Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.
Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.
Dr. Kathryn Kaiser is an Assistant Professor in the Department of Health Behavior at the University of Alabama at Birmingham (UAB) School of Public Health. She holds concurrent appointments in multiple research centers including the Center for Clinical and Translational Science and the Nutrition Obesity Research Center (NORC). Her research focuses on meta-research methodologies, race/sex disparities in obesity, systematic reviews of nutrition interventions, and advancing FAIR data principles for scientific communication. Education: B.S. Microbiology (Texas A&M), B.S. Medical Technology (University of Texas Health Science Center), Ph.D. in Health Psychology (University of North Texas Health Science Center). Postdoctoral training in Vascular Biology/Hypertension at UAB. Extensive background in medical diagnostics instrumentation and laboratory science prior to academia. Research Interests: Systematic review methodologies, FAIR data standards, obesity disparities with a focus on neuroendocrine mechanisms, and translational research in bariatric surgery outcomes. Specializes in methodological rigor for clinical trials and evidence synthesis. Grants: NIH-funded projects on FAIR principles implementation, dairy intake research, obesity energetics, and lifespan studies. Recent grants include $2.1M for metadata education programs and $1.8M for knowledge mapping initiatives. Awards: Recognized as 2015 Top Reviewer for American Journal of Preventive Medicine. Serves as Associate Editor for Frontiers in Nutrition. Active member of Cochrane Collaboration and multiple professional societies. Teaching: Graduate courses in Health Program Evaluation, Psychophysiology, and Systematic Review Design. Supervises doctoral students in health behavior research through 30+ dissertation committees since 2011. Labs/Teams: Key contributor to UAB's Nutrition Obesity Research Center (NORC) and Center for Outcomes and Effectiveness Research (COERE). Collaborates with international metadata initiatives like Metadata 2020 and science dialogue mapping projects.
Naim U. Rashid, PhD, is an Associate Professor with tenure in the Department of Biostatistics at the UNC Gillings School of Global Public Health and holds a joint appointment as Research Associate Professor at the Lineberger Comprehensive Cancer Center. He serves as Associate Director of the Lineberger Biostatistics Shared Resource and co-directs the Biostatistics Cores of the UNC Pancreatic and Breast Cancer SPOREs. His work bridges statistical methodology development with collaborative cancer research, focusing on translating genomic discoveries into clinical applications. Dr. Rashid's research spans precision medicine, genomics, statistical computing, and machine learning with specific applications to pancreatic and breast cancers. His lab develops novel statistical methods for high-throughput genomic data analysis, cancer subtyping, missing data problems in deep learning, and clinical trial design. Recent work includes developing an AI tool that recommends optimal clinical trials to pancreatic cancer patients, funded by a $311,000 Department of Defense grant in 2024. His methodological contributions focus on improving replicability in gene signature selection and clinical prediction, with emphasis on addressing racial disparities in cancer outcomes. His publication record shows consistent output in top statistical and medical journals, with recent work focusing on high-dimensional statistics, missing data methods, and cancer genomics. His research demonstrates a clear trajectory from methodological innovation to clinical implementation, particularly in pancreatic cancer where his PurIST classifier has gained recognition. The work increasingly incorporates machine learning approaches while maintaining strong statistical foundations. Delta Omega Faculty Award (2021, UNC Chapel Hill) IBM and R.J. Reynolds Junior Faculty Development Award (2017, UNC Chapel Hill) Barry H. Margolin Dissertation Award (2013, UNC Chapel Hill) Training Grant recipient (2006-2011, Genomics and Cancer) Dr. Rashid actively mentors graduate students and serves as trial statistician on multiple cancer clinical trials. He teaches BIOS 735, a doctoral-level course on statistical computing, and is involved with the Translational Breast Cancer Research Consortium Statistical Working Group. His lab collaborates extensively with clinicians at UNC Lineberger and beyond, with recent work including the PROCLAIM Study examining mHealth apps to improve diverse recruitment in pancreatic cancer trials. The Rashid Lab focuses on developing computational tools that directly impact clinical decision-making while addressing methodological challenges in genomic data analysis.
Prof. Valentina Boeva is an Assistant Professor at the Department of Computer Science, ETH Zürich, specializing in biomedical informatics. Her research focuses on integrating machine learning and computational methods to address challenges in genomics, oncology, and precision medicine. She holds a position in the Professur für Biomedizininformatik (Biomedical Informatics) and is based at CAB G32.2, Universitätstrasse 6, Zürich, Switzerland. Her work emphasizes applications such as cancer biomarker discovery, tumor heterogeneity analysis, and epigenetic profiling. She teaches courses including Machine Learning Seminar, Data Science Lab, and Machine Learning for Genomics. Her research group develops computational tools like CDState and UniversalEPI to decode complex biological systems. She actively publishes in top-tier journals, with recent work on exosome-driven diagnostics and chromatin interaction modeling. Her scientific contributions span methodologies for single-cell data analysis, survival modeling, and drug response prediction. She collaborates across disciplines to bridge computational science with clinical applications in cancer research.
Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.
Jonathan Huggins is an Assistant Professor at Boston University, affiliated with the Department of Mathematics & Statistics and the Faculty of Computing & Data Sciences. He holds a Ph.D. in Computer Science from MIT (2018) and a B.A. in Mathematics from Columbia University (2012). His research focuses on developing fast, trustworthy machine learning and Bayesian methods that balance computational efficiency and statistical optimality, with applications in ecological forecasting and genomic data analysis. Education: Ph.D. in Computer Science, Massachusetts Institute of Technology (2018) B.A. in Mathematics, Columbia University (2012) Research Interests: Large-scale machine learning and Bayesian computation Robust statistical inference Applications in genomics and ecological modeling Algorithmic development for scalable inference Key Projects: Stochastic Methods for Data Science: A book on stochastic processes and algorithms VIABEL: A Python package for variational inference and diagnostics ShorTeX: A LaTeX package for mathematical writing Recent Articles: Focus on scalable Bayesian methods, error bounds for iterative algorithms, and mutational signature discovery. His work emphasizes reproducibility and robustness in statistical inference. Awards: Blackwell–Rosenbluth Award (Outstanding Junior Bayesian Researcher) Grants & Funding: Supported by NIH, NSF, and the Department of Defense. Active in advising students across multiple BU programs. Labs/Teams: Affiliated with the BU URBAN Program, Program in Bioinformatics, and Department of Computer Science.
Frédéric Vrins is a Professor at the Louvain School of Management (LSM) , UCLouvain , affiliated with the Louvain Institute of Data Analysis and Modeling in economics and statistics (LIDAM) and Louvain Finance (LFIN). His work bridges theoretical and applied finance, with a focus on risk modeling, portfolio optimization, and machine learning applications. His research interests include: Quantitative Finance: Derivatives pricing, stochastic processes, and model calibration. Risk Management: Credit concentration risk, recovery rates, and wrong-way risk in financial markets. Portfolio Optimization: Mean-variance strategies, diversification metrics, and robustness under parameter uncertainty. Machine Learning in Finance: Applications to recovery rate prediction and option pricing frameworks. Recent publications highlight trends in: Credit risk modeling for Collateralized Loan Obligations (CLOs) and consumer credit. Machine learning integration in derivatives pricing and portfolio construction. Stochastic methods for Brownian bridges, CDS spreads, and recovery rates. Empirical studies on Loan-to-Value policies and business cycle impacts. Affiliations and locations: Louvain School of Management (LSM) - Building B, Chaussée de Binche 151, 7000 Mons Louvain Finance (LFIN) - Traverse d'Esope 1, 1348 Louvain-la-Neuve Louvain School of Management (LSM) - BATA Building, Chaussée de Binche 151, 7000 Mons
Joseph Alejandro Gallego Mejia is an Assistant Teaching Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He holds a PhD with meritorious distinction in Systems and Computing Engineering from the National University of Colombia, along with a Master’s and dual Bachelor’s degrees in Systems and Computing Engineering and Industrial Engineering. PhD in Systems and Computing Engineering, National University of Colombia (Meritorious Distinction) Master of Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Systems and Computing Engineering, National University of Colombia Bachelor of Engineering in Industrial Engineering, National University of Colombia His research focuses on artificial intelligence, machine learning, computer vision, quantum machine learning, natural language processing, and cybersecurity. He explores robustness estimation, anomaly detection, incremental learning, and scalable software architectures for AI systems. His work bridges theoretical foundations and practical applications in health, remote sensing, and edge computing. The recent publications reflect a strong trend in interdisciplinary AI research, combining machine learning with quantum computing, cybersecurity, and natural language understanding. His work spans domains such as satellite imagery analysis, medical diagnostics, IoT security, and conversational AI, demonstrating a commitment to scalable and robust intelligent systems. Keywords across publications include Computer Science, Machine Learning, Quantum Computing, and Cybersecurity, with subfields ranging from adversarial robustness to hybrid quantum-classical models. Scientific distinctions include: PhD with meritorious distinction, National University of Colombia Postdoctoral fellow, Frontier Development Lab (Trillium), supported by NASA and ESA He has served as a reviewer for top-tier journals and conferences including Neurocomputing, IEEE Access, Radioscience, NeurIPS, and NLDL. Though no formal grants are listed, his postdoc was funded by NASA and ESA, indicating significant external support. He teaches courses in programming, data science, machine learning, deep learning, NLP, and software engineering. He founded the tech company Sammu and mentors students through instruction and research supervision. He is actively involved in research and teaching, contributing to innovative programs in AI and computing education. His lab and team affiliations are not explicitly stated, but his work suggests collaboration with AI, quantum computing, and cybersecurity research groups.
Alberto Viglione is an Associate Professor at the Politecnico di Torino , Department of Environment, Land and Infrastructure Engineering (DIATI), and a member of the Interdepartmental Center SmartData@PoliTO. He has been a faculty member since 2019, following a decade as a Research Fellow at the Vienna University of Technology. University: Politecnico di Torino Department: DIATI – Department of Environment, Land and Infrastructure Engineering Rank: Associate Professor Email: alberto.viglione@polito.it His research focuses on flood hydrology, water resources, and hydro-meteorological extremes , integrating statistical analysis, climate change impacts, land use dynamics, and socio-hydrological modeling. He investigates the spatio-temporal dynamics of climatic, hydrological, and human processes in river basins and their implications for extreme event risks. His work emphasizes data integration, conceptual modeling, and risk assessment across scales. The recent publications highlight a strong trend in analyzing European flood dynamics , the impacts of climate change , and the development of socio-hydrological frameworks that incorporate human behavior and societal memory into flood risk modeling. His research spans from statistical hydrology in ungauged basins to large-scale assessments of climate-flood interactions. Scientific Awards and Honors: AMGA Award for best PhD thesis on water resources (2009) Editorial and Professional Service: Associate Editor, Water Resources Research (2014–present) Associate Editor, Hydrological Sciences Journal (2012–2018) Associate Editor, WIRES Water (2012–2020) Associate Editor, Journal of Hydrology and Hydromechanics (2019–present) Scientific Committee Member, European Geosciences Union (2019–2023) Secretary, International Commission on Water Resources Systems, IAHS (2015–present) Teaching and Advising: He teaches courses such as Bayesian Inference , Applied Hydrology , Fundamentals of Environmental Geosciences , and Hydro-meteorological Risk Assessment . He is a PhD supervisor and member of multiple PhD colleges in Civil and Environmental Engineering at Politecnico di Torino. He currently advises PhD students including Tsion Ayalew Kebede , Emanuele Mombrini , Luigi Cafiero , Luca Lombardo , and Matteo Pesce . His research is supported by grants from national (PRIN), EU, and commercial sources, including projects like Clim2FlEx , RETURN , and ATO4WATER . Research Labs and Teams: He is affiliated with the SmartData@PoliTO laboratory, focusing on big data and data science applications in hydrology and environmental systems.
Guang Lin is the Associate Dean for Research and Innovation in the College of Science and a Full Professor in the School of Mechanical Engineering and Department of Mathematics at Purdue University. He leads the Data Science Consulting Services and has dual appointments in Statistics and Earth, Atmospheric, and Planetary Sciences. His research focuses on AI, machine learning, uncertainty quantification, and computational science, with applications in fluid mechanics, materials science, and healthcare. Lin holds a Ph.D. from Brown University (2007) and has received numerous awards, including the NSF CAREER Award and Purdue’s University Faculty Scholar distinction. He has authored over 250 publications and secured grants totaling millions, including DOE and NIH funding. His interdisciplinary work bridges academia and industry, emphasizing AI-driven solutions for complex systems. Education: Ph.D. Applied Mathematics (Brown, 2007), M.S. Applied Mathematics (Brown, 2004), M.S. Mechanics (Peking University, 2000), B.S. Mechanics (Zhejiang University, 1997). Research Grants: Includes DOE-funded projects on machine learning for plasma-wall interactions and NSF grants for multiscale modeling. Service: Editorships in SIAM MMS, ASME Journal, and leadership in Purdue’s AI initiatives. Teaching: Courses on Uncertainty Quantification, Fluid Mechanics, and Data Science.
Maarten De Vos is a Professor at the Department of Electrical Engineering (ESAT) , KU Leuven , with dual appointments in the Faculty of Medicine and Faculty of Engineering Science . He leads interdisciplinary research at the intersection of artificial intelligence and biomedical signal processing.
Kristian O'Connor is a Professor in the Department of Kinesiology within the College of Health Sciences at the University of Wisconsin-Milwaukee, where he also serves as Associate Vice Provost for Research Education. His academic credentials include a Ph.D. in Exercise Science from the University of Massachusetts (2002), an M.S. in Exercise Science from Arizona State University (1998), and a B.A. in Physics from Colorado College (1994). Dr. O'Connor's research centers on the biomechanics of musculoskeletal injury, with particular emphasis on knee injury mechanisms during sports activities. His work investigates how neuromuscular fatigue contributes to increased injury risk in conditions like anterior cruciate ligament (ACL) tears and anterior knee pain, and explores its role in osteoarthritis progression. A significant innovation in his research involves developing portable single-camera 3D motion capture technology for clinical settings, eliminating the need for specialized laboratory environments. Analysis of his 15 most recent publications reveals consistent focus on age-related movement changes, knee biomechanics during dynamic tasks, and fatigue-induced injury mechanisms. His work spans geriatric mobility, athletic performance, and clinical rehabilitation, with strong methodological emphasis on motion analysis and neuromuscular assessment. Key trends include transition step descent biomechanics, visual-motor integration in aging populations, and foot-joint coupling dynamics in runners. Dean’s Award for Outstanding Service (2016), College of Health Sciences As Associate Vice Provost for Research Education, Dr. O'Connor oversees research training programs while maintaining active NIH-funded research. His recent grants include NIH R44HD068147-02 ($1.5M) and R43HD068147-01 ($215k) for developing clinical gait assessment tools, plus industry funding from Sport Biomechanics, Inc. His research program integrates motion capture technology development with fundamental injury mechanism studies, focusing on translating laboratory findings to clinical applications for injury prevention and rehabilitation. Dr. O'Connor leads a biomechanics research laboratory focused on developing portable motion analysis systems and investigating injury mechanisms across diverse populations including athletes, older adults, and clinical patients. His team's development of single-camera 3D motion tracking represents a significant advancement toward accessible clinical biomechanics assessment.
Prof. Liqiu Meng serves as Chair of Cartography and Visual Analytics at the Technical University of Munich (TUM). He specializes in advanced geospatial research, digital cartography, and human-technology collaboration frameworks. Current Faculty at TUM Chair of Cartography and Visual Analytics Research Focus: His work bridges cartographic theory with cutting-edge technology, covering topics like 3D urban modeling, AI ethics visualization, geovisual analytics, and spatiotemporal data interpretation. Urban Morphology Analysis AI Ethics Cartography Geovisual Analytics 3D City Data Integration Location-Based Service Design Publications: Recent works (2025-2024) demonstrate expertise in explainable AI for urban analysis, multi-agent systems for geospatial interaction, and advanced spatial modeling techniques. Contact: liqiu.meng@tum.de | contact.lfk@ed.tum.de
Chris Volinsky is a Clinical Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, joining in September 2023. His work bridges industry-scale data science and academic research, focusing on practical applications of machine learning and statistical modeling in business contexts. Education: PhD in Statistics, University of Washington BA in Statistics and Mathematics, University of Buffalo His research interests lie at the intersection of data science and business operations, with a focus on recommender systems , personalization , social network analysis , and mitigating bias in machine learning models . He also emphasizes data visualization and the ethical implications of data usage, particularly in balancing innovation with privacy concerns and regulatory compliance. While no specific publications are listed in the provided text, his career has been defined by high-impact, real-world applications of data science, particularly in telecommunications and entertainment industries. Scientific Awards: $1M Netflix Prize (2009) as member of BellKor's Pragmatic Chaos team Volinsky has extensive experience in advising and leading data science teams. He led a team of 40 data scientists at AT&T, where he oversaw projects with significant business impact, including fraud detection, customer complaint prediction, and computer vision applications. Although formal student advising is not detailed, his leadership roles imply substantial mentorship and team development. He has not disclosed specific grants, but his work at AT&T and NYU suggests engagement with large-scale, industry-funded research initiatives. He was instrumental in pioneering work on large-scale recommender systems and continues to contribute to the evolution of data-driven decision-making in enterprise settings.
