Ishani Karmarkar
I am a PhD student at Stanford University in the Institute for Computational and Mathematical Engineering. My research primarily focuses on the design and analysis of algorithms for problems arising in graph theory, machine learning, and optimization.
I am advised by Aaron Sidford and Ellen Vitercik.
Previously, I earned a BS in Applied and Computational Mathematics with a Minor in Computer Science from the California Institute of Technology (Caltech), where I did undergraduate research with Bamdad Hosseini, Andrew Stuart, and Ashish Mahabal.
I also had the opportunity to work as an intern at Facebook (now Meta), where I worked on a variety of machine learning projects.
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From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection
Vaggos Chatziafratis, Ishani Karmarkar, and Ellen Vitercik (alphabetical)
in submission, 2023
Clustering is a fundamental problem in machine learning. There are a
variety of clustering algorithms in the literature; however, there is
scant guidance on choosing a good clustering algorithm for a dataset
at hand. In this paper we approach the problem of clustering
algorithm selection using a semi-supervised framework. We present
algorithms that use a small subset of the data to provably estimate the
performance of three classic clustering algorithms: k-means++, k-centers, and
single linkage hierarchical clustering on the full dataset. We support
our theoretical results with experiments on several real-world clustering
instances. (arxiv)
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Towards Optimal Effective Resistance Estimation
Rajat Dwaraknath, Ishani Karmarkar, and Aaron Sidford (alphabetical)
Neural Information Processing Systems (NeurIPS), 2023
Computing the effective resistance of nodes in a network (or, undirected graph) is a fundamental task with many applications, for instance, in graph machine learning, graph data analysis, and the design of efficient graph algorithms. In this paper we introduce new efficient algorithms for estimating effective resistances in well-connected graphs. We also prove improved time lower bounds for effective resistance estimation. In addition, we provide natural extensions
of our work to related problems on symmetric diagonally dominant (SDD) and
positive semi-definite (PSD) matrices. (arxiv)
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Real-bogus classification for the Zwicky Transient Facility using deep learning
Dmitry A Duev, Ashish Mahabal, Frank J Masci, Matthew J Graham, Ben Rusholme, Richard Walters, Ishani Karmarkar, Sara Frederick, Mansi M Kasliwal, Umaa Rebbapragada, and Charlotte Ward
Royal Astronomical Society, 2019
In this project, we present a convolutional neural network classifier for classifying real versus bogus data in astronomical images from the Zwicky Transients Facility (ZTF), a new robotic time-domain astronomical survey. (arxiv)
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Internship Projects
Webpage in progress, to be updated soon :)
Other Projects
Webpage in progress, to be updated soon :)
Teaching Experience
Aside from research, I also enjoy teaching mathematics. At Stanford, I had the opportunity to serve as a teaching assistance for CME 302 (Numerical Linear Algebra) and ME 300 (Linear Algebra with Engineering Applications.)
At Caltech, I enjoyed being a teaching assistant for CME 213 (Markov Chains, Discrete Stochastic Processes, and Applications), ACM 104 (Applied Linear Algebra), CS 157 (Statistical Inference), and ACM 95/100 (Introductory Methods of Applied Math).
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