Aaron Smith is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Stanford University. His research focuses on applied probability, computational statistics, Monte Carlo methods, and Markov chains, with an emphasis on advancing theoretical understanding and practical applications of these methodologies. Dr. Smith's work includes contributions to community detection algorithms, Markov chain mixing times, and synthetic health data generation. His recent publications explore topics such as nonstandard Dirichlet form representations, perturbation analysis of MCMC algorithms, and sparse Bayesian multidimensional scaling. He advises students in applied probability and has supervised postdoctoral researchers in related fields. His research interests span a wide range of topics, including stochastic processes, statistical inference, and algorithm design. He is particularly known for his analysis of convergence rates in Markov chains and the development of efficient sampling techniques for complex models. His interdisciplinary work bridges theoretical mathematics and practical computational challenges in data science and healthcare. Dr. Smith collaborates on projects involving synthetic data frameworks for privacy-preserving applications and has contributed to foundational work on mixing times and perturbation effects in stochastic systems.
Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).
Jaline L Gerardin is an Associate Professor in Preventive Medicine (Epidemiology) and McCormick School of Engineering at Northwestern University. She is affiliated with the Center for Global Health, Institute for Public Health and Medicine (IPHAM), Northwestern Institute on Complex Systems, and Robert J. Havey, MD Institute for Global Health. Her career focuses on malaria modeling and public health interventions. Current affiliations: Northwestern University, IPHAM, NICO, Center for Global Health Prior role: Malaria lead at Institute for Disease Modeling Research Focus: Gerardin specializes in malaria transmission modeling and public health intervention optimization . Her work addresses subnational tailoring of malaria strategies, intervention mix analysis for elimination, and ethical modeling practices. She integrates multidisciplinary data (entomology, immunology, demography) into agent-based models to guide policy in resource-limited settings. Article Trends: Recent publications emphasize subnational malaria intervention prioritization (Guinea, Nigeria), diagnostic performance evaluation, human mobility impacts on transmission, and wastewater surveillance applications for disease modeling. Leadership Roles: Co-chair: American Society of Tropical Medicine and Hygiene symposia Member: WHO working groups, Malaria Modeling Consortium Advisor: WHO Malaria Multi-Model Comparison initiatives Education: PhD from University of California, San Francisco (2013)
Gautam Kamath is an Assistant Professor at the University of Waterloo's Cheriton School of Computer Science, a Faculty Member at the Vector Institute, and a Canada CIFAR AI Chair. He leads The Salon, a research group focused on statistics, algorithms, machine learning, and optimization. His work bridges theoretical foundations with practical applications in data privacy and robustness, contributing to both academic research and real-world deployments that impact millions of users. Dr. Kamath earned his PhD and SM degrees in Electrical Engineering and Computer Science at MIT, where he was advised by Costis Daskalakis. Prior to MIT, he graduated from Cornell University in May 2012 with a degree in Computer Science and Electrical and Computer Engineering, where he worked with Bobby Kleinberg. His academic journey reflects a strong foundation in both theoretical computer science and practical applications. His research focuses on developing solutions for trustworthy and reliable machine learning and statistics, with particular emphasis on data privacy and robustness. He addresses fundamental problems in these areas, revitalizing statistical toolkits for the modern data era where privacy preservation is paramount. His work spans theoretical foundations of differential privacy to practical applications that have been deployed at scale, including contributions to systems that protect the sensitive information of hundreds of millions of individuals. Analysis of his recent publications reveals a strong trend toward addressing the interplay between privacy, robustness, and machine learning performance. His work increasingly examines practical deployment challenges while maintaining theoretical rigor, with growing emphasis on diffusion models, generative AI, and the legal implications of AI systems. There's a clear trajectory from foundational privacy theory toward addressing real-world implementation challenges across diverse application domains. Canada CIFAR AI Chair Ontario Early Researcher Award 2024 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies STOC Best Student Presentation Award ICML 2024 Best Paper Award Microsoft Research Fellow at the Simons Institute for the Theory of Computing Dr. Kamath actively mentors students, with notable successes including Valentio Iverson winning the Germain-Erdős Undergraduate Award and Chris Trevisan receiving the CRA Outstanding Undergraduate Researcher Award. His service to the research community is extensive, serving as Editor-in-Chief of TMLR, on the Executive Committee of the Learning Theory Alliance, and on steering committees for major conferences including ICML, COLT, and ALT. He has organized numerous workshops focused on privacy-preserving machine learning and differential privacy. Through The Salon research group, Dr. Kamath fosters a collaborative environment where postdoctoral fellows, graduate students, and undergraduates work together on cutting-edge problems at the intersection of statistics, algorithms, machine learning, and optimization. The group maintains strong connections with industry partners and participates in major research initiatives, including the Vector Institute's privacy and security research efforts. Looking forward, Dr. Kamath will be moving to the Computer Science department at NYU's Courant Institute of Mathematical Sciences in September 2026, where he plans to expand his research program.
Sarah Kang is the Director of the Department of Climate Dynamics at the Max Planck Institute for Meteorology in Hamburg, Germany, a position she has held since August 2023. She leads the Director's Research Group (CDY) focusing on fundamental climate dynamics. Prior to this, she served as Professor in the Department of Urban and Environmental Engineering at Ulsan National Institute of Science and Technology (UNIST) in South Korea from 2011-2023, progressing through assistant, associate, and full professor ranks. Her research examines complex climate system dynamics, with emphasis on: Large-scale atmosphere and ocean circulation patterns Tropical-extratropical climate interactions Hydrological cycle responses to climate change Mechanisms of polar amplification Teleconnections between ocean basins and climate zones Analysis of her recent publications reveals dominant research themes: Ocean-atmosphere coupling mechanisms Radiative forcing and climate sensitivity Hemispheric climate asymmetries Tropical precipitation dynamics Polar warming impacts on global circulation with consistent methodology employing high-resolution climate modeling and observational verification. Major scientific recognitions include: AGU Atmospheric Sciences Ascent Award (2022) AOGS Kamide Lecture Award (2018) Editor's Citation for Excellence in Refereeing (GRL 2018) UNIST Teaching Excellence Award (2012) NCAR Advanced Studies Fellowship (2009) She maintains extensive professional engagement as: Co-chair of CLIVAR Climate Dynamics Panel Science Steering Committee member for CFMIP Associate Editor for Frontiers in Climate Editor for AGU Advances Board member of Korean Meteorological Society
Raquel E. Aldana is the Martin Luther King Jr. Professor of Law at UC Davis School of Law, where she rejoined as a full-time faculty member in 2020 after serving as Associate Vice Chancellor for Academic Diversity (2017–2020). She holds a J.D. from Harvard Law School and B.A. degrees in English and Spanish from Arizona State University (summa cum laude). Her academic career includes prior roles at the McGeorge School of Law and the University of Nevada, Las Vegas, as well as a Fulbright Scholarship in Guatemala (2006–2007). Her research focuses on transitional justice, criminal justice reform, sustainable development in Latin America, and immigrant rights. Aldana has authored or edited five books and over thirty law review articles, earning recognition through grants such as the UC Davis Academic Senate Interdisciplinary Grant for the project Building Bridges: Narrowing the Legal-Scientific Divide in Immigration Forensic Assessments . She teaches courses on immigration law, asylum, comparative forced displacement, and critical race theory. Aldana leads the Aoki Center for Critical Race and Nation Studies, co-directing its mission to advance interdisciplinary scholarship on race, ethnicity, and law. She co-founded the Critical Race Theory Book Series with UC Press, aiming to globalize and diversify critical race discourse. Her awards include the ABA’s Margaret Brent Women Lawyers of Achievement Award and the UC Davis Chancellor’s Achievement Award for Diversity. Her service includes roles on the American Law Institute, American Bar Foundation, and the Council on Foreign Relations. She chairs the ABA’s Latin America and Caribbean Council and co-chaired the UC Davis taskforce to achieve Hispanic Serving Institution (HSI) status, realized in 2024. Her work integrates legal education, policy reform, and grassroots advocacy to address systemic inequities in migration, criminal justice, and global development.
Paul-Christian Burkner is a researcher in the Department of Computer Science at Aalto University. His work focuses on Bayesian statistical methods, computational modeling, and probabilistic programming. He collaborates with Professor Aki Vehtari's research group and has published extensively on topics like model sensitivity, spatiotemporal analysis, and variable selection techniques. His research interests include: Bayesian inference and model comparison Computational statistics Probabilistic programming Machine learning algorithms Statistical modeling in social sciences Neuroimaging data analysis Recent publications demonstrate expertise in simulation-based calibration, spatiotemporal modeling, and Gaussian process approximations. Collaborations span psychology, neuroscience, and machine learning domains. Contact: ext-paul-christian.burkner@aalto.fi
Lieselotte Ahnert serves as a Visiting Professor for Developmental Psychology at the Free University of Berlin's Department of Education and Psychology, specifically within the Department of Developmental Science and Applied Developmental Psychology. Her academic journey began at Humboldt University in East Berlin, where she studied psychology during a period of significant political change in the GDR. After completing her doctorate in 1982, she held various research positions including director of the Interdisciplinary Center for Applied Socialization Research (IZAS e.V.) from 1991-1996 and a visiting scientist position at the National Institutes of Health in Washington from 1996-1999. Prior to her current position at FU Berlin (since 2019), she was a Professor of Developmental Psychology at the University of Vienna from 2008-2019. Professor Ahnert's research focuses on early childhood development, attachment theory, and the specific contributions of fathers to child development. Her work examines how children develop within various caregiving contexts, including the transition from home to childcare settings, the effects of socioeconomic disadvantage on attachment security, and the unique role fathers play in children's language development and emotional regulation. She has conducted extensive research on stress responses in infants during childcare transitions and how care providers influence these responses. Her interdisciplinary approach draws from behavioral biology, genetics, neuroscience, pediatrics, and education to understand the complex interplay between genetic and environmental influences in early development. Ahnert's publication record demonstrates significant contributions to understanding father-child relationships, with recent work exploring how fathers' attachment security and education contribute to early child language skills beyond mothers' influence. Her research has also examined the 'terrible twos' phase, investigating how children cope with frustration and tantrums and how parents support them through these developmental challenges. She has published extensively in high-impact journals including Child Development, Developmental Psychobiology, and Attachment and Human Development. Professor Ahnert has received recognition for her expertise, including being commissioned to provide expertise for the Federal Chancellery of Austria in 2021. Her work has practical applications in early childhood education and intervention programs, particularly in understanding how public childcare can compensate for developmental risks in socioeconomically disadvantaged children. In addition to her research, Ahnert has made significant contributions to academic training and supervision. Her book publications, including 'It's the fathers who matter' (2023) and 'How much mother does a child need?' (2020), bridge the gap between academic research and public understanding of child development. Her work continues to influence both academic discourse and practical approaches to early childhood care and education.
John Drake is an Associate Professor in the Department of Sustainable Resources Management at the State University of New York College of Environmental Science and Forestry (SUNY-ESF). His research focuses on plant physiology and ecosystem ecology, particularly the physiological mechanisms underlying tree function and their scaling to ecosystem processes under climate change. He holds a Ph.D. from the University of Illinois (2010) in Ecology, Evolution, and Conservation Biology and a B.S. from Hope College (2005) in Biology. Prior roles include Research Fellow and Postdoc positions at institutions like the Hawkesbury Institute for the Environment and Boston University. His work emphasizes tree responses to temperature and water availability, including photosynthetic acclimation, carbon allocation, and stable isotope analysis. He leads the Drake Lab, which investigates tree ecophysiology and forest resilience. Current advisees include Sofia Arreaga (MS), Leslie Morrison (PhD), Jacob Olichney (PhD), and Taylor Wegner (MS). Notable grants include studies on New York forest responses to environmental change and climate resilience strategies for American chestnut restoration. Dr. Drake has received the University Distinguished Fellow award and contributed to over 40 peer-reviewed publications since 2007. His lab supports interdisciplinary research on forest carbon dynamics, soil-microbe interactions, and climate adaptation, with projects funded by agencies like the USDA and SUNY-ESF.
Dr. Haiyan Liu is an Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, within the School of Social Sciences, Humanities, and Arts. She earned her Ph.D. in Quantitative Psychology from the University of Notre Dame (2018). Her research focuses on advanced statistical modeling of psychological and educational data, including high-dimensional, longitudinal, and social network data. She develops Bayesian methodologies and machine learning techniques to enhance understanding of human behavior, with recent emphasis on structural equation modeling, network dynamics, and nonparametric growth curves. Her work addresses challenges in survey methodology and behavioral data analysis. Dr. Liu’s educational background includes a Ph.D. in Quantitative Psychology from the University of Notre Dame (2018), complementing her current academic role. Her lab, accessible at https://sites.google.com/view/ucmhaiyanliu , supports her research activities. Her research interests span Bayesian SEM, social network analysis, and applications of machine learning to behavioral data, aiming to bridge methodological innovation with practical psychological inquiry. Her recent articles highlight advancements in Bayesian model selection, longitudinal sentiment analysis, and social network mediation. She emphasizes prior specification rigor in Bayesian frameworks and explores nonlinear relationships in social dynamics. Though no awards are explicitly listed, her contributions to statistical methodologies in psychological research reflect significant scholarly impact. Dr. Liu advises students in quantitative methods and has developed software tools like logistic4p for misclassification correction in logistic regression. Her work integrates computational methods with theoretical advancements, positioning her as a key contributor to modern quantitative psychology.
Dr. Rajesh Bhargave is an Associate Professor of Marketing at the Department of Analytics, Marketing and Operations within Imperial Business School, Imperial College London. He holds a B.B.A. from the University of Texas at Austin and a Ph.D. from the Wharton School of the University of Pennsylvania. His research focuses on consumer behavior, particularly how social contexts and digital tools influence decision-making and product evaluations. Dr. Bhargave’s academic journey includes prior faculty positions at the University of Texas at San Antonio before joining Imperial College London. He teaches across programs including MBA, Executive Education, and Pre-experience Masters, emphasizing practical application of marketing principles. His research explores two primary areas: (1) the impact of social environments on product preferences, analyzing shared versus solitary consumption experiences, and (2) how online technologies reshape consumer decision-making processes. Key topics include hedonic judgments, numerical processing in choices, and the psychological effects of digital cues like 'cloud' reminders. His publications span journals such as Journal of Consumer Research and Psychological Science , reflecting a strong focus on behavioral economics and consumer psychology. Notable themes include 'collective satiation,' 'cue-of-the-cloud effects,' and the role of round numbers in consumer perceptions of product longevity. Dr. Bhargave has advised students such as JORGE PENA-MARIN and Nicole Votolato Montgomery. While no scientific awards are explicitly listed, his work contributes significantly to marketing theory and practice. His research and teaching underscore the intersection of technology, social dynamics, and consumer behavior.
Eunjung Lee serves as Assistant Professor of Business Analytics in the College of Business at Lewis University, bringing extensive industry experience from Samsung and LG alongside prior academic roles at Indiana State University. Her expertise bridges business analytics and educational technology with a pronounced focus on equity-driven research. Her educational foundation includes: Ph.D. in Information Technology Management, University of Wisconsin-Milwaukee M.B.A., Korea University B.S., Kwangwoon University Dr. Lee's research trajectory evolved from business analytics toward educational equity, with recent work emphasizing computational thinking in mathematics education, teacher preparation models, and machine learning applications for gifted identification. She investigates how digital tools transform pedagogy while addressing systemic barriers for underrepresented students. Analysis of her 2022-2025 publications reveals dominant themes in equitable gifted identification through cross-cultural validation of the HOPE rating scale, computational thinking integration in teacher training, and longitudinal studies of enrichment program impacts. Her methodology consistently combines quantitative analysis with equity-centered frameworks. No scientific awards were documented in available sources. While specific advising details remain unreported, her courses in Business Intelligence and Forecasting demonstrate applied analytics instruction. Research grants weren't specified in source materials. Collaborative structures like the Lowell Stahl Center for Entrepreneurship provide institutional context, though dedicated labs or research teams weren't explicitly referenced.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Professor Inge Hoff is affiliated with the Norwegian University of Science and Technology (NTNU) in the Department of Civil and Environmental Engineering, where he has served since 2009. Prior to this, he held roles as senior researcher and research leader at SINTEF. Research Interests : Materials for road construction, frost protection, laboratory testing, pavement dimensioning, road rehabilitation, state development modeling, ground-penetrating radar surveys, and concrete/natural stone coverings. Students : Mentors active PhD fellows Lisa Hannasvik, Arman Hamidi, Clara Weber, and Shoiab Ahmad. Teaching : Coordinates courses like TBA4204/BYGT1102 Transport Infrastructure , BYGT2204 Road and Railway Construction , and BA8600 Pavement Structure Dimensioning . Recent publications highlight his expertise in granular material behavior, asphalt durability under climate stressors, and advanced structural assessment techniques. Collaborations with international researchers and presentations at major conferences (TRB, International Conference on Bituminous Mixtures) demonstrate his ongoing contributions to road engineering.
Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.
