Pathways to Discovery 2027
Calling undergraduate and master’s students at Prairie View A&M University and Morgan State University! Interested in AI research? Join us in Summer 2027 at UC San Diego for a paid summer research internship.
Applications due September 30, 2026. Click here to apply!
Why apply?
Some of best jobs in the world are in AI research. Think jobs like Research Scientist at OpenAI, or becoming a university faculty studying AI. To get these jobs, you need to have a strong track record of research in AI, which often means getting a PhD. To get into a top PhD program, you'll want to have experience doing AI research. That's where this program comes in!
As a research intern in Pathways to Discovery, you will:
- Get hands-on experience doing cutting-edge AI research at UC San Diego, a top-5 institution in the world in computer science and AI.
- Work with world-class professors, researchers, and graduate students.
- Live on the beautiful UCSD campus for 8 weeks during Summer 2027.
- Gain an advantage when applying to PhD programs, especially at the University of California campuses.
- Get paid while doing so!
Eligibility
- Open to undergraduate and master’s students from Prairie View A&M University and Morgan State University.
- Students should have some coursework or experience in data science, computer science, statistics, or a related field.
- Prior research experience is a plus, but not required.
- International students are eligible.
- Graduating seniors are eligible (e.g. you will not be a student during Fall 2027).
Funding and support
Interns selected for the program will receive:
- A paid summer research fellowship with a $5,000 stipend.
- Fully covered on-campus housing and dining.
- Round-trip travel to UC San Diego.
- Mentorship, networking, and career development opportunities.
- Field trips to the San Diego Zoo, beaches, and more.
Timeline
- Applications due
- September 30, 2026, Anywhere on Earth
- Acceptance notification
- October 31, 2026
- Program dates
- June 20, 2027 - August 14, 2027 (tentative, exact dates to be announced)
Mentors and projects
Explore the potential research projects below. Mentors are listed in alphabetical order by last name.
Emily AikenComputational social science
Main research question
How can computational and design-oriented methods improve the design and delivery of development and humanitarian aid programs?
What a summer intern will do
Analyze and clean quantitative data, train machine learning models, and help design or run surveys, experiments, and user studies.
What excites the lab
Using data science methods to improve the efficiency of high-stakes humanitarian work.
Alex CloningerMachine learning theory
Main research question
How can you determine information about every element in a dataset from only a subset of the data?
What a summer intern will do
Develop and implement algorithms, analyze graphs, and run experiments.
What excites the lab
Building mathematical tools that make algorithms more powerful and efficient.
Haojian JinHuman-computer interaction and data privacy
Main research question
How can we make privacy risk assessment more accessible and accurate?
What a summer intern will do
Design and refine multiple-choice questions for privacy assessments and run user studies.
What excites the lab
Turning complex privacy concepts into simple, understandable tools.
Victor MincesInteractive applications for manipulating, learning about, and playing with sound
Main research question
How can we design applications that let people interact with digital sound in ways that improve their lives?
What a summer intern will do
Help create browser-based applications for manipulating and creating sound.
What excites the lab
Seeing children excited to play with and learn about sound.
Gal MishneMachine learning, applied math, and computational neuroscience
Main research question
How can we measure similarity between two potentially large graphs?
What a summer intern will do
Analyze large datasets, code in Python, and learn machine learning methods.
What excites the lab
Graphs, neural recordings, and ice cream.
Benjamin SmarrTime series in health AI and human–AI health collaboration
Main research question
Can we support decision making informed by health data using AI?
What a summer intern will do
Learn statistical methods for dissecting time-series health data.
What excites the lab
How new data sources empower historically underserved populations.
Yusu WangGeometric deep learning, generative AI, graph learning, and neural algorithmic reasoning
Main research question
How do generative models form internal concepts, and how can that understanding steer them toward better outputs? Can neural networks learn generalizable algorithmic procedures, and how can classical algorithms be combined with machine learning?
What a summer intern will do
Run experiments with machine learning models, implement improvements to existing models, analyze results, and contribute to paper writing when appropriate.
What excites the lab
Understanding what drives the behavior of modern machine learning models and using those insights to design better, more robust, and safer models.
Duncan Watson-ParrisMachine learning for climate
Main research question
Can we use machine learning to better model climate physics?
What a summer intern will do
Build machine learning models of atmospheric physics, analyze results, and compare them with climate models.
What excites the lab
Thinking about new ways of modeling the Earth!
Past cohorts
In 2026, we successfully ran Pathways to Discovery for the first time, bringing students and mentors together at UC San Diego for a summer of research, learning, and community.
Acknowledgements
This program is generously funded by the UC-HBCU Initiative.