Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.
Xi Gong is an Associate Professor in the Department of Biobehavioral Health and the Institute of Computing and Data Sciences at Pennsylvania State University, where he leads the Gong Lab. His research focuses on Geospatial Data Science, integrating GIScience, computational methods, and statistical analysis to study environmental health and social dynamics. He holds a PhD in Geographic Information Science from Texas State University, an M.Sc. from the University of Chinese Academy of Sciences, and a B.Eng. from Wuhan University. Dr. Gong's research interests include spatio-temporal data mining, environmental exposure modeling, and visual analytics for big data. His work bridges Environmental Health Science (EHS) and Spatially Integrated Social Science (SISS), addressing public health concerns through geospatial modeling and interdisciplinary collaboration. He currently accepts graduate students and postdoctoral scholars for projects in Geospatial Data Science and EHS. Education: PhD (2016, Texas State University), M.Sc. (2011, University of Chinese Academy of Sciences), B.Eng. (2008, Wuhan University) Labs/Teams: Director of Gong Lab, focused on geospatial big data analysis for health and environmental studies Advising: Mentors PhD and MS students in Biobehavioral Health and related fields
Ahmed Eldawy is an Associate Professor in the Department of Computer Science at the University of California, Riverside. He leads groundbreaking research in databases, big data management, and spatial data processing, with a focus on scalable exploratory analytics through systems like Beast , UCR-Star , Raptor , and Spider . His work spans geospatial data infrastructure, distributed computing, and computational geometry. Research Interests : Databases, big data management, spatial data processing, geospatial analytics, distributed systems, computational geometry. Awards : NSF CAREER award (2021), 10-year Influential Paper Award (ICDE 2025), Best Demo award (SIGSPATIAL 2020). Grants : NSF (IIS-2046236, IIS-1954644, CNS-1924694), USDA (USDA NIFA 2020-69012-31914), UC Office of the President (M21PL3368). Labs & Centers : RAISE@UCR, Data Science Center, Center for Robotics and Intelligent Systems (CRIS), Affiliate of Center for Geospatial Sciences and Winston Chung Global Energy Center. His recent publications focus on LLM-driven geospatial visualization (LASEK), distributed raster analytics (RDPro), learned spatial query optimization, and scalable spatiotemporal systems. He advises numerous PhD and Master’s students, many of whom now work at top tech companies like Amazon, Microsoft, and Meta.
Fabio Miranda is an Assistant Professor at the Department of Computer Science, University of Illinois at Chicago (UIC) . His research bridges visualization , machine learning , data management , and computer graphics to enable interactive visual analysis of large-scale urban datasets . He has developed systems like UrbanRama (for VR navigation) and The Urban Toolkit (a grammar-based framework), which have been deployed in academia, industry, and government agencies. Education: Ph.D., Computer Science , New York University (2018) M.S., Computer Science , Pontifical Catholic University of Rio de Janeiro (2011) B.S., Computer Science , Federal University of Minas Gerais (2009) Research Interests center on urban visual analytics , 3D analytics , and machine learning for accessibility . His work addresses challenges like sunlight access , sidewalk quality assessment , and commuting flow modeling , often collaborating with urban planners, climate scientists, and occupational therapists. Scientific Recognition includes awards at IEEE VIS , SIBGRAPI , and SIGMOD . His research is funded by NSF , NIH , DOT , and DPI , with media coverage in The New York Times , The Economist , and Architectural Digest . Teaching includes courses like CS 524: Big Data Visualization and Analytics and CS 424: Visualization and Visual Analytics . He emphasizes web-based systems, dataflow frameworks, and interdisciplinary collaboration, with open positions for PhD , MSc , and undergraduate researchers .
Professor Kay O'Halloran serves as Chair Professor and Head of Department of Communication and Media within the School of the Arts at the University of Liverpool since August 2019. She concurrently holds the position of Co-Director for the Digital Media and Society Institute (DMSI), demonstrating significant leadership across academic and research domains. Her career spans prestigious institutions including Curtin University (2013-2019) and National University of Singapore (1998-2013), where she directed the Multimodal Analysis Lab and served as Deputy Director of the Interactive & Digital Media Institute. PhD from Murdoch University (1996) Postdoctoral position at Martin Luther University (1997-1998) Visiting Distinguished Professor at Shanghai Jiaotong University (2017-2020) Professor O'Halloran's research focuses on multimodal discourse analysis, particularly the interaction of language with visual and mathematical resources. Her pioneering work in systemic functional multimodal discourse analysis (SF-MDA) has significantly impacted mathematics education and digital communication studies. Current research emphasizes digital tools for multimodal analysis and mixed methods approaches to big data analytics, addressing contemporary challenges in misinformation and public health communication. Her recent publications reveal strong thematic concentration in pandemic-related communication analysis, misinformation resistance mechanisms, and multimodal argumentation across social and cultural contexts. The 15 most recent articles demonstrate consistent application of multimodal frameworks to urgent societal issues including public health crises, political discourse, and digital literacy challenges. Founding editor of Routledge Studies in Multimodality book series (57+ volumes) Over 150 publications in leading international journals 50+ plenary/keynote presentations globally Competitive funding from National Research Foundation, MOE Singapore, US Air Force, ARC, NHMRC, and others Professor O'Halloran has directed interdisciplinary research teams comprising social scientists, computer scientists, and designers. Her leadership extends to developing commercial multimodal analysis software (Multimodal Analysis Image and Video) adopted internationally. Current projects include Eurovision 2023 wellbeing evaluation and COVID-19 misinformation immunity development, demonstrating continued relevance to contemporary societal challenges.
Prof. Dr. Andreas Butz is a Full Professor and Chair for Human-Computer Interaction at the Department for Informatics, Ludwig-Maximilians-Universität München (LMU Munich). He leads the Media Informatics Group, focusing on innovative interaction techniques and interfaces in immersive environments like VR/AR, automotive systems, and smart spaces. His research emphasizes perceptual user interfaces, social robotics, and designing systems that balance invisibility with transparency. Key research areas include: Virtual/Augmented Reality interfaces for productivity and social interaction AI-driven decision support in safety-critical domains (aviation, healthcare) Haptic and wearable interaction technologies Automotive UI design for driver assistance systems Principles of explainable AI and human-AI collaboration His work bridges theory and practice through projects like: VR-based movement training systems AI trust calibration mechanisms Multi-modal interaction frameworks for automotive environments Systems for analyzing long-term music listening behavior Recent articles explore topics ranging from AI support in pilot decision-making to haptic wearables and creative writing interfaces. His team collaborates with industry partners on electric vehicle information systems and in-car interaction challenges.
Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.
Dr. Baijian "Justin" Yang serves as the Associate Dean for Research at Purdue Polytechnic Institute and is a Professor in the Department of Computer and Information Technology at Purdue University. He earned his Ph.D. in Computer Science from Michigan State University, with Master's and Bachelor's degrees in Automation (EECS) from Tsinghua University. Dr. Yang has established himself as a leader in multiple interdisciplinary research domains. Dr. Yang's educational background includes: PhD in Computer Science, Michigan State University (2002) MS in Automation (EECS), Tsinghua University (1998) BS in Automation (EECS), Tsinghua University (1995) His research interests span multiple cutting-edge domains with practical applications: Cybersecurity : Developing novel approaches for threat intelligence, security education, and network defense Big Data : Creating innovative algorithms for dimension reduction, regression with categorical variables, and tensor decomposition Applied Machine Learning : Implementing AI solutions in healthcare, manufacturing, and forestry applications Digital Forestry : Using UAV imagery and remote sensing for forest management and tree species classification Dr. Yang's publication record demonstrates significant impact across multiple disciplines, with recent work focusing on spatial transcriptomics analysis (SiGra), delirium detection using limited-lead EEG, and visual localization technologies. His research bridges theoretical advances with practical applications in healthcare, manufacturing quality control, and environmental monitoring. The interdisciplinary nature of his work is evident in collaborations spanning computer science, healthcare, forestry, and manufacturing domains. His scientific achievements have been recognized with numerous awards: 2023 HRSA Building Bridges to Better Health Competition Winner (Phase 1) and 2nd place ($100,000 prize) in Phase 3 2023 Outstanding Faculty Award in Engagement, Department of Computer and Information Technology, Purdue University 2021 Leadership in Manufacturing Award, Manufacturing Times Digital (MxD) 2021 Good to Great Award, Purdue Polytechnic 2020 Outstanding Faculty Award in Discovery, Department of Computer and Information Technology 2019 University Faculty Scholars, Purdue University As an educator and mentor, Dr. Yang has advised numerous graduate students through their PhD and Master's research. His leadership extends to significant service roles including serving as Faculty Champion for the Holistic Safety and Security research impact area at Purdue Polytechnic from 2018 to 2021, board membership with ATMAE (2014-2016), and participation in the IEEE Cybersecurity Initiative Steering Committee (2015-2017). He holds valuable industry certifications including CISSP, MCSE, and Six Sigma Black Belt, demonstrating his commitment to bridging academic research with industry practice. Dr. Yang leads multiple research projects including "Digital Forestry" for developing tools to quantify forest function, "CHEESE" (Cyber Human Ecosystem of Engaged Security Education), and "CICI" (Supporting Controlled Unclassified Information with a Campus Awareness and Risk Management Framework). His work on "Applied Machine Learning" focuses on solving real-world problems, while his "Dimension Reduction and Memory Amnestic Big Data Regression" project innovates computational algorithms for large-scale data analysis.
Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.
Marketta Kyttä is a Professor of Land Use Planning at Aalto University's Department of Built Environment. She holds a PhD in Architecture from Helsinki University of Technology (2004) and leads the Spatial Planning and Transportation Engineering Master’s program. Research Interests: Child- and age-friendly urban environments SoftGIS methodology for participatory mapping Social sustainability and urban health Public participation in planning Activity space modeling Scientific Awards: Lea Pulkkinen award Tapio Nummenmaa award Geospatial World Forum's WebGIS Innovation Award (2010) Oskari Vilamo award (2020) Oppikirja-palkinto for urban planning literature Methodological Contributions: SoftGIS (PPGIS innovation) Commercialization as Maptionnaire software International Collaborations: Affiliated with universities in Italy, Australia, and the US. Her work spans 80+ countries through Maptionnaire’s global adoption.
Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.
Yuankai (Kenny) Tao is an Associate Professor of Biomedical Engineering at Vanderbilt University's School of Engineering and an SPIE Faculty Fellow. He directs the Graduate Studies program in Biomedical Engineering and leads research in optical imaging systems for clinical diagnostics and therapeutic monitoring in ophthalmology, gastroenterology, and oncology. His lab develops technologies like intraoperative OCT and SECTR, focusing on noninvasive subcellular visualization and biomarker monitoring. Collaborations span engineering, basic sciences, and medicine to translate innovations into clinical tools. Education: Ph.D., Biomedical Engineering, Duke University M.S., Biomedical Engineering, Duke University B.S.E., Biomedical Engineering and Electrical Engineering, Duke University Research Interests: Biomedical optics, optical coherence tomography (OCT), image-guided surgery, therapeutic monitoring, big data analytics, and high-throughput imaging for drug discovery. His work bridges engineering and medicine, emphasizing real-time feedback systems and interdisciplinary innovation. Grants & Labs: Director of the Vanderbilt Institute for Surgery and Engineering (VISE)-affiliated lab, focusing on surgical imaging and translational research. Projects include automated instrument tracking, SECTR systems, and AI-driven imaging analysis. Collaborations involve clinicians and researchers across disciplines.
Naoki Yoshinaga is a tenured Associate Professor at the Institute of Industrial Science, The University of Tokyo, with extensive experience in natural language processing and computational linguistics. He has held academic positions since 2008 and currently leads research on pragmatic NLP models and multilingual systems. PhD in Computer Science, The University of Tokyo (2005-2008) MSc in Information Science (2000-2002) BSc in Information Science (1996-2000) His research focuses on mechanistic interpretability in NLP models, multilingual/multimodal NLP , and efficient model design using trie structures and conjunctive features. He also investigates knowledge acquisition from social data and evaluation metrics for language generation . Recent publications include work on neuron empirical gradient analysis (ACL-25), multilingual knowledge representation (EACL-24), and compact embedding methods (CoNLL-24). His research has been funded by multiple grants, including the University of Tokyo Excellent Young Researcher program and JSPS fellowships. Committee Special Award, Association for NLP (2023) JSAI SIG Research Award (2022) Best Interactive Award, DEIM Forum (2019, 2016) He developed widely-adopted NLP tools like pecco (fast classification library), RenTAL (LTAG-to-HPSG grammar converter), and J.DepP (Japanese dependency parser). His lab emphasizes strong equivalence in formalism comparisons and pragmatic model design .
