Hi, I'm Sam! I research deep learning methods for protein design. I have a strong background in computer science, machine learning, and biology, which has allowed me to excel in this field. I am passionate about using deep learning to better understand protein structure and function, and I have developed innovative algorithms and models in this area. Let's connect!
Mutational Effect Transfer Learning
Keep an eye out for my upcoming preprint!
I am excited to share how we are using molecular simulations and transfer learning to improve protein sequence-function modeling when there is limited experimental training data available.
Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein’s behavior and properties. We show that neural networks can learn the sequence–function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequence–function relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate that learned models can be applied to design new proteins with properties that exceed natural sequences.
2023University of Wisconsin-Madison
Ph.D. Computer Science
I earned a Ph.D. in Computer Science from the University of Wisconsin-Madison. I was advised by Anthony Gitter and Philip Romero. My research focused on deep learning methods for protein engineering. I was fortunate to receive two distinguished fellowships, including a pre-doctoral fellowship from the PhRMA Foundation and a short-term traineeship from UW-Madison's Genomic Sciences Training Program.
2016George Mason University
M.S. Computer Science
I obtained an M.S. in Computer Science from George Mason University in 2016. I was advised by Zoran Duric and Naomi Lynn Gerber. My research focused on methods for tracking human movement with depth cameras, and my master's thesis is titled A method for estimating motions of contours with an application to gait recognition. I received the Outstanding Graduate Teaching Assistant award for my efforts assisting the teaching of CS 321: Software Engineering.
2014George Mason University
B.S. Computer Science
I obtained a B.S. in Computer Science from George Mason University in 2014. I graduated from the Honors College and received several awards, including the Schwartzstein Best Freshmen Research Paper Scholarship, the Student Excellence Award, and Outstanding Undergraduate Teaching Assistant.
2017-PresentUniversity of Wisconsin-Madison
Graduate Research Assistant
I am a graduate research assistant in the Gitter Lab at the University of Wisconsin-Madison.
- Research novel methods for predicting the functional activity of protein variants
- Implement custom algorithms, data processing pipelines, and machine learning frameworks
- Utilize high-throughput computing clusters to accelerate GPU and CPU-based workflows
- Communicate research to broad audiences in talks and manuscripts
- Collaborate with multi-disciplinary teams including computer scientists and chemists
- Stay current with new research in the area
2015-2016U.S. Naval Research Laboratory
Student Research Scientist
- Researched novel method for tracking motions of contours
- Applied method for gait recognition with depth cameras
2014National Institutes of Health
Research Scientist Intern
- Developed computer vision system for tracking lab mice
- Designed custom graphical tools for efficiently annotating video