جست‌وجوی استاد و پژوهشگر

John McCallمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Nature-inspired Computing
• +۵ مورد دیگر

Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.

Robert Ostrowskiمشاهده پروفایل

استادیار

• Graph Theory
• Distributed Algorithms
• Multi-agent Systems
• +۳ مورد دیگر

Dr. Robert Ostrowski serves as an Assistant Professor at the Department of Algorithms and System Modeling within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His academic work focuses on theoretical computer science with emphasis on graph-based network problems. His research primarily explores graph theory and distributed algorithms , specializing in multi-agent systems for network security applications. Key investigation areas include heterogeneous agent searching, energy-constrained mobile agents in tree networks, and non-monotone graph searching strategies where traditional monotonicity assumptions do not hold. His work bridges theoretical foundations with practical network security implementations. Dr. Ostrowski's publication record demonstrates consistent focus on algorithmic solutions for network problems. His recent work shows increasing specialization in heterogeneous agent models and energy-aware protocols, with publications appearing in journals like Journal of Computer and System Sciences and Theoretical Computer Science . His academic advising and grant activities are not publicly documented in available sources. Similarly, no scientific awards or fellowships are mentioned in the provided materials.

• stochastic approximation
• reinforcement learning
• sampling
• +۴ مورد دیگر

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.

• Machine Learning
• Operations Research
• Supply Chain Management
• +۷ مورد دیگر

Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.

Jie Liuمشاهده پروفایل

استاد

• Big Data Intelligent Analysis
• Machine Learning Systems
• Intelligent Software Engineering
• +۳ مورد دیگر

Jie Liu is a Researcher at the Institute of Software, Chinese Academy of Sciences and a Professor and Doctoral Supervisor at University of Chinese Academy of Sciences. He is also a Member of the Youth Innovation Promotion Association of the Chinese Academy of Sciences and an Executive Committee Member of the System Software Committee of the CCF Computer Society. His research is conducted within the Software Engineering Technology R&D Center. Dr. Liu received his Ph.D. from the University of Science and Technology of China in 2011 and his B.A. from the same institution in 2004. He has progressed through the ranks at the Institute of Software, CAS, starting as an Assistant Research Fellow (2011-2014), then Associate Research Fellow (2014-2024), and currently as a Researcher (since 2024). His research spans Big Data Intelligent Analysis Models and Systems at the intersection of AI, Software Engineering, and System Software. Specifically, his work covers three main areas: Big Data and Machine Learning Systems (statistics and AI algorithm model libraries, data quantitative analysis tools, LLM reasoning optimization, Earth Big Data); Intelligent Software Engineering (code model constraint decoding, data science agents, system log analysis agents); and Knowledge-Enhanced Intelligent Model Construction (knowledge extraction, knowledge graphs, domain AI model design). His research has resulted in innovative approaches to handling complex data analysis challenges across multiple domains. Dr. Liu's research has produced significant outcomes including EarthDataMiner, which supports SDG indicator calculations and won the 2024 Beijing Municipal Science and Technology Progress First Prize. His work on RISC-V software migration technology has been integrated into the Ruiqian tool (https://rvpt.top/), demonstrating practical applications of his research in emerging computing architectures. Beijing Science and Technology Progress Award, First Prize, 2024 2023 Surveying and Mapping Science and Technology Award, Special Prize, 2023 DASFAA Best Paper Runner-up, Second Prize, 2013 Dr. Liu has successfully guided numerous graduate students who have secured positions at major technology companies including Alibaba, ByteDance, Southern Power Grid, and Agricultural Bank of China. He has secured funding through multiple National Natural Science Foundation projects, National Key R&D Program projects, and over ten other research initiatives. His research collaborations span industry leaders like Huawei, JD.com, and TravelSky, as well as academic institutions within the Chinese Academy of Sciences. He teaches graduate courses such as 'Machine Learning Systems' and 'Cloud Computing and Big Data Technology' at University of Chinese Academy of Sciences, and has established a research group focused on developing innovative solutions at the intersection of AI and software engineering with real-world applications in earth sciences, healthcare, and intelligent systems.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

Beomjoon Kimمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Humanoid Robotics
• +۳ مورد دیگر

Beomjoon Kim is an Associate Professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST). He directs the Humanoid Generalization (HuGe) lab, which focuses on creating general-purpose humanoids capable of efficient decision-making in complex environments. Education: Ph.D. in Computer Science from MIT CSAIL M.Sc. in Computer Science from McGill University B.Math in Computer Science and Statistics from University of Waterloo Professor Kim's research spans multiple areas of robotics and artificial intelligence, with a particular focus on humanoid robotics, task and motion planning, and robot learning. His work aims to bridge the gap between high-level task planning and low-level motion control, enabling robots to operate effectively in complex, real-world environments. He has made significant contributions to the field of geometric task and motion planning, developing novel algorithms that improve the efficiency and effectiveness of robot decision-making processes. Analysis of Professor Kim's recent publications reveals a strong focus on humanoid robotics, with particular emphasis on motion planning, object manipulation, and learning-based approaches. His work increasingly integrates deep learning techniques with traditional robotics algorithms, demonstrating a trend toward more data-driven approaches in robotics research. Many of his papers address the challenge of generalization in robotics, seeking to develop systems that can handle novel objects and environments without extensive retraining. Scientific Awards: Best Cognitive Robotics Paper award at ICRA 2017 Oral presentation (top 6% of accepted papers) at AAAI 2020 Oral presentation (top 6% of accepted papers) at AAAI 2019 Oral presentation (top 6% of accepted papers) at AAAI 2018 Spotlight presentation (top 3.5% of accepted papers) at NeurIPS 2018 Spotlight presentation (top 4% of accepted papers) at NeurIPS 2013 Plenary talk (top 12% of accepted papers) at CoRL 2020 Professor Kim actively mentors students at various levels, currently advising multiple Ph.D. and Master's students in the HuGe lab. His research is supported by grants that enable his team to pursue ambitious projects in humanoid robotics and AI. Through collaborations with institutions like MIT and industry partners, his work has significant impact on both academic research and practical applications of robotics technology. The HuGe lab, under Professor Kim's direction, has established itself as a leading research group in humanoid robotics. The lab maintains strong connections with other research institutions and regularly publishes in top-tier robotics and AI conferences. Current research directions include developing more efficient motion planning algorithms, improving robot manipulation capabilities, and exploring the integration of large language models with robotic systems.