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 for computationally expensive simulations.
As an Associate Instructor, I am currently teaching CS 006 (Effective Use of World Wide Web) to 100+ students covering web tools, HTML, CSS,generative AI, and verification of information, privacy, and other legal and societal issues. I also taught CS 009A (Intro to Programming and Data Science) covering computational thinking, problem-solving, and data analysis using the Python language as a primary instructor of record.
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 and Publications
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.
Advisor: Dr. Christian Shelton | Institution: UC Riverside
FLARE MCMC: Fidelity-based Layer-Adaptive REcursive proposals for MCMC
Harini Venkatesan, Christian Shelton, Ming-Feng Ho, Simeon Bird, and Mengxuan Wu.
SIAM/ASA Journal on Uncertainty Quantification, In Press, 2026. PDF
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.
Current Courses
CS 006: Effective Use of World Wide Web
Role: Primary Instructor | Students: 100+ | Audience: Non-Engineering majors | Quarter: Fall 2025, Winter 2026, Spring 2026
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.