Friday, October 10th 11:00am – 12:30pm

Lecture Center C4

Deep Learning for Smarter Algorithms in Detection, Estimation, and Navigation: From Underwater Environment Estimation to Robotic Path Planning

Toros Arikan, PhD

Assistant professor in Electrical Engineering

University of Notre Dame

Abstract: By deploying deep learning methods as global optimizers that learn algorithmic solutions, we can solve long-standing ill-posed problems in joint parameter estimation. Neural networks can also produce human-interpretable results that de-mystify the algorithm design process, allowing us to rigorously determine hyperparameters such as training lengths. I will introduce two recent works where this methodology is used to tackle open problems in remote sensing and navigation. In underwater environment estimation, I will present a U-Net method for the acoustic mapping of reflective boundaries such as the sea surface and seafloor, with state-of-the-art performance and the new capability of jointly estimating the number of boundaries in the environment. In the field of robotic path planning, I will present a recurrent convolutional neural network (RCNN) method for solving a sub-class of Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problems. By learning an algorithm via reinforcement learning, whose intermediate stages are visible to the user, we devise augmentations that yield strong accuracy and runtime performances, which can lead to lower power consumption. Beyond their immediate applications, these new methods point to a general strategy of solving a broad class of joint parameter estimation problems via deep learning.

Bio: Toros Arikan was born in Mount Kisco, NY, USA, in 1993. He obtained his B.S. and M.S. degrees from the University of Illinois at Urbana-Champaign, with a focus on digital communications and signal processing; and his Ph.D. degree from the Massachusetts Institute of Technology in 2023, specializing in localization, tracking, and remote sensing. In 2023-25 he was a postdoctoral researcher at Rice University, where he researched deep learning methods for environment estimation, and robotic path planning algorithms for cluttered environments. He is currently a new assistant professor in EE at the University of Notre Dame, where he teaches Array Signal Processing and is actively recruiting graduate students and postdoctoral candidates. 

Faculty Host: Dr. Ahmet Enis Cetin (aecyy@uic.edu)