Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application

Spline-Based Minimum-Curvature Trajectory Optimization for Autonomous Racing

We propose a novel B-spline trajectory optimization method for autonomous racing. We consider the unavailability of sophisticated race car and race track dynamics in early-stage autonomous motorsports development and derive methods that work with limited dynamics data and additional conservative constraints. We formulate a minimum-curvature optimization problem with only the spline control points as optimization variables. We then compare the current state-of-the-art method with our optimization result, which achieves a similar level of optimality with a 90% reduction on the decision variable dimension, and in addition offers mathematical smoothness guarantee and flexible manipulation options. We concurrently reduce the problem computation time from seconds to milliseconds for a long race track, enabling future online adaptation of the previously offline technique.Comment: Submitted to ICRA 202