About Me
I am a PhD candidate in Computer Science at UC Riverside with a passion for both research and teaching. My research focuses on developing novel multi-fidelity inference techniques using Markov chain Monte Carlo methods and normalizing flows to leverage low-fidelity approximations, working in collaboration with UCR's Physics and Astronomy department to approximate posteriors using computationally expensive simulations.
As an Associate Instructor, I am currently teaching CS 006 (Effective Use of World Wide Web) to over 100 students covering web tools, e-communities, e-commerce, generative AI, and verification of information, privacy, and other legal and societal issues. I also taught CS 009A (Intro to Programming and Data Science) to over 100 students covering computational thinking, problem-solving, and data analysis using the Python language.
I previously served as a Teaching Assistant for Machine Learning, Data Mining and Object-Oriented Programming courses, assisting over 500 students total.
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Research Interests
My research focuses on developing innovative multi-fidelity methods for probabilistic inference, with particular emphasis on recursive multi-level approaches that can approximate complex, real-world distributions more efficiently than traditional methods.
Current Research Focus
I'm working on extending Markov chain Monte Carlo (MCMC) methods to solve the challenging problem of proposal function selection, especially in high-dimensional and computationally expensive simulations. This work has direct applications to astronomy and hydrology, where we need to efficiently sample from complex probability distributions that describe physical models of the universe, for instance. I have developed extensive convergence rates for these methods as well as established empirical and theoretical hyperparameter selection
Collaborative Work
I collaborate closely with researchers in UCR's Physics and Astronomy department to apply these methods to real and complex problems. This interdisciplinary approach allows me to work at the intersection of computer science, statistics, and astronomy, developing solutions that are both theoretically sound and practically useful.
Advisor: Dr. Christian Shelton | Institution: UC Riverside
Teaching
I am passionate about making computer science accessible and engaging for students from all backgrounds. My teaching philosophy is grounded in the belief that active learning fosters deeper understanding and long-term retention of core concepts and foundations. In entry level programming courses, I emphasize collaborative problem-solving, hands-on activities, and use live coding demonstrations to model the thought process behind programming. In more advanced machine learning courses, I strive to bridge the gap between theory and practice by incorporating real-world datasets and applications, which allow students to see the relevance of methods beyond abstract concepts.
Current Courses
CS 006: Effective Use of World Wide Web
Role: Primary Instructor | Students: 100+ | Audience: Non-Engineering majors | Quarter: Fall 2025
Designed for students outside engineering, this course covers web development fundamentals including HTML, CSS, and modern topics like Generative AI tools. We also cover basics of computer networks and security, with an emphasis on privacy. The goal is to give students practical skills they can use in any field.
Previously Taught Courses
CS 009A: Data Oriented Introduction to Computing
Role: Primary Instructor | Students: 100+ | Audience: Non-CS majors | Schedule: 3 lectures per week | Quarter: Fall 2023
This course introduces students to programming using Python with a focus on data science applications to non-cs majors. We cover NumPy, PyTorch, and other essential tools for modern data analysis, making the concepts accessible to students regardless of their technical background. Concepts include variables, expressions, branches, loops, functions, parameters, lists, strings, file I/O, and exception handling. Also covers software design, testing, and debugging.
Teaching Assistant Experience
CS 171: Machine Learning and Data Mining
Role: Teaching Assistant | Students: 250+ | Schedule: 1 discussion per week | Quarters: Spring 2022, Winter 2023, Spring 2025
This course introduces formalisms and methods in data mining and machine learning. Topics include data representation, supervised learning, classification, regression and clustering. Also covers rule learning, function approximation, and margin-based methods. As a TA, my goal is to support students in understanding machine learning algorithms by leading discussion sections and providing coding demonstrations.
CS 010B: Introduction to Object-Oriented Programming
Role: Teaching Assistant | Students: 250+ | Schedule: 3 hours lab per week | Quarters: Fall 2021, Winter 2022
Helping students master object-oriented programming concepts in C++. It emphasizes good programming principles and development of substantial programs. Topics include recursion, pointers, linked lists, abstract data types, and libraries. Also covers software engineering principles. I focus on building students' problem-solving skills through hands-on programming projects.
Teaching Approach
I believe in active learning and student engagement. My classes feature live coding demonstrations, in-class problem-solving sessions, and collaborative projects. I maintain regular office hours and provide detailed feedback on assignments to help students learn and grow as the quarter goes on. I also mentor undergraduate students in research projects, helping them develop both technical skills and research methodology.