Uploaded April 2025 | Updated September 2026, 2 weeks ago
Allen School Colloquia Series
Title: From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models
Speaker: Yangruibo Ding (Columbia University)
Date: April 7, 2025
Abstract: Large language models (LLMs) have revolutionized the way how humans write code, but they still have limitations in comprehensively reasoning about software programs to assist with more involved software engineering tasks. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with (1) symbolic reasoning for program semantics and (2) global reasoning for software dependencies. Finally, I will discuss the future of AI-driven software engineering, envisioning a path to approach full-stack automation in a trustworthy way.
Bio: Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
This video is closed captioned.
Allen School Colloquia Series
Title: From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models
Speaker: Yangruibo Ding (Columbia University)
Date: April 7, 2025
Abstract: Large language models (LLMs) have revolutionized the way how humans write code, but they still have limitations in comprehensively reasoning about software programs to assist with more involved software engineering tasks. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with (1) symbolic reasoning for program semantics and (2) global reasoning for software dependencies. Finally, I will discuss the future of AI-driven software engineering, envisioning a path to approach full-stack automation in a trustworthy way.
Bio: Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
This video is closed captioned.
![[ASL] Toward Total Scene Understanding for Autonomous Driving—Drago Anguelov (Waymo)](https://i.ytimg.com/vi/pK5ChzMsfE0/mqdefault.jpg "[ASL] Toward Total Scene Understanding for Autonomous Driving—Drago Anguelov (Waymo)
Ben Taskar Distinguished Memorial Lecture
Title: Toward Total Scene Understanding for Autonomous Driving
Speaker: Drago Anguelov (Waymo)
Host: Anat Caspi
Date: January 25, 2024
Abstract: Machine learning has proven to be a key ingredient in building a performant and scalable Autonomous Vehicle stack, spanning key capabilities such as perception, behavior prediction, planning and simulation and evaluation. I will describe recent Waymo research on performant ML models and architectures that help us handle the variety and complexity of real world environments. I will also discuss how progress in building Autonomous Driving agents can impact people with disabilities and cover some current open questions about how to further enhance embodied AI agent capabilities with ML.
Bio: Drago joined Waymo in 2018 to lead the Research team, which focuses on pushing the state of the art in autonomous driving using machine learning. Earlier in his career he spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Prior to joining Waymo, Drago led the 3D Perception team at Zoox.
This video is closed captioned.
A version of this video without ASL interpretation is available here: https://youtu.be/zCJO7ONdPZM.")
