MPCS Pre-Doctoral Program

Core course requirements (4 courses)

• ​1 Theory course (MPCS or PhD level)
• 2 Systems core classes (MPCS or approved 300-level PhD level)
• 1 Machine Learning (MPCS 53111 or approved 300-level PhD course)

Research requirements (4 courses)

• 2 Practicum courses with MS research advisor
• 2 Reading & Research courses to work on research

Elective course requirements (4 courses)

• CMSC 300-level PhD courses providing depth in a specific area.

Internship requirements

• Pre-doctoral students should plan to work with their MS research advisor in the summer after their first academic year of study.

Sample Pre-Doc Course Schedule

Pre-Doctoral students are expected to concentrate most of their research work in their first year, allowing them to build a competitive PhD application in their final quarter. A typical schedule could look like this (research courses appear highlighted in bold):

The Pre-Doc program is intended for students with a substantial background in computer science who can immediately contribute to their assigned research group. Research experience in your area of focus is preferred. Unlike the 9-Course and 12-Course MPCS programs, the Pre-Doc program is not intended for students who need the immersion classes.

Most students who are admitted to the Pre-Doc program have completed an undergraduate major in Computer Science, or a similar field, and have significant research experience. The Pre-Doc program is highly competitive, admitting only a few students per year.

Admissions decisions for the Pre-Doc program are done in conjection with the CS faculty advisors. Most advisors are looking for students who have the skills and background to make contributions to their research teams.

Below is the list of Department of Computer Science faculty who have expressed interest in advising students in the MPCS Pre-Doctoral program, and their research interest(s) for the 2024 – 2025 academic year.

Please note: these are the only CS faculty that have agreed to review Pre-Doctoral applications for the 2024-2025 academic year. If you submit an application to work with a faculty member not listed here, or in a research area not listed, your application will not be reviewed.

• Raul Castro Fernandez – Data Science, the Economics of Data, Data Management, Large language models for data discovery and integration
• Haryadi Gunawi – Machine Learning for Storage Systems, Storage Systems for Machine Learning
• Rana Hanocka – Computer Graphics, Computer Vision, Deep Learning, Generative AI
• Grant Ho – Security and Privacy, AI/ML and Security, Data Science
• William Hoza – Computational Complexity Theory, Pseudorandomness, Derandomization, Circuit Complexity
• Junchen Jiang – Computer Networks, Machine Learning, Mobile Computing
• Yanjing Li – Computer Architecture, Systems and Machine Learning, Emerging Technologies, Robust System Design
• Pedro Lopes – Wearables, Haptics and New Sensory Experiences in Virtual/Augmented Reality, Human Computer Interaction
• Ken Nakagaki – Human-Computer Interaction, Interaction Design, Actuated and Shape-Changing Tangible User Interfaces, Interactive Media Art
• Aaron Potechin – Complexity Theory, Discrete Math
• Robert Rand – Quantum Computing, Programming Languages, and Formal Verification
• Chenhao Tan – Human-Centered AI, Natural Language Process, Computational Social Science, Human-Computer Interaction

These research advisors are only available to students admitted to the Pre-Doctoral program.