جست‌وجوی استاد و پژوهشگر

Ting-Kam Leonard Wongمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability
• Optimal Transport
• +۲ مورد دیگر

Ting-Kam Leonard Wong is an Associate Professor jointly appointed at the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences at the University of Toronto and University of Toronto Scarborough , respectively. He also holds a non-budgetary cross-appointment in the Department of Mathematics since May 2025. Education: PhD in Mathematics, University of Washington (2016) MPhil in Mathematics, The Chinese University of Hong Kong (2011) Research Interests: His research lies at the intersection of mathematical finance, probability theory, optimal transport, and information geometry . He explores deep theoretical questions in stochastic processes and their applications in financial modeling, market analysis, and data science. His work often involves developing geometric frameworks to understand complex probabilistic systems, including the study of Wasserstein distances, Bregman divergences, and their roles in optimization and inference. Research Trends: Across his recent publications, a clear trend emerges toward integrating geometric methods with stochastic modeling and financial applications . He has contributed to the development of new divergence measures, optimization algorithms, and portfolio frameworks. His work often bridges theoretical probability with practical tools in finance and machine learning, such as portfolio optimization, PCA under transport metrics, and scalable inference methods. Awards & Funding: NSERC Discovery Grants (2019, 2025) Connaught New Researcher Award (2020) Data Science Institute Seed Funding for Methodologists (2022) Best Paper Award, Geometric Science of Information Conference (2019) Advising & Grants: He currently supervises several PhD students including Elijah French , Madhu Gunasingam , and Amanjit Kainth . He previously co-supervised Steven Campbell , who successfully defended in 2023. His research is supported by multiple federal and institutional grants, reflecting his active role in advancing methodological research in statistics and finance. Editorial & Service Roles: He serves as an Associate Editor for the journal Information Geometry (2022–present), actively contributing to the dissemination of research in geometric methods for data science and inference.

Ilse C.F. Ipsenمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +۸ مورد دیگر

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Andrea Torselloمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Andrea Torsello is a Full Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on Computer Vision, Machine Learning, and Artificial Intelligence with applications in quantum computation, graph theory, and 3D modeling. He is actively involved in teaching courses on quantum computation, artificial intelligence foundations, and computer science fundamentals at both undergraduate and graduate levels. Prof. Torsello's research interests include graph neural networks, quantum machine learning, and structural pattern recognition. His work often bridges theoretical computer science with practical applications in cultural heritage digitization and environmental monitoring. His recent articles highlight advancements in graph generation via spectral diffusion, quantum lens-based 3D shape analysis, and interpretable graph neural networks. He serves on the editorial boards of journals like Pattern Recognition and has contributed to projects funded by the EU’s H2020 program, including the VEiL initiative for visualizing engineered landscapes. His teaching spans topics such as quantum computation, advanced algorithms, and machine learning across multiple engineering programs.

Niko Eskelinenمشاهده پروفایل

پژوهشگر

• Sociology
• Social Inequality
• Intergenerational Mobility
• +۶ مورد دیگر

Niko Eskelinen is a Doctoral Researcher at the University of Turku, affiliated with the Department of Sociology within the Faculty of Social Sciences, and a member of the INVEST Research Flagship Centre. His research focuses on social inequality, intergenerational disadvantage, and youth labor market outcomes, particularly NEET status. He utilizes advanced quantitative methods and Finnish population data to explore causal pathways in social stratification and well-being. His research interests include: Social stratification and mobility Intergenerational transmission of disadvantage Youth exclusion from education and employment (NEET) Birth outcomes and population health Welfare policy and subjective well-being Quantitative and longitudinal social research methods The trend in his recent publications shows a consistent focus on using sibling fixed-effects and large-scale register data to disentangle causal mechanisms in social reproduction. His work bridges demography, sociology, and public policy, with strong implications for early intervention and equity-focused social programs. Key scientific contributions have been published in peer-reviewed journals, policy briefs, and research reports. Notable works include analyses of NEET persistence, seasonality of birth weight, and intergenerational socioeconomic disadvantage. Niko Eskelinen actively contributes to policy discussions through INVEST policy briefs and blog posts. He collaborates with leading scholars in sociology and population research but there is no public information about formal student supervision or research grants. He is part of the INVEST Research Flagship team, which supports interdisciplinary research on well-being and inequality.

Ryoko Susukidaمشاهده پروفایل

پژوهشگر

• Substance Use Disorders
• Health Service Use
• Prevention
• +۶ مورد دیگر

Ryoko Susukida, PhD, is an Associate Scientist in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. Her research focuses on data harmonization, integrative data analysis, and evidence synthesis in substance use disorder treatment and suicide prevention. She holds a PhD from the Johns Hopkins Bloomberg School of Public Health (2017) and a BA from the University of Tokyo (2010). Research Interests: Her work spans substance use disorders, health service utilization, prevention strategies, psychiatric epidemiology, and methodological innovations in clinical trials and observational studies. She emphasizes generalizability of study findings and data harmonization across diverse datasets. Awards: Phi Beta Kappa (2017) Fulbright Scholarship (2013–2016) Lucy Shum Memorial Scholarship Award (2015) Grants & Projects: Leads the Maryland Overdose Data Collaborative and contributes to projects analyzing overdose mortality patterns and public health interventions. Her work integrates epidemiological, economic, and clinical perspectives to address substance use and mental health challenges.

Eric Millerمشاهده پروفایل

استاد

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +۴ مورد دیگر

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.

• Financial Literacy
• Behavioral Economics
• Microeconometrics
• +۳ مورد دیگر

Andrej Cupak is a Slovak Associate Professor at the Department of Banking and International Finance, Faculty of Economics and Finance, University of Economics in Bratislava. He holds advanced degrees in Economics and Management from Slovak University of Agriculture in Nitra and has worked as a Senior Researcher at National Bank of Slovakia since 2020. His research focuses on financial literacy, behavioral economics, gender differences in financial decision-making, and retirement savings. Recent work explores crypto-asset investments through environmental/social lenses. 2024: Environmental and social attitudes in crypto-asset investments 2022: Investor confidence & financial literacy interactions 2021: Cross-country financial literacy variations 2021: Gender differences in risky asset behavior 2019: Financial literacy's causal impact on retirement savings 2018: Gender gap decomposition in financial literacy Cupak has participated in multiple research projects including: APVV-23-0329 (Principal Investigator): Financial literacy & information sources APVV-21-0174 (Investigator): Rural development & agricultural employment VEGA 1/0607/21 (Investigator): Banking regulation in Eurozone VEGA 1/0221/21 (Investigator): Interest rates with CBDC APVV-20-0359 (Principal Investigator): Pandemic impacts on food security He has supervised 19 bachelor's and 10 master's theses, contributed to microeconometric research, and conducted STATA workshops at the University of Economics in Bratislava.

• Information Retrieval
• Search Engine Technologies
• Data Compression
• +۴ مورد دیگر

Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.

• Quantum Computing
• Hybrid Quantum-Classical Algorithms
• Workflow Scheduling
• +۳ مورد دیگر

Katarzyna Rycerz is a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków, Poland. She holds the academic rank of Lecturer as part of the assistant professors group, with her office located at D-17, ul. Kawiory 21, room II, 3.54. Her research spans quantum computing, hybrid quantum-classical algorithms, and distributed systems, with significant contributions to workflow scheduling and complex network optimization. Dr. Rycerz's primary research focuses on quantum optimization techniques, particularly quantum annealing and variational algorithms applied to combinatorial problems. Her work bridges theoretical quantum computing with practical high-performance computing challenges, including load rebalancing in HPC systems and community detection in complex networks. She has developed software tools like QHyper for hybrid quantum-classical optimization and explored quantum solutions for thermoelectric properties in graphene and quantum game theory paradoxes. Her 15 most recent publications (2019-2025) reveal a dominant trend toward quantum-classical hybrid methods, with 80% focused on quantum annealing applications for workflow scheduling, network analysis, and optimization problems. Key themes include D-Wave system implementations, quantum game theory extensions, and thermoelectric material simulations, demonstrating a consistent shift from earlier multiscale simulation work toward cutting-edge quantum computing applications. No scientific awards are documented in the available information. Details regarding student advising, research grants, laboratory affiliations, or collaborative teams are not provided in the source materials.

AYSUN BARANSEL ISIRمشاهده پروفایل

استاد

• Forensic Medicine
• Child Abuse and Neglect
• Age Estimation in Forensic Contexts
• +۴ مورد دیگر

Professor AYSUN BARANSEL ISIR is a distinguished academic at Gaziantep University's Faculty of Medicine, Department of Internal Medicine, with a specialization in Forensic Medicine. She has held the position of Professor since 2016 and previously served as Associate Professor at both Gaziantep University and Ankara University. Her extensive academic career spans over two decades, beginning as a Research Assistant in 1998 at Gaziantep University. She also holds an appointment at Gazi University's Acupuncturia and Complementary Medicine Application and Research Center since 2021, reflecting her interdisciplinary expertise. Her educational background includes a Medical License from Erciyes University Faculty of Medicine (1989-1996) followed by Medical Specialization in Forensic Medicine at Gaziantep University (1998-2000). Her academic progression demonstrates consistent advancement from Research Assistant (1998-2003) to Doctor Lecturer (2004-2010), Associate Professor (2010-2016), and ultimately Professor (2016-present). Professor Baransel Isir's research spans multiple interdisciplinary domains at the intersection of forensic medicine, genetics, and child protection. Her work focuses on forensic age estimation methodologies, child abuse and neglect documentation, genetic markers related to substance abuse and criminal behavior, and medical malpractice in traditional and complementary medicine. She has made significant contributions to understanding the genetic basis of cannabis addiction through studies on CNR1 and DRD2 gene polymorphisms. Her research on child abuse has informed multidisciplinary team approaches for child protection in Turkey. Analysis of her recent publications reveals a strong focus on forensic genetics applications, particularly Y-chromosome STR analysis for human identification, and growing interest in the forensic implications of complementary medicine practices. Her work bridges traditional forensic medicine with emerging fields like genetic epidemiology and alternative medicine safety. Gökhan Eriş scientific incentive award (2012) from Adli Tıp Vakfı Best oral presentation award (2006) from Acil Tıp Uzmanları Derneği Professor Baransel Isir has supervised numerous graduate students through their thesis work, with a particular emphasis on forensic applications in child protection, age estimation methodologies, and medical malpractice cases. Her research projects have received funding from higher education institutions, including studies on new psychoactive substances and forensic toxicology. She has served in significant administrative roles including as Chair of the Department of Forensic Medicine (2017-2019) and President of the Domestic Clinical Research Ethics Committee (2018-present). She is actively involved in multiple professional organizations including serving as Vice President of ÇİİÖDER (Association for the Prevention of Child Abuse and Neglect), and as a member of the Association of Forensic Medicine Specialists. Her work extends to public education through media appearances discussing child abuse identification and prevention.

Matteo Fumagalliمشاهده پروفایل

مدرس ارشد

• Population Genetics
• Bioinformatics
• Machine Learning
• +۲ مورد دیگر

Dr. Matteo Fumagalli is Senior Lecturer in Genetics at Queen Mary University of London , leading the Biology Department within the School of Biological and Behavioural Sciences. His research focuses on computational methods for genomic data analysis, particularly in population genetics, human evolution, and machine learning applications. Affiliations : Digital Environment Research Institute, Alan Turing Institute His work integrates genomic data with machine learning to infer demographic history, selection events, and functional variation. He develops tools like ngsTools and ImaGene for analyzing next-generation sequencing data. Recent research involves transfer learning for selection detection, deep learning in population genetics, and balancing selection analysis using spatial-temporal models. Grants : Barts Charity (2025), NERC (2023-2026), DSIT (2025) He has received prestigious fellowships including Human Frontier Science Program and EMBO awards, and is a Fellow of the Higher Education Academy. Dr. Fumagalli supervises PhD students and postdocs, emphasizing inclusive education and ethical publishing advocacy.

Laura Carrelمشاهده پروفایل

استاد

• Genetics
• Molecular Biology
• Epigenetics
• +۳ مورد دیگر

Laura Carrel is a Professor at the Department of Molecular and Precision Medicine, Penn State University College of Medicine, and a member of the Huck Institutes of the Life Sciences. Her research focuses on X-chromosome inactivation, autoimmune diseases, and the role of genetic/epigenetic mechanisms in health disparities. Key Affiliations: Penn State College of Medicine, Huck Institutes of the Life Sciences Research Interests Dr. Carrel's work explores: X-chromosome inactivation and escape gene regulation Sex-biased genetic variants in autoimmune diseases like systemic lupus erythematosus Integrative approaches combining genomics, epigenetics, and electronic health records Mechanisms of X-linked inheritance in the genome era Scientific Awards 2020 - Career Citation Milestone Award (M (1000) Club) 2021 - Dean's Award for Excellence in Teaching (co-recipient) Grants & Collaborations She leads projects funded by the National Institutes of Health (NIAID, NICHD) on: Trans-ancestry genetic studies of lupus etiology Functional analysis of inactive X chromosome expression Autoimmune disease mechanisms in multi-omics datasets Labs & Teams Her lab is part of the Huck Institutes of the Life Sciences and collaborates on large-scale genomic initiatives, including reference genome sequencing and autoimmune disease consortia.

Kenneth G Rossمشاهده پروفایل

استاد

• Population Biology
• Molecular Evolutionary Genetics
• Molecular Systematics
• +۴ مورد دیگر

Dr. Kenneth G Ross is a Professor in the Department of Entomology at the University of Georgia's College of Agricultural & Environmental Sciences . His research focuses on population biology , molecular evolutionary genetics , and comparative genomics of social insects. University: University of Georgia School: College of Agricultural & Environmental Sciences Department: Entomology His work explores genetic variation across social insect populations, including fire ants ( Solenopsis invicta ), yellowjacket wasps , and tent caterpillars , to infer breeding biology, gene flow, and evolutionary mechanisms. Recent studies examine supergene regulation of social organization and gene expression patterns in hybrids. Key article trends include invasive species genetics , social behavior genetics , and ecological adaptation . Publications span journals like Science , Nature , and PLoS ONE .

Thomas Fredrik Hansenمشاهده پروفایل

استاد

• Evolutionary Biology
• Theoretical Biology
• Quantitative Genetics
• +۵ مورد دیگر

Thomas Fredrik Hansen is a Professor at the Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, where he is affiliated with the Centre for Ecological and Evolutionary Synthesis (CEES). His research primarily focuses on evolutionary biology, theoretical biology, and quantitative genetics, with particular emphasis on evolvability, macroevolution, and phylogenetic comparative methods. Hansen's research interests span evolutionary quantitative genetics, morphological evolution, evolvability, and macroevolutionary dynamics. His work integrates theoretical frameworks with empirical data to understand how genetic architecture influences evolutionary potential and constraints. He has made significant contributions to understanding how evolvability shapes macroevolutionary patterns, the role of epistasis in morphological divergence, and the application of phylogenetic comparative methods to study evolutionary processes across diverse taxa. His theoretical work on evolutionary constraints and adaptive landscapes has provided new insights into the mechanisms that govern long-term evolutionary change. Analysis of Hansen's recent publications reveals a consistent focus on the intersection of quantitative genetics and macroevolution. His work demonstrates how microevolutionary processes scale up to macroevolutionary patterns, with particular attention to evolvability as a key determinant of evolutionary trajectories. He has pioneered approaches to measuring and modeling the relationship between genetic architecture and evolutionary potential across different time scales. His research often involves collaborations with empirical evolutionary biologists to test theoretical predictions using data from diverse organisms including plants, insects, fish, and mammals. Hansen has been actively involved in mentoring students and collaborating with researchers across various institutions. His work has contributed significantly to methodological advances in evolutionary biology, particularly in the areas of phylogenetic comparative methods and quantitative genetics. He has also contributed to important theoretical debates regarding evolutionary constraints, the measurement of selection, and the interpretation of evolutionary patterns in the fossil record. Hansen maintains an active research program through the Centre for Ecological and Evolutionary Synthesis (CEES), where he collaborates with researchers studying diverse aspects of evolutionary biology, ecology, and biodiversity. His work bridges theoretical and empirical approaches to evolutionary biology, making substantial contributions to our understanding of how genetic architecture shapes evolutionary potential across different biological scales.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.