جست‌وجوی استاد و پژوهشگر

Alyssa M. Bilinskiمشاهده پروفایل

استادیار

• Policy evaluation
• Health modeling
• Population health
• +۵ مورد دیگر

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.

Dmitri Krioukovمشاهده پروفایل

دانشیار

• Network Science
• Complex Networks
• Mathematical Physics
• +۷ مورد دیگر

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.

• Statistics
• Data Science
• Applied Probability
• +۱ مورد دیگر

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 Csathoمشاهده پروفایل

استاد

• Remote Sensing
• Glaciology
• Climate Change
• +۳ مورد دیگر

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.

• Machine Learning
• Artificial Intelligence
• Explainability of AI
• +۳ مورد دیگر

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 Wakeleyمشاهده پروفایل

استاد

• Theoretical Population Genetics
• Mathematical Biology
• Evolutionary Biology
• +۳ مورد دیگر

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 Chevalierمشاهده پروفایل

استادیار

• Data Science
• Interdisciplinary Research
• Statistics Education
• +۶ مورد دیگر

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

Magnus Bakke Botnanمشاهده پروفایل

استادیار

• Topological Data Analysis
• Representation Theory of Quivers
• Computational Topology
• +۳ مورد دیگر

Magnus Bakke Botnan is an Assistant Professor at the Department of Mathematics, Vrije Universiteit Amsterdam, holding a VIDI career grant (€850,000) since 2018. His research bridges pure and applied mathematics within topological data analysis (TDA), focusing on multiparameter persistence, computational topology, and applications to sciences. PhD in Mathematics, Norwegian University of Science and Technology (NTNU), 2015 Postdoc at TU Munich, 2016-2018 His research group includes postdocs Hannah Rocio Santa Cruz Baur and Rui Dong, and PhD student Enes Devecioğlu. Recent work involves signed barcodes, rank decompositions, and stability of persistence modules. He co-authored the first comprehensive tutorial on multiparameter persistence with Mike Lesnick. Notable contributions include proving the NP-hardness of computing interleaving distance, establishing universality of bottleneck distance for extended persistence diagrams, and developing computational methods for non-branching complexes. Publications span journals like Foundations of Computational Mathematics , Discrete & Computational Geometry , and conferences SoCG, NeurIPS, and ICRA. Scientific Awards: VIDI Career Grant (€850,000) He has taught courses including Complex Analysis, Calculus, Topological Data Analysis, and seminars on analysis and dynamical systems. Actively organizes Applied Topology Days and collaborates on projects integrating TDA with physics, computer science, and statistics.

• Information Retrieval
• Interactive Information Retrieval
• Personalisation
• +۴ مورد دیگر

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.

• Big Data
• Complex Networks
• Graph Neural Networks
• +۱ مورد دیگر

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-Grandaمشاهده پروفایل

دانشیار

• Machine Learning
• Medical AI
• Climate Science
• +۷ مورد دیگر

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

Ben Seiyon Leeمشاهده پروفایل

استادیار

• Computational Statistics
• Spatiotemporal Modeling
• Climate Change Impact Analysis
• +۳ مورد دیگر

Ben Seiyon Lee is an Assistant Professor in the Department of Statistics at George Mason University's College of Science. His work bridges computational statistics, climate modeling, and environmental risk assessment. Education: PhD in Statistics, Pennsylvania State University (2020) Lee specializes in computational methods for high-dimensional spatiotemporal data and uncertainty quantification in climate models. His research explores climate change impacts on extreme hydrological events, wildfire emissions, and medical decision-making. Recent publications focus on Bayesian spatiotemporal frameworks for extreme precipitation analysis, zero-inflated spatial models, and multisector uncertainty quantification. His work addresses challenges in flood risk assessment, agricultural yield projections, and healthcare compliance metrics.

Maria Timofeevaمشاهده پروفایل

دانشیار

• Cancer Epidemiology
• Genetic Risk Factors
• Colorectal Cancer
• +۷ مورد دیگر

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.

• Differential Privacy
• Machine Learning
• Privacy-Preserving Data Analysis
• +۳ مورد دیگر

Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.

Laura Ascenzi-Morenoمشاهده پروفایل

استاد

• Bilingual Education
• Translanguaging
• Biliteracy
• +۷ مورد دیگر

Laura Ascenzi-Moreno serves as Professor of Bilingual Education and Bilingual Program Coordinator in the Childhood, Bilingual and Special Education Department at Brooklyn College, City University of New York (CUNY), School of Education. Her academic leadership focuses on developing educators capable of serving linguistically diverse student populations through equity-centered pedagogical frameworks. Her educational foundation includes a B.A. in Anthropology and Education from Swarthmore College (1994), an M.A. in Individualized Studies in Education from Harvard Graduate School of Education (1999), New York State Permanent Certification for PreK-6 (2002), a Bilingual ESL and Teacher Leadership Program from Bank Street College (2004), and a Ph.D. in Urban Education from CUNY Graduate Center (2012). Dr. Ascenzi-Moreno's research pioneers translanguaging applications across literacy instruction, assessment, and computational learning environments. She investigates how emergent bilinguals' linguistic repertoires can transform reading development, teacher knowledge construction, and multimodal assessment practices. Her work consistently centers equity through frameworks like syncretic reasoning and accompáñamiento, challenging deficit ideologies while promoting asset-based approaches to multilingual education. Analysis of her 2020-2024 publications reveals an accelerating interdisciplinary trajectory where translanguaging principles increasingly intersect with computer science education. This evolution demonstrates strategic expansion from foundational literacy research into computational literacies, with growing emphasis on co-design methodologies and teacher agency in developing multilingual CS curricula. Her scientific recognition includes the 2024 NCTE Outstanding Elementary Educator Award, Language Arts Distinguished Article Award, and major NSF grants totaling $1.3 million for computational literacy projects. Additional honors encompass Fulbright Scholarship work in Colombia, PSC-CUNY research awards, and Westinghouse Science Talent Search distinction. As Principal Investigator of NSF-funded PiLa-CS and former CUNY-NYSIEB Associate Investigator, she secures substantial grant support while mentoring teacher candidates. Her service includes NCTE Elementary Steering Committee leadership (2022-2026), journal reviewing, and Brooklyn's New York Teacher Table participation addressing educator recruitment/retention. Dr. Ascenzi-Moreno co-directs the Participating in Literacies in Computer Science project and maintains active collaboration with NYC schools through translanguaging professional development initiatives. Her work with the CUNY-NYSIEB project established foundational frameworks now implemented across New York bilingual programs.