YOUNG ALUMNUS/ALUMNA ACHIEVER AWARDEE 2026
Prof. Gauri Joshi is currently an Associate Professor in the Department of Electrical and Computer Science and Engineering (ECE) at Carnegie Mellon University (CMU), Pittsburgh, Pennsylvania, USA.
After graduating with a dual degree from IIT Bombay, where she received the Institute Gold Medal, she went on to pursue her Ph.D. in Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), USA. Her doctoral thesis focused on job scheduling algorithms for cloud computing systems.
During her master’s and Ph.D. studies, she interned at Qualcomm R&D, Bell Labs, and Google’s headquarters in Mountain View. These industry experiences shaped her research philosophy—drawing insights from practical challenges when defining and solving theoretical problems. After completing her Ph.D. in 2016, she joined the IBM T. J. Watson Research Center in New York as a Research Staff Member. In 2017, she joined Carnegie Mellon University as an Assistant Professor in the ECE department and was promoted to Associate Professor in 2022. Over the years, she has advised 17 Ph.D. students (9 graduated), a postdoctoral researcher, and more than 25 master’s and undergraduate students. She continues to maintain a strong connection with IIT Bombay—six of her Ph.D. students are IIT Bombay graduates, and her former postdoctoral researcher, Mr. Pranay Sharma, is now a faculty member at C-MInDS, IIT Bombay.
Prof. Joshi’s research focuses on democratising machine learning. While the success of modern ML systems has been driven by big data and big computers, their high resource demands have concentrated training and deployment within a few large organisations. Her work aims to enable scalable learning across networks of resource-constrained devices. Her group has been at the forefront of federated learning, making impactful contributions in tackling computational heterogeneity and client selection. Through a long-term collaboration with Google, her team produced two comprehensive surveys on advances and open problems in federated learning, which together have garnered over 10,000 citations.
During her Ph.D. at MIT, she provided the first rigorous understanding of how job replication and erasure coding can mitigate tail latency in computing systems. She introduced and analysed the (n,k) fork-join system, a generalisation of the (n,n) fork-join model in queueing theory. This work helped catalyse a new subfield of information theory known as coded computing. Her team has since contributed to multiple dimensions of coded computing, including adapting to varying node speeds (Best Paper Award at ACM SIGMETRICS 2020), varying traffic demand (Best Paper Award at ACM MobiHoc 2022), and non-linear computations.
Prof. Joshi has received several recognitions, including the MIT Technology Review 35 Under 35, the NSF CAREER Award, the ONR Young Investigator Award, the Google Research Scholar Award, and the IEEE Goldsmith Lecturer Award, along with multiple best paper and thesis awards.
Interests & Hobbies: I enjoy cooking, solving puzzles, traveling, running, and music. During my IIT Bombay years, I served as the Music and Dance (MAD) secretary of Hostel 10 and sang in Surbahar and other music events at Mood Indigo and Malhar.
A Personal Note: I grew up in a family of engineers—both my parents are civil engineers. My father is also an IIT Bombay alumnus, and I am proud to carry forward the legacy. I visited the IITB campus several times during my childhood and always dreamed of walking down its beautiful tree-lined paths as a student. My dream came true in 2005, when I entered the campus as a freshman and spent five amazing years there. My husband, Shreerang, is also an IITB alumnus.
