Trenton Chang

Stanford, CA · (435) 225-1659 ·

I am a Master's student in Computer Science, Artificial Intelligence track at Stanford University. I currently do research with Chris Ré at HazyResearch in robustness in video-based machine learning, working with Sharon Li (Asst. Prof., University of Wisconsin-Madison) and Dan Fu (PhD Candidate, Stanford). After graduation, I hope to pursue a PhD in computer science. I am most interested in studies of machine learning model robustness and fairness in real-world situations. I am passionate about ethical AI, and am always searching for connections between my undergraduate field -- ethnic studies -- and responsible, fair AI devleopment.



Stanford University

Master of Science
Computer Science - Artificial Intelligence Track

GPA: 4.05

January 2020 - June 2021 (expected)

Stanford University

Bachelor of Arts
American Studies - Concetration in Asian American Representation

GPA: 3.97

September 2016 - June 2020


Beyond the Pixels: Robustness in video machine learning models

Supervised Research

Studies in robustness in video machine learning models under bit-level network and file corruptions in data. We find that video corruptions cause an up to 77% drop in accuracy on the action recognition task using 3D-ResNets. Furthermore, corrupted videos that the model predicted incorrectly were up to 1.6 times more perturbed (L2 distance) than those correctly predicted. Baseline model-side defenses for dealing with corrupted data (out-of-distribution detection, adversarial training, training with data augmented by corruptions) fail to restore performance on clean data, though augmentation shows some promise. Initial robustness study presented at ECCV 2020 (first-author), Workshop on Adversarial Robustness in the Real World. You can view my talk here.

March 2020 - present

Multi-label few-shot domain adaptation

Course Project, CS 330 (Stanford): Deep Multi-Task and Meta Learning

Studies in the viability of multi-label problem transformation techniques in the few-shot domain adaptation case, for plug-and-play training for Memory Augmented Neural Networks, Model-Agnostic Meta-Learning, and Prototypical Networks. Evaluated utility of powerset labels vs. binary relevance labels (multi-headed approach), the latter requiring architectural modifications. On BigEarthNet dataset, class-balanced sampling technique achieved top performance (86.3 F1, 3-label case, MANN), outweighing choice of label representation. Ablations suggest exponential growth of problem difficulty with increase in label set cardinality, as well as linear gains from exponential support set growth. Future work points to better scaling for these techniques.

September 2020 - November 2020

Drowsiness Detection in Single-Channel EEGs

Supervised Research

Under supervision of Henwei Huang (Postdoc, MIT), in Giovanni Traverso's (Asst. Prof., MIT Mechanical Engineering) lab. Investigated practical methods for single-channel EEG-based drowsiness detection. Previous work has used frequency features (i.e. power spectral density, entropy) to predict mental/neurological states, but practical methods for single-channel drowsiness detection remain underexplored. Developed a data collection and ML model pipeline using the OpenBCI headset and a Jetson Nano. Between classical (i.e. SVM, decision trees) and deep ML architectures, we found a Fourier-domain KNN search yielded the best accuracy.

June 2020 - November 2020

Kuzushiji-to-Text Image Transcription

Independent Project

Bounding box regression and character-level classification on over 3000 scanned, expert-labeled images of Japanese calligraphy pages. Objective is OCR on Japanese calligraphy. studied multiple architectures:w in the image-to-text domain, from R-CNN techniques to image captioning architectures. Baseline model is a CNN with VGG-19 feature extraction that serves as an encoder, which is then fed into a LSTM decoder with self-attention. Baseline results have approx. 40% training classification accuracy.

July 2019 - December 2019

Classifying Activity from Single-Channel EEG Data

Final Project, CS 229 (Stanford): Machine Learning

Implemented multiple classification algorithms and signal processing techniques on the UCI Epilepsy dataset. Baseline models included softmax regression and k-nearest neighbors, which achieved moderate accuracy. Best model was a 1D CNN, borrowing from a text sequence classification architecture. Algorithms were tested based on raw time-series data as well as frequency data extracted by the Fourier transform and the spectral entropy of the singal. My contribution to the group was proposing and implementing the Fourier transform as a feature extraction technique, and creating a Hidden Markov Model for classification.

October 2019 - December 2019
For more projects, check out my Github!


Alexa Prize 4 Team Member

Stanford NLP Group

Member of Stanford Alexa Grand Challenge Prize Team. Investigating super secret cool NLP stuff (sorry, it's all internal for now 😊).

October 2020 - present

Graduate Student Researcher

Led robustness study of video action recognition networks against naturally-occurring network and file corruptions. See above description under "Projects" for more details.

March 2020 - present

Graduate Student Researcher

Traverso Lab, MIT

Examined methods for drowsiness detection in single-channel EEGs. See above description under "Projects."

June 2020 - November 2020


Developed and taught AI Ethics curriculum, a project-based introduction to mathematical definitions of model fairness using the COMPAS ProPublica dataset. Delivered intro lecture. Taught advanced high school cohort introductory machine learning concepts (linear regression, logistic regression, neural networks)

May 2020 - July 2020

Residential Counselor/Teaching Assistant

Stanford Pre-Collegiate Studies

Led 2 labs of ~20 high school students each in the Artificial Intelligence course at Stanford Pre-Collegiate Studies, reviewing concepts like search algorithms, game-playing algorithms (e.g. minimax), and reinforcement learning. Supervised and advised projects in computer vision, price prediction, sentiment analysis, and more. Wrote problem set and code solutions (Python and Unity C#) for student reference.

June 2019 - August 2019


When not thinking about AI, I enjoy making music. I am a jazz pianist and songwriter, and am heavily involved in the music side of musical theatre at Stanford, where I am piloting a intiative for student-taught workshops in the arts as a member of the Ram's Head Theatrical Society Board of Directors.

My past musical theatre credits include Gaieties 2020: Unprecedented Times (Recording Engineer) Gaieties 2019: Midterm Impossible (Composer, Lyricist, and Music Director), Cabaret (Pianist), The Addams Family (Pianist), Gaieties 2018: Jane Stanford and the Chamber of Secrets (Pianist), The Wiz (Music Director and Pianist), Gaieties 2017: Bearanormal Activity (Pianist), Ragtime (Rehearsal Pianist), and Pippin (Assistant Producer).

Other interests include bullet chess (find me @tchainzzz), watching football, and getting 8 hours of sleep every night -- it's really good for productivity!