Yuliya Martsynyuk is an Associate Professor in the Department of Statistics at the University of Manitoba, located within the Faculty of Science. She holds an office in 256 Parker and can be reached via email at Yuliya.Martsynyuk@umanitoba.ca. Her research interests align with core statistical disciplines, including theoretical and applied statistics, probability, and data analysis methodologies. Specific subfields are not explicitly detailed in the provided text, but her affiliation with the Statistics department suggests expertise in areas such as statistical modeling, computational statistics, and interdisciplinary applications of statistical methods. No awards, publications, grants, or student advising records are explicitly listed in the provided information. Further details on her academic contributions would require additional sources.
Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).
Dr Jonathan Green is a seabird biologist and currently serves as Programme Director for the BSc Environmental Science at the University of Liverpool. He has held roles such as Head of Discipline in Ecology & Marine Biology and Deputy Assessment Officer within the School/Institute. His professional activities include chairing conferences, convening sessions, and serving on editorial boards for journals like Emu - Austral Ornithology (since 2008) and Endangered Species Research (2009–2013). Education: Zoology, University of Cambridge (1995) PhD in Zoology, University of Birmingham (2001) Postdoctoral Research, University of Birmingham Fellowship at La Trobe University, Australia (2005–2008) Research Interests: Jonathan Green's research explores the intersection of ecology, physiology, and behavior in seabirds. He investigates how seabirds adapt to contrasting marine and terrestrial environments, particularly regarding foraging strategies, energy expenditure, and moult ecology. His work addresses anthropogenic threats such as overfishing, climate change, and offshore wind farm developments. He also leads conservation projects in the Caribbean UK Overseas Territories, collaborating with local organizations and government bodies to protect seabird populations. Advising and Grants: Jonathan Green supervises PhD student Elayna Daniels through the CASE studentship grant. His research is supported by grants from NERC, Defra's Darwin+ programme, and UKRI, focusing on topics like marine bird energetics, wind farm impacts, and Caribbean conservation. These projects often involve collaboration with government agencies and industry partners to ensure applied outcomes. Labs/Teams: Jonathan Green collaborates with interdisciplinary teams and institutions, including the Joint Nature Conservation Committee (JNCC), RSPB, BTO, and renewable energy developers. His fieldwork is conducted at sites such as Puffin Island, North Wales, and various locations in the Caribbean UK Overseas Territories.
Shanna Swan is a renowned epidemiologist and Professor of Environmental Medicine and Public Health at the Icahn School of Medicine at Mount Sinai. She holds a PhD in Statistics from UC Berkeley (1963), an MA in Biostatistics from Columbia University, and a BA in Mathematics from City College of New York. Her career spans academia, public health institutions, and research on environmental health impacts. Notable roles include work at Kaiser Permanente, California Department of Health Services, University of Missouri, and University of Rochester. Her research focuses on endocrine-disrupting chemicals (EDCs), sperm count decline, and reproductive health. Her groundbreaking 2017 study revealed a 50% sperm count drop in Western men over 40 years, later updated to show acceleration since 2000. She authored the influential book Count Down (2021), addressing environmental threats to human fertility. Key contributions include forming California’s reproductive health group and leading National Academy of Sciences committees on EDCs. Swan advocates for science-driven public health policy, emphasizing the need to address chemical exposures. Her work bridges statistical rigor with real-world impact, influencing global discussions on fertility and environmental safety. Awards include the Ward Medal in Logic (CCNY). She remains active in advancing research, education, and community action to safeguard human health and reproduction.
Alyssa M. Bilinski is the Peterson Family Assistant Professor of Health Policy at Brown University School of Public Health, with joint appointments in the Departments of Health Services, Policy & Practice and Biostatistics. Her work focuses on integrating policy evaluation and modeling to identify efficient interventions for improving population health and well-being. Education: PhD in Health Policy (Evaluative Science & Statistics) from Harvard University MSc in Medical Statistics from London School of Hygiene and Tropical Medicine as a Marshall Scholar AM in Statistics from Harvard University BA from Yale College Research Areas: Dr. Bilinski's research bridges methodological innovation and practical application, with emphasis on: Modeling strategies for safe school reopening during pandemics Defining adaptive mitigation frameworks for public health Parental mental health impacts of education policies Leveraging absenteeism data for health interventions Scientific Recognition: As a Marshall Scholar, she demonstrated exceptional academic achievement through advanced training in medical statistics. Collaborations with state, local, and federal public health agencies Publications in interdisciplinary journals: JAMA, Annals of Internal Medicine, PNAS, Health Affairs, Journal of Econometrics, Value in Health Her current work focuses on real-time data systems for public health decision-making, particularly through absenteeism monitoring in educational settings.
Prof. Dmitri Krioukov is an Associate Professor in the Department of Physics at Northeastern University and holds an affiliated faculty position in Electrical and Computer Engineering. He directs the DK-Lab at the Network Science Institute, focusing on theoretical aspects of complex networks, including latent network geometry, random geometric graphs, and navigation in networks. His work bridges mathematical physics and applied network science, with applications to real-world data such as the Internet's structure. Research interests revolve around the interplay between network topology and geometry, including studies of causal sets, graph curvature, and dynamics in complex systems. He has pioneered frameworks linking network growth to hyperbolic geometry, enabling efficient routing algorithms. Notable contributions include the discovery of latent geometric structures underlying real-world networks and their implications for navigation and scalability. He has been recognized for high-impact publications, including multiple Stanford University Annual Assessments placing him among the top 2% most-cited scientists in his field (2024, 2023, 2022). His lab's interdisciplinary approach integrates principles from physics, mathematics, and computer science to address fundamental questions in network science.
Alex Shkolnik is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB). His email is shkolnik@pstat.ucsb.edu. While specific research interests, educational background, grants, or lab affiliations are not detailed in the provided text, his department affiliation suggests expertise in statistical methodologies and applied mathematical sciences. No awards, advised students, or publications are listed in the current information. Further details about his academic trajectory or professional activities would require additional sources.
Beata Csatho PhD is a Professor in the Department of Earth Sciences at the University at Buffalo, affiliated with the College of Arts and Sciences. Her research focuses on remote sensing, glaciology, climate change, and geophysics. She holds a PhD in Geophysics from the University of Miskolc, Hungary (1993). Her work integrates geophysical, remote sensing, and climatic data to study ice sheet dynamics and cryospheric changes. She leads the Remote Sensing lab and teaches courses like GLY 465/565 (Environmental Remote Sensing) and GLY 325 (Geophysics). Recent research emphasizes Greenland and Antarctic ice dynamics, ICESat-2 validation, and developing tools like Ghub for collaborative glaciology. She advises PhD and Master's students and collaborates on major projects like ISMIP7 and IceBridge. Her lab focuses on advancing laser altimetry, DEM correction, and cryosphere observation techniques.
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University and serves as Director of the Stanford AI Lab (SAIL) and Senior Fellow at the Institute for Human-Centered AI (HAI). He holds dual roles as Chief Scientist at Visual Layer and Virtue AI. His research focuses on machine learning methods, explainability, fairness, and ethics of AI, alongside systems for scalable AI deployment. Education details are not explicitly provided, but his work spans foundational contributions to machine learning systems (e.g., XGBoost) and explainable AI frameworks like Anchors and LIME. He emphasizes ethical AI through projects like CheckList for model testing and Model Equality Testing for API transparency. His scientific contributions include advancing optimization techniques (AdaScale SGD, TVM compiler) and ethical benchmarks for generative AI. He has been recognized as a Member of the National Academy of Engineering for his transformative impact on AI systems and their societal applications. Guestrin leads interdisciplinary initiatives at SAIL and HAI, fostering collaboration between technical innovation and human-centered design. His work bridges theory and practice, addressing challenges in healthcare (diabetes management systems) and AI security.
John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.
Fanny Chevalier is an Assistant Professor at the University of Toronto, cross-appointed to the Department of Computer Science and Department of Statistical Sciences. Her research bridges data visualization and human-computer interaction, focusing on interactive tools for visual analytics and creative data exploration. Research Focus: Key themes include design and evaluation of visual exploration tools for complex datasets, statistics education through visualization, perception of animated transitions, and sketch-based interfaces. Her work emphasizes both theoretical and applied aspects of data-human interaction. Education & Career: Holds an MSc (2004) and PhD (2007) from Université de Bordeaux, with postdoctoral experience at Inria (France), OCAD University, and University of Toronto. Transitioned from research scientist roles to academia, joining the University of Toronto faculty in 2017. Contact: Email: fanny@cs.toronto.edu
Ralf Bierig joined Maynooth University's Computer Science Department in 2017, teaching topics including information retrieval, software testing, interaction design, and virtual reality. He is the programme director of the Higher Diploma in Human-Computer Interaction (HCI) and User Experience (UX). He earned his BSc (2002) from University of Furtwangen and PhD (2008) from Robert Gordon University. Research Interests His work spans information retrieval, interactive information retrieval, personalisation, information search behavior, usability (UX), and virtual reality (VR). Recent publications focus on multimodal concept indexing, hybrid IR approaches, and contextual adaptation in search systems. Publication Trends His research combines statistical semantics, graph modeling, and multimodal data analysis across academic collaborations in Austria, Germany, and international venues like ECIR and SIGIR.
Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.
Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications
Maria Timofeeva is an Associate Professor in the Epidemiology, Biostatistics and Biodemography (EBB) department at the University of Southern Denmark (SDU), with additional affiliation at the Danish Institute for Advanced Study (DIAS). She holds an Honorary Fellow position at the University of Edinburgh since December 2019. Her research focuses on cancer prevention and prediction, particularly studying the effects of environmental and genetic factors on cancer risk and progression. Dr. Timofeeva earned her Dr.sc.hum in Epidemiology from Heidelberg University (2005-2009), with a dissertation on genetic polymorphisms as risk factors for early onset lung cancer. Prior to her current position, she worked as a Statistical Geneticist at the University of Edinburgh (2013-2019) and as a Postdoctoral Fellow at the International Agency for Research on Cancer (2009-2013). Her research interests center around understanding the genetics of cancer risk through multi-omic analysis. She leads several significant projects, including the Interdisciplinary Project on Adherence to Colorectal Cancer Screening, meta-analysis of factors associated with false-positive and false-negative FOBT results (registered in PROSPERO ID: CRD42022315767), and the COlorectal Cancer screening Among RElatives (CoCARE) twin-family study in Denmark. Her methodological expertise spans observational epidemiological studies (case-control, population-based cohort studies, twin studies), meta-analysis, umbrella reviews, and multi-omics data analysis. Analysis of her recent publications reveals a strong focus on colorectal cancer genetics, with particular emphasis on genome-wide association studies, Mendelian randomization approaches, and trans-ancestry analyses. Her work frequently leverages large datasets including the UK Biobank and international consortia, with applications in cancer risk prediction and understanding gene-environment interactions. Dr. Timofeeva has an extensive publication record with 73 publications listed in her profile. Her research has been cited across multiple platforms, with mentions in news outlets, social media, and academic readership platforms like Mendeley. She is actively involved in academic service, serving as a peer reviewer for journals including BMC Cancer and Scientific Reports, and participating in conferences such as the 26th Nordic Congress of Gerontology. She also serves on evaluation committees, including with the World Cancer Research Fund International (April-May 2024). Her teaching activities include courses on evidence-based drug utilization and biostatistics, as well as supervision of research projects on gene expression in twins. Dr. Timofeeva has engaged with the public through media contributions, including an interview titled 'Jeg vil forstå, hvorfor vi får kræft' (November 15, 2021), where she discussed understanding why we get cancer.
