Ke Wang is a Professor in the School of Computing Science at Simon Fraser University . His research focuses on Data Mining , Database Systems , Data Privacy , and Graph and Network Data . He holds a Ph.D. and M.Sc. from the Georgia Institute of Technology (1986 and 1984, respectively). Teaching includes courses like Database Systems II , Introduction to Data Mining , and Special Topics in Databases . He has advised numerous students and alumni, many of whom now work in tech, academia, and industry. Notable awards include the 2013 Faculty of Applied Sciences Research Excellence Award and the ECIR 2019 Best System Paper . His work emphasizes privacy-preserving techniques and has led to contributions like the Introduction to Privacy-Preserving Data Publishing textbook. He has served as a conference chair for major data mining events like SDM 2015/2016 and holds editorial roles in journals like ACM TKDD. His lab, the Database and Data Mining Laboratory , focuses on actionable solutions for real-world data challenges.
Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.
Xiaohui Yu is a Professor and Graduate Program Director in the School of Information Technology at York University. He holds a BSc from Nanjing University, an MPhil from the Chinese University of Hong Kong, and a PhD from the University of Toronto. His research focuses on the intersection of data management and machine learning, including ML-based database systems, large-scale machine learning, and spatio-temporal data analysis in contexts like intelligent transportation systems and social networks. Supported by grants from NSERC and industry partners, his work has been published in top venues such as SIGMOD, VLDB, and TKDE. He serves as an Associate Editor for journals like IEEE TKDE and ACM TKDD, and actively participates in conference program committees. Education: BSc (Nanjing University), MPhil (Chinese University of Hong Kong), PhD (University of Toronto). Research Interests: Big data management, database systems, machine learning, spatio-temporal data analytics, and video query processing. Recent articles emphasize ML-driven database components, efficient video query optimization, and scalable algorithms for large-scale data. His work addresses challenges in query processing, indexing, and real-time systems. Service: Serves on editorial boards (e.g., Information Systems), and chairs/workshops (e.g., Symposium on Data Markets). Active in program committees for SIGMOD, ICDE, and other leading conferences. Advising & Grants: Directs graduate programs and leads research groups. Collaborates with industry on data marketplaces and AI model integration. No specific student names listed, but actively recruits PhD/Master’s candidates.
Lee Miller is a Professor in the Department of Neurobiology, Physiology, and Behavior at the University of California, Davis, College of Biological Sciences. His research integrates neural engineering, physiology, and computational methods to develop communication restoration technologies and investigate sensory processing mechanisms. His primary research interests include neural engineering for speech neuroprosthetics, electrophysiological analysis of speech production, auditory neuroscience, and geometric approaches to neuromuscular signal decoding. He employs surface electromyography (EMG), electroencephalography (EEG), and computational modeling to study brain-machine interfaces for speech restoration and multisensory integration. Recent publications reveal a dominant focus on EMG-based speech neuroprostheses, with geometric and topological analysis of neuromuscular signals emerging as a key methodology. His lab has pioneered non-invasive approaches to speech articulation decoding, created standardized EMG databases, and investigated neural mechanisms of attention in speech-in-noise processing. This work bridges engineering innovation with fundamental neuroscience to address communication disorders. Professor Miller leads the Miller Lab at UC Davis, which specializes in neural engineering for communication restoration. The lab develops real-time speech synthesis systems from neural signals and investigates the physiological basis of speech production and perception using multimodal recording techniques.
See Kiong Ng serves as Professor of Practice in the Department of Computer Science at the School of Computing, National University of Singapore (NUS), while concurrently holding leadership roles as Director of AI Technology at AI Singapore and Deputy Director of NUS's Institute of Data Science (IDS). His work focuses on translational data science research and developing integrated capabilities for Singapore's Smart Nation initiative through industry and public agency collaborations. His academic credentials include a B.S. in Applied Mathematics (Computer Science Track) from Carnegie Mellon University (1989), an M.S.E. in Computer & Information Science (Artificial Intelligence) from the University of Pennsylvania (1990), and a Ph.D. in Computer Science from Carnegie Mellon University (1998), supported by Singapore's National Computer Board overseas scholarship. Professor Ng's research bridges artificial intelligence with real-world applications across diverse domains. His primary interests span Data Mining, Machine Learning, Natural Language Processing, Smart Cities, and Computational Biology, with emphasis on extracting value from big data through interdisciplinary approaches. He actively pioneers applications in urban systems and bioinformatics, demonstrating data science's transformative potential beyond traditional boundaries. His publication record reveals consistent innovation in algorithm development for complex data challenges, with recent work focusing on taxonomy construction, single-cell genomics analysis, urban transportation systems, and imbalanced time series classification. These contributions demonstrate his commitment to solving practical problems through cutting-edge data science techniques. His major recognitions include: MTI Borderless Award (2014) as Green Growth Working Group project member Minister for National Development's R&D Award 2017 (Distinguished Award) for city-level analytics platform innovation A*STAR Borderless Award (2014) as Urban Systems Initiative team leader MTI Innovation Award (2013) for Strategic Technology Translation in Business Analytics Professor Ng has established significant research infrastructure including founding A*STAR's Data Analytics Department and leading the Urban Systems Initiative. His translational research model emphasizes industry partnerships and practical implementation, particularly in smart city development where he connects data science with urban planning challenges across Singapore's government agencies.
Jon Atle Gulla is a Professor at NTNU and Director of the Norwegian Research Centre for AI Innovation (NorwAI). He holds academic leadership roles, including former Head of the Department of Computer Science and Informatics at NTNU. His expertise spans Semantics, Language Technology, Recommender Systems, and AI-driven innovation. He has nearly 150 international publications and advised over 100 students across MSc, PhD, and postdoctoral levels. Education: MSc in Computer Science (1988), PhD in Computer Science (1993) from Norwegian Institute of Technology (NTH) MSc in Linguistics (1995), University of Trondheim MSc in Management (Sloan fellowship, 2003), London Business School Research interests focus on Semantics and Language Technology applied to Recommender Systems, Information Retrieval, and Text Analysis. He explores AI-based innovations in digitalization and entrepreneurship, advising industry on AI adoption and commercialization. Notable projects include Big Data collaborations with DNB, RecTech for news recommendation, and Trondheim Analytica analyzing political texts/social media. Publications emphasize AI applications in news recommendation, political text analysis, and Scandinavian language models. His work addresses ethical AI, copyright implications, and cross-lingual NLP challenges. Awards: Member of the Royal Norwegian Society of Arts and Sciences. Advising/grants: Supervised 30 PhD students and 70 MSc students. Involved in startups like Fast Search & Transfer (acquired by Microsoft) and Mito.ai/Strise.ai. Active in reviewing for journals like Data & Knowledge Engineering and conferences like ACL. Labs/teams: Leads NorwAI, co-founder of INRA and NOBIDS workshops. Collaborates with industry and academia on AI-driven solutions.
Dr. Jonathan Lenoir is a CNRS Researcher at the Ecology and Dynamics of Anthropized Systems (EDYSAN) laboratory, University of Picardie Jules Verne , France. His work bridges Ecology and Biostatistics , focusing on ecological dynamics under spatial and temporal global changes, particularly biotic responses to climate change. His research spans broad-scale biodiversity patterns, species distribution modeling, and microclimate ecology, with special attention to forest systems. Dr. Lenoir leads and contributes to multiple research projects including MaCCMic (Impact of forest Management and Climate Change on understory Microclimate) and IMPRINT (Impacts of Microclimatic Processes on forest Biodiversity redistribution under macroclimaTe warming). These projects utilize advanced technologies like LiDAR and microclimate sensors to model understory temperature dynamics and predict biodiversity responses to climate change. His recent publications analyze microclimate buffering in forests ( 2024 ), species thermophilization ( 2024 ), and the application of deep learning to habitat identification ( 2024 ). His work also explores interdisciplinary connections like eco-oncology , comparing invasion dynamics in ecology and medicine. Dr. Lenoir actively mentors researchers and supervises fieldwork campaigns, emphasizing rigorous data collection ( 180 monitoring plots across French forests ) and advanced statistical analyses in R . He collaborates with European institutions and participates in large-scale initiatives like ReSurveyEurope , a database of resurveyed vegetation plots.
Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.
Sibel Adali is a Professor in the Department of Computer Science at Rensselaer Polytechnic Institute’s School of Science. She joined RPI in 1996 after earning her PhD from the University of Maryland. Her academic roles include Associate Head and Graduate Program Director of the Computer Science Department (2015-2018) and currently serving as Associate Dean of Science for Research and Graduate Studies at RPI. She teaches foundational Computer Science courses in problem-solving and databases. Education: PhD from University of Maryland. Adali’s research focuses on trust modeling, social networks, information retrieval, and semantic analysis. She has led cross-cutting projects addressing cognitive science intersections with network theory, including roles as ARL-lead CTA Trust Coordinator and SCRNARC Associate Director. Her work explores misinformation mitigation, semantic shifts in language, and expert identification in social media ecosystems. Recent publications highlight her contributions to fake news detection, semantic change modeling, and AI-driven misinformation interventions. Her research trends emphasize interdisciplinary approaches combining machine learning, computational linguistics, and behavioral studies. Scientific Awards: Trustees' Outstanding Teacher Award (2015), Rensselaer Polytechnic Institute’s highest teaching honor. Adali actively collaborates on grants and projects through research centers including MOCA, IDEA, SCNARC, and ARL CTA partnerships. She advises students on advanced computational research and participates in military communications technology initiatives.
David Basin is a Full Professor at the Department of Computer Science, ETH Zurich, and heads the Information Security Group. He has held academic positions since 2003, including roles at the University of Freiburg (1997–2002) and the Max-Planck-Institut für Informatik (1992–1997). His research focuses on Information Security, including methods and tools for secure systems, formal verification, and cryptographic protocols. He is Editor-in-Chief of the ACM Transactions on Privacy and Security and Springer's Information Security and Cryptography book series. Basin founded the Zurich Information Security Center (ZISC) in 2003 and led it until 2011. Education: B.Sc. in Mathematics (Reed College, 1984), Ph.D. (Cornell University, 1989), and Habilitation (University of Saarbrücken, 1996). Research interests span formal methods for security protocol verification, privacy-preserving systems, and cryptographic implementations. He has contributed to foundational work on security protocols, including the Tamarin verification framework. His work addresses real-world systems like payment protocols (EMV), DNS security, and database isolation guarantees. Awards: ACM Fellow (2018) for contributions to Information Security and Formal Methods, IEEE Fellow. He has organized numerous conferences, including IEEE S&P, Euro S&P, and ACM CCS. Labs/Teams: Leads the Information Security Group at ETH Zurich and co-founded Anapaya Systems, a startup focused on network security solutions. His team develops tools like VeriMon (formally verified monitoring) and Tamarin for protocol analysis.
Ying Cai is an Associate Professor in the Department of Computer Science at Iowa State University, joining in 2003 after earning his Ph.D. in Computer Science from the University of Central Florida (2002). His research focuses on AI, machine learning, data science, cybersecurity, privacy protection, and database systems. He leads projects funded by the Air Force Research Laboratory, including work on authentication data structures for rank-aware queries, requiring U.S. citizenship and expertise in linear algebra/cryptography. Dr. Cai’s work spans cybersecurity (e.g., adversarial example defense, secure secret sharing), spatio-temporal systems (e.g., traffic risk prediction, check-in time modeling), and healthcare AI (e.g., cervical spine diagnosis with transformers). His publications emphasize practical applications of ML in privacy, security, and distributed systems. Professional roles include Associate Editor for Multimedia Tools and Applications (since 2009), Co-chair for COMPSAC TAIN/NCIW symposium (2014–2017), and TPC Chair for Mobilware 2010. His service includes contributions to INFOCOM, ICDCS, and MDM conferences. Current research opportunities exist for graduate students with strong programming/math skills, particularly in cryptography and linear algebra. He emphasizes interdisciplinary work, such as bridging AI with social sciences via large language models.
Piotr Indyk is the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He is co-director of the Foundations of Data Science Institute (FODSI) and a member of MIT's Theory of Computation Group, Computer Science and Artificial Intelligence Lab (CSAIL), and multiple research initiatives like Wireless@MIT and Big Data@CSAIL. Education: Magister (MA) in Computer Science, University of Warsaw (1995) Ph.D. in Computer Science, Stanford University (2000), advised by Rajeev Motwani Research Interests: Focuses on high-dimensional computational geometry, data stream algorithms, sparse recovery, compressive sensing, and machine learning. His work includes foundational contributions like locality-sensitive hashing (LSH), the Sparse Fourier Transform, and efficient similarity search algorithms. Key Contributions: Known for developing FALCONN (Fast Approximate Nearest Neighbor Search library), and for pioneering work in sub-linear algorithms, streaming algorithms, and geometric computing. Awards: ACM Paris Kanellakis Award (2012) ACM Fellow (2015) Simons Investigator (2013) Member, National Academy of Sciences (2024) Member, American Academy of Arts and Sciences (2023) Teaching & Mentorship: Advised numerous PhD/MSc students and postdocs, and taught courses on geometric computation, streaming algorithms, and algorithmic aspects of embeddings. Labs & Teams: Leads research in areas like FODSI, geometric algorithms, and data science at MIT's CSAIL.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.
Mohsen Lesani is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin School of Engineering. His research focuses on reliability and security of software systems, particularly concurrent and distributed systems, with recent emphasis on secure replicated systems and distributed machine learning. Dr. Lesani received his PhD from UCLA, MS in artificial intelligence from Sharif University of Technology, and BS in software engineering from University of Tehran. He was previously a postdoc at MIT. His educational background provides a strong foundation for his interdisciplinary research spanning programming languages, distributed systems, and security. His research interests center on creating reliable and secure distributed systems. Current projects include resilient and secure distributed systems, heterogeneous and reconfigurable secure distributed systems, automatic analysis and synthesis of replicated objects, verification of distributed systems, data analytics, secure exchange across blockchains, machine learning for performance models, domain-specific languages and type systems, and automatic fence insertion for concurrent systems. His work bridges theoretical foundations with practical implementations to address real-world challenges in distributed computing. Lesani's research has been recognized with several prestigious awards including the NSF CAREER award in 2020 and DARPA YFA award in 2022. His work has also received the SIGPLAN Research Highlight in 2019, a distinguished paper award at OOPSLA 2018, and a best paper award at ISSRE 2015. These accolades reflect the impact and quality of his contributions to the field. He actively mentors PhD students in the Safe and Secure Software (S3) lab, including Xiao Li, Eric Chan, Javad Saber-Latibari, and Tejas Mane. His research has been supported by multiple NSF grants, demonstrating sustained funding for his innovative work. Lesani serves on program committees for major conferences including POPL, PLDI, OOPSLA, and DISC, contributing to the academic community. Lesani leads the Safe and Secure Software (S3) lab at UC Santa Cruz, where his team works on cutting-edge research in distributed systems, programming languages, and security. The lab fosters a collaborative environment where theoretical insights are translated into practical systems that address real-world challenges in reliability and security of distributed applications.
