I am an Assistant Professor in the Electrical and Computer Engineering Department at UW-Madison, with affiliated appointments in the Departments of Mathematics and Statistics. I am a fellow of the Grainger Institute for Engineering. My research interests lie in the areas of information theory, machine learning, and network science.
From 2015-2016, I was a postdoctoral fellow at the Warren Center for Network and Data Sciences at the University of Pennsylvania. I was hosted jointly by the Statistics and CIS departments. I completed my PhD in the EECS Department at UC Berkeley in 2015, where I was advised by Professor Venkat Anantharam.
I graduated with a Bachelor of Technology (B. Tech) degree in Electrical Engineering from IIT Bombay in 2010. I was advised by Professors Abhay Karandikar and Sibi Raj B Pillai. I also spent three exciting months at the Department of Information Engineering, Chinese University of Hong Kong, working with Professor Chandra Nair in 2009.
My office address is 4611 Engineering Hall. I can be reached by email at vjog at wisc dot edu.
Fall 2016: ECE 730, "Modern Probability Theory and Stochastic Processes"
Spring 2017: ECE 729, "Theory of Information Processing and Transmission"
Fall 2017: ECE 730, "Modern Probability Theory and Stochastic Processes"
Spring 2017: ECE 203, "Signals, Information, and Computation" (shadowing Prof. Barry Van Veen)
Fall 2018: ECE 203, "Signals, Information, and Computation"
Spring 2019: ECE 203, "Signals, Information, and Computation"
- Adrian Tovar Lopez (Department of Mathematics)
- Jing Hao (Department of Mathematics, advised by Prof. Nigel Boston)
- Ankit Pensia (Department of Computer Sciences, co-advised with Prof. Po-Ling Loh)
- Jinnian Zhang (Department of Electrical and Computer Engineering)
- Bhumesh Kumar (Department of Electrical and Computer Engineering)
MS in Electrical and Computer Engineering
Machine Learning and Signal Processing accelerated program
- To MLSP students on campus: Please email me if you would like to do your MLSP project at the intersection of Machine Learning and Medical Imaging.
- Yogesh Balaje Mahendran
- Unifying the Brascamp-Lieb inequality and the Entropy Power inequality. Venkat Anantharam, Varun Jog, and Chandra Nair. (January 2019)
- Dual Loomis-Whitney inequalities using information theory. Jing Hao and Varun Jog. (January 2019)
- Teaching and learning in uncertainty. Varun Jog. (January 2019)
- Generalization error bounds using the Wasserstein metric. Adrian Tovar Lopez and Varun Jog. ITW 2018.
- Convexity of mutual information along the Ornstein-Uhlenbeck flow. Andre Wibisono and Varun Jog. ISITA 2018.
- Graph-Based Ascent Algorithms for Function Maximization. Muni Pydi Sreenivas, Varun Jog, and Po-Ling Loh. Allerton Conference, 2018.
- Convexity of mutual information in the heat flow. Andre Wibisono and Varun Jog. ISIT 2018.
- Generalization bounds for noisy, iterative algorithms. Ankit Pensia, Varun Jog, Po-Ling Loh. ISIT 2018.
- An entropy inequality for symmetric random variables. Jing Hao and Varun Jog. ISIT 2018.
- A convolution inequality for entropy over Z_2. Varun Jog. ISIT 2017.
- Information and estimation in Fokker-Planck channels. Andre Wibisono, Varun Jog, Po-Ling Loh. ISIT 2017.
- Computing and maximizing influence in linear threshold and triggering models. Justin Khim, Varun Jog, Po-Ling Loh. NIPS 2016.
- Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence. Varun Jog, Po-Ling Loh. Allerton Conference, 2015.
- On the geometry of convex typical sets. Varun Jog, Venkat Anantharam. Best student paper award at ISIT 2015.
- On model misspecification and KL separation for Gaussian graphical models. Varun Jog, Po-Ling Loh. ISIT 2015.
- A geometric analysis of the AWGN channel with a (sigma, rho)-power constraint. Varun Jog, Venkat Anantharam. ISIT 2015.
- An energy harvesting AWGN channel with a finite battery. Varun Jog, Venkat Anantharam. ISIT 2014.
- Convex relative entropy decay in Markov chains. Varun Jog, Venkat Anantharam. CISS 2014.
- The entropy power inequality and Mrs. Gerber's lemma for groups of order 2^n. Varun Jog, Venkat Anantharam. ISIT 2013.
- An information inequality for the BSSC channel. Varun Jog, Chandra Nair. ITA 2010.
- Optimal rates for community estimation in the weighted stochastic block model. Min Xu, Varun Jog, and Po-Ling Loh. To appear in the Annals of Statistics, 2019.
- Intrinsic entropies of log-concave distributions. Varun Jog, Venkat Anantharam. IEEE Transactions on Information Theory, 2017.
- Persistence of centrality in random growing trees. Varun Jog, Po-Ling Loh. Random Structures and Algorithms, 2017.
- Computationally efficient influence maximization in stochastic and adversarial models: Algorithms and analysis. Justin Khim, Varun Jog, Po-Ling Loh. Submitted, 2017.
- Analysis of centrality in sublinear preferential attachment trees via the Crump-Mode-Jagers branching process. Varun Jog, Po-Ling Loh. IEEE Transactions on Network Science and Engineering, 2016.
- A geometric analysis of the AWGN channel with a (sigma, rho)-power constraint. Varun Jog, Venkat Anantharam. IEEE Transactions on Information Theory, 2016.
- The entropy power inequality and Mrs. Gerber's lemma for groups of order 2^n. Varun Jog, Venkat Anantharam. IEEE Transactions on Information Theory, 2014.
- An information inequality and evaluation of Marton's inner bound for binary input broadcast channels. Yanlin Geng, Varun Jog, Chandra Nair, Vincent Zizhou Wang. IEEE Transactions on Information Theory, 2013.
- (01/19) I was the R. Narasimhan Memorial Lecturer at Tata Institute of Fundamental Research, Mumbai
- (10/18 , 11/18, 12/18) Invited talks at Rutgers University, Michigan University, Columbia University, and University of Maryland, College Park.
- (08/18) ML4MI pilot grant awarded! Thanks to the Department of Radiology and the Grainger Institute of Engineering for supporting my collaboration with Dr. Alan McMillan.
- (07/18) Thanks to NSF for funding my EAGER proposal with Po-Ling Loh (co-PI)!
- (04/18) Congratulations to Yogesh for winning the Hilldale Undergraduate/Faculty Research Fellowship! Yogesh will be collaborating with Prof. Dhavan Shah's group to analyze the spread of misinformation in social networks.
- Prof. Jiamian Hu and I will be co-organizing the TGIF Seminar series in Spring 2019
- I am co-organizing the Machine Learning for Medical Imaging (ML4MI) initiative at UW-Madison, along with Prof. Po-Ling Loh (ECE) and Prof. Diego Hernando (Departments of Radiology and Medical Physics)
- James Melbourne and I are guest editors for the special issue on entropy inequalities for the Entropy Journal