Alec Helbling

I am an undergraduate Computer Science student at Georgia Tech.

I currently work with Chris Rozell at Georgia Tech, where my work focuses on techniques allowing for fine tuned control of deep generative models, specifically Variational Autoencoders.

I previously interned at IBM Research with Achille Fokue where my work focused on applying graph neural networks at large scale language models to the task of document summarization. Prior to that I was a software engineering intern at Microsoft where I worked on scaling a data analytics service. I also was an intern in the Machine Learning and Instrument Autonomy Group at NASA Jet Propulsion Laboratory with Lukas Mandrake. At NASA my work focused on developing web-based visualization tools for interacting with machine learning models. I started my research career working with David Koes at the University of Pittsburgh. There I worked on machine learning applications to computational drug discovery, specifically trying to understand protein-ligand interactions. I also worked on a web-based molecular visualization library called 3Dmol.js..

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

profile photo
Research

I am interested in machine learning and software engineering. I am specifically interested in generative modeling, biologically inspired machine learning, visualization, and meta-learning.

Oracle Guided Image Synthesis with Relative Queries
Alec Helbling, Christopher John Rozell, Matthew O'Shaughnessy, Kion Fallah
International Conference on Learning Representations
Workshop on Deep Generative Models for Highly Structured Data, 2022
OpenReview

We developed a method for guiding the generative process of VAEs by asking user's relative queries of of the form "do you prefer image a or image b?" From these queries we could infer what images a user prefers and use that understanding of user preferences to generate reccomendation images with our VAE.

Visualizing Convolutional Neural Network Protein-Ligand Scoring
Joshua Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes
Journal of Molecular Graphics and Modeling, 2018
arXiv

We applied convolutional neural networks to the problem of predicting whether or not a protein-ligan pair are likely to bind. We developed methods of visualizing and understanding the learned structure of these models.

Projects
Manim Machine Learning
Alec Helbling
Github (Currently 52 ⭐)

Manim Machine Learning is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library. I want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering.


This website uses a template from https://jonbarron.info/