arXiv subjects

Shubham Jha

Publications and source records attributed to Shubham Jha.

4 recordsLinked to original sources

arXiv2025

Empa: An AI-Powered Virtual Mentor for Developing Global Collaboration Skills in HPC Education

High-performance computing (HPC) and parallel computing increasingly rely on global collaboration among diverse teams, yet traditional computing curricula inadequately prepare students for cross-cultural teamwork essential in modern computational research environments. This paper presents Empa, an AI-powered virtual mentor that integrates intercultural collaboration training into undergraduate computing education. Built using large language models and deployed through a progressive web application, Empa guides students through structured activities covering cultural dimensions, communication styles, and conflict resolution that are critical for effective multicultural teamwork. Our system addresses the growing need for culturally competent HPC professionals by helping computing students develop skills to collaborate effectively in international research teams, contribute to global computational projects, and navigate the cultural complexities inherent in distributed computing environments. Pilot preparation for deployment in computing courses demonstrates the feasibility of AI-mediated intercultural training and provides insights into scalable approaches for developing intercultural collaboration skills essential for HPC workforce development.

cs.CY↗

arXiv2023

Fundamental Limits of Distributed Optimization over Multiple Access Channel

We consider distributed optimization over a $d$-dimensional space, where $K$ remote clients send coded gradient estimates over an {\em additive Gaussian Multiple Access Channel (MAC)} with noise variance $σ_z^2$. Furthermore, the codewords from the clients must satisfy the average power constraint $P$, resulting in a signal-to-noise ratio (SNR) of $KP/σ_z^2$. In this paper, we study the fundamental limits imposed by MAC on the {convergence rate of any distributed optimization algorithm and design optimal communication schemes to achieve these limits.} Our first result is a lower bound for the convergence rate, showing that communicating over a MAC imposes a slowdown of $\sqrt{d/\frac{1}{2}\log(1+\SNR)}$ on any protocol compared to the centralized setting. Next, we design a computationally tractable {digital} communication scheme that matches the lower bound to a logarithmic factor in $K$ when combined with a projected stochastic gradient descent algorithm. At the heart of our communication scheme is carefully combining several compression and modulation ideas such as quantizing along random bases, {\em Wyner-Ziv compression}, {\em modulo-lattice decoding}, and {\em amplitude shift keying.} We also show that analog schemes, which are popular due to their ease of implementation, can give close to optimal convergence rates at low $\SNR$ but experience a slowdown of roughly $\sqrt{d}$ at high $\SNR$.

cs.IT↗

arXiv2023

Universal Gaussian Quantization with Side Information using Polar Lattices

We consider universal quantization with side information for Gaussian observations, where the side information is a noisy version of the sender's observation with noise variance unknown to the sender. In this paper, we propose a universally rate optimal and practical quantization scheme for all values of unknown noise variance. Our scheme uses Polar lattices from prior work, and proceeds based on a structural decomposition of the underlying auxiliaries so that even when recovery fails in a round, the parties agree on a common "reference point" that is closer than the previous one. We also present the finite blocklength analysis showing an sub-exponential convergence for distortion and exponential convergence for rate. The overall complexity of our scheme is $O(N^2\log^2 N)$ for any target distortion and fixed rate larger than the rate-distortion bound.

cs.IT↗

arXiv2022

Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization

Communication efficient distributed mean estimation is an important primitive that arises in many distributed learning and optimization scenarios such as federated learning. Without any probabilistic assumptions on the underlying data, we study the problem of distributed mean estimation where the server has access to side information. We propose \emph{Wyner-Ziv estimators}, which are communication and computationally efficient and near-optimal when an upper bound for the distance between the side information and the data is known. As a corollary, we also show that our algorithms provide efficient schemes for the classic Wyner-Ziv problem in information theory. In a different direction, when there is no knowledge assumed about the distance between side information and the data, we present an alternative Wyner-Ziv estimator that uses correlated sampling. This latter setting offers {\em universal recovery guarantees}, and perhaps will be of interest in practice when the number of users is large and keeping track of the distances between the data and the side information may not be possible. With this mean estimator at our disposal, we revisit basic problems in decentralized optimization and compression where our Wyner-Ziv estimator yields algorithms with almost optimal performance. First, we consider the problem of communication constrained distributed optimization and provide an algorithm which attains the optimal convergence rate by exploiting the fact that the gradient estimates are close to each other. Specifically, the gradient compression scheme in our algorithm first uses half of the parties to form side information and then uses our Wyner-Ziv estimator to compress the remaining half of the gradient estimates.

cs.IT↗