Current

PhD Students & Postdocs

• Salva Rühling Cachay
• Yadi Cao
• Vivian Chen
• Jiahe (Chloe) Huang
• Ian Li
• Brooks (Ruijia) Niu
• Sophia Sun
• Aysin Tumay
• Alex Rojas
• Sharvaree Vadgama
• Sumanth Varambally
• Hansen Lillemark
• Jianke Yang
• Bo Zhao
• Zihao Zhou

Undergraduates & Master's Students

• Manu Bhat
• Sonia Fereidooni

Alumni

PhD Students & Postdocs

• Dongxia (Allen) Wu -->Postdoc, Stanford
• Rui (Ray) Wang -->Postdoc, MIT --> Applied Scientist, Amazon
• Jedrzej (Jacob) Kozerawski --> Research Scientist, Apple
• Robin Walters --> Assistant Professor, Northeastern University

Undergraduates & Master's Students

• Kenneth (Theo) Carr --> PhD, MIT
• Peter Eckmann --> PhD, Stanford
• Chintan Shah --> PathAI
• Mayank Sharan --> Microsoft Research --> Google
• Manish Singh --> Qualcomm AI Research --> Google
• Raechel Walker --> PhD, MIT
• Kun Wang --> PhD, Princeton

CSE 291 (B00) Generative AI

Fall 2025   Fall 2023   Fall 2022   Fall 2020

Description: Deep generative models combine the generality of probabilistic reasoning with the scalability of deep learning. This research area is at the forefront of deep learning and has given state-of-the-art results in text generation, video synthesis, and molecular design, among many others. This course will cover recent advances in deep generative models, including variational autoencoders, generative adversarial networks, autoregressive models, normalizing flow, and diffusion models. This is a graduate-level course with an emphasis on mathematical principles as well as practical know-how. The course will be a combination of lectures, student presentations, and team projects.

CSE 251B/151B Deep Learning

Spring 2025   Spring 2023

Description: This course covers the fundamentals of deep neural networks at the graduate level. We introduce multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, Transformers, and advanced topics in deep learning. The course will be a combination of lectures, presentations, and machine learning competitions.

CSE 151B Deep Learning

Spring 2023   Spring 2022   Spring 2021

Description: This course covers the fundamentals of deep neural networks at the undergraduate level. We introduce linear regression, multi-layer perceptrons, back-propagation, and automatic differentiation. We will also discuss Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. The course will be a combination of lectures, presentations, and machine learning competitions.

CSE 259 AI Seminar

Spring 2022

Description: This seminar course focuses on discussing the state-of-the-art research and cutting edge technology in AI. We will invite researchers from academia and industry to share their most recent work in AI and machine learning.

CSE 291 (5) Deep Reinforcement Learning

Fall 2021

Description: Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research is at the forefront of machine learning. Deep RL is able to solve a wide range of complex decision-making tasks, opening up new opportunities in domains such as healthcare, robotics, smart grids, finance, and many more. This class will cover recent advances in deep RL, including imitation learning, Policy Gradients, Deep Q-learning, Actor-Critic algorithms, model-based RL, and inverse RL. The course will be a combination of lectures, student presentations, and projects.

Courses Taught at Northeastern University

CS 7140 Advanced Machine Learning, Spring 2020

Course Website

CS 3950/4950 Introduction to Computer Science Research, Fall 2019

3950 Course Website    4950 Course Website

CS 7180 Special Topics in AI: Deep Learning, Spring 2019

Course Website

CS 6140 Machine Learning, Fall 2018

Course Website    Course Syllabus

Podcast Interviews

Into the Impossible

• The Computer EXPERT That Just Solved Google's Hardest Challenge | Rose Yu [ Youtube]

Carry the Two

• Rose Yu on Automatic Symmetry Discovery [Webpage] [Youtube]

The TWIML AI Podcast

• Spatiotemporal Data Analysis with Rose Yu [Webpage][ Youtube]

Featured Articles

Quanta Magazine

• Improving Deep Learning With a Little Help From Physics [Webpage]
• AI Comes Up with Bizarre Physics Experiments. But They Work [Webpage]

MIT Technology Review

• Innovators Under 35: Rose Yu [Webpage]