Adi Acharya

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I'm a Ph.D. candidate in Computer Science at the University of Maryland, College Park, advised by Prof. David Mount. My research focuses on computational geometry and data structures/algorithms, with applications in data science and machine learning.

In my previous life, I was a lecturer in Computer Science at the University of Maryland, a computational scientist at IISc Bangalore, and an electrical engineer at IIT Kharagpur.

I expect to graduate in Spring 2026 and am seeking full-time opportunities in industry, with primary interests in data science, machine learning, and algorithms.

Research

My research primarily lies in the field of computational geometry and topology, with a strong emphasis on theoretical foundations and their practical applications in machine learning. As of late, I have been working towards the development of efficient algorithms and optimized frameworks, for analyzing high-dimensional categorical and stochastic data.

We study algorithms for maintaining hypotheses of large, dynamically evolving geometric datasets, where objects move in d-dimensional space and their locations are uncertain. Using a motion model where each object's movement is limited by its nearest neighbor, we provide an algorithm that maintains a close approximation (measured by KL-divergence) to the true state, and prove its asymptotic optimality.

We study the linear SVM problem in the Hilbert metric, a non-Euclidean geometry over convex bodies. We present efficient algorithms for SVM classification in this setting, for convex polygons in the plane and polytopes in higher dimensions, and also consider related problems in the Funk distance.

The evolving data framework studies algorithms that maintain an approximate sketch of a structure as it changes over time. We focus on tracking labeled nodes in the plane, where labels can be swapped by an unseen evolver, and updates require physically moving them. Applications include tracking disease hot-spots and UAVs. Our approach uses an Oracle to guide efficient updates over a sparse graph.

We establish a novel framework for evolving geometric data. In this framework, we study maintaining labels on tree vertices as they evolve over time. Our results show the algorithm keeps labels close to their true locations, with nearly matching lower bounds.

The contour tree is a topological structure that tracks the connectivity of level sets in a scalar function, supporting visual exploration and analysis. This paper presents a fast, parallel, and memory-efficient algorithm for constructing contour trees on large datasets, outperforming existing methods in speed and memory usage.

This paper analyzes EEG signals during 36 hours of sleep deprivation using phase synchronization. Weighted networks are built from EEG data at different wavelet levels, and network parameters are tracked to study brain integration and segregation. Some parameters show clear patterns in specific frequency bands as sleepiness and fatigue increase.

Based on a modified version of Jon Barron's website.