Alexander Ihler

Alexander Ihler is a Professor in the Department of Information and Computer Science at the University of California, Irvine. His work bridges artificial intelligence and machine learning with a focus on statistical methods for data analysis and approximate inference in graphical models. Applications span sensor networks, computer vision, and computational biology.

His research emphasizes graphical models for structuring probability distributions and enabling efficient reasoning. Key contributions include analyzing convergence in belief propagation, extending variational techniques to continuous systems, and developing bounds for marginal MAP and decision-making problems. He also explores adaptive inference algorithms for dynamic problems, such as tracking changes in sensor data or biological systems.

Recent publications highlight advancements in

• AI for protein design and astrophysics
• UAV mobility modeling
• deep learning integration with classical algorithms
• dynamic mode decomposition for forecasting

Scientific accolades include the NSF CAREER award and first-place UAI 2014 Approximate Inference Challenge in five categories. He has advised numerous PhD and MS students, with graduates in fields like robotics, computational biology, and statistical learning.

His group collaborates with institutions like Microsoft Research, DARPA, and the NIH, focusing on scalable methods for real-world systems. Additional personal details include a PhD from MIT under Alan Willsky and a prior role as co-program chair of the 2016 Uncertainty in Artificial Intelligence (UAI) conference.