جست‌وجوی استاد و پژوهشگر

Ilhan Akbasمشاهده پروفایل

دانشیار

• Complex Systems
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dr. Ilhan Akbas is an Associate Professor in the Electrical Engineering and Computer Science Department within the College of Engineering at Embry-Riddle Aeronautical University. He serves as Director of the Flexible & Intelligent Complex Systems (FICS) Research Group, Co-director of the Wireless Devices and Electromagnetics (WIDE) Lab, and holds leadership positions in the Center for Aerospace Resilient Systems (CARS) and the Advanced Air Mobility Research and Innovation Lab (AAMRIL). With over 20 years of experience in machine learning, complex systems, and modeling & simulation, Dr. Akbas's research focuses on validation and verification of autonomous systems, particularly in aerospace applications. His work develops methods for decision-making in high-dimensional, probabilistic scenarios with applications in autonomous vehicles, cybersecurity for unmanned aerial systems, and cyber-physical systems operating in dynamic/uncertain environments. Dr. Akbas's research has secured funding through grants from the National Science Foundation, Federal Aviation Administration, U.S. Air Force, Office of Naval Research, and industry partners. His publication record includes more than 30 journal and 80 conference papers in IEEE, ACM and AIAA venues, with recent work focusing on AI validation frameworks, autonomous vehicle safety testing, and cybersecurity for unmanned systems. His scientific contributions are recognized through his status as a Senior Member of IEEE and membership in several professional organizations including ACM, AIAA, SAE, the International Alliance for Mobility Testing and Standardization, the Complex Systems Society, and the Cyber Safety Commercial Aviation Team. Dr. Akbas actively mentors students through research projects and teaches courses including Computer Science Capstone Design, Digital Communications, and graduate thesis supervision. His research group utilizes advanced tools including Python, Julia, Rust, simulation environments like AirSim and CARLA, and hardware platforms including NVIDIA Jetson for edge computing applications.

Huibin Zhouمشاهده پروفایل

استاد

• Statistics
• Data Science
• High-dimensional Statistics
• +۵ مورد دیگر

Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University, where he has held faculty positions since 2004. He served as Chair of the Department of Statistics and Data Science (2017-2021) and earlier as Chair of the Department of Statistics (2012-2017). Professor Zhou earned his Ph.D. from Cornell University in 2004, progressing through academic ranks from Assistant Professor to full Professor at Yale. His research spans high-dimensional statistics, nonparametric estimation, machine learning, and statistical decision theory. Key focus areas include minimax optimality in estimation, Bayesian methods, network analysis, and functional data. Zhou's work frequently bridges theoretical foundations with applications in social networks, signal processing, and computational biology. Analysis of his publications reveals consistent emphasis on theoretical guarantees for statistical methods, with recent work exploring spectral clustering, Gaussian mixtures, variational inference, and privacy-preserving estimation. His research demonstrates deep engagement with minimax optimality, high-dimensional challenges, and computational-statistical tradeoffs. Honors & Awards: Henry Ford II Professorship at Yale University Professor Zhou has taught graduate and undergraduate courses including Probability Theory, Statistical Decision Theory, Nonparametric Estimation, Data Mining, and Functional Data Analysis. He organized several NSF workshops including the 2012 Workshop on High-Dimensional Data and the 2015 Workshop on Empirical Processes.

Guillaume Mercereمشاهده پروفایل

استاد

• System Identification
• Model Learning
• Automatic Control
• +۶ مورد دیگر

Guillaume Mercere is a Full Professor in Automatic Control and Systems at ENSIP (Ecole Nationale Supérieure d'Ingénieurs de Poitiers), University of Poitiers. He maintains dual affiliations with Laboratory LIAS at both ENSIP in Poitiers and ISAE-ENSMA in Chasseneuil, conducting research in system identification and control theory. Professor Mercere teaches automatic control and signal processing at the Master's level, with additional expertise in numerical optimization, machine learning, and time series analysis. His teaching materials are available upon request, reflecting his commitment to educational transparency. Research Focus: Model learning, system identification, estimation theory, state space modeling, gray box modeling, linear parameter varying (LPV) systems, linear fractional representation (LFR), and subspace-based methods Application Areas: Electrical engineering, aeronautics, heat transfer, flexible/cable-driven manipulators, vehicle tire/road interactions, and image processing Analysis of his recent publications reveals a strong emphasis on recursive estimation methods (particularly total least squares), theoretical developments in LPV system representations, predictive control methodologies, and noise covariance estimation for Kalman filtering. His work bridges theoretical advances in identification methodologies with practical applications across multiple engineering domains, demonstrating both depth and breadth in his research program. Professor Mercere leads the Automatic Control Team at Laboratory LIAS, where he collaborates with researchers on theoretical and applied projects. His research group focuses on developing identification methodologies with practical implementation in real-world engineering systems, maintaining an active publication record through 2025 that demonstrates ongoing contributions to the field of system identification and control engineering.