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.
Karel Keesman is an Associate Professor at Wageningen University & Research, specializing in mathematical and statistical methods applied to environmental engineering and biotechnology. He leads research in aquaponics, wastewater treatment, and sustainable energy systems, focusing on optimizing resource use and environmental impact. His work integrates advanced modelling techniques with real-world applications, such as bioreactor stability, nutrient cycling in aquaculture, and renewable energy storage. His research interests span aquaponics system design, desulfurization processes, and sensor-based monitoring in water networks. He actively contributes to interdisciplinary projects, including the development of off-river pumped hydro energy storage and nutrient recovery from biofloc systems. Keesman supervises multiple PhD candidates exploring topics like aquaponics sustainability, anaerobic digestion, and energy mixes in Indonesia. He has authored over 300 publications and datasets, emphasizing open-access research. His work bridges theoretical models with practical solutions for environmental challenges.
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.
Alicia L Carriquiry is a Distinguished Professor and President's Chair at Iowa State University, serving as Director of the Center for Statistics and Applications in Forensic Evidence (CSAFE). She holds a PhD in Statistics from Iowa State University (1989), an MS from the University of Illinois at Urbana-Champaign/ISU (1985/1986), and a BS from Universidad de la Republica in Uruguay (1981). Her research focuses on applying statistical methods to forensic science, nutrition epidemiology, and plant and animal breeding, with a particular emphasis on Bayesian frameworks. Recent research emphasizes forensic evidence analysis, including footwear impression algorithms, handwriting software development, and probabilistic evidence assessment tools. She also explores dietary intake patterns in populations across Latin America and Southeast Asia, addressing nutrient deficiencies and public health interventions. Her work bridges statistical rigor with practical applications in criminal justice, improving forensic methodologies through algorithmic innovation and interdisciplinary collaboration. As CSAFE Director, she leads initiatives to enhance statistical foundations in forensic disciplines, train practitioners, and develop open-source datasets. Notable contributions include database search methodologies, error rate analyses, and software tools like handwriter for handwriting analysis. Her research underscores the importance of probabilistic reasoning in legal contexts and addresses challenges in multi-camera source identification and nonlinear image distortion correction. Carriquiry’s leadership extends to editorial roles and professional service, advancing statistical standards in forensic science. She remains active in training programs and collaborative research projects, fostering reproducibility and relevance in scientific inquiry.
Stephanie S. Shipp is a Research Professor in the Department of Statistics at Iowa State University. Her career spans federal statistics, science and technology policy, and academia. She previously led economic evaluation programs at the National Institute of Standards and Technology (NIST) and the IDA Science and Technology Policy Institute (STPI), and co-founded a data science research group at Virginia Tech and the University of Virginia. Her work focuses on modernizing statistical products for the Census Bureau through innovative methodologies beyond traditional surveys. Education: BA in Economics from Trinity University, Washington DC PhD in Economics from George Washington University Her research interests bridge data science, policy analysis, and statistical evaluation, emphasizing decadal modernization of census operations and advanced survey methods. She maintains strong collaborations with Iowa State University, where she joined the Statistics Department to continue her interdisciplinary work. Professional Affiliations: American Statistical Association American Economic Association American Association for the Advancement of Science International Statistical Institute Stephanie has no listed awards, grants, or publications in the provided text.
Ezra Miller is a Professor of Mathematics at Duke University, specializing in algebraic geometry, combinatorics, and their applications to biology and statistics. His work bridges pure mathematics with interdisciplinary research, including studies in phylogenetic trees, geometric probability, and algebraic statistics. He holds positions in the Mathematics Department at Duke and has contributed to the Statistical and Applied Mathematical Sciences Institute (SAMSI) programs. Education: Details not explicitly provided in the text, but his academic journey includes a Ph.D. from UC Berkeley and postdoctoral research. His research interests span geometry, algebra, probability, statistics, topology, combinatorics, algorithms, and computational biology. He has advised students in algebraic combinatorics and related fields. Research emphasizes geometric and combinatorial structures, with notable projects on phylogenetic data analysis, hypergeometric systems, and Gröbner basis theory. His work often integrates computational methods with theoretical frameworks. Notable collaborations include studies on metric phylogenetic trees, topological data analysis, and combinatorial game theory. He has taught advanced courses in algebra, combinatorics, and applied mathematics at Duke. Labs/Teams: Active in SAMSI's Analysis of Object Data program and collaborates with statisticians and biologists on interdisciplinary projects.
Benoît Monin is the Bowen H. and Janice Arthur McCoy Professor of Ethics, Psychology, and Leadership at Stanford University, holding dual appointments in the Department of Psychology (School of Humanities and Sciences) and the Organizational Behavior area of the Stanford Graduate School of Business (GSB) . He has been with Stanford since 2001, advancing from Assistant Professor to full Professor by 2012. His research focuses on moral psychology, identity dynamics, leadership ethics, and societal implications of technology , with notable work on viral outrage, AI’s impact on human identity, and moral licensing. Monin earned a PhD in Psychology from Princeton University (2002) , an MSc in Social Psychology from the London School of Economics (1995) , and an ESSEC Diploma in Management (1994) . He has held visiting roles at the University of Michigan and Paris X Nanterre. His teaching includes courses like Acting with Power and executive programs on leadership and sustainability. Research Highlights: Monin’s work bridges social psychology and organizational behavior, exploring how people navigate moral dilemmas, react to identity threats, and perceive ethical leadership. Recent studies address the psychological effects of AI advancements and viral social media dynamics. Awards: Recognized with prestigious fellowships including the Hank McKinnell-Pfizer Faculty Fellow (2023–24), and the Dean’s Award for Distinguished Teaching (2005). His military honor, the Médaille de Bronze de la Défense Nationale (1996) , underscores his multidimensional career. Leadership & Outreach: Co-Director of the Leading with an Improv Mindset initiative, emphasizing adaptive leadership skills. Engages widely in media discussions on ethics, AI, and social psychology through platforms like Stanford Business Insights .
Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology
Prof. Constantin A. Rothkopf is a W3 Professor at the Department of Psychology, Technische Universität Darmstadt, and a secondary member of the Department of Computer Science. He serves as Founding Director of the Centre for Cognitive Science and founding member of the Hessisches Zentrum für Künstliche Intelligenz (hessian.ai). He is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the DAAD Konrad Zuse Schools of Excellence in Artificial Intelligence (ELIZA). His research focuses on the interplay between perception and action, using computational models and experimental studies in humans. Current work includes eye-tracking studies in naturalistic environments, inverse optimal control models, and developing algorithms for virtual agents. Education: Ph.D. in Neuroscience and Informatics from the University of Rochester, followed by postdoctoral research at Frankfurt Institute for Advanced Studies (FIAS). He has held visiting professorships at Central European University (2017) and Columbia University (2023). Awards include an ERC Consolidator Grant (2022) and SCENE Project Funding (2025). Research Interests: Active vision, decision-making under uncertainty, sensorimotor control, and computational modeling. Key themes include how humans use sensory input to form beliefs, make decisions, and act in dynamic environments. Grants/Awards: ERC Consolidator Grant (2022), SCENE Funding (2025) Labs/Teams: Centre for Cognitive Science, hessian.ai, ELLIS Unit Darmstadt
Nicola McConkey is an Ernest Rutherford Fellow and Lecturer in Particle Physics at the School of Physical and Chemical Sciences, Queen Mary University of London. She joined the Particle Physics Research Centre in 2024 and leads experimental work in neutrino interactions and detector development. Her affiliations include the Centre for Fundamental Physics and Centre for Experimental and Applied Physics. McConkey is an active member of international collaborations including SBND, DUNE, and MicroBooNE, where she contributed to the assembly of SBND and pioneered high-statistics measurements of electron-neutrino interactions using liquid argon detectors. Her research focuses on three primary domains: neutrino physics (particularly neutrino-argon scattering cross-sections), quantum technology applications for neutrino mass measurement, and liquid argon time projection chamber (LArTPC) detector development. McConkey's investigations aim to advance fundamental particle physics through precision measurements and technological innovation, with emphasis on improving detection capabilities for next-generation neutrino experiments. Publications predominantly explore neutrino interaction dynamics, cross-section measurements, and detector performance optimizations across MicroBooNE, SBND, and DUNE collaborations. Research trends demonstrate consistent focus on refining LArTPC technologies, developing machine learning applications for particle reconstruction, and probing beyond-Standard-Model physics through neutrino interactions. Scientific Awards: Ernest Rutherford Fellowship (2022) McConkey advises two PhD students (Oscar Chow, Yoshita Dabburi) and leads significant research funding including: STFC Grant: 'Piecing together the neutrino mass puzzle' (£431,666; 2024-2027) STFC Outreach Grant: 'Quantum Technologies for Neutrino Mass' (£99,999; 2024-2025) She coordinates research within the Particle Physics Research Centre laboratory and collaborates extensively within the SBND, DUNE, and MicroBooNE international teams, alongside leading the Quantum Technologies for Neutrino Mass collaboration developing novel measurement techniques.
Matt Soosalu serves as a Lecturer in Economics, currently instructing undergraduate courses including Intermediate Macroeconomics (Econ 2102), Introductory Statistics for Economics (Econ 2210), and Intermediate Microeconomics (Econ 2030) across Summer 2024, Fall 2024, and Winter 2025 terms. His academic engagement spans active course instruction since Summer 2024 following a five-year tenure as a Teaching Assistant for diverse economics curricula. His pedagogical expertise covers core economic disciplines, with demonstrated proficiency in quantitative methods through courses like Econometrics and Research Methods. Teaching responsibilities encompass both foundational undergraduate instruction (e.g., Introduction to Macroeconomics, Managerial Economics) and advanced topics including International Trade Theory and Honours-level seminars. Professional activities reflect specialization in applied economic theory, particularly in microeconomic systems, macroeconomic modeling, and statistical analysis frameworks essential for economic research.
Vinothan N. Manoharan is a Professor in the School of Engineering and Applied Sciences and the Department of Physics at Harvard University. He joined Harvard in 2005 after a postdoctoral fellowship at the University of Pennsylvania and a PhD in Chemical Engineering at the University of California, Santa Barbara. His research bridges colloidal science, biophysics, and materials engineering, focusing on self-assembly processes and advanced imaging techniques.
Ana Filipa Sequeira is a researcher affiliated with INESC TEC, Porto, Portugal, and the University of Porto. Her work spans biometrics, fairness in AI, and explainable artificial intelligence. She has contributed to advancements in face recognition, synthetic data applications, and bias mitigation through techniques like knowledge distillation and model compression. Institution: INESC TEC (Porto, Portugal) Research Themes: Face recognition, fairness, explainability, synthetic data, biometric security Her recent publications focus on addressing demographic biases in face recognition systems, developing privacy-preserving explainable methods, and evaluating synthetic data's impact. She collaborates extensively with researchers like Pedro C. Neto, Jaime S. Cardoso, and Naser Damer. Sequeira has participated in organizing and analyzing competitions such as FRCSyn, BIOSIG, and SYN-MAD, emphasizing robust evaluation frameworks. Her work intersects technical innovation with ethical considerations, advocating for responsible AI applications in biometrics.
Dr Davoud Cheraghi Home is a Reader (Associate Professor) in Pure Mathematics at Imperial College London, affiliated with the Pure Analysis and PDEs and Geometry research groups. His research focuses on complex analysis and dynamical systems, particularly holomorphic maps, rigidity phenomena, and small divisors problems, bridging analysis, geometry, and combinatorics. He has organized numerous conferences and workshops, including events at Imperial College and the School of Mathematics in Tehran. Dr Cheraghi teaches advanced courses in geometric complex analysis, analysis, and dynamical systems at Imperial College, as well as at the University of Warwick and Stony Brook University. He has mentored PhD students and postdoctoral researchers, contributing significantly to the field through his publications and collaborations. His research interests encompass geometric analysis (quasi-conformal mappings, elliptic PDEs), renormalization operators, and low-dimensional analytic dynamics. Notable publications include studies on irrationally indifferent attractors, Siegel polynomials, and combinatorial rigidity. Dr Cheraghi’s work is supported by extensive teaching and mentorship activities, reflecting his commitment to advancing both research and education in pure mathematics.
Daniela M Witten is a Professor of Statistics and Biostatistics at the University of Washington, holding the Dorothy Gilford Endowed Chair in Mathematical Statistics. Her research focuses on developing statistical machine learning methods for high-dimensional data, with a particular emphasis on unsupervised learning and theoretical foundations. Witten earned her BS in Math and Biology with Honors and Distinction from Stanford University in 2005 and her PhD in Statistics from Stanford University in 2010 under Robert Tibshirani. Her academic journey established her expertise in bridging mathematical theory with biological applications. Her research program centers on high-dimensional statistical learning , where she develops methods for unsupervised learning and graphical modeling when features outnumber observations. She pioneers statistical models for neural activity through collaborations with the Allen Institute for Brain Science and Princeton University, addressing functional connectivity and neuron sub-population identification. Her groundbreaking work on selective inference solves the "double-dipping" problem in hypothesis generation and testing, enabling valid inference after hierarchical clustering and regression trees. Additionally, she advances multi-view data analysis to integrate complementary data sources like clinical and genomic measurements. Applications span genomics, neuroscience, microbial ecology, and pathology, demonstrating her commitment to solving real-world biomedical challenges. Her 2025 publications reveal a cohesive trend toward developing theoretically rigorous inference frameworks for high-dimensional settings, with emphasis on linear regression validity, semi-supervised efficiency, Gaussian decomposition, and PCA variance quantification—showcasing her signature blend of methodological innovation and practical applicability. Witten's exceptional contributions are recognized through extensive honors: Presidents’ Award, Committee of Presidents of Statistical Societies (COPSS) (2022) Mortimer Spiegelman Award, American Public Health Association (2019) Simons Investigator Award (2018-2023) Sloan Research Fellowship (2013-2015) NSF CAREER Award (2013-2018) NIH Director’s Early Independence Award (2011-2016) 23 major awards including named lectureships, fellowships, and editorial leadership As a dedicated mentor, she has guided students like Olivia McGough (NSF GRFP winner), Dwight (Zichun) Xu (ASA Nonparametrics Student Paper Award winner), Yiqun Chen (Hopkins Biostat faculty), and Anna Neufeld (Williams College faculty). Her research is sustained by major grants from NIH, NSF, and Simons Foundation. Witten co-authored the seminal textbook "Introduction to Statistical Learning" and currently serves as Joint Editor of the Journal of the Royal Statistical Society, Series B (2023-2025), shaping the field through both scholarship and community leadership.
