Summary
Disha Shur is a research-focused computer scientist and graduate teaching assistant at Purdue with eight years of experience spanning algorithm design, signal processing, and wireless systems. Currently pursuing a PhD in Computer Science, she develops scalable, low-dimensional and hypergraph representation learning algorithms while teaching data mining and signals & systems. Her background blends theoretical work on randomized algorithms and spectral graph theory with hands-on systems engineering—building a 5G testbed, implementing PHY protocols, and working with USRPs and vector signal analyzers. She has contributed to projects across leading Indian institutes and participated in Google's CS Research Mentorship, giving her a strong mix of academic rigor and practical experimentation. Notably, her research combines ideas from finite rate of innovation and majorization-minimization approaches to tackle sample-efficient signal reconstruction, revealing a knack for marrying classical signal techniques with modern learning problems. Based in West Lafayette, she brings interdisciplinary depth to problems at the intersection of signals, networks, and representation learning.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Electronics and Telecommunication Engineering, 8.34/10, Bachelor's degree, Electronics and Telecommunication Engineering, 8.34/10 at Indian Institute of Engineering Science and Technology (IIEST), Shibpur
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Purdue University