Deep Density Estimation Professors and Supervisors

Michelle Zeibotsمشاهده پروفایل

مدرس ارشد

University of Technology SydneyAustralia

Dr. Michelle Zeibots is a Senior Lecturer in the School of Civil and Environmental Engineering within the Faculty of Engineering & Information Technology at the University of Technology Sydney (UTS). She also serves as the Co-Leader of Program 5 — Transport Economics, Planning & Service Engineering within the UTS Transport Research Centre, a multi-disciplinary research hub dedicated to applied transport research and teaching. Her academic journey includes a PhD in Sustainable Futures from UTS, a BA(Hons) from Murdoch University, and a BSc(Arch) from the University of Sydney. Zeibots integrates operational, behavioral, and governance aspects of multi-modal urban transport networks in her research, consultancy, and teaching. Her expertise spans sustainable urban passenger transport systems, induced traffic growth, public and active transport models, and Least Cost Planning approaches to reduce road traffic congestion. Zeibots' recent research output demonstrates a strong focus on shared space modeling, pedestrian-vehicle interaction, traffic management optimization, and sustainable transport solutions. Her work bridges computer vision applications for crowd analysis with traditional transportation engineering, creating innovative approaches to urban mobility challenges. She has developed the Integrated Pedestrian-Vehicle Model (IPVM) and conducted significant research on effort-based pedestrian route choice behavior. Engineers Australia Transport Medal (2024) CRC Association Excellence in Innovation (2019) UTS Vice-Chancellor's Award for Research Excellence through Collaboration (2018) Finalist, CILTA 2018 Professional Women of the Year UTS Research Excellence Award for Collaboration (2018) As an educator, Zeibots teaches 48370 Road & Transport Engineering and has led continuing professional development courses in Travel Planning and Integrated Transport & Land-use. She has supervised PhD students through iMOVE CRC scholarships and secured significant research funding from Transport for NSW, Railway Manufacturing Cooperative Research Centre, ARC Linkage Projects, and other sources. Her media presence is notable, with frequent appearances where she translates technical transport concepts into accessible language for public discussion. Michelle Zeibots maintains active professional memberships with Engineers Australia (Transport Australia Society), Planning Institute of Australia, and Chartered Institute of Logistics & Transport Australia Inc. (CILTA), and her work aligns with UN Sustainable Development Goals 9, 11, 12, and 13.

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Frederick Matsenمشاهده پروفایل

استاد

Fred Hutchinson Cancer CenterUnited States

Dr. Frederick "Erick" Matsen is a Professor in the Public Health Sciences Division at Fred Hutchinson Cancer Research Center, where he serves as Associate Program Head of the Herbold Computational Biology Program. He is also an Affiliate Associate Professor of Genome Sciences and Statistics at the University of Washington and a Howard Hughes Medical Institute Investigator. Dr. Matsen leads the Matsen Lab, which focuses on computational approaches to evolutionary biology and immunology. Dr. Matsen's research centers on computational biology, particularly developing algorithms to analyze biological data from DNA sequencing of viruses, immune cells, and complex environmental samples. His team works on phylogenetic methods, evolutionary tree construction, and software development for computational biology. Key research areas include Bayesian phylogenetics via optimization , phylodynamics with many sequences , antibody sequence analysis , and learning immune mechanisms from receptor sequences . His work bridges mathematics, computer science, and biological discovery, with applications to HIV, SARS-CoV-2, and other pathogens. The Matsen Lab has developed numerous influential resources including phyloseminar.org, B-T.CR, and phylobabble.org. Recent publications demonstrate a strong focus on machine learning applications in phylogenetics, antibody evolution analysis, and computational approaches to viral evolution. His team's work has significant implications for understanding immune responses to pathogens and developing better vaccines and treatments. Howard Hughes Medical Institute Investigator (2021-present) Howard Hughes Medical Institute-Simons Foundation Faculty Scholar Dr. Matsen maintains an active mentoring program, with an impressive track record of trainees securing tenure-track faculty positions. His lab fosters a collaborative environment where biologists learn analytical tools and computational scientists work directly with biological problems. Current projects include developing computational methods for outbreak control and analyzing antibody responses to SARS-CoV-2 using phage-immunoprecipitation sequencing. The Matsen Lab operates within Fred Hutch's collaborative ecosystem, including membership in the Immunotherapy Integrated Research Center, Pathogen-Associated Malignancies Integrated Research Center, and Translational Data Science Integrated Research Center. The lab has recently moved to the Lake Union Steam Plant facility, specifically designed to facilitate collaboration between clinical researchers, data scientists, and evolutionary biologists.

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Maryam Hamidiمشاهده پروفایل

دانشیار

Lamar UniversityUnited States

Dr. Maryam Hamidi serves as Associate Professor in the Department of Industrial and Systems Engineering at Lamar University, where she leads research at the intersection of reliability engineering, data analytics, and transportation systems. Her work bridges theoretical frameworks with practical applications in maritime logistics and railway infrastructure, supported by active collaborations with government agencies and industry partners including Texas Department of Transportation and Buckeye Partners. Dr. Hamidi's academic foundation includes: Ph.D. in Systems and Industrial Engineering from the University of Arizona MBA from Sharif University of Technology B.S. in Electrical Engineering from Amir-Kabir University of Technology Her research program focuses on Reliability Engineering, Statistical Data Analysis, and Maintenance Optimization, with recent emphasis on applying machine learning to maritime transportation challenges. Current projects analyze Automatic Identification System (AIS) data to model vessel traffic patterns, predict congestion in narrow waterways like Houston Ship Channel, and assess port resiliency. She also develops game-theoretic models for warranty contracts and maintenance scheduling in railway systems, addressing critical infrastructure challenges through data-driven methodologies. Dr. Hamidi's publication trajectory shows increasing focus on AI-driven transportation analytics since 2020, with 80% of her recent work centered on maritime applications and machine learning. Her research consistently incorporates student collaboration, with doctoral candidates contributing to 90% of recent publications in transportation engineering journals. Dr. Hamidi's recognition includes: Certified Reliability Professional designation (2016) from ReliaSoft Society Hans Reiche Scholarship (2016) University of Arizona Student-Faculty Interaction Award (2015) Professional Opportunities Development Grant (2015) She currently mentors three doctoral students on projects involving port management resiliency, vessel traffic optimization, and industrial data science applications, while maintaining active industry partnerships that provide students with real-world research opportunities and career pathways. Her funded projects total over $500K, with current focus on AIS applications for Gulf Coast waterways and anomaly detection in energy infrastructure.

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Chevaleyre Yannمشاهده پروفایل

استاد

Paris-Dauphine UniversityFrance

Chevaleyre Yann is a Professor of Computer Science at LAMSADE, Paris-Dauphine PSL University, where he has been working since 2017. Previously, he served as Professor of Computer Science at Paris-Nord University from 2009 to 2017 and as Director of the "data science" team at the LIPN laboratory. His academic journey includes a Lecturer position at LAMSADE, Paris-Dauphine University from 2002 to 2009, a Habilitation thesis at Paris-Dauphine University in 2009, and a Doctorate at Pierre and Marie Curie University under the supervision of Jean-Daniel Zucker from 1998 to 2001. Professor Chevaleyre's research spans multiple areas within artificial intelligence and computer science, with particular expertise in multi-agent systems, computational social choice, and machine learning. His work on preference modeling, voting theory, and resource allocation has significantly contributed to the field of computational social choice. More recently, his research has expanded into adversarial machine learning, robust classification, and generative models, reflecting the evolving landscape of AI research. His publication record demonstrates consistent contributions across multiple domains, with recent work focusing on precision-recall optimization in generative models, the role of randomization in adversarial robustness, and novel approaches to graphical bilinear bandits. His research shows a clear trajectory from foundational work in multi-agent systems toward more contemporary challenges in machine learning security and evaluation. Professor Chevaleyre has maintained active collaborations with researchers across France and internationally, evidenced by his extensive co-authorship network. His work bridges theoretical computer science with practical applications in areas ranging from robotics to bioinformatics.

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Jacek Rumińskiمشاهده پروفایل

استاد

Gdańsk University of TechnologyPoland

Prof. Jacek Rumiński serves as Professor and Head of the Department of Biomedical Engineering within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His leadership drives innovation at the critical intersection of engineering and clinical medicine, focusing on computational solutions for healthcare challenges. His research program spans: Advanced medical image segmentation for wound assessment and body composition analysis using dual-attention U-Net architectures Convolutional neural networks for extracting clinical metrics from low-resolution dual-energy X-rays Respiratory signal processing from thermal imaging during pandemic conditions Rule-based systems and LLMs for protecting electronic health information in DICOM objects Analysis of his 2025 publications reveals a consistent emphasis on overcoming class imbalance in medical imaging through ensemble methods and Bayesian optimization, while addressing critical clinical needs in lipedema diagnosis, wound management, and medical data privacy. His work demonstrates rigorous validation using Polish clinical datasets and practical implementation frameworks. As Department Head, Prof. Rumiński leads a multidisciplinary team advancing biomedical technologies through close collaboration with clinical partners, establishing Gdańsk Tech as a hub for healthcare AI innovation in Central Europe.

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Denis Vitalievich Belomestnyمشاهده پروفایل

پژوهشگر ارشد

HSE UniversityRussia

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

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Nicolas Verstaevelمشاهده پروفایل

دانشیار

Toulouse Capitole UniversityFrance

Nicolas Verstaevel is an Associate Professor at the University of Toulouse Capitole and researcher at the Toulouse Institute of Computer Science Research (IRIT), where he contributes to the SMAC (Cooperative Multi-Agent Systems) team. He holds a PhD in Artificial Intelligence and an HDR (Habilitation à Diriger des Recherches). His career includes international experience as an Associate Research Fellow at the University of Wollongong, Australia, and industrial R&D roles at Capgemini. His research explores complex systems through: Agent-based modeling and simulation frameworks Machine learning applications in urban environments IoT-enabled smart city infrastructure Pedestrian dynamics and crowd behavior analysis Real-time traffic monitoring systems He leads projects funded by ANR and EU programs, focusing on high-density crowd simulations and sustainable urban mobility. His publications demonstrate strong emphasis on multi-agent systems, sensor data fusion, and scalable simulation architectures. Key lab affiliations: Cooperative Multi-Agent Systems (SMAC) at IRIT: https://www.irit.fr/-Equipe-SMAC-

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