Find Academic Supervisors

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.

• Machine Learning
• Artificial Intelligence
• Data Science
• +7 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Marina Papatriantafilou is an Associate Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. Her research focuses on distributed computing, fault-tolerance, parallel algorithms, and concurrency control. She has contributed to methods for fault-tolerant distributed systems, visualization tools for distributed algorithms, and scalable overlay networks. Her academic roles include teaching advanced courses on distributed systems, computer communication, and operating systems. She advises graduate students in areas like distributed algorithms and parallel computing. Key research interests include lock-free synchronization, memory reclamation, and self-stabilizing systems. She has authored over 100 publications in top-tier conferences and journals, with recent work on data streaming frameworks, energy-sharing optimization, and vehicular network processing. Professional involvement includes roles in program committees for conferences like OPODIS, SWAT, and SSS, plus membership in research evaluation boards for Swedish and European funding agencies. She pioneered educational tools like the Lydian environment for distributed algorithm visualization.

• Distributed Computing
• Distributed Algorithms
• Fault-Tolerance
• +8 more

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +5 more

Yan Huang (黄炎) is a postdoctoral researcher at KTH Royal Institute of Technology, affiliated with the Division of Decision and Control Systems. He earned his Ph.D. in Control Science and Control Engineering from Zhejiang University (2020-2024) under Prof. Jinming Xu and Prof. Jiming Chen, with prior degrees from Southeast University and Anhui University. His work focuses on distributed optimization and machine learning. Ph.D. in Control Science and Control Engineering, Zhejiang University, 2020-2024 M.S. in Control Science and Control Engineering, Southeast University, 2017-2020 B.S. in Automation, Anhui University, 2013-2017 Yan Huang specializes in distributed and federated optimization theory , applying these techniques to large-scale machine learning and networked control systems . His research addresses data heterogeneity, robustness to failures, and computational efficiency in decentralized networks. He has developed flexible frameworks for gradient tracking and personalized optimization. His recent publications emphasize stochastic gradient tracking , variance reduction , and differential privacy in distributed settings. Key topics include adaptive stepsizes , Byzantine-robust algorithms , and computation-communication trade-offs , reflecting his focus on scalable and secure optimization methods. Outstanding Graduate, Zhejiang University, 2024 Outstanding Doctoral Student, Zhejiang University, 2022 and 2023 2nd prize in China Post-graduate Mathematical Contest in Modeling, 2017 Outstanding Graduate, Anhui University, 2017 Huang has taught courses on Principles of Automatic Control , Modern Control Theory , and Distributed Control and Optimization . He has also presented at ICML 2022 and Zhejiang University's academic forum, highlighting his work on decentralized SGD and data heterogeneity.

• Distributed Optimization
• Federated Optimization
• Large-Scale Machine Learning
• +5 more

Siyi Wang is a Postdoctoral Researcher at the Division of Decision and Control Systems (DCS) , KTH Royal Institute of Technology, under the mentorship of Prof. Karl Henrik Johansson. She earned her Ph.D. from Technical University of Munich (TUM) under Prof. Sandra Hirche and holds B.Eng and M.Eng degrees from Harbin Institute of Technology under Prof. Ligang Wu. Education: B.Eng & M.Eng in Electrical Engineering, Harbin Institute of Technology Ph.D. in Electrical Engineering, Technical University of Munich (TUM) Research Focus: She investigates the value of information for control , stochastic control and optimization , and online learning and control , addressing challenges in networked control systems (delays, packet loss) and developing risk-averse reinforcement learning frameworks. Key Publications: Her work spans networked control systems under cyber-physical threats, sliding mode control for multi-agent systems, and distributional reinforcement learning for LQR optimization. Recent papers emphasize adaptive event-triggered scheduling, disturbance handling, and safety-critical control strategies. Academic Contributions: Advising: Supervised master's (2022), bachelor's (2021), and research internships (2021–2022) on topics like event-triggered learning and information value simulations Professional Service: Reviewer for ISA Transactions , IEEE JSAC , IFAC , and IEEE CDC Laboratory Affiliation: Division of Decision and Control Systems, KTH Royal Institute of Technology, focusing on networked systems, cyber-physical security, and learning-based control methodologies.

• Value of information for control
• Stochastic control and optimization
• Online learning and control
• +3 more