Hill (1996, summa cum laude). Current affiliations: Constitutional Law Workshop, International Law, Public Service and Public Interest Law Program Prior: Visiting Professor at University of British Columbia (2024), University of Toronto (2023), and Stanford Law School (2019) His research spans constitutional design , democratic erosion , AI governance , and national security law . Articles analyze presidential immunity , algorithmic fairness , and judicial data stewardship , with keywords in Law, Computer Science, Political Science, and Criminology. Recent work examines digital privacy and reproductive rights post-Roe . Notable scientific awards include the Graduating Students Award for Teaching Excellence (2015) and Project Syndicate's global '30 Forward Thinkers' list (2025). He serves on the boards of the ACLU of Illinois and Seminary Coop, and has held editorial roles for journals like Law and Social Inquiry .

• Data Mining
• Graph Algorithms
• Network Analysis
• +2 more

Florian Adriaens is a Postdoctoral Researcher in the Department of Computer Science at the University of Helsinki, specializing in data mining, graph algorithms, and theoretical computer science with significant contributions to algorithmic fairness and network analysis. His work bridges theoretical foundations and practical applications in complex graph structures. His core research interests include: Data Mining Graph Algorithms Network Analysis Algorithmic Fairness Theoretical Computer Science Adriaens has published 15 peer-reviewed articles from 2017-2025 in top venues including KDD and WWW, demonstrating evolving expertise from foundational graph problems (diameter minimization, signed graph clustering) to cutting-edge hypergraph optimization and fairness-aware diversification. Recent publications (2024-2025) show increased focus on cardinality-constrained hypergraph cuts, multilayer correlation clustering, and fair representative selection, reflecting growing complexity in his research trajectory. His collaborative work includes researchers like Gionis, Tatti, and Wang, indicating strong interdisciplinary connections within the data mining community. Contact: florian.adriaens@helsinki.fi | ORCID

Qi LiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +7 more

Qi Li is an Assistant Professor at Iowa State University, specializing in Artificial Intelligence, Machine Learning, and Data Science. Their research focuses on advancing techniques in natural language processing, graph theory, federated learning, and knowledge graph construction. Key contributions include improving distant supervision for named entity recognition, enhancing fairness in graph-based systems, and developing adaptive methods for knowledge extraction and crowdsourcing. Research interests span a broad range of topics, including but not limited to: Named Entity Recognition (NER), fairness in machine learning, federated learning frameworks, knowledge graph completion, and optimization of crowdsourcing systems. Their work often combines theoretical advancements with practical applications, such as improving model robustness and scalability in real-world scenarios. Recent publications highlight innovations in entity linking re-ranking (GenDecider), personalized federated learning (pFedClub), and individual fairness in graph structures. These contributions reflect a commitment to addressing challenges in AI ethics, data efficiency, and system reliability. Qi Li’s research has been presented at top-tier conferences such as COLING, WWW, NeurIPS, and AIES, demonstrating a strong track record in impactful and interdisciplinary work.

Rocky Tong ChenView profile

Senior Lecturer

• Data Mining
• Recommender Systems
• Predictive Analytics
• +2 more

Dr. Rocky Tong Chen is a Senior Lecturer and ARC DECRA Fellow at the Data Science Discipline within the School of Electrical Engineering and Computer Science at The University of Queensland. He maintains active affiliations with the Centre for Enterprise AI and the ARC Centre of Excellence for Children and Families Over the Lifecourse. His research focuses on developing accurate, efficient, and trustworthy data mining solutions to extract actionable patterns from large-scale user data. Chen's research interests span Data Mining, Recommender Systems, Predictive Analytics, Machine Learning, and Health Informatics. His work demonstrates a strong emphasis on practical applications, particularly in developing lightweight and efficient algorithms for recommendation systems that can operate on resource-constrained devices. His research trajectory shows a clear progression from traditional recommender systems toward more privacy-preserving, decentralized, and trustworthy approaches that address real-world deployment challenges. Chen has published over 70 peer-reviewed papers in top-tier venues including KDD, SIGIR, WWW, ICDM, ICDE, AAAI, and IJCAI, as well as leading journals like VLDBJ, IEEE TKDE, IEEE TNNLS, ACM TOIS, and WWWJ. His publication trends reveal a strong focus on federated learning, graph neural networks, and on-device recommendation systems in recent years. 3 Best Paper Awards 1 Best Paper Nomination 2 Travel Awards Dr. Chen has secured multiple ARC grants including the Discovery Early Career Researcher Award (2023-2026) for 'Scalable and Lightweight On-Device Recommender Systems,' and is actively supervising numerous PhD students across various projects related to recommendation systems, data analytics, and machine learning. His supervision portfolio includes both principal and associate advisory roles, demonstrating his collaborative approach to research mentorship. His laboratory work focuses on developing practical implementations of recommender systems with an emphasis on efficiency, privacy, and real-world applicability.

• Machine Learning
• Recommendation Systems
• Federated Learning
• +3 more

Xin Zhou is a researcher at the School of Computer Science and Engineering, Nanyang Technological University (Singapore), affiliated with the Cloud Computing and Application Platform (CAP) Group. His work focuses on machine learning, recommendation systems, federated learning, and multimodal data analysis. He holds a PhD from Kyoto University's Department of Social Informatics (Japan). Key research areas include collaborative filtering, graph neural networks, privacy-preserving techniques, and large-scale learning patterns in MOOCs. Notable contributions include frameworks like SelfCF for self-supervised collaborative filtering and studies on personalized federated knowledge graphs. His publications span top venues such as ACM Multimedia, WWW, and IEEE Transactions on Neural Networks. Zhou collaborates extensively with institutions like Peking University and Baidu Research, focusing on scalable solutions for recommendation systems and distributed machine learning challenges.

• Machine Learning
• Natural Language Processing
• Climate Modeling
• +6 more

Professor Andreas Hotho leads the Data Science Chair at the University of Würzburg's Faculty of Mathematics and Computer Science, while serving as founding spokesman of the Center for Artificial Intelligence and Data Science (CAIDAS). His academic journey includes senior researcher roles at the University of Kassel and research positions at the AIFB Institute (University of Karlsruhe) and L3S Research Center (Hannover). Research Focus: Machine Learning, Large Language Models, Climate Modeling, Semantic Web, and Knowledge Graphs Methodologies: ConvMOS architecture, DenseLoss approach, LanZ-DML, and BibSonomy system Applications: Environmental monitoring, Digital Humanities, Smart Beehive Analysis, and Social Media Analytics With over 15 recent publications, his work spans climate model output statistics, zero-shot metric learning, imbalanced regression techniques, and German language model development. He serves as editor-in-chief for the Transactions on Graph Data and Knowledge and maintains active roles in academic governance through PC memberships at ECML-PKDD, WWW, and other major conferences. Scientific awards include: NAACL 2024 Best Paper Honorable Mention ICDM 2023 Best Paper Award NeurIPS 2020 Best ML Innovation Award ISWC 2018 SWSA Ten-Year Award WWW 2015 Best Paper Award FAIML 2020 Best Student Paper His research group includes doctoral researchers like Jan Pfister, Julia Wunderle, and Albin Zehe. Key projects include LitBERT for literary analysis, BigData@Geo series for climate modeling, and BeeConnected for ecosystem monitoring.

• Data Science
• Network Science
• Computational Social Science
• +5 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information , University of Pittsburgh , and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber) . With secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program, their interdisciplinary work bridges computational methods with social science inquiry. Research interests focus on: Social and political network analysis Computational social science Data mining and visualization Digital accountability in political communication Policy diffusion analytics Trust-distrust dynamics in networked societies Key research themes include: Developing network theories for misinformation and extremism Designing visual analytics for policy diffusion and group bias detection Studying collective behavior during emergencies Creating computational tools for social data analysis Major grants include NSF awards for: Public policy diffusion analytics COVID-19 misinformation mitigation Urban mobility and smart city initiatives Understanding extremism in cyber-social spaces Scientific leadership roles: Editor-in-Chief of ICWSM Editorial board member at Frontiers in Big Data, EPJ Data Science, PLOS ONE, and Nature Scientific Reports Co-chair roles at KDD Humanitarian Mapping Workshop, WWW Web & Society Track, and multiple SBP-BRiMS graduate programs As founder of the Pitt Computational Social Dynamics Lab (PICSO LAB) , they lead research on social media analysis, network modeling, and urban informatics through projects like PittSmartLiving – a $1.4M NSF-funded initiative exploring real-time transit incentives and community engagement.

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +3 more

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Xiangnan KongView profile

Associate Professor

• Data Mining
• Machine Learning
• Biomedical Informatics
• +5 more

Xiangnan Kong is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), serving as the Graduate Coordinator of the Data Science Program. His academic journey includes a BS and MS from Nanjing University (2006, 2009) and a PhD from the University of Illinois at Chicago (2014). He maintains an active research program with significant contributions to data mining and machine learning. Dr. Kong's primary research interests focus on data mining and machine learning, with emphasis on addressing data science problems in biomedical and social applications. His work specifically targets data variety issues across multiple domains including neuroscience, biomedical informatics, social networks, and business intelligence. He has published extensively in top-tier conferences and journals such as KDD, ICDM, SDM, WWW, WSDM, CIKM, and TKDE. His recent publications demonstrate a strong trend toward interdisciplinary applications, with significant work in neuroimaging analysis, healthcare applications, urban traffic estimation, and efficient deep learning architectures. His research bridges theoretical advances in graph mining, heterogeneous information networks, and deep learning with practical applications in biomedical domains and social computing. Dr. Kong has successfully advised numerous PhD and Master's students, with former students now working at prominent organizations including Facebook Research, VISA Research, MIT CSAIL, and Amazon AI. His research is supported by multiple grants including NSF awards, Adobe Gift Grant, Huawei Research Grant, and WPI TRIAD Grant.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Rohit Babbar is a Senior Lecturer in the Department of Computer Science at the University of Bath, specializing in Artificial Intelligence and Machine Learning. His research focuses on extreme multi-label classification, deep learning, and algorithmic optimization for complex performance metrics. He contributes to UN Sustainable Development Goals through his work in these technical domains. Dr. Babbar leads the NERC GW4+ project on leveraging deep learning to uncover hidden patterns in FDA adverse event data, demonstrating expertise in applying machine learning to real-world problems. His recent research emphasizes scalable solutions for high-dimensional classification tasks and improving algorithmic efficiency. His publications span conferences like ACL, KDD, and WWW, reflecting contributions to computational linguistics, neural network architectures, and online learning systems. He actively collaborates on international projects, such as FAERS data analysis and label correlation studies in extreme classification scenarios.

Muhammad IkramView profile

Senior Lecturer

• Cybersecurity
• Privacy-Preserving Systems
• Machine Learning for Security
• +5 more

Dr. Muhammad Ikram is a Senior Lecturer in Cybersecurity at Macquarie University's School of Computing, affiliated with the Information Security and Privacy (ISP) group and the Macquarie Cybersecurity Hub (MCHUB). His research focuses on privacy/security in mobile/Web platforms, leveraging machine learning for fraud detection and vulnerability analysis. He holds a PhD in Electrical Engineering from UNSW, a Master's from Ajou University, and a Bachelor's from UET Peshawar. Ikram has published 64+ works in top conferences/journals like Usenix Security, NDSS, and ACM TOPS. His notable contributions include analyzing iOS app vulnerabilities, tracking prevention mechanisms, and mobile health app privacy risks. He received the Best Paper Award at AsiaCCS 2019 and Outstanding Paper at Mobiquitous 2021. Research Interests: Mobile/Web Security, Malware Detection, Privacy-Preserving Systems Grants: Led projects on network security, telecommunications obligations, and cybersecurity hubs Media Impact: Featured in The Guardian and over 50 outlets for work on web resource loading (~11M audience reach) Service Roles: Technical Program Committee member at WWW/PETS, reviewer for CCS/PETS He previously held roles as a Postdoc at University of Michigan and Research Scientist at Data61. His work bridges academic research with real-world impact in cybersecurity policy and industry standards.