Available Research Projects

New Algorithm for Explainable AI
Faculty Member
Xin Liu

Description
The project aims to develop a new post-hoc explainability algorithm for deep learning models, specifically for computer vision applications. The idea revolves around enhancing the existing and widely used method of "Integrated Gradients".

Requirements
Applicants should have expertise in machine learning, deep learning and basic computer vision concepts, and ample experience with Python and Pytorch.

How to Apply: Fill the Google form: https://forms.gle/yv9u1mhFq8XJ5xkc7
Deep Networks to Decode Brain Signals in Listeners with Cochlear Implants
Faculty Member
Lee Miller

Description
The Speech Neuroengineering and Cybernetics Lab (PI: Lee Miller) in collaboration with the Cognitive Neurolinguistics Lab (PI: David Corina) and Dr. Doron Sagiv (Otolaryngology / Head & Neck Surgery) has a research project investigating speech perception in listeners with cochlear implants, including children born deaf as well as very old adults.

Cochlear implants are the most widespread and successful neural prosthesis ever, with over a million users worldwide. They can restore speech perception even in children born deaf. We use EEG to study how children’s brains change and improve as they learn to hear with an implant. One of the greatest challenges for clinicians and researchers, however, is that the implant itself adds electrical noise, thereby obscuring the brain signals. The overall goal of this project is to develop a deep neural network to separate neural signals from cochlear implant noise. This will have a profound effect on design and clinical evaluation the implants. This project will prepare candidates for careers in domains such as medical devices, consumer tech, data science, brain-computer interfaces, and many more.

Requirements
- Candidates will need to be comfortable training and testing deep neural networks (e.g. with Pytorch) and basic signal processing.
- The expected deliverable will be a first-author, peer-reviewed journal publication or conference proceeding, so a demonstrated ability to write well is also required.
- Specific skills may include any of the following (helpful but not required): Time series analysis, machine learning, data augmentation, blind source separation, and working with autoencoders.
How to Apply: If interested, please send an email to leemiller@ucdavis.edu with subject line “MS Project…” including a brief explanation of why you’re interested in this project, your resume, unofficial transcript, graduation timeline, details on programming abilities, time available (hours/week now, over summer, and through next year), and one example of a report that you have written on your own (for a class or project).
Prediction-Oriented Methods in Handling Missing Data
Faculty Member
Norm Matloff, Emeritus

Description
Many, probably most, real-world datasets contain missing values. A wealth of methods have been developed to deal with this problem, but few if any are prediction-oriented, and not many are suitable for machine learning. Here we will develop novel approaches to this problem.
A software package will be developed, and a paper written that explores the efficiency of the methods. The software will be written in R, with Python interfaces.
Requirements
Some background in linear models and machine learning methods. Previous exposure to R is nice but not required; strong coding skills, including debugging, are a must.

How to Apply: Please contact nsmatloff@ucdavis.edu.
Machine Learning Assisted Gamification for Education
Faculty Member
Setareh Rafatirad

Description
This project aims at developing a machine learning assisted gamification framework to promote equity and inclusion in education.

Requirements
Applicants need to have experience with python programming and a basic machine learning experience.

How to apply: Please contact Prof. Setareh Rafatirad srafatirad@ucdavis.edu and send your resume and transcript and the reason for choosing this research topic.
Python programming for physics modeling
Faculty Member
Emilie Roncali
Description
The Roncali lab in the Department of Biomedical Engineering at UC Davis (https://roncalilab.engineering.ucdavis.edu/) is looking for a highly motivated graduate student (MSc.) with an interest in medical physics and AI programming. Our lab develops physics simulation and AI-based simulations (e.g. GANs), which need to be refactored in Python, specifically for GPU computing.
Requirements
A strong background in computer science and advanced programming skills (Python) are required. The candidate should be familiar with Matlab and C++. The student will work closely with the postdoctoral researchers in the lab to translate their code in Python, implementing good programming practice.

Qualified candidates can apply by sending their CV and a short statement of research interests to Dr. Emilie Roncali (eroncali@ucdavis.edu).
Scientific software development for DNA nanotechnology
Faculty Member
David Doty

Description
The project will involve:
1. scientific software development, for instance on scadnano (https://github.com/UC-Davis-molecular-computing/scadnano#readme) for structural DNA nanotech design, and nuad (https://github.com/UC-Davis-molecular-computing/nuad#readme) for DNA sequence design.
2. (optional for MS project) Algorithmic and modeling research in support of DNA sequence design.
3. (optional for MS project) Collaborations with partner institutions on wet-lab experiments in nucleic acid strand displacement and self-assembly to tune the modeling and design software.
Requirements
Background in computer science/computer engineering/software engineering (either through a formal degree, or experience with programming)

How to apply:
Contact David Doty at doty@ucdavis.edu and indicate your background with software development.
Analysis and Visualization of Unstructured Climate Data
Faculty Member
Paul Ullrich

Description
Professor Paul Ullrich is leading a team to develop tools for analysis and visualization of climate data, particularly global, unstructured climate datasets defined in spherical geometry. These tools are widely employed throughout the climate science community, including the U.S. Department of Energy, the National Oceanic and Atmospheric Administration, and National Center for Atmospheric Research. Interested students can work with Prof. Ullrich and his team to develop new visualization or analysis capabilities in C++ or Python. Our core software repositories for this project can be found at:
https://github.com/SEATStandards/ncvis
https://github.com/UXARRAY/uxarray

Requirements
- If interested in visualization of climate data, experience with C++
- If interested in analysis of climate data, experience with Python
- Familiarity with Linux operating systems
- Basic familiarity with version control (Git)

How to apply:
If intersted, email Prof. Paul Ullrich (paullrich@ucdavis.edu)