Tony Dear

Tony Dear

Senior Lecturer in Computer Science at Columbia University

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Posts

publications

Deepcollide: Scalable data-driven high dof configuration space modeling using implicit neural representations

Published in , 1900

Recommended citation: Guo, G., Goldfeder, J., Ray, A., Dear, T. and Lipson, H., 2025. Deepcollide: Scalable data-driven high dof configuration space modeling using implicit neural representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2689-2699). https://openaccess.thecvf.com/content/ICCV2025W/E2E3D/html/Guo_DeepCollide_Scalable_Data-Driven_High_DoF_Configuration_Space_Modeling_using_Implicit_ICCVW_2025_paper.html

teaching

COMS 4733, Computational Aspects of Robotics

Fall 2026, Spring 2024, Fall 2021, Spring 2021, Spring 2019

This course is an introduction to fundamental problems and algorithms in robotics from a computer scientist’s perspective. While robotics is inherently a broad and interdisciplinary field, we will primarily focus on ideas with roots in computer science, as well as the roles that a computer scientist would play in robotics research or engineering tasks. Topics include configuration spaces, kinematics, search and sampling-based planning, state estimation, localization and mapping, perception, and learning.

COMS 4701, Artificial Intelligence

Summer 2026, Summer 2025, Spring 2025, Summer 2024, Spring 2024, Spring 2023, Spring 2022, Summer 2021, Fall 2020, Fall 2019, Fall 2018

Artificial intelligence (AI) is a broad, interdisciplinary, and rapidly evolving field of computer science concerned with the design of systems that can perceive, reason, learn, and act. This course provides an overview of the fundamental methods and applications of AI, with an emphasis on algorithms, computation, and the evolution of the field from classical approaches to modern AI systems. Topics include search and planning, probabilistic reasoning, machine learning, reinforcement learning, deep learning, foundation models, and agentic AI. Students will develop intuition and algorithmic thinking through written and programming problems in Python, and will apply these concepts to building and evaluating intelligent systems. By the end of the course, students will be able to formulate AI problems, reason and make decisions under uncertainty, learn from data, and design modern AI systems that use foundation models and other AI techniques.

COMS 4771, Machine Learning

Spring 2026

COMS 4771 is a graduate-level introduction to the statistical principles and algorithmic paradigms of machine learning (ML). Broadly speaking, ML is concerned with the tasks of learning models from data, generalizing to unseen scenarios, and solving problems without explicit instructions. We will focus mostly on supervised learning, including both classical and deep learning methods. We will also see how ML is used in various applications in NLP, vision, robotics, etc. throughout the course.

COMS 3203, Discrete Mathematics

Spring 2026, Fall 2025, Spring 2025, Fall 2024, Fall 2023, Fall 2022, Spring 2019

The study of discrete mathematics provides an important foundation for basic theoretical principles in computer science. We first build a strong foundation in logic, formal proofs, and mathematical induction. We then examine the discrete structures of functions, relations, and graphs, which provide different ways of representing relationships, connections, and networks. Finally, we move onto numerical and computational aspects: number theory and modular arithmetic, counting and combinatorics, and discrete probability. Throughout the course, we will also see computer science applications and practice writing implementations in Python.

COMS 3251, Computational Linear Algebra

Spring 2023, Spring 2022, Fall 2021, Spring 2021, Spring 2020

This course is an introduction to linear algebra and its usage in computational applications. The study of linear equations, linear functions, and their representations pervades numerous fields of study. Students will learn and practice fundamental ideas of linear algebra and simultaneously be exposed to and work with real-world applications of these ideas. This course emphasizes a rigorous approach to mathematics, which will serve as a foundation for future courses like computer graphics, machine learning, and robotics. The learning and usage of Python and libraries such as NumPy is an essential component of the course, as is the development of basic skills of computational programming.

16-384, Robot Kinematics and Dynamics

Spring 2013 - Spring 2018 (Carnegie Mellon)

Although I was never a formal instructor for this course, I initially was a graduate student TA and then worked closely with my advisor in subsequent semesters to implement an online curriculum for present offerings of the course.