جست‌وجوی استاد و پژوهشگر

Vladimir Makarenkovمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Roles and Affiliations: Full Professor of Computer Science at Université du Québec à Montréal (UQAM), Director of the DESS in Bioinformatics program. Regular member of LaCIM and affiliate member of Mila (Quebec AI Institute). Served as Assistant Professor (2001–2006) and Associate Professor (2006–2011) before becoming Full Professor in 2011. Education: B.Sc./M.Sc. in Applied Mathematics and Computer Science, Moscow State University (1987–1992) DEA in Computer Science, University Paris V (1993–1994) Ph.D. in Computer Science and Mathematics, École des Hautes Études en Sciences Sociales (EHESS), Paris (1994–1997) Research Interests: Focuses on machine learning applications in bioinformatics, including clustering algorithms, phylogenetic analysis, and genomics. Specializes in CRISPR/Cas9 off-target prediction, SARS-CoV-2 origin analysis, and high-throughput screening (HTS) data correction. Develops tools like T-REX web server for phylogenetic analysis and DUNEScan for skin cancer uncertainty estimation. Integrates deep learning and reinforcement learning for clustering and medical diagnostics. Publications and Grants: Over 100 peer-reviewed articles spanning bioinformatics, AI, and computational biology. Key grants include projects on HTS data correction, phylogenetic networks, and machine learning for biomedical applications. Collaborates with institutions like Mila and Université de Montréal. Labs and Teams: Leads research teams at UQAM’s Department of Computer Science, focusing on interdisciplinary projects combining AI and life sciences. Maintains collaborations with international groups in computational biology and data science.

Alexandros Stergiouمشاهده پروفایل

استادیار

• Video Understanding
• Action Recognition
• Model Interpretability
• +۵ مورد دیگر

Alexandros Stergiou is an Assistant Professor in the Data Management & Biometrics (DMB) group at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) of the University of Twente. His research focuses on video understanding, model interpretability, and spatio-temporal analysis. PhD from Utrecht University (2021) Postdoctoral work at VUB and University of Bristol Key research areas include: Action Recognition and Interaction Modeling Temporal and Motion Analysis Audio-Visual Recognition Deep Learning for Video Tasks Recent publications span large-scale benchmarks (LAVIB), temporal attention mechanisms, and motion consistency modeling. Awards include Outstanding Reviewer at ECCV 2022. Teaching roles: Course administrator for REDI and Deep Learning Guest Lecturer for INFOMCV

Andrew Kempمشاهده پروفایل

دانشیار

• Coastal Processes
• Climate Change
• Sea Level Rise
• +۳ مورد دیگر

Dr. Andrew Kemp is an Associate Professor in the Department of Earth and Climate Sciences at Tufts University and currently serves as the Interim Director of Environmental Studies. His research focuses on reconstructing relative sea-level changes over the past 2000 years using interdisciplinary methods such as coastal stratigraphy, geochemical proxies, and advanced dating techniques. His work aims to understand sea-level responses to historical climate events like the Medieval Climate Anomaly and the Little Ice Age, providing critical data for IPCC climate projections and coastal management strategies. Dr. Kemp holds a PhD from the University of Pennsylvania (2009) and a BSc from Durham University (2002). His research emphasizes the interplay between physical processes driving sea-level changes and the societal implications of future projections. He collaborates globally, with recent studies in locations like Newfoundland, Florida, and Remote Oceania, addressing regional sea-level variability and subsidence dynamics. His key contributions include statistical modeling frameworks for sea-level trends, replication of regional sea-level histories, and assessments of coastal vulnerability under climate change scenarios. Kemp's work bridges geology, climatology, and policy, with implications for urban resilience and environmental conservation.

Christopher McCartyمشاهده پروفایل

پژوهشگر

• Social Network Analysis
• Survey Methodology
• Public Health
• +۵ مورد دیگر

Dr. Christopher McCarty is the Director of the University of Florida’s Bureau of Economic and Business Research and previously served as Associate Dean in the College of Liberal Arts and Sciences. He holds a PhD in Anthropology from the University of Florida (1992) and a BA in Anthropology from West Virginia University (1980). His research focuses on social networks, including methodological advancements in large-scale surveys, hard-to-count populations, and professional research networks. He developed the EgoNet software for personal network data analysis. His work spans global collaborations with USAID, anthropological field studies in Mexico and West Africa, and interdisciplinary projects addressing public health, disaster resilience, and education. Education: Bachelor of Arts in Anthropology, West Virginia University, 1980 Doctor of Philosophy in Anthropology, University of Florida, 1992 Research interests include social network analysis, survey methodology, public health epidemiology, and quantitative research innovations. His recent work emphasizes network-driven interventions in healthcare, disaster response, and education reform. Over 150 peer-reviewed articles reflect his interdisciplinary approach to network science. Grants and Advising: Oversees Bureau research activities, advises doctoral committees, and leads projects funded by NSF, NIH, and USAID. Notably, he co-leads interventions integrating social networks into tuberculosis care and cancer caregiver support systems. Labs/Teams: Directs the Bureau of Economic and Business Research, a multidisciplinary unit analyzing economic trends and policy impacts.

• Computer Vision
• Machine Learning
• Stereo Matching
• +۴ مورد دیگر

Wei Qin Chuah is a Lecturer at the School of Engineering at RMIT University, Australia, specializing in computer vision and machine learning. His research focuses on developing robust and generalizable models for various computer vision applications, particularly in the domains of stereo matching, depth estimation, and autonomous vehicle systems. Dr. Chuah's research interests span several key areas with particular emphasis on domain generalization and domain adaptation techniques that enable models trained on synthetic data to perform well on real-world applications. He has made significant contributions to stereo matching and depth estimation systems, developing methods to improve long-range perception for autonomous vehicles. His work on Neural Radiance Fields (NeRF) addresses challenges in learning from corrupted or uncurated image collections. Additionally, he has explored applications of computer vision in diverse domains including industrial inspection , veterinary assistance systems , and traffic management around construction sites. Analysis of Dr. Chuah's publication trends reveals a consistent focus on addressing the challenge of synthetic-to-real domain shift , developing innovative methods like Information-Theoretic Shortcut Avoidance (ITSA) to prevent models from learning spurious correlations in synthetic training data. His recent work extends to keypoint estimation for industrial applications using micro-CT imaging and transfer learning, as well as developing artifact-free Neural Radiance Fields from uncurated image collections. Dr. Chuah is currently supervising research projects including Learning Robust and Generalisable Models for Computer Vision Using Animation , indicating his ongoing commitment to training the next generation of computer vision researchers. His collaborative work spans multiple institutions, with frequent co-authorship patterns suggesting strong research partnerships with colleagues including Tennakoon R, Hoseinnezhad R, and Bab-Hadiashar A. His research bridges theoretical advances in machine learning with practical applications in autonomous systems, industrial automation, and healthcare.

• Nuclear Physics
• Neutron Physics
• Nuclear Astrophysics
• +۲ مورد دیگر

Adria Casanovas Hoste is a researcher in the Department of Physics at the Barcelona School of Industrial Engineering (ETSEIB), part of the Polytechnic University of Catalonia (UPC). With 71 documented research activities spanning from 2012 to 2026, Casanovas Hoste is actively involved in nuclear physics research, particularly in neutron capture cross-section measurements, nuclear instrumentation development, and nuclear astrophysics applications. The researcher is affiliated with the Advanced Nuclear Technologies Research Group (ANT) and the Institute of Energy Technologies at UPC, collaborating extensively with CERN's n_TOF facility. Research interests focus on nuclear physics , neutron physics , and nuclear astrophysics , with particular emphasis on neutron capture cross-section measurements for stellar nucleosynthesis, development of advanced neutron detectors, and applications in nuclear technology and medical physics. The work contributes significantly to understanding the s-process in stellar evolution and improving nuclear data for reactor applications, medical dosimetry, and cosmological models. Analysis of recent publications reveals a strong focus on measurements at the n_TOF facility at CERN, with particular attention to isotopes relevant for nuclear astrophysics (such as Pb, Tl, Se, and Ce) and nuclear technology applications. The research combines experimental measurements with astrophysical modeling to address questions about stellar nucleosynthesis and the chemical evolution of the universe, while also developing practical applications in radiation detection and dosimetry. 35 journal articles in high-impact publications including Physical Review Letters and Physical Review C 24 conference presentations at international venues 6 competitive R&D projects including Spanish national research programs Significant collaborations with CERN and European research institutions Casanovas Hoste is actively involved in the development of advanced nuclear instrumentation, including neutron detectors for time-of-flight experiments, dosimeters for pulsed neutron fields, and fission ionization chambers. The researcher's work supports Sustainable Development Goal 7: Affordable and Clean Energy through contributions to nuclear data for energy applications.

Christophe Goléمشاهده پروفایل

استاد

• Dynamical Systems
• Mathematical Biology
• Symplectic Geometry
• +۵ مورد دیگر

Christophe Golé is a full-time Professor in the Department of Mathematical Sciences at Smith College . He has held academic positions at the University of Minnesota, ETH Zurich, SUNY Stony Brook, and the University of California, Santa Cruz. His research bridges Dynamical Systems and Mathematical Biology , with a focus on Phyllotaxis and Fibonacci Spirals in plant patterns. Golé co-founded the NSF-funded 4 College Biomath Consortium , which evolved into the Five College Biomathematical Sciences Certificate Program . He directed the Smith Geneva and Paris Junior Year Abroad programs in 2003-04 and 2014-15, respectively. Ph.D. , Boston University M.A. , University of California, Santa Cruz B.A. , Université Paris Golé's work on Phyllotaxis explores the mathematical underpinn'tion of Fibonacci spirals in plants and deviations from them. His earlier research in Hamiltonian Systems and Symplectic Twist Maps advanced understanding of periodic orbits and ghost tori. Articles span Mathematical Biology , Dynamical Systems , and Sub-Riemannian Geometry , with trends in Pattern Formation , Biological Modeling , and Hamiltonian Dynamics . Collaborations include the Smith Botanic Garden , where he co-created the exhibit Plant Spiral: Beauty You Can Count On . His courses include Introduction to Real Analysis , Topics in Continuous Applied Math: Dynamical Systems , and Topology . Golé is active in advising students through projects like zebrafish neurogenesis and crab-snail antagonism . He also participates in the Smith French and Spanish Tables and is involved in cross-disciplinary initiatives at the intersection of mathematics and biology.

Jacob Calvertمشاهده پروفایل

پژوهشگر ارشد

• Collective Behavior
• Probability Theory
• Data Science
• +۴ مورد دیگر

Dr. Jacob Calvert is a Research Fellow at Georgia Institute of Technology and a visiting postdoctoral fellow at the Santa Fe Institute. Previously, he was a Staff Research Scientist at a stealth startup and a PhD alumnus from the University of California, Berkeley (2022), where he studied under Professor Alan Hammond. He holds M.Sc. degrees from the University of Oxford (2017) and University of Bristol (2016), along with a B.S. from the University of Illinois (2015). Ph.D., University of California, Berkeley (2022) M.Sc., University of Oxford (2017) M.Sc., University of Bristol (2016) B.S., University of Illinois (2015) His research focuses on collective behavior, nonequilibrium self-organization, and the application of probability theory to data science. He explores how systems like ant colonies and programmable matter exhibit phase transitions dependent on individual counts (critical numerosity) and investigates principles like rattling theory to predict steady states in nonequilibrium systems. His publications span Markov processes, statistical physics, and computational biology, with recent work on stationary distributions in random Markov chains, hierarchical self-organization algorithms, and numerosity-driven clustering in collective systems. Key contributions include formalizing critical numerosity as a zero-one law and demonstrating how local dynamics govern global self-organization in nonequilibrium settings. Notable affiliations include the Simons Laufer Mathematical Sciences Institute (Berlekamp Postdoctoral Fellow, Spring 2025) and collaborations with interdisciplinary teams studying programmable matter and biological collectives.

Peyman Afshaniمشاهده پروفایل

دانشیار

• Geometric algorithms and data structures
• Algorithmics
• Algorithms for hierarchical memory
• +۳ مورد دیگر

Peyman Afshani is an Associate Professor in the Department of Computer Science at Aarhus University, Denmark. His research focuses on geometric algorithms, data structures, and algorithms for hierarchical memory systems. He maintains an active research profile with consistent publications since 2009, including 61 research outputs as documented. His primary research interests include: Geometric algorithms and data structures Algorithmics Algorithms for hierarchical memory Range Query optimization Computational Geometry Analysis of his publication trends since 2009 shows sustained output with 3-5 publications annually, peaking at 5 in multiple years (2011, 2012, 2014, 2017, 2019, 2021, 2023, 2024). His recent work (2023-2025) demonstrates continued focus on computational geometry problems including simplex range reporting, semialgebraic range reporting, and convexity in 3D search algorithms. His research shows strong theoretical foundations with practical applications in data structure design and query optimization. Collaboration networks indicate significant engagement with international researchers, particularly in computational geometry and data structure research domains. Professor Afshani supervises PhD students (at least one documented case) and maintains an active research program with consistent output across two decades.

Roberto Iglesias Rodríguezمشاهده پروفایل

دانشیار

• Machine Learning
• Robotics
• Continual Learning
• +۶ مورد دیگر

Roberto Iglesias Rodríguez is an Associate Professor at the University of Santiago de Compostela, Spain, with a focus on robotics and machine learning. He holds a B.S. and Ph.D. in Physics from the same institution (1996, 2003). His work addresses lifelong robot learning, federated learning, and deployment of intelligent systems in heterogeneous environments. Current affiliation: University of Santiago de Compostela Academic rank: Associate Professor Education: B.S. and Ph.D. in Physics (1996, 2003) His research spans robotics, machine learning, and sensor fusion , emphasizing adaptive algorithms for non-IID data, concept drift, and real-time environmental interaction. Projects include service robots learning from humans, distributed control architectures, and federated learning strategies. Recent publications analyze continual learning , scene recognition , and human-robot collaboration . Key themes: robust navigation, multi-sensor systems, and explainable AI.

Akihito Kamataمشاهده پروفایل

استاد

• Psychometrics
• Educational and Psychological Measurement
• Item Response Theory
• +۵ مورد دیگر

Akihito Kamata is a Professor at Southern Methodist University (SMU) in the Department of Education Policy & Leadership (Simmons School of Education & Human Development) and Department of Psychology (Dedman College of Humanities and Sciences). He also directs the Quantitative Methods Lab (QML) at SMU's Center on Research and Evaluation (CORE). Academic Rank: Professor Education: Ph.D. in Measurement and Quantitative Methods from Michigan State University (1998) Dr. Kamata's research focuses on psychometrics and educational/psychological measurement, with expertise in item response theory , multilevel modeling , and structural equation modeling . His work includes model development for oral reading fluency (ORF) assessment data, supported by three IES grants from the U.S. Department of Education. Recent publications emphasize Bayesian approaches to ORF scoring, count data modeling , and longitudinal analysis of student development. He has contributed to advanced psychometrics through special issues and book chapters, including the Handbook of Advanced Multilevel Analysis (2011). Scientific Awards: Michigan State University Distinguished Alumni Award (2022) Students and Collaborators: Current Ph.D. Students: Patrick Lan, Nancy Le, Kuo Wang, Ji Li Former Ph.D. Students: Hao Ma Collaborators: Yusuf Kara (Senior Data Analyst), Xinya Liang (Associate Professor), Chalie Patarapichayatham (Research Assistant Professor), Cornelis Potgieter (Assistant Professor), Xin Qiao (Post-Doctoral Fellow)

• Statistical Methods
• Clinical Trials
• HIV Research
• +۷ مورد دیگر

Michael David Hughes is a Professor of Biostatistics at Harvard T.H. Chan School of Public Health, where he also serves as Director of the Center for Biostatistics in AIDS Research. His primary affiliation is with the Department of Biostatistics, and he serves as Principal Investigator of the Statistical and Data Management Center (SDMC) for the AIDS Clinical Trials Group (ACTG), funded by the National Institutes of Health. Dr. Hughes' research program focuses on statistical methods related to the design and analysis of clinical trials and observational studies, particularly in the context of HIV research. He is well-known for his work on the development of methods for evaluating surrogate markers using meta-analysis of results from multiple clinical trials, with an important early application being the assessment of CD4 cell counts and viral load as surrogate markers for AIDS development. His statistical research is strongly motivated by problems encountered in collaborative HIV research, with ongoing interests including the design and analysis of phase I/II clinical trials, bridging trials between populations, and statistical methods for diagnostic and biomarker evaluation. His work on dose-finding studies is particularly notable, including research to identify drug doses that maximize treatment success (both safety and efficacy) rather than just finding maximal tolerated doses, and methodology for dose-finding in special populations like infants and pregnant women. Much of his biomarker research is motivated by the need to identify less expensive and more accessible technologies for resource-limited countries. Dr. Hughes has led major research initiatives through multiple NIH grants, including the current UM1AI068634 grant running until 2027 for the Statistical and Data Management Center of the ACTG. His recent publication record shows a strong dual focus on HIV research and COVID-19 pandemic response, with significant contributions to platform trial design (such as the ACTIV-2 trial), viral kinetics, and treatment resistance studies. His work bridges biostatistics methodology with practical clinical applications in infectious disease treatment. As Principal Investigator of the Statistical and Data Management Center for the AIDS Clinical Trials Group, Dr. Hughes leads a substantial research team that undertakes numerous studies of HIV interventions globally, including research on co-infections like tuberculosis and hepatitis. His team collaborates extensively on international health research addressing HIV treatment challenges across diverse populations and resource-limited settings.

Ismail Ilkan Ceylanمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Graph Machine Learning
• +۳ مورد دیگر

Dr. Ismail Ilkan Ceylan is an Associate Member at the University of Oxford's Department of Computer Science, specializing in artificial intelligence and machine learning with a focus on graph-based approaches. His research spans multiple disciplines within computer science, with particular emphasis on developing methods that integrate machine learning, knowledge representation, and theoretical computer science. Dr. Ceylan's research interests center on graph machine learning, which involves developing methods to learn from relational patterns and reason over them. He explores how techniques from deep learning, graph representation learning, probabilistic methods, logical reasoning, and complexity theory can be combined to address challenging problems in relational structures. His work has applications across diverse domains including life sciences, chemical and biological systems, and social networks. His publication record shows a strong trend toward foundational work in graph neural networks, with recent papers examining theoretical properties, representation capacity, and practical applications. The research spans theoretical computer science aspects like homomorphism counts and zero-one laws, while also addressing practical challenges in knowledge graph completion, link prediction, and representation learning for specialized graph structures. Best Paper Prize for A Dichotomy for Homomorphism-Closed Queries on Probabilistic Graphs Sister Conference Best Paper Track for Open-World Probabilistic Databases: An Abridged Report Marco Cadoli-Best Student Paper Award for Open-World Probabilistic Databases Dr. Ceylan actively supervises both current and past graduate students, indicating a strong commitment to academic mentorship. His research group includes students working on various aspects of graph machine learning, knowledge representation, and probabilistic reasoning. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier conferences suggests successful funding of research activities. His work appears to be centered around advancing the theoretical foundations of graph machine learning while simultaneously developing practical applications in knowledge representation and reasoning. The collaborative nature of his publications, often with multiple co-authors from different institutions, suggests participation in a broader research network focused on the intersection of AI, machine learning, and knowledge representation.

Sherzod Akhundjanovمشاهده پروفایل

دانشیار

• Applied Microeconomics
• Industrial Organization
• Environmental and Resource Economics
• +۶ مورد دیگر

Sherzod Akhundjanov is an Associate Professor in the Department of Applied Economics at Utah State University's College of Agriculture and Applied Sciences. His research spans applied microeconomics, industrial organization, environmental/resource economics, and regional science, with a focus on empirical regularities and statistical models governing economic, social, and epidemiological phenomena. He has received multiple awards including the Quality of Research Discovery Award (2022, AARES) , Faculty Researcher of the Year (2022, CAAS) , and Outstanding Published Research Award (2020, WAEA) . His work appears in journals like the Journal of the Royal Statistical Society, American Journal of Agricultural Economics, and The World Economy. His recent publications analyze topics such as: COVID-19's impact on agricultural trade Statistical distributions in public health and economics Regulatory compliance costs Consumer behavior with food labels Environmental policy preferences by firms He has mentored graduate students in Applied Economics and taught advanced econometrics courses (APEC 7330, 7350) since 2016. Scientific Awards: Quality of Research Discovery Award, Early/Mid-Career Researchers (AARES, 2022) Faculty Researcher of the Year (CAAS, 2022) Quality of Research Discovery Award, Honorable Mention (AAEA, 2021) Outstanding Published Research Award (WAEA, 2020) Outstanding Doctoral Dissertation Award, Honorable Mention (AAEA, 2017)

• Probability
• Statistics
• Extreme Events
• +۴ مورد دیگر

Anna (Ania) Panorska is a Professor at the University of Nevada, Reno within the Graduate Program of Hydrologic Sciences. She holds a Ph.D. in Mathematics (Statistics and Applied Probability program) from UC Santa Barbara (1992) and an M.S. in Statistics from UT El Paso (1988), with prior graduate coursework in Applied Mathematics at the University of Warsaw. Her career spans academia, research institutions (Desert Research Institute), industry (Blue Cross Blue Shield of Tennessee), and statistical consulting. Her educational background includes: M.S. in Statistics, University of Texas at El Paso, 1988 Ph.D. in Mathematics (Statistics and Applied Probability program), University of California, Santa Barbara, 1992 Professor Panorska's research centers on theoretical and applied probability and statistics, with specialized expertise in limit theorems and extreme event modeling within probability theory, and multivariate model development in statistics. Her work is characterized by extensive interdisciplinary collaboration across ecology, engineering, climate science, hydrology, and medicine. Key contributions include novel distributional frameworks for stochastic episodes, heavy-tailed data analysis, and extreme value applications in environmental systems. Analysis of her recent publications reveals a sustained focus on developing new probability distributions (e.g., slash, truncated Zipf, generalized Pareto variants) and their application to multivariate dependencies and extreme events. Her methodological innovations consistently bridge theoretical statistics with practical challenges in hydrology (precipitation extremes), climate science (intense weather events), and ecology (species interaction networks), demonstrating a cohesive trajectory in environmental statistics.