Alex Huang

Hello there! My name is Alex, and I currently work as a Machine Learning Engineer at Fetch Rewards . My research interests focus on applied deep learning, particularly in affective computing.

I earned my Bachelor’s degree in Computer Science from UW–Madison, where I had the privilege of working with Prof. Yin Li and Prof. Tim Rogers. Starting Fall 2026, I’ll be joining the University of Michigan to work with Prof. Emily Mower Provost.

Email  /  Scholar  /  LinkedIn  /  Github

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Publications

A Data-driven Approach to Facial Expression Recognition

Wei-Chun Huang
Senior Thesis
(In Progress)

Using Transformer based VaDE model to cluster facial expressions extracted using a 3D face reconstruction model (EMOCA).

Uniting theory and data: The promise and challenge of creating an honest model of facial expression

Sophie Wohltjen, Y. Ivette Colon, Zihao Zhu, Karina Miller, Wei-Chun Huang, Bilge Mutlu, Yin Li, Paula Niedenthal1
Cognition and Emotion
(In Press)

Discuss the resources that are available to help researchers build a more ecologically valid model of facial expressions.

Measuring large group synchrony and social connection with Machine Learning and Computer Vision

Michelle Marji, Siddharth Suresh, Wei-Chun Huang, Alexis Liu, Karina Miller, Joshua Jackson, Christian Andresen, Corey Pompey, Paula Niedenthal
Affective Science
(In Press)

Track and measure synchrony and spatial configurations of large groups using Faster R-CNN.

Categories vs Semantic Features: What shape the similarities people discern in photographs of objects?

Siddharth Suresh, Wei-Chun Huang, Kushin Mukherjee, Timothy Rogers
ICLR Workshop, 2024
paper

Discovered that model trained to produce category labels and model trained to generate semantic features learned very different representational geometries throughout the network.

Conceptual structure coheres in human cognition but not in Large Language Models

Siddharth Suresh, Kushin Mukherjee, Xizheng Yu, Wei-Chun Huang, Lisa Padua, Timothy Rogers
EMNLP, 2023
paper

Compared how Large Language Models such as FLAN and GPT structure concepts relative to human cognition.

Other Projects

Below are some of the non-research projects I’ve worked on. Feel free to take a look!

3D Facial Feature Extraction

repo

Extract 3D Facial Features from videos and images using SOTA 3D face reconstruction model - EMOCA .

Video Feature Extraction

repo

This directory contains the code to extract features from video datasets using mainstream vision models such as Slowfast, i3d, c3d, CLIP, etc.


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