Hi! I am a Stanford BS & MS candidate and machine learning engineer. Below are some portfolio projects.
Reach out if you want access to private repos of listed projects. Enjoy!
Major Project Experience (to date)
For each of these, see “Longer Summaries” section for more details!
SUMMER 2021 UPDATE: I’m interning with Spotify‘s ML Platform team this summer from June-August, living off campus in the Bay Area.
FALL 2021 UPDATE: WOOHOO! I signed an offer with TikTok. I will start working in their Search Team as a Machine Learning Engineer next fall in Mountain View!
Stanford Network Analysis Project RAship (supervisor Jure Leskovec)
- Surpassed SOTA results on the “cold start” problem in recommendation systems via a hybrid matrix factorization and “content basket” approach
- Introduced a novel deep autoencoder architecture DeepNaniNet using jointly learned graph and language encoders for reconstructing user-item preferences
- Compiled the largest anime recommendation dataset featuring 10,000 shows, 13,000+ users, 130,000 reviews and more by crawling online discussion forums
- Founded and maintaining only existing NLP driven anime recommendation site OtakuRoll built on DeepNaniNet!
- Submitted manuscript to a conference as Lead Author (contact for preprint)
Machine Learning Intern @ Samsung Research America Think Tank Team
- Trained the SARAM robot in a goal-based OpenAI gym environment
- Replicated two OpenAI papers on dexterous hand solving a Rubik’s cube and ran hundreds of experiments with policy gradient actor-critic algorithms
- Developed a new exploration-based environment for more human-like movements and presented successful PoC to team including SRA VP Joon Lee
- Worked on practical methods to making RL policies transfer sim2real
Stanford QIAI Deep Learning RAship (supervisor Daniel Rubin)
- Applying techniques of multi-modal representation learning on chest X-ray images, following this dataset with ideas from this paper (some initial code)
- Training object detection models on Stanford’s largest video/EEG dataset for seizure detection with SOTA video understanding technologies, like this unsupervised deep tracker to track patients and this for video event composition
Software Engineer – Machine Learning Intern @ Synaptics
- Replicated the SOTA paper (SSD) in object detection
- Engineered datasets and data pipeline for the task of logo detection
- Developed a high-recall end-to-end realtime logo detection pipeline for smart TV
- Experimented and tested deployment on Synaptics VSR 371 SoC
- Presented findings to company AI/CV team and CTO
Co-Founder, CTO @ Demodraft – Led a team of part-time volunteers to deploy our Beta within 1.5 months, got accepted into Berkeley Sky Deck, one of nation’s top college accelerators with <10% acceptance rate
Quizkly (Live app/demo)– Full-stack React + Django web app with end-to-end deep learning pipeline that auto-generates quizzes from any corpus of text [UPDATE: Service stopped as we ran out AWS credits :(]
aiRoute (App Store) – iOS native app that randomly generates a running route in your area and provides turn-by-turn map and voice navigation
Course Final Projects + Misc.
- OtakuRoll (NLP Rec Sys)
- Samsung Research
- Stanford QIAI
- aiFood + MealApp
I am an undergraduate at Stanford University, pursuing Mathematical and Computational Science with honors, expected 2021/22. I expect to graduate 2022 with a Masters in Computer Science.
I was born in Torrance, California. I am a US citizen and permanent resident.
I studied overseas from elementary to high school, as an expat.
During high school, I:
- Won Second Place Grand Award in Mathematics Category at Intel International Science and Engineering Fair 2017
- Project was a conjecture and proof of new upper bounds on the metric dimension for planar graphs with applications to GPS-less navigation systems
- Scored 14 on the 2017 United States of America Mathematical Olympiad (ranking me around top ~110 nationwide) as the first qualifier in my high school’s history and one of only three from China
- Co-authored of a paper in combinatorial graph theory on the Metric Dimension for Planar Graphs, readable here: [1704.04066] Bounds on metric dimension for families of planar graphs (also included below)
- Achieved Distinguished Honor Roll Recipient on both AMC 12 and AMC 10 (top 1% on the nation’s largest math competition)
- Ranked top 30 in the national American Invitational Mathematics Examination (the invitation round after the AMC), an invitation-only round for the US Math Team selection process
- Attended Ross Mathematics Camp at Ohio State University 2017 (one of USA’s oldest pure math summer program and an intense 6-week study of number theory)
- Started school’s only math club, growing the club in three years to having twenty regular members, lecturing on a weekly basis on a topic of interest, hosting contests and activities, and promoting math at school assemblies
- Started a collaborative blog for competitive math students training for Olympiads and became one of the most popular blogs on the Art of Problem Solving, an online forum for math problem solvers around the world
- Selected in the two dozen cohort among hundreds of entrepreneurial-inclined Stanford students to be in this year’s Pear VC Garage Fellow program
- Published two end-to-end iOS apps on the App Store
- Started Quizkly with a partner, a machine learning driven automated MCQ quiz generator tested with Stanford Medical School students
- Helped organizing of China-US conferences for delegates (Stanford FACES)
- Partake in the Arete Fellowships on Effective Altruism (Stanford EA)
- Research at the Rubin Lab (PI Dr. Rubin) on applying deep learning to unsupervised patient video for seizure detection (Stanford QIAI Lab)
- Research in the Stanford Network Analysis Project on deep recommender systems and knowledge-augmented language models
- Serve as a MCS Student Advisor senior year
Choice Set of Coursework
- Computer Science
- CS 103 Mathematical Foundations for Computing, CS 106L Standard C++ Programming Laboratory, CS 106X Programming Abstractions Accelerated, CS 107 Computer Organization & Systems, CS 110 Principles of Computer Systems, CS 161 Design and Analysis of Algorithms, CS 199 Independent Research (2x), CS 221 Artificial Intelligence: Principles and Techniques, CS 224N Natural Language Processing with Deep Learning, CS 246 Mining Massive Data Sets, CS 229 Machine Learning, CS 236 Deep Generative Models, CS 261 Optimization and Algorithmic Paradigm, CS 330 Deep Multi-task and Meta Learning (audited), CS 47 Cross Platform Mobile Development (audited)
- Mathematics + Statistics
- MATH 104 Applied Matrix Theory, MATH 171 Fundamental Concepts of Analysis, MATH 61DM, 62DM, 63DM Modern Mathematics: Discrete Methods
- STATS 116 Theory of Probability, STATS 200 Introduction to Statistical Inference
- MS&E 111X Introduction to Optimization (Accelerated), MS&E 221 Stochastic Modeling, PHIL 150 Mathematical Logic
- deeplearning.ai Deep Learning Specialization [certificate]
- Alberta Machine Intelligence Institute Reinforcement Learning Specialization [certificate]
- AwesomeMath Summer Program twice – an Olympiad-prep math camp
- Worldwide Online Olympiad Training – high school online Olympiad-prep math camp
- Ross Mathematics Camp 2017 – one of US’s oldest/most prestigious math enrichment camps, guiding students on proof-based exploration from classical to modern number theory results
- Summer Program on Applied Rationality and Cognition 2018 – a highly selective program of nationwide’s top STEM talent
- Make School Summer Academy 2018 – program with curriculum in iOS app dev and design thinking in which students ship apps by the end
- Multiple Stanford Effective Altruism Fellowships (2019)
Technical (non-code) writing samples
- Analysis paper on Fourier Transform and an application to CNNs
- (published in Towards Data Science)
- ISEF-winning combinatorial graph theory paper back in HS
Mentoring Resources (more to come)
Get in Touch
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“He who says he can, and he who says he can’t… are both correct.”