Zirui "Colin" Wang

I am a Staff Research Associate at the University of California, San Diego. I obtained B.S. in Data Science at the Halicioglu Data Science Institute (HDSI) and B.A. in Cognitive Science at the CogSci Department at the University of California, San Diego (UCSD). I work on the practical aspect of domain generalization and transfer in machine learning. I am a recipient of the HDSI Undergraduate Scholarship.

During my undergraduate studies & research, I have acquired/am currently acquiring experience in Reinforcement Learning, Hierachical Visual Reasoning, as well as Question-Answering in Language Models. I am fortunate to be advised by Prof. Zhuowen Tu and Prof. Zhiting Hu at UCSD. I have been primarily mentored by Dr. Yifan Xu and Nicklas Hansen for research.

I have also been committed to education. I have served as a Teaching Asssiant for various CS/ML courses at UCSD, tutored 10+ class offerings across the Computer Science and Engineering department, Data Science department, and Cognitive Science department, helped 1,000+ individual students, and worked with 6 teaching faculties for building better course experiences for students. I was primarily mentored by Prof. Marina Langlois for teaching.

Email  /  Resume  /  CV  /  GitHub  /  Twitter  /  Google Scholar  /  LinkedIn

Schedule

My calendar. Note that all the schedulings are tentative and subjective to change

Research

On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning

We investigate whether internal models learned by modern model-based RL algorithms can be leveraged to solve new, distinctly different tasks faster.

Teaching

Full lists and details about classes I have served as a teaching assistant in the past. Instructor names are listed based on the time I worked with them. Staff names are listed in alphebatical order based on their first names. Instructor names and staff names are separated by a semicolon. Instructor evaluation is attached if available.

Deep Learning

This course covers the fundamentals of neural networks. We introduce linear regression, logistic regression, perceptrons, multilayer networks and back-propagation, convolutional neural networks, recurrent networks, and deep networks trained by reinforcement learning.

Introduction to Machine Learning

The topics include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting, and perceptrons; and topics in unsupervised learning, such as k-means and hierarchical clustering. In addition to the actual algorithms, the course focuses on the principles behind the algorithms.

Practical Data Science in R

Learn coding for data analysis using the R programming language. Course focus will be on practical and applied skills in asking data-informed questions, data wrangling, data visualization, building statistical learning models, and communication.

The Practice and Application of Data Science

Students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems.

Data Structures and Algorithms for Data Science (x4)

Programming techniques including encapsulation, abstract data types, interfaces, algorithms and complexity, and data structures such as stacks, queues, priority queues, heaps, linked lists, binary trees, binary search trees, and hash tables with Java.

Programming and Basic Data Structures for Data Science (x2)

Programming techniques including recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists.

Principles of Data Science

This introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets, emphasizing an end-to-end approach to data science, introducing programming techniques in Jupyter Notebook that cover data processing, modeling, and analysis.

Other Projects

These include courseworks, projects and other research-related work not for publishing. contents to be updated (10/11/2022). To remind myself what to put: DSC180 Capstone, DSC190 DataMining, COGS 108, MATH 189, DSC 102, DSC 106, COGS 189, DataHacks Adv, DataHacks Bus, Tencent, SS (CMU)

On the Domain Robustness with Prompt & Prefix Tuning

We analyze the robustness of Language Models using prompt tuning and prefix tuning toward a domain shift (i.e. learning a task from data in a specific domain and evaluating that model on the same task, but data is out-of-domain).

EEG Transformer

We implement a naive EEG transformer that explores the possibility of using a ViT based transformer for inferring 3-class motor imagery based on multichannel time-series EEG data recorded at 1000 Hz for 8 seconds (in which 4 seconds are used). We propose future directions.

Design and source code from Jon Barron's website. Forked from the Jekyll varient by Leonid Keselman.