Adam Sun

Hi! I'm Adam Sun.

I'm a junior at Stanford University studying computer science. I'm interested in artificial intelligence research, particularly computer vision for scene understanding and medical applications. I'm passionate about finding new ways to use AI to benefit humanity.

At Stanford, I'm a student researcher at Stanford Vision and Learning Lab (SVL), where I am fortunate to work with Professors Fei-Fei Li and Ehsan Adeli at the Partnership in AI-Assisted Care. In my work there, I've recently published a paper to ICCV 2023 on rendering humans from object-occluded monocular videos using neural radiance fields. I'm also one of the head TA/Course Organizers for Stanford's CS21SI (AI for Social Good).

When I'm not in class, you can probably find me beatboxing with my a cappella group, sampling the local food scene, or biking aimlessly around campus.

My resume is here. You can also reach me at adsun@stanford.edu.

Education

Stanford University

Sep 2021 - June 2025 (GPA: 4.0/4.0)

B.S. Computer Science, AI track (Advised by Ron Fedkiw)

Clubs: Stanford O-Tone East Asian A Cappella (Vocal Percussionist), CS + Social Good (AI + Social Good Team Lead)

Relevant Coursework: Vision (CS 231N), NLP (CS 224N), AI Principles (CS 221), AI for Biomedical Imaging (CS 235), Systems I/II (CS 107/111), Algorithms (CS 161), Linear Algebra/Multivariable Calculus (MATH 51, CS 205L), Probability (CS 109), Graphics (CS 148), Information Theory (ENGR 76)

Planned Coursework: Decision Making Under Uncertainty (CS 238), Real Analysis (MATH 115), Applied Number Theory (MATH 110)

Work Experience

A list of the things I've done and places I've been.

Stanford Vision Lab

Sep 2022 - Present

I work with Professors Fei-Fei Li and Ehsan Adeli on 3D vision and multimodal learning research for applications in the ICU.

Ford Motor Company

Jun 2021 - Aug 2021

I processed vehicle sensor data into trajectories and implemented a model for vehicle trajectory prediction.

Whiterabbit.ai

Jun 2023 - Sep 2023

I designed deep learning models that could diagnose breast cancer from digital breast tomosynthesis images with high accuracy.

University of Michigan

Jun 2020 - Sep 2020

I worked with Prof. David Fouhey on using fully-convolutional neural networks for monocular depth estimation.

Wells Fargo

Jun 2022 - Sep 2022

I investigated explainable AI for high-risk applications. I wrote a report assessing the fidelity of explanation methods.

MeeGo Education

Jun 2021 - Sep 2021

I designed and taught a 10-week online deep learning course for high school students during the COVID pandemic.

Publications

Here's a list of my publications. Stay tuned!

Rendering Humans from Object-Occluded Monocular Videos
Tiange Xiang, Adam Sun, Jiajun Wu, Ehsan Adeli, and Li Fei-Fei
IEEE/CVF International Conference on Computer Vision (ICCV), 2023

We introduce OccNeRF, a neural rendering method that can render humans in severely occluded scenes.
We propose surface-based rendering by integrating geometry and visibility priors.
[Project Page/Paper]

Projects

Here you'll find some of the projects I've worked on, mostly class projects.

ASR Error Correction on ICU Clinical Narration Dataset (CS 224N Final Project)

An ASR error corrector finetuned on a small nurse-corrected ICU clinical narration dataset.

[Paper/Code]

Medical Image Dataset Rebalancing with cDCGANs (CS 231N Final Project)

Rebalancing medical image datasets by generating minority class images with conditional deep generative adversarial networks.

[Paper]

Towards Lightweight and Efficient Melanoma Diagnosis (CS 235 Final Project)

Diagnosing melanoma with a lightweight and efficient CNN-based architecture.

[Paper]

Evaluating LIME and SHAP Fidelity with Inherently Interpretable Models (Wells Fargo Internship Report)

Comparing fidelity of LIME and SHAP with a variety of datasets, models, and model complexities using exact interpretations from inherently interpretable models.

[Report]

Linear Probing for VLM Activity Recognition (Under Review)

A video-only linear probing baseline to classify fine-grained activities from vision-language model encodings.

[Code/Paper Under Review]

An Image Captioning Approach to Food Identification (CS 221 Final Project)

Framing food identification as an image captioning problem so that it can identify new dishes.

[Paper]