Find Academic Supervisors

Ihab IlyasView profile

Professor

• Data Management
• Data Cleaning
• Machine Learning for Data Quality
• +6 more

Professor Ihab Ilyas is a leading figure in data management and machine learning at the Cheriton School of Computer Science , University of Waterloo , where he holds the Thomson Reuters Research Chair in Data Quality . He is currently on leave from the university, serving as a Distinguished Engineer, Proactive Intelligence at Apple Inc . His research focuses on data cleaning , large-scale data integration , knowledge graphs , and machine learning applications in data quality . He has pioneered systems like HoloClean and Saga , with commercial impacts through co-founded startups Inductiv (acquired by Apple) and Tamr . PhD in Computer Science from Purdue University Co-founder of Inductiv (acquired by Apple) Co-founder of Tamr Research Interests include: Probabilistic and uncertain data management Machine learning for data quality and enrichment Big data systems and information extraction Knowledge graph construction and optimization Scientific Awards and Recognitions include: Fellow of the Royal Society of Canada (2024) C.C. Gotlieb Computer Award (2024) IEEE Fellow (2021) ACM Fellow (2020) Cheriton Faculty Fellowship (2013-2016) Ontario Early Researcher Award

• Databases
• Data Mining
• Information Retrieval
• +2 more

Dr. Arnab Bhattacharya is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Kanpur since December 2020. He previously served as Associate Professor (2014–2020) and Assistant Professor (2007–2014) at IIT Kanpur. Education: PhD in Computer Science (2007), University of California, Santa Barbara MS in Computer Science (2007), University of California, Santa Barbara Bachelor of Computer Science and Engineering (2001), Jadavpur University Research Focus spans Databases , Data Mining , Information Retrieval , and Artificial Intelligence . His work emphasizes graph analytics , skyline queries , probabilistic data , and knowledge graph management . Article Trends reveal expertise in graph neural networks , trajectory-aware systems , statistical significance in databases , and legal/medical data mining . His methodologies often integrate chi-square statistics , approximate indexing , and provenance tracking . Scientific Awards: IBM Faculty Research Award Yahoo! Faculty Research and Engagement Program Award Best Paper at COMAD 2011 Best Student Paper at COMAD 2010 Top-Five Student Paper at ICDM 2005 ICDM Student Travel Award sponsored by IBM Contact: Office RM 409, Department of Computer Science and Engineering, IIT Kanpur Email: arnabb@iitk.ac.in | Phone: +91-512-259-7650

Jennifer WidomView profile

Professor

• Nontraditional Data Management
• Data Streams
• Crowdsourcing Quality Management
• +3 more

Jennifer Widom is the Frederick Emmons Terman Dean of Stanford University's School of Engineering and holds the Fletcher Jones Professorship in Computer Science and Electrical Engineering. She previously served as Chair of the Computer Science Department (2009–2014) and Senior Associate Dean (2014–2016). Widom earned her Ph.D. in Computer Science from Cornell University (1987) and completed her undergraduate degree in Music at Indiana University (1982). She joined Stanford in 1993 after research at IBM Almaden. Education: Ph.D., Computer Science, Cornell University, 1987 MS, Computer Science, Cornell University, 1985 MS, Computer Science, Indiana University, 1983 BS, Music, Indiana University Jacobs School of Music, 1982 Research Interests: Widom's work focuses on nontraditional data management, including data streams, uncertain databases, crowdsourcing, and query processing systems like STREAM and Deco . She has pioneered methods for managing and querying uncertain data, optimizing graph algorithms, and integrating human computation into data systems. Key Contributions: Developed the STREAM system for real-time data stream management Advanced techniques for crowdsourcing quality management Contributed to foundational work in uncertain databases and provenance tracking Awards & Recognition: ACM Fellow (2005) Member, National Academy of Engineering (2005) Edgar F. Codd Innovations Award (2007) ACM-W Athena Lecturer (2015) EPFL-WISH Erna Hamburger Prize (2018) Teaching & Leadership: Widom teaches courses on data analytics and database systems, advising students like Arnav Joshi. She has led major initiatives in computational education and institutional leadership at Stanford.

Matteo MagnaniView profile

Professor

• Network Science
• Social Network Analysis
• Computational Social Science
• +10 more

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.

Cheng, Reynold C.K.View profile

Professor

• Data Science
• Big Graph Analytics
• Uncertain Data Management
• +3 more

Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.

• Distributed Systems
• Database Management Systems
• Operating Systems
• +7 more

Natacha Crooks Dr. Natacha Crooks is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her research focuses on distributed systems, databases, and security, with a particular emphasis on consistency models, BFT protocols, and cloud computing. She is a founding member of the SkyLab and a core contributor to the Data Systems and Foundations Group at Berkeley. She holds a Ph.D. in Distributed Systems from the University of Texas at Austin (2019) and a BA in Computer Science and Law from the University of Cambridge (2012). Affiliations & Roles: Assistant Professor, UC Berkeley EECS Visiting Researcher at Azure Research, Security & Privacy Founding Member of SkyLab Member of Berkeley Center for Decentralized Intelligence Former Scientific Advisor at Improbable and Astronomer Research Interests: Her work bridges distributed systems and database research, addressing challenges in transactional consistency, fault-tolerant protocols, and cloud resource optimization. Key themes include: Designing scalable BFT consensus algorithms Secure multi-cloud storage solutions Optimizing distributed transaction processing Privacy-preserving distributed systems Awards & Recognition: Sloan Research Fellow (2025) NSF CAREER Award ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2020) IEEE CS TCDE Early Career Award (2024) Teaching: Fall 2025: CS 294-282 (Research Culture and Community Norms), Wheeler 130. Industrial Collaboration: Prior roles include visiting researcher at Cornell University, Microsoft Research (DMX group), and Imperial College London. Industrial partnerships include work with Materialize, Astronomer, and Improbable.

Benjamin C.M. KaoView profile

Professor

• Database Management Systems
• Data Mining
• Real-time Systems
• +1 more

Professor Benjamin C.M. Kao is a faculty member in the Department of Computer Science at The University of Hong Kong (HKU), affiliated with the School of Computing and Data Science. He holds a BSc from HKU (1989) and a PhD from Princeton University (1995). His career includes roles as a teaching/research assistant at Princeton (1989-1991) and a research fellow at Stanford University (1992-1995). His research focuses on Database Management Systems, Data Mining, Real-time Systems, and Information Retrieval Systems. Notable contributions include S-OLAP for sequence data analysis, collaborative resource discovery in social tagging systems, and algorithms for mining periodic patterns in sequences. He has led research grants such as the GRF-funded 'Online Analytical Processing on Sequence Data' (2008) and computational studies in uncertain data mining (2006). Professor Kao has served on program committees for major computer science conferences and reviewed for leading journals. His work bridges theoretical foundations with practical applications in data systems and information retrieval.

Kyle DeMarsView profile

Associate Professor

• Space situational awareness
• Navigation, guidance, and control
• Bayesian filtering
• +6 more

Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.

Chenhao MaView profile

Assistant Professor

• Graph Computing
• Data Mining
• Database Systems
• +3 more

Dr. Chenhao Ma is an Assistant Professor at the School of Data Science , The Chinese University of Hong Kong, Shenzhen , where he works on large-scale data management and data mining. Previously, he was a Postdoctoral Fellow at the University of Hong Kong (2021–2022) and earned his PhD in Computer Science from the University of Hong Kong (2021) and B.Eng. from Shandong University (2017). Current research focuses on graph computing (dense subgraph discovery, motif analysis, graph learning), AI+DB (Text-to-SQL, vector search), and traffic data mining (trajectory analysis, outlier detection). He has published over 40 papers in top venues including SIGMOD, PVLDB, KDD and received the ACM SIGMOD Research Highlight Award 2021 and Best of SIGMOD 2020 (4/458). Scientific Awards : ACM SIGMOD Research Highlight Award 2021 Best of SIGMOD 2020 (4/458) Presidential Young Fellow at CUHK-Shenzhen (2023) Hong Kong and China Gas Scholarship (2019-2020) Reaching Out Award (2019) HKU Postgraduate Scholarship (2017-2021) ACM-ICPC Gold Medal (2015) National Scholarship (2014, 2015) Advising and Research Team : He leads a team including Postdoc Dr. Yuanyuan Zeng, PhD students Lujie Ban, Yuwei Xu, and MPhil students Yi Yang, Yuyang Liang. Former mentees like Yichen Xu (PhD at Berkeley) and Jiayang Pang (Master at UC San Diego) have achieved academic placements. Professional Service : He has served as PC member/reviewer for VLDB, KDD, ICDE, WWW, NeurIPS, TKDE , and guest editor for Applied Sciences and Frontiers in Big Data . He chairs sessions at ICDE and VLDB.

Oliver KennedyView profile

Associate Professor

• Databases
• Programming Languages
• Data Science
• +2 more

Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.

Peter HaasView profile

Professor

• Data Management
• Machine Learning
• Simulation
• +6 more

Peter Haas is a Professor at the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, with an adjunct role in Industrial Engineering. Previously, he spent 30 years as a Principal Research Staff Member at IBM Research and held a consulting professorship in Management Science and Engineering at Stanford University. His research focuses on applying probability and statistics to data management, simulation of complex systems, and machine learning scalability. Education : PhD, Operations Research, Stanford University, 1986 MS, Statistics, Stanford University, 1984 MS, Environmental Engineering, Stanford University, 1979 SB, Engineering and Applied Physics, Harvard University, 1978 Research Interests : Haas’s work spans stochastic systems, probabilistic databases (e.g., MCDB and SimSQL), sampling techniques, and simulation optimization. He pioneered methods for managing uncertain data and scalable machine learning, including compressed linear algebra for declarative systems. His recent focus includes in-database decision support and hybrid simulation metamodeling with neural networks. Key Contributions : He developed the Online Aggregation framework (SIGMOD 1997), which earned a Test-of-Time Award in 2007. His work on matrix factorization and distributed stochastic gradient descent (DSGD) revolutionized large-scale machine learning. He also advanced techniques for estimating distinct-values and correlation discovery in databases. Awards : A six-time recipient of IBM’s Pat Goldberg Memorial Award, he is an ACM and INFORMS Fellow. His honors include the VLDB Best Paper Award (2016), EDBT Best Paper (2018), and recognition in Communications of the ACM. Advising & Grants : He advises four current PhD students and has graduated Matteo Brucato. His IBM career included over 30 patents, including foundational work for DB2’s sampling capabilities and IBM Watson analytics. He leads the DREAM Lab, focusing on data systems for exploration and analytics. Labs/Teams : Directs the Data systems Research for Exploration, Analytics, and Modeling (DREAM) Lab, advancing projects like Splash (health system simulation) and SuDocu (document summarization by example).

Arijit KhanView profile

Associate Professor

• Graph Neural Networks
• Blockchain
• Data Management
• +5 more

Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.

Nikos GiatrakosView profile

Assistant Professor

• Big Data Management
• Internet of Things (IoT)
• Distributed Systems
• +3 more

Nikos Giatrakos is an Assistant Professor at the School of Electronic & Computer Engineering, Technical University of Crete, and a core member of the Software Technology and Network Applications Lab (SoftNet) . His work bridges Big Data systems, IoT, and advanced analytics, with a focus on real-time processing and scalable architectures. Previously, he served as a postdoctoral researcher at the same laboratory. Education PhD in Computer Science, University of Piraeus (2012) Postgraduate Diploma in Information Systems, Athens University of Economics and Business (2008) BSc in Computer Science, University of Piraeus (2006) Research Focus : Nikos specializes in software architectures for Big Data streaming, including Distributed Big Data Processing , Federated Machine Learning , Cloud-to-Edge Data Management , and Approximate Query Processing . His work has also advanced Complex Event Processing and Outlier Detection in decentralized environments. Scientific Contributions : His research has led to the DAG* workflow optimizer for IoT, the SuBiTO framework for real-time neural learning, and the INFORE approach for cross-platform analytics. He received the Best System Demonstration Award at ACM CIKM 2020 for INforE. Academic Leadership : Nikos teaches Object-Oriented Programming, Data Science, and Distributed Systems. He has supervised numerous European and national grants as Principal Investigator and served on program committees for top-tier conferences like SIGMOD, VLDB, and DEBS.

Fabian PanseView profile

Researcher

• Database Systems
• Data Quality
• Probabilistic Data Management
• +3 more

Dr. Fabian Panse is a Researcher at the Database and Information Systems (DBIS) group within the Department of Informatics at the University of Hamburg. His work focuses on database systems, data quality, and probabilistic data management, with significant contributions to polyglot persistence, duplicate detection, and data simulation frameworks like SmartOpenHamburg and HADeS. Research Assistant since 2009 PhD in Computer Science Research interests center on polyglot persistence , probabilistic databases , duplicate detection , and data pollution techniques . His publications span conferences like VLDB, ICDE, and workshops on database fundamentals. He has supervised over 20 theses including Master's and Bachelor's projects on topics ranging from data synthesis to smart city applications . Key collaborations include Prof. Norbert Ritter and Dr. Wolfram Wingerath.

Xin HuangView profile

Associate Professor

• Graph Data Management
• Big Graph Mining & Visualization
• Social Network Analysis
• +4 more

Xin Huang is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University (HKBU), Faculty of Science. He serves as Coordinator of Research Postgraduate Programme (PhD&MPhil) and Coordinator of Programming Contest Teams. His educational background includes: Ph.D. in Systems Engineering and Engineering Management from The Chinese University of Hong Kong (2014) B.Eng. in Computer Science from Xiamen University (2010) Huang's research focuses on innovative technologies for large-scale graph data management and analysis. His work spans graph data management including keyword search and graph indexing, graph mining techniques for summarization and decomposition, community search algorithms, and graph learning approaches including graph neural networks. His research integrates theoretical foundations with practical applications in social network analysis and privacy-aware computing. His publication record shows consistent output in top-tier venues with a clear evolution from foundational graph algorithms to more complex applications involving dynamic and uncertain graphs, reflecting the growing importance of graph analytics in real-world systems. Notable awards include: Best Student Paper Award at ACM/IEEE IWLS 2023 Best Faculty Article Award from Chinese Communication Association (2022) HKBU President's Award for Outstanding Performance as Young Researcher (2021) RGC Early Career Award (2020) Best Paper Award at WISE 2019 Huang actively mentors PhD students and postdoctoral fellows, currently supervising five PhD candidates and four postdocs. His research has been supported by competitive grants including the RGC Early Career Award. He serves as Associate Editor for Data Science and Engineering and World Wide Web Journal, and participates extensively in program committees for major conferences including VLDB, ICDE, and WWW. As part of HKBU's Database Research Group, Huang contributes to a vibrant research environment that has secured over $10 million in research funding and published more than 200 papers in top venues.