Selected Publications
You Reap What You Sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work. We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages. We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections
This blog post discusses the ICLR 2021 paper "On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections" by Li et al., highlighting the importance of its theoretical results while critically examining the notions and applications of dyadic fairness provided.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
We explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.
Fairness and Bias Mitigation: A practical guide into the AllenNLP Fairness module
As models and datasets become increasingly large and complex, it is critical to evaluate the fairness of models according to multiple definitions of fairness and mitigate biases in learned representations.
allennlp.fairness aims to make fairness metrics, fairness training tools, and bias mitigation algorithms extremely easy to use and accessible to researchers and practitioners of all levels.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
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 − V) color. Such a calibration has utility in population studies of FGK dwarfs for further understanding of the chemical evolution of the Milky Way.
Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management
Trustworthy artificial intelligence (AI) has become an important topic because trust in AI systems and their creators has been lost. Researchers, corporations, and governments have long and painful histories of excluding marginalized groups from technology development, deployment, and oversight. As a result, these technologies are less useful and even harmful to minoritized groups. We argue that any AI development, deployment, and monitoring framework that aspires to trust must incorporate both feminist, non-exploitative participatory design principles and strong, outside, and continual monitoring and testing. We additionally explain the importance of considering aspects of trustworthiness beyond just transparency, fairness, and accountability, specifically, to consider justice and shifting power to the disempowered as core values to any trustworthy AI system. Creating trustworthy AI starts by funding, supporting, and empowering grassroots organizations like Queer in AI so the field of AI has the diversity and inclusion to credibly and effectively develop trustworthy AI. We leverage the expert knowledge Queer in AI has developed through its years of work and advocacy to discuss if and how gender, sexuality, and other aspects of queer identity should be used in datasets and AI systems and how harms along these lines should be mitigated.
How to Make Virtual Conferences Queer-Friendly: A Guide
Queer in AI frequently gets inquires about making virtual conferences more inclusive from both conference organizers and queer community organizers. The purpose of this document is to provide a tutorial for D&I organizers on how to make virtual conferences queer friendly.
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.
Honors
- AI2 2021 Outstanding Intern of the Year Award (2022) ~ 1 of 3 interns awarded for going above and beyond as a researcher and as a colleague while at AI2, receiving $10,000 and an invitation to return to AI2 for another internship
- MLH Top 50 Class of 2021 ~ out of 135,000 students who participated in hackathons, story was one of 50 recognized due to projects and impact on other students in community
- UCLA Samueli School-Wide Outstanding Bachelor of Science (2021)
- UCLA Chancellor's Service Award (2021)
- UCLA Samueli Engineering Achievement Award in Student Welfare (2021)
- 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)
Invited Talks and Panels
- Guest Lecture: Bias in Natural Language Processing, COM SCI 263: NLP, UCLA (2022)
- UPE Graduate School Panel, UCLA (2022)
- Co-Opting AI: Queer, NYU's Institute for Public Knowledge (2022)
- Queer in AI: Making AI Queer-Inclusive and Prioritizing Grassroots D&I Activism, Humlab, Umeå University (2022)
- Prioritizing Grassroots D&I Activism: Queer in AI, AAAI 2022 Workshop on Diversity in Artificial Intelligence
- Prioritizing Grassroots D&I Activism: Queer in AI and "How Do We Improve DEI in AI?" Panel, Nike Sport+AI Conference 2022
- Rebuilding Trust: Making Artificial Intelligence Queer-Inclusive, QWER Hacks 2022
- Eye on A.I.: Equity & Inclusion in A.I. Technology, Toronto Public Library (2021)
- ACM AI at UCLA Research Panel, UCLA (2021)
- Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies, EMNLP 2021
- Safer Privacy-Preserving Friend Suggestions, Snap, Inc. (2021)
- Machine Learning Justice, Catalysts for Change (2021)
- How Can I Make My Hackathon Queer-Inclusive? (Slides, Video), Hackcon IX (2021)
- Intersectionality Panel, NAACL 2021
- Queer in AI Inclusive Conference Guide DEI Update, Allen Institute for Artificial Intelligence (2021)
- Queer in AI Panel at UCLA (2021)
- Fair Machine Learning (adapted from Barocas and Hardt's 2017 NeurIPS talk), Microsoft Garage Brown-Bag (2020)
- An Automated and Cost-Effective System for Early Antimicrobial Susceptibility Testing Using Optical Fibers and Deep Learning, UCLA HHMI Day 2019 (2019)
Press
- .Tech Domains x Major League Hacking: 24 Student Programmers Share Their #MyStartInTech Stories (.Tech Domains, 2021)
- UCLA Engineering Outstanding Bachelor Awardee Champions Equity for LGBTQ+ Community (UCLA Samueli Newsroom, 2021)
- Queer in AI with Arjun Subramonian (500 Queer Scientists, 2021)
- UCLA Samueli Announces 2021 Commencement Awards (UCLA Samueli Newsroom, 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)
Service