Viveck received his Ph.D. from the University of California, Irvine in 2011, and was subsequently a postdoctoral researcher jointly with the Research Laboratory of Electronics (RLE) at MIT and the Electrical and Computer Engineering Department at Boston University. He was on the faculty at Pennsylvania State University from 2014 to 2024, and is currently an Associate Professor in the School of Electrical and Computer Engineering at Georgia Tech.
His research group works on the mathematical and information-theoretic foundations of reliable and trustworthy communication, computation, storage, and learning. Research areas include resilience and privacy in distributed learning, erasure-coded cloud storage, distributed algorithms, and wireless communications. The group combines theoretical analysis with systems prototyping and implementation.
He has been recognized with numerous awards, including the 2009 IEEE Information Theory Society Best Paper Award, the 2014 IEEE International Symposium on Network Computing and Applications (NCA) Best Paper Award, an NSF Career Award in 2016, and a 2019 Google Faculty Award. He has served an associate editor for IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications and the IEEE Journal on Selected Areas in Information Theory.
- Ph.D., Electrical and Computer Engineering, University of California, Irvine, 2012
- M.Tech, Electrical, Electronics, and Communications Engineering, Indian Institute of Technology, Madras, 2006
- B.Tech, Electrical Engineering, Indian Institute of Technology, Madras, 2006
Prof. Cadambe's research uses mathematical tools — drawn from information theory and coding theory — to solve practical engineering problems that arise in modern AI and cloud infrastructure. His interests span several interconnected areas: designing wireless communication networks that can handle interference; building AI and machine learning systems that are fault-tolerant and efficient at scale; protecting privacy in distributed computation and data storage; and developing coding techniques modern storage and database systems.
Prof. Cadambe's research efforts actively involve both graduate and undergraduate students.
Prof. Cadambe's teaching is centered on foundational and advanced electrical and computer engineering courses at both undergraduate and graduate teaching. His teaching spans core topics in electrical and computer engineering, including signal processing, communication systems, information theory, and error correcting codes. His courses emphasize mathematical foundations while connecting to modern applications in AI, wireless networks, and large-scale computing systems. Professor Cadambe’s teaching approach encourages active engagement and supports students’ development in both fundamental principles and research methodologies.
- 2009 Information Theory Society Best Paper Award
- 2014 NSF CRII Award
- 2016 NSF Career Award
- Google Faculty Award
- 2014 IEEE Network Computing and Applications Conference Best Paper Award
- Senior Member, IEEE
- V. R. Cadambe and S. A. Jafar, "Interference Alignment and the Degrees of Freedom of the K User Interference Channel," IEEE Transactions on Information Theory, vol. 54, no. 8, pp. 3425–3441, Aug. 2008.
- V. R. Cadambe and A. Mazumdar, "Bounds on the Size of Locally Recoverable Codes," IEEE Transactions on Information Theory, vol. 61, no. 11, pp. 5787–5794, Nov. 2015.
- S. Dutta, V. R. Cadambe, and P. Grover, "Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products," IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6171–6193, Oct. 2019.
- S. Dutta, M. Fahim, F. Haddadpour, H. Jeong, V. Cadambe, and P. Grover, "On the Optimal Recovery Threshold of Coded Matrix Multiplication," IEEE Transactions on Information Theory, vol. 66, no. 1, pp. 278–301, Jan. 2020.
- H. Zare, V. R. Cadambe, B. Urgaonkar, C. Sharma, P. Soni, N. Alfares, and A. Merchant, "LEGOStore: A Linearizable Geo-Distributed Store Combining Replication and Erasure Coding," Proceedings of the VLDB Endowment, vol. 15, no. 10, pp. 2201–2215, Jun. 2022.
- S. Vithana, V. R. Cadambe, F. P. Calmon, and H. Jeong, "Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation," IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2025.