Selected Publications

Motif-Driven Contrastive Learning of Graph Representations
Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.
MOTIF-Driven Contrastive Learning of Graph Representations
We propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a self-supervised manner so that it can automatically mine motifs from large graph datasets. Our framework achieves state-of-the-art results on various graph-level downstream tasks with few labels, like molecular property prediction.
Automated, Cost-Effective Optical System for Accelerated Antimicrobial Susceptibility Testing (AST) Using Deep Learning
We demonstrate an automated, cost-effective optical system that delivers early AST results, minimizing incubation time and eliminating human errors, while remaining compatible with standard phenotypic assay workflow. The system is composed of cost-effective components and eliminates the need for optomechanical scanning. A neural network processes the captured optical intensity information from an array of fiber optic cables to determine whether bacterial growth has occurred in each well of a 96-well microplate.
Estimating the Ages of FGK Dwarf Stars Through the Use of GALEX FUV Magnitudes
Stellar age cannot be directly measured, yet age determinations are fundamental to understanding the evolution of stars, planets, and galaxies. The work presented here builds upon the idea of a stellar-activity age. We utilized far-ultraviolet (FUV) photometry acquired by the Galaxy Evolution Explorer (GALEX) space telescope as an indicator of chromospheric activity to infer ages of late-F, G, and K type dwarf stars. We derived a purely empirical correlation between FUV magnitudes and stellar age in conjunction with (B βˆ’ V) color. Our attention is restricted to Sun-like stars with color range 0.55 ≀ (B-V) ≀ 0.71 and absolute magnitude range 4.3 ≀ M V ≀ 5.3. This correlation is functional up to 6 Gyr for FGK dwarfs. With such a correlation, one only needs Johnson (B βˆ’ V) and FUV measurements to estimate the stellar age for Population i dwarf stars of solar-like temperature and metallicity. Such a calibration has utility in population studies of FGK dwarfs for further understanding of the chemical evolution of the Milky Way.
Queer | Inclusive | Badass
Employing future ML capabilities and ML-generated artifacts as a proxy, my poster presents how the tech community, by 2025, will prioritize the creation of fair, intersectional, and ethical technology.

Selected Projects

Robust Model-Agnostic Meta-Learning for Binary Content Moderation Tasks in Natural Language Processing
We investigated applying Model-Agnostic Meta-Learning (MAML) to boost performance on binary content moderation tasks in low-resource contexts. Using PyTorch, we compared the ability of a model pre-trained with MAML to adapt to unseen binary content moderation tasks to those of a model pre-trained using traditional transfer learning approaches and a model trained from scratch.
On the Complexity and Convergence of Approximate Policy Iteration Schemes
We surveyed relevant literature in approximate policy iteration, and provided theoretical proof sketches involved in the analysis of the complexity bounds, convergence guarantees, and rates of convergence for various approximate policy iteration algorithms.
Model-Agnostic Meta-Learning for a Policy Gradient Approach to MuJoCo Continuous Control Tasks
We investigated the adaptive power of Model Agnostic Meta-Learning on a policy gradient approach to MuJoCo continuous control tasks.
MovieLens Recommender System
We created a recommender system to predict the binary rating for 4M unseen UserID-MovieID pairs in the MovieLens dataset. We surveyed the performance of content-based (e.g. TF-IDF, genre-based decision tree, etc.) and collaborativefiltering (e.g. SVM, SVD, element-wise matrix factorization, tabular matrix factorization, hybrid matrix factorization, etc.) methods. We achieved the third highest ROC-AUC on the test set in our data mining class.
Quantum Programming Algorithms
We implemented Deutsch-Jozsa, Bernstein-Vazirani, Grover’s algorithm, and Simon’s algorithm using PyQuil and Qiskit. We then evaluated the implementations and modern quantum compile and runtime capabilities using the Rigetti and IBM quantum simulators and IBMQX quantum devices.


  • UCLA Eugene V. Cota-Robles Fellowship (2021) ~ one of most prestigious graduate fellowships awarded by UCLA
  • UCLA Graduate Research Assistantship (2021)
  • Boeing Company Scholarship (2021)
  • Brian J. Lewis Endowment (2021)
  • Computing Research Association Outstanding Undergraduate Researcher Honorable Mention (2020)
  • AAAI Undergraduate Consortium (2020) ~ presenting at AAAI Undergraduate Research Symposium and receiving mentorship from leading researchers in AI; 1 of 14 accepted out of 82 applicants for inspiring personal statement and exemplary service and research in self-supervised methods for learning graph-level representations
  • IBM Quantum Challenge (2020) ~ decomposed a large unitary gate for a minimal gate set with Qiskit; 1 of 574 winners out of 1745 participants
  • Out for Undergrad Tech Conference (2020) ~ 1 of 300 applicants accepted for superb academics, exemplary leadership, and work experiences, as well as diverse and unique viewpoints
  • Google Queer Tech Voices Conference (2020) ~ 1 of 32 accepted out of hundreds of applicants
  • 3rd Place Award for Best Hack @ Rose Hack, Major League Hacking (2019) ~ developed application that produces mashups of songs and evaluates which two songs form the best mashup
  • Siemens Competition Regional Finalist (2017) ~ 1 of 101 finalists selected from 4092 entrants
  • Award of Achievement, Association for Computing Machinery, San Francisco Bay Area Professional Chapter (2016) ~ developed automated digital music transposer
  • Dean's Honors List (2018-2021)



  • QWER Hacks: A Case Study on How to Build an Inclusive Hackathon (UCLA Samueli Newsroom, 2021)
  • UCLA’s ACM AI Podcast Addresses AI and Diversity, Featuring Guests from Underrepresented Communities (UCLA Samueli Newsroom, 2021)
  • Student-run tech podcast aims to make computer science more diverse, accessible (Daily Bruin, 2021)
  • ACM AI at UCLA, Outreach + Events Feature (A.I. For Anyone, 2020)
  • Students code software to help underrepresented groups in LGTBQ+ hackathon (Daily Bruin, 2020)
  • Equality in America Town Hall with Tom Steyer (CNN, 2019)
  • Washington, California Students Win Regional Siemens Competition at California Institute of Technology (citybizlist, 2017)
  • Indian American STEM Whiz Kids Named 2017 Siemens Regional Finalists (IndiaWest, 2017)
  • Three MVHS students make it to semifinal round of Siemens competition (El Estoque, 2017)
  • Local Charity Map of Bay Area (ArcGIS, 2016)

Justice, Equity, Diversity, and Inclusion (JEDI) in AI

I am a fervent champion of justice, equity, diversity, and inclusion in AI research and beyond!

  • I organize with Queer in AI, hosting socials (AAAI-21, ICML '21) at AI conferences to build a strong community of queer and trans researchers in AI. Furthermore, I organize the undergraduate mentoring program, which gets junior queer and trans folks involved with AI research. Additionally, I advise AI conferences on diversity and inclusion and accessibility issues. Finally, I meet weekly to discuss administrative issues.

  • As an organizer of the inaugural UCLA Computer Science Summer Institute (CSSI), I recruited and interviewed diverse Undergraduate Tutors for to lead interactive coding and problem-solving sessions with the high school students for both the Introductory and Intermediate tracks. I am working with Professor Yizhou Sun and Professor Parvaneh Ghaforyfard to onboard the selected Undergraduate Tutors, organizing pedagogy and technical knowledge preparation sessions.

  • I co-founded QWER Hacks, Major League Hacking's first-ever LGBTQIA+ event and the first collegiate LGBTQIA+ hackathon in the nation, which increases the visibility of and celebrates the queer and trans community in STEM.

  • I led JEDI initiatives within ACM at UCLA, employing actionable goalsetting and reflection to take concrete steps towards making the organization more inclusive of everyone.

  • I created and produced the "You Belong in AI!" podcast, which inspires youth and college students of all identities and backgrounds, especially those who are underrepresented, to pursue AI opportunities.

  • I strongly advocate to make an AI education accessible to everyone. With the prevalence of AI in modern society and the harms it poses to already-marginalized communities, it is especially paramount that we empower individuals from these communities to have informed conversations about AI and fight against algorithmic injustices. As Outreach Director of ACM AI at UCLA, I co-founded, led, and taught a machine learning course at North Hollywood High School (in Los Angeles) that emphasizes foundational concepts and fairness, ethics, accountability, and transparency in modern AI. Since my Outreach team and I unfortunately cannot teach at every school, we leveraged educational technology to open-source our content in a fun, digestible manner; a prime example of this is through ACM Teach LA at UCLA's online, interactive learning labs on topics like gradient descent, mean-squared error, convolutional filters, biases in machine learning, etc.