Four SCS Students Named Facebook Fellows

SCS Ph.D. students Devendra Singh Chaplot, Sai Krishna Rallabandi (top) Hubert Tsai and Juncheng Yang have been named Facebook fellows.

Four Ph.D. candidates in the School of Computer Science are among 36 outstanding students in computer science and engineering from 16 universities who have been named 2020 recipients of the Facebook Fellowship Program.

Each Facebook fellow receives tuition and fees for up to two academic years and a stipend of $42,000, which includes conference travel support. Facebook received applications from 1,876 students at more than 100 universities for this year's program.

The fellowship program chose Juncheng Yang, a Ph.D. student in the Computer Science Department, to be a fellow in computer storage and efficiency. Yang is broadly interested in the reliability, performance and availability in the storage and caching subsystems of internet-scale web services.

Devendra Singh Chaplot, a Ph.D. student in the Machine Learning Department (MLD), was named a fellow in computer vision. His research aims to design algorithms capable of "physical intelligence," i.e., building intelligent embodied autonomous agents capable of learning to perform complex tasks in the physical world that involve perception, natural language understanding, reasoning, planning and sequential decision making.

Sai Krishna Rallabandi, a Ph.D. student in the Language Technologies Institute, and Hubert Tsai, a Ph.D. student in MLD, were both named fellows in spoken language processing and audio classification.

Rallabandi, who also earned a master's degree in language technologies at CMU, is developing a framework referred to as "De-Entanglement," which aims to isolate relevant causal factors of variation in the data distribution. He hopes to apply De-Entanglement to various tasks such as unsupervised acoustic unit discovery from speech, flexible and expressive Text to Speech synthesis, acoustic search, and visual question answering.

Tsai's research goal is to understand computational and statistical principles in spoken language modeling. He plans to use these principles to enhance representation interpretability and improve data efficiency.

For More Information, Contact:

Byron Spice | 412-268-9068 | bspice@cs.cmu.eduVirginia Alvino Young | 412-268-8356 | vay@cmu.edu