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.
Oliver Grau is a Chair Professor for Image Science and a leading figure in Media Art research, affiliated with Hong Kong Baptist University (Academy of Visual Arts, Distinguished Fellow since 2023) and Danube University Krems (Chair Professor for Image Science from 2005–2022). He founded the Archive for Digital Art (ADA) and the international MediaArtHistories Conference Series , with key roles at institutions across Germany, Austria, and Australia. His work bridges art, science, and technology, focusing on immersive images, digital heritage, and emotion research. Key Positions: Director of ADA (since 2000), Founding Director of MediaArtHistories Conference Series (since 2005), PI for high-resolution digitization of the Goettweig Graphic Print Collection (2005–2017). Research Grants: Funded by DFG, Austrian Science Fund, Australian Research Council, VW Foundation (total 8.3 Mio EUR), and Erasmus+ Joint Master program (5.4 Mio EUR). Research Interests: Grau’s scholarship spans the history of media art, telepresence, artificial life, emotion research, and digital humanities. His work emphasizes the evolution of immersive visual experiences from historical art forms to digital media, integrating theoretical and practical approaches. Article Trends: His recent publications focus on digital art’s sociopolitical dimensions, archiving challenges in the digital era, and cross-disciplinary methodologies. Themes include media art conservation, web-based tools for digital humanities, and the intersection of artistic expression with contemporary global issues like climate change and surveillance. Scientific Awards: Distinguished Fellow at Hong Kong Baptist University (2023) Science Award of Lower Austria (2019) Honorary Doctorate from University of Oradea (2014) Invitations to G-20 summit, Olympic Games, and international symposia Labs & Teams: He leads the Archive for Digital Art, managing a global team of 28 staff, and co-founded the Erasmus+ Joint Master in Media Arts Cultures. His projects emphasize collaborative archiving tools (e.g., Web 2.0/3.0 archives) and large-scale digitization initiatives.
Simen Grung is a Doctoral Research Fellow at the Department of Teacher Education and School Research, Faculty of Educational Sciences, University of Oslo. His work focuses on assessment practices and English language pedagogy. Current projects: EDUCATE (Subject Renewal Evaluation), LANGUAGES (Language Instruction Contexts), NAVIKO-LU (Video-based Teacher Training), VIST (Video Excellence in Teaching) Research Groups: SISCO (Studies of Instruction across Subjects and Competences) His research explores feedback mechanisms in international classroom contexts, formative assessment challenges, and the use of video representations to enhance teacher training programs across Norway, England, and France. Recent publications demonstrate expertise in comparative education studies, language proficiency analysis, and assessment research methodologies. Key themes include cross-cultural pedagogical practices, assessment literacy, and video-based professional development. Active in academic collaborations, he works with researchers like Lisbeth M. Brevik, Eva Thue Vold, and Kirsti Klette on projects examining instructional quality and student experiences in teacher education.
Alan Marco is an Associate Professor at the Jimmy and Rosalynn Carter School of Public Policy within Georgia Tech's Ivan Allen College of Liberal Arts. His research focuses on innovation policy, patent economics, and intellectual property strategy. Previously, he served as Chief Economist at the U.S. Patent and Trademark Office (USPTO), where he co-created PatentsView.org and led the Cancer Moonshot Patent Challenge. Research interests span antitrust regulation, patent valuation, technology markets, and empirical legal studies. Marco holds a Ph.D. in economics from UC Berkeley and a B.A. from Skidmore College. Publications demonstrate expertise in patent analytics, with recent work examining strategic citation behaviors, FRAND royalty calculations, and links between antitrust enforcement and innovation. Research methodologies emphasize large-scale data analysis of USPTO records, litigation outcomes, and market transactions.
Jeremy Gibbons is a Professor of Computing at the University of Oxford, affiliated with the Department of Computer Science within the Faculty of Computer Science. He serves as Director of the Professional Programmes, overseeing part-time postgraduate degrees in Software Engineering. His roles include Chair of the Faculty of Computer Science (2012–2016), Director of the Software Engineering Programme, and Fellow of Kellogg College. Gibbons' research focuses on programming methodologies, particularly functional and object-oriented languages, with an emphasis on program calculation, design patterns, and bidirectional transformations. He leads the Algebra of Programming research group and is Editor-in-Chief of the Journal of Functional Programming and The Art, Science, and Engineering of Programming . Education includes a D.Phil. from Oxford University. His work spans formal methods, domain-specific modeling for clinical trials (e.g., CancerGrid project), and semantic frameworks for software systems. He has advised numerous students and contributed to open-access initiatives in publishing. Key collaborations include roles in ACM SIGPLAN and IFIP Working Groups 2.1 and 2.11. Research interests emphasize foundational aspects like profunctor optics, categorical programming, and algorithm design. Notable projects include datatype-generic programming and metadata-driven engineering for clinical trials. His work bridges theoretical computer science with practical applications in software architecture and system design.
Eetu Mäkelä is a Professor of Digital Humanities at the University of Helsinki, leading the research group at the Helsinki Centre for Digital Humanities. He focuses on computational methods in humanities and social sciences, including datafication and interdisciplinary collaboration. He serves as Technical Director of DARIAH-FI and a Research Programme Director at the Helsinki Institute for Social Sciences and Humanities. Currently, he heads the preparatory group for the Helsinki Liberal Arts and Sciences Bachelor’s Programme. His research emphasizes technological and theoretical foundations of computational research, with notable contributions to linked open data, sociolinguistic analysis, and historical text mining. He has developed widely used tools like Recon, Palladio, and Octavo, which are employed in academic and public sectors. His work has garnered over 20 awards, including best paper and open science recognitions. Key areas of expertise include digital humanities methodologies, data integration, and open science practices. He actively contributes to teaching, designing courses such as 'Methods for Digital Humanities' and demonstrating innovative pedagogical approaches. His recent projects involve analyzing 18th-century philosophical texts and sociolinguistic change using computational methods. Awards: Multiple best paper awards, open data awards, and open science awards. Grants/Advising: Leads research initiatives funded by the Academy of Finland and other bodies. Mentors interdisciplinary research teams but no specific student names listed. He maintains active roles in academic infrastructure, including the DARIAH-FI initiative and the Helsinki Institute’s datafication program. His work bridges technical innovation with humanities scholarship, emphasizing practical, enduring systems for academic and public use.
Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.
Steven Swanson is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He is the Director of the Non-Volatile Systems Laboratory (NVSL), where he leads cutting-edge research in non-volatile memory, storage systems, and hardware-software co-design. His work bridges computer architecture, systems, and software to develop efficient, reliable, and secure computing platforms. Ph.D., University of Washington, 2006 B.S., University of Puget Sound, 1999 Dr. Swanson's research centers on non-volatile and persistent memory systems , exploring how next-generation storage technologies can transform computing. His lab develops full-stack solutions including file systems like NOVA and Orion , programming models such as NV-Heaps , and hardware prototypes like Moneta and Onyx . The team also works on low-power co-processors (e.g., GreenDroid ) and tools for debugging and verifying persistent memory programs. Research spans system reliability, security, energy efficiency, and performance optimization. His recent publications reveal a strong focus on persistent memory safety , zero-copy I/O , RDMA-based distributed file systems , and real-world characterization of Intel Optane . These works appear in top venues including ASPLOS, FAST, MICRO, and USENIX ATC, demonstrating sustained innovation in storage and systems research. Scientific honors include: NSF CAREER Award Google Faculty Award Facebook Faculty Award NetApp Faculty Fellow Dr. Swanson has advised 15 PhD students and 4 postdocs , many now faculty or senior engineers at Google, Microsoft, Intel, and other leading tech firms. He has secured significant research funding and leads major community initiatives such as the annual Non-Volatile Memories Workshop and Persistent Programming In Real Life (PIRL) . His educational efforts include innovative courses on robotic system design, quadcopter building, and modern storage systems, emphasizing hands-on learning and real-world implementation. The Non-Volatile Systems Laboratory (NVSL) under his leadership fosters a collaborative, international research environment, hosting visitors and postdocs from around the world. The lab is recognized globally as a pioneer in storage systems research and a key contributor to the adoption of persistent memory technologies in industry.
Dr. Joshua New is a Distinguished R&D Staff Member at Oak Ridge National Laboratory (ORNL) and holds a Joint Faculty Position at The University of Tennessee since 2012. He leads research in building energy modeling, climate change science, and supercomputing. His work focuses on urban-scale energy systems, AI-driven analytics, and high-performance computing applications. Education: Ph.D. in Computer Science (University of Tennessee, 2009), M.S. in Computer Systems, B.S. in Computer Science and Mathematics (Jacksonville State University). His research interests include optimizing building energy efficiency, simulating climate impacts on urban infrastructure, and developing tools like AutoBEM and ModelAmerica to model 122.9 million U.S. buildings. He has over 150 peer-reviewed publications and led 45+ projects involving supercomputing, visual analytics, and AI for big data. Awards include the R&D 100 Award (2016), ASHRAE Distinguished Service Award (2018), and Lab-Corps (2015). His teams prioritize utility use cases and validate models against real-world data. He is a Senior IEEE member, Certified Energy Manager (CEM), and holds certifications in project management and energy efficiency. Key contributions include the Roof Savings Calculator Suite, AutoGen/AutoSim tools, and the ModelAmerica initiative. His work addresses national energy policy, heatwave resilience, and sustainable city design through interdisciplinary collaborations.
Victoria Lemieux is a Professor at the University of British Columbia (UBC) Faculty of Arts, School of Information, and Cluster Lead for Blockchain@UBC, Canada’s largest research cluster focused on blockchain technology. Her research centers on risks to trustworthy records in blockchain systems and their impact on transparency, financial stability, and human rights. She has pioneered Canada’s first research-oriented graduate blockchain training program and organized multiple interdisciplinary summer institutes. Education: Ph.D. in Archival Studies from University College London (2002), Certified Information Systems Security Professional (CISSP, 2005). Affiliated with UBC’s Peter Wall Institute for Advanced Studies, Sauder School of Business, and Institute for Computers, Information and Cognitive Systems (ICICS). Research interests span blockchain technology , trustworthy records , risk management , information governance , and visual analytics , with recent work addressing healthcare data frameworks, Web3 AI integration, and socio-cultural dynamics of decentralized systems. She has published extensively on blockchain applications in archives, land transactions, and privacy-preserving technologies. Scientific Awards : 2015 Emmett Leahy Award 2015 World Bank Big Data Innovation Award 2016 Emerald Literati Award 2016 Emerald Literati Outstanding Paper Award Supervision: Currently accepts doctoral students in Computational Archival Science and blockchain-related archival research. Affiliated with the Blockchain@UBC cluster and multidisciplinary research teams exploring decentralized systems for social good.
Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.
Professor George Buchanan is a leading researcher in human-computer interaction and digital libraries at RMIT University . His work bridges information science, digital humanities, and health informatics, focusing on usability in sensitive contexts like healthcare and misinformation. Deputy Dean, Research at RMIT University Former Director, University of Melbourne iSchool Research Interests: Digital information interaction Health and aging informatics Disinformation analysis Mobile interface design Digital library systems Key Contributions: Developed mobile web usability benchmarks, spatial hypertext tools, and thermal feedback interfaces. Currently seeking PhD students for 2025 projects on digital browsing and view change dynamics. Awards: Over twenty best paper awards and Honorary Life Fellow of the Royal Society of Arts. Advising: Accepting Masters/PhD supervision in information interaction and digital health domains.
Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.
Professor Ruth Ahnert holds a Chair in Literary History & Digital Humanities at Queen Mary University of London, affiliated with the School of English and Drama and the Digital Environment Research Institute (DERI). Her research bridges early modern literary studies, digital humanities, and data science, focusing on epistolary culture, surveillance, and interdisciplinary collaboration. She leads the UKRI-funded Living with Machines project, exploring the social impact of 19th-century technology through computational methods. Educated at the University of Cambridge (BA, MPhil, PhD), she has published widely on topics including Tudor networks, prison literature, and quantitative history. Her work emphasizes collaborative methodologies, as seen in projects like Networking Archives (integrating early modern correspondence metadata) and the interactive Tudor Networks visualization. Awards include grants from the AHRC, Folger Shakespeare Library, and the British Library-Alan Turing Institute partnership. She supervises PhD students examining early modern communication networks and deviance, and her teaching reflects her commitment to blending traditional humanities with computational innovation. Ruht Ahnert’s interdisciplinary approach has been featured in documentaries, radio programs, and public engagement initiatives, including Niall Ferguson’s BBC/PBS series Networlds . Her recent publications include The Network Turn (2020, co-authored) and the forthcoming Tudor Networks of Power (2023). She co-edits the Text Technologies series at Stanford University Press, bridging book history and digital scholarship.
Arash Joorabchi is an Assistant Professor at the Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland. His research focuses on the intersection of machine learning, educational technology, and digital library systems, with particular emphasis on automated assessment, text mining, and knowledge organization techniques. Research Trends: Analysis of his publications reveals sustained contributions to automated short-answer grading, Arabic text classification, and semantic integration of Wikipedia with academic resources. Key methodologies include sentence transformers, hybrid text representation models, and citation-based indexing techniques. Technical Domains: His work spans natural language processing, educational data mining, metadata management, and semantic web technologies. Specific applications include Q&A platform analysis, library resource discovery, and curriculum development systems.
