Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.
Lukas Grasmann is a Researcher at the Faculty of Informatics, Vienna University of Technology (TU Wien), based in the Databases and Artificial Intelligence research group (Institute E192-02, Room HA0302). His contact details include email lukas.grasmann@tuwien.ac.at and phone +43-1-58801-192216, with professional activities centered on cutting-edge database technologies and AI applications. His educational qualifications comprise: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) in Engineering Grasmann's research spans database systems and artificial intelligence with emphasis on big data analytics, distributed query processing, and skyline query optimization. His work focuses on integrating specialized query paradigms into Apache Spark SQL for efficient large-scale data analysis, particularly applied to perishable food supply chain optimization for waste reduction. This intersects computer science fundamentals with sustainability-driven practical implementations. His 2022-2023 publications reveal a concentrated research trajectory in enhancing Spark SQL's capabilities for skyline queries, addressing both theoretical optimization challenges and real-world deployment in distributed environments. The work demonstrates consistent innovation in bridging database theory with big data infrastructure, advancing scalable solutions for multi-criteria decision problems. Scientific recognition: No formal awards or fellowships documented in source materials Research funding and collaborations include: Lead researcher on FFG-funded project "AI-driven collaborative supply and demand matching platform for food waste reduction" (2022-2025) Contributor to HyperTrac project (2018-2022) focusing on traceability systems Active participant in KnowledgeGraph initiative (2020-2028) developing semantic knowledge networks He operates within TU Wien's Databases and Artificial Intelligence research ecosystem (Institute E192), collaborating with specialists like Pichler and Selzer on database scalability challenges. The group maintains strong industry connections through applied projects targeting supply chain intelligence and food waste analytics.
Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.
Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.
Mioara Mandea is a distinguished geophysicist currently serving as Solid Earth Programmes Manager at the French Space Center (CNES) in Paris since 2011. She maintains strong academic affiliations with Sorbonne University through her long-standing association with the Institut de Physique du Globe de Paris (IPGP), where she held multiple research positions from 1991-2011 including Head of the National Magnetic Observatory (1994-2004). Her career also includes leadership roles at the European Center for the Arctic at Versailles University and the Helmholtz Center in Potsdam. Her educational background includes dual PhDs in Geophysics (1993 from Bucharest University and 1996 from IPGP) followed by an HDR (Habilitation à diriger les recherches) in Physics of the Earth from Université Paris VII in 2001. This highest French academic qualification authorizes her to supervise doctoral candidates and apply for professorial positions. Mandea's research spans Earth observation from space, geopotential fields analysis, and geomagnetic field studies using historical archives, modern observatories, and satellite data. She specializes in adapting advanced mathematical tools to analyze magnetic and gravity data, with particular focus on Earth's deep interior, planetary magnetism (Moon, Mars, Mercury), and Arctic region geophysical changes. Her work bridges theoretical geophysics with practical space-based observation techniques. Analysis of her recent publications reveals a consistent focus on integrating satellite-derived gravity and magnetic data to understand Earth's interior dynamics. Her research shows increasing sophistication in mathematical modeling techniques, particularly wavelet analysis and multi-sensor data integration. The publications demonstrate strong international collaboration patterns, with frequent co-authorship across European institutions and the United States. Membre associé de l'Académie Royale de Belgique (2018) Medal 'Petrus Peregrinus' of European Geosciences Union (2018) Chevalier - Ordre National du Mérite (2016) Member of Academia Europaea (2015) Member of the Bureau des Longitudes (2014) International Award of American Geophysical Union (2014) Mandea has held significant leadership roles in the international geoscience community, including Secretary General of the International Association of Geomagnetism and Aeronomy since 2009, Chair of the Science Committee at the International Space Science Institute since 2016, and former Secretary General of the European Geosciences Union (2012-2016). She serves on multiple advisory boards for major research projects including EPOS and MED-SUV, and has chaired numerous award committees for the AGU and EGU. Her editorial work includes associate editor roles for Surveys in Geophysics and special issues for Physics of the Earth and Planetary Interior. Her research has been supported through leadership roles in major international space-based Earth observation initiatives, particularly through her position at CNES where she manages solid Earth science programs. She has contributed to numerous collaborative projects involving satellite missions for geomagnetic and gravity field measurements.
Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.
Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.
Dr. Adam Green is a Lecturer in Sustainability at the University of York, with dual appointments in the Department of Archaeology and Department of Environment and Geography. His research focuses on the relationship between inequality and sustainability, drawing on interdisciplinary methods from archaeology, economics, and agronomy. He specializes in South Asian archaeological studies, particularly the Indus Civilization, and collaborates with global researchers to address contemporary sustainability challenges. Green holds a PhD in Anthropology from New York University (2015) and has held positions at the University of Cambridge and King’s College, Cambridge. His work integrates computational methods to analyze large-scale archaeological datasets, exploring long-term economic trends and equitable governance models. He leads projects like the NSF-funded Gini Project and collaborates with institutions such as Punjab Agricultural University and the International Crops Research Institute. His teaching includes modules on past environments and sustainability frameworks. Green actively promotes dialogues between academia and communities to advance sustainable development, emphasizing historical insights to inform present policies.
Jonathan Balkind is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). His research focuses on the intersection of computer architecture, programming languages, and operating systems, with an emphasis on pragmatic system design and open-source hardware. He leads the ArchLab at UCSB and is affiliated with the OpenPiton project, an open-source manycore research framework. Education includes a PhD and MA in Computer Science from Princeton University (adviser: Prof. David Wentzlaff), an MSci in Computing Science from the University of Glasgow (advisers: Prof. Joseph Sventek and Dr. John O'Donnell), and exchange studies at UCSB. His work has been supported by awards such as the NSF Early CAREER Award (2023) and the Open Hardware Trailblazer Fellowship (2022). Research interests span heterogeneous computing, cache-coherent systems, FPGA integration, and domain-specific architectures. Notable projects include the 25-core Piton chip, the CIFER SoC with embedded FPGA, and the DECADES manycore processor. Recent publications address fused-kernel operating systems (Stramash), control logic synthesis, and hyperloop data-center architectures. His awards reflect contributions to open-source hardware and academic mentorship, including Siebel Scholarship (2018), Gordon Y.S. Wu Fellowship (2013–2017), and multiple teaching/research recognitions. He actively collaborates with industry (e.g., Microsoft Research, ARM) and advises on open-source projects.
Beng Chin Ooi is a Lee Kong Chian Centennial Professor at the National University of Singapore (NUS), School of Computing. He has been with NUS since 1991, progressing through the ranks from Lecturer to his current distinguished position. He previously served as Dean of the School of Computing from 2007 to 2013 and as Director of the Smart Systems Institute from 2011 to 2021. His educational background includes: 1985: B.Sc. (1st Class Honors) from Monash University, Melbourne, Australia 1989: Ph.D. in Computer Science from Monash University, Melbourne, Australia Beng Chin Ooi's research focuses on database systems, large scale analytics, and distributed systems. His work has been instrumental in advancing the field of data management technology, particularly in the context of "big data" in large-scale parallel and distributed systems. He has made significant contributions to spatio-temporal and distributed data management, as well as pioneering research in distributed database management and peer-to-peer based enterprise quality management. His recent publications demonstrate a strong focus on blockchain technology, machine learning systems, and healthcare informatics. There's a clear progression from foundational database research to applications in emerging technologies like blockchain and AI. His work bridges theoretical advances with practical system implementations, as evidenced by multiple open-source projects associated with his publications. His notable awards include: 2021: NUS Research Recognition Award 2020: ACM SIGMOD E.F. Codd Innovations Award 2020: ACM SIGMOD Research Highlight Award 2019: VLDB Best Paper Award 2016: Fellow of Singapore National Academy of Science 2016: China Computer Federation Overseas Outstanding Contributions Award 2014: VLDB Best Paper Award 2014: IEEE TCDE CSEE Impact Award 2013: Singapore National Day's Public Administration Medal (Silver) 2013: NUS Outstanding Researcher Award 2012: IEEE Computer Society Kanai Award 2011: ACM Fellow 2011: Singapore President's Science Award 2009: IEEE Fellow 2009: ACM SIGMOD Contributions Award Throughout his career, Professor Ooi has demonstrated exceptional leadership in the database community, promoting high standards of database research at both international and regional levels. His BLOCKBENCH framework became the world's first benchmarking tool for private blockchains, and his work on data provenance on blockchain systems earned both the VLDB Best Paper Award and the ACM Research Highlight Award. He has led several major research initiatives, including the Smart Systems Institute at NUS. Professor Ooi has established multiple open-source projects including FabricSharp for blockchain data provenance and Cool for cohort online analytical processing. His research group has consistently produced high-impact work that bridges theoretical advances with practical system implementations.
Prof. Dr. Peter Sanders is a full professor in Theoretical Computer Science at the Karlsruhe Institute of Technology (KIT), leading the Algorithm Engineering group. His academic career includes a doctoral degree from Karlsruhe University and research stints at institutions like the Max Planck Institute for Informatics. He specializes in algorithm theory and engineering, focusing on parallel computing, large-scale data processing, and graph partitioning. His research bridges theoretical foundations with practical implementations, emphasizing real-world applications in optimization, route planning, and distributed systems. Education: Ph.D. in Computer Science, Karlsruhe University (1996) Bachelor/Master studies at Karlsruhe University (1988-1996) Research Interests: Algorithm design and analysis Parallel and distributed algorithms Graph algorithms and partitioning Algorithm engineering for big data High-performance computing Publications: Over 250 papers, emphasizing parallel algorithms, distributed systems, and graph theory. Recent work includes scalable SAT solving, hypergraph partitioning, and distributed string sorting. His contributions have advanced practical applications in route planning, load balancing, and large dataset processing. Awards: Recipient of the prestigious Leibniz Prize (DFG) and Baden-Württemberg State Research Prize. He coordinated the DFG Priority Program on Algorithm Engineering and is an active reviewer for major funding bodies. Consulting: Engages with companies like SAP and Google, focusing on optimization, route planning, and database algorithms. Leads projects on algorithm scalability and real-world problem-solving. Labs/Teams: Heads the Algorithm Engineering group at KIT, fostering collaborations in distributed computing and algorithmic research.
Dinand Webbink is a Full Professor of Policy Evaluation at the Erasmus School of Economics, Erasmus University. He is actively engaged in empirical economic research with a focus on assessing the impact of public policies in education, labor, health, and crime. He holds fellowships at the Tinbergen Institute and IZA Bonn, and is an academic partner at the Netherlands Bureau of Economic Policy Analysis (CPB), highlighting his national and international recognition. His research interests span a wide range of topics in applied microeconomics and econometrics. Key areas include policy evaluation using causal inference methods, education reform, labor market dynamics, health economics, and socioeconomic inequality. His methodological expertise includes difference-in-differences, fixed effects modeling, and analysis of large-scale survey and administrative data. His work frequently contributes to evidence-based policymaking and aligns with several UN Sustainable Development Goals, particularly those related to quality education and reduced inequalities. The most recent articles highlight his ongoing contributions to pressing policy debates—such as the shift from grants to income-contingent loans in Dutch higher education, the behavioral economics of happiness and altruism in sports contexts, and the effects of forced school attendance on student outcomes. His publications appear in leading journals like the Journal of Applied Econometrics , Economics of Education Review , and Scandinavian Journal of Economics , indicating sustained scholarly impact. Scientific Honors and Affiliations: Fellow, Tinbergen Institute Fellow, IZA Bonn Academic Partner, Netherlands Bureau of Economic Policy Analysis (CPB) Webbink has supervised 15 academic works, reflecting his commitment to mentoring the next generation of economists. Although specific grant details are not listed, his affiliations with CPB and Tinbergen Institute suggest involvement in major research initiatives and policy advisory roles. He has also contributed datasets to public repositories, supporting open science and reproducibility in empirical economics. He is associated with research groups and networks including the Tinbergen Institute, IZA, and CPB, which serve as collaborative hubs for economic research in Europe. These affiliations enable interdisciplinary and policy-relevant research with real-world impact.
Stephen Taylor is a Professor of Computer Engineering at Dartmouth College's Thayer School of Engineering. His research focuses on cybersecurity, distributed computing, and embedded systems security. He has held leadership roles including DARPA Program Manager and Air Force Research Laboratory IPA. Taylor's work includes foundational contributions to the National Cyber Range and Air Force Cyber Experimentation Environment. Education: BSc in Computer Systems from Essex University (1982), MSc in Computer Science from Columbia University (1985), and PhD in Computer Science from the Weizmann Institute (1989). Research emphasizes resilient operating systems for cloud computing, security mechanisms for embedded systems, and large-scale experimentation infrastructure. Awards include the USAF Exemplary Civilian Service Medal and Secretary of Defense Medal for Public Service. Teaches courses on microprocessors, software design, and cyberspace technology. Advises on multi-disciplinary projects and has authored 4 books and over 25 journal articles. His lab explores hardware-software co-design for cyber resilience and soil-based sustainable computing.
Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).
Hanjun Kim is a researcher at Yonsei University, focusing on compiler design, machine learning optimization, and hardware-aware programming techniques. His work bridges theoretical research with practical implementations in embedded systems and security domains. Research Interests Compiler-driven optimization for PIM (Processing-in-Memory) architectures Homomorphic encryption compiler design Parallel computing for DNN/LLM inference Network function program analysis Recent research trends include: application of compiler techniques to optimize resource utilization in heterogeneous computing environments, particularly for AI workloads and secure computation. His publications demonstrate expertise in tackling performance bottlenecks through architectural and compiler co-design. Conference Service 2025 SPLASH OOPSLA Review Committee 2025 LCTES Program Committee 2024 CGO Program Committee 2023 LCTES Program Committee 2022 CGO Organization Committee 2020 LCTES Program Committee
