Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.
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Xiaobai Liuمشاهده پروفایل
استادیار
Xiaobai Liu is an Assistant Professor in the Department of Computer Science at San Diego State University, College of Sciences. His research bridges Computer Vision, Machine Learning, and Computational Statistics, with applications in Clinic Diagnosis, Sports, Transportation, Surveillance, and Video Games. Education: PhD in Computer Science from HuaZhong University of Science and Technology (2012) His research focuses on image parsing, video analysis, and deep learning techniques for 3D reconstruction, object tracking, and marine mammal detection. Recent publications highlight advancements in LiDAR generation, scene text recognition, and automated spectrogram processing. Notable grants include NSF-funded projects on autonomous vehicle safety simulations, marine mammal classification, and AI-driven recycling systems. He has advised over 30 students in real estate analytics, robotics, and computer vision projects. Scientific Awards: 2018 San Diego State University President’s Excellence Award
Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.
Prof. Dr.-Ing. Dirk Roos is a Professor for Computersimulation und Design Optimization at the Department of Mechanical Engineering and Process Engineering, Niederrhein University of Applied Sciences, where he has served since March 2011. He is also the Head of the Institute for Modeling and High Performance Computing (IMH) since December 2016. His academic career includes previous positions as Head of Robust Design Optimization at DYNARDO Dynamic Software and Engineering GmbH (2002-2011) and Technical Solutions Specialist at CADFEM GmbH (2000-2008). Prof. Roos specializes in machine learning, robust design optimization, stochastic analysis, and probabilistic modeling for virtual product development. His research focuses on developing mathematical methods for stochastic structural and fluid simulation, robustness and reliability analysis, multidisciplinary optimization, and software development for complex system development. He has established strong collaborations with academic institutions including RWTH Aachen, Ruhr-Universität Bochum, and industrial partners like Siemens AG and Robert Bosch GmbH. His recent work explores Probabilistic Intelligence, cyber-physical systems, digital twins, and Big Data Analysis, with applications spanning power plant flexibility, renewable energy, turbomachinery, automotive, aerospace, and medical technology. Prof. Roos has successfully secured numerous third-party funded research projects totaling over 1.8 million euros, including collaborations with Siemens AG, Robert Bosch GmbH, and various universities. optiSLang Award 2012 Weimar Optimization and Stochastic Days Gutachter BMBF im Programm FH-Kooperativ since 2019 Mitglied des Scientific Committee International Probabilistic Workshop since 2017 Mitgliedschaft im Graduierteninstitut für angewandte Forschung der Fachhochschule in NRW since 2017 Prof. Roos supervises multiple doctoral students and has successfully completed several cooperative PhD projects. His current research projects include AI-driven optimization for medical care, reinforcement learning for emergency room planning, and machine learning algorithms for power plant component lifetime prediction. The Institute for Modeling and High Performance Computing (IMH) under his leadership develops mathematical methods and software competence in machine learning and CAE-based robust design optimization for virtual product development.