Actively looking for full-time software engineer opportunies starting August 2025

Hi everyone, I am currently a 5th-year Computer Science Ph.D. student at Georgia Tech. My advisor is Vivek Sarkar. Prior to my Ph.D. journey, I earned my bachelor degree from Washington University in St. Louis in December 2019.

My research area is dynamic HPC/Parallel program analysis. My Ph.D. thesis focused on understanding, detecting and fixing data races in parallel programs. I have developed several dynamic debugging tools for OpenMP and HClib programs. Additionaly, my reserach achieves theoretical breakthroughs and utilizes Large Language Model in industrial settings.

Email: fjin35 at gatech.edu

Research problems

I am proud that my research pushes the boundary of human’s understanding of data races and program behavior.

Determinism: given the same input, how do we guarantee the programs will return the same output? How do we even define “same” here? In my ECOOP paper, I give the formal definition of determinism for task-parallel programs with promises, and prove that data race freedom guarantees determinism for such program.

Happens-before relation: task A happens before task B if and only if in all possible scheduing of the program, task A has to finish before task B starts.

Concurrent access: if two tasks does not have happens-before relationship between them, we say they can happen in parallel (or concurrently). The exact order will be determined by the runtime. In one schedule, task A may happen before task B. In the other schedule, it may be the other way around.

Data race: if two tasks concurrently access the same memory location, and at least one of them perform a write, we define this as a data race. Data race is notorious because it makes program non-deterministic. Imagine task A writes 5 to variable x, and task B writes 3 to it. When the program ends, we have no idea what the value of x will be: 5 or 3, that’s a question.

Dynamic race detection: To detect data races while running the program, we use compiler to “instrument” each read and write in the program. Instrument means we insert a check function after each read and write. This is done on the IR level. When the program is running, the check function records each read and write, and see if a previous access causes a data race with the current access.
My ECOOP paper designs a new race detection algorithm and evalutes the implementation of it. It is sound and precise: meaning it will not report non-existing race or miss any races.