Raja Sunkara

I am a second year PhD student in the Computer Science department at Missouri S&T, advised by Prof. Ardhendu Tripathy. Additionally, I am a member of the Statistical Machine Learning Lab. My research interests broadly include machine learning, signal processing, and optimization. Currently, I am working on developing sequential decision making algorithms for multi-armed bandits and black-box optimization.

Previously, I obtained my Master’s thesis from Missouri S&T under the guidance of Prof. Thomas Tie Luo. I earned my Bachelor’s degree in Aerospace Engineering from the Indian Institute of Technology, Madras, where my thesis project was supervised by Prof. Ranjith Mohan. In industry, I worked as a Data Analyst, Data Scientist, Research Engineer, and R&D Data Scientist at Enfrien Innovations, Agrometrics, Matdun Labs, and Alcon, respectively.

CV

Download my latest CV (PDF)

news

Oct 16, 2026	Our ICML 2025 paper, Adaptive Partitioning Schemes for Optimistic Optimization, is now on arXiv, with code, a pip-installable package and a Colab demo.
Dec 14, 2024	I will be attending the Neural Information Processing Systems (NeurIPS) conference from December 10 to December 15, 2024, to present my paper.
Jul 15, 2024	I attended the International Conference on Machine Learning (ICML) virtually in July 2024.
Sep 15, 2022	I presented my paper at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) in September 2022.

selected publications

ICML
Adaptive Partitioning Schemes for Optimistic Optimization

Raja Sunkara, and Ardhendu Tripathy

In Proceedings of the 42nd International Conference on Machine Learning, 2025

Abs arXiv Bib HTML PDF Code Website

Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically known and whose gradients are not available. Practitioners often have a fixed budget for the number of function evaluations and the performance of an optimization algorithm is measured by its simple regret. In this paper, we study the class of "Optimistic Optimization" algorithms for black-box optimization that use a partitioning scheme for the domain. We develop algorithms that learn a good partitioning scheme and use flexible surrogate models such as neural networks in the optimization procedure. For multi-index functions on an m-dimensional subspace within d dimensions, our algorithm attains \tildeO(n^-β/ d) regret, where β= 1 + \fracd-m2m-1, as opposed to \tildeO(n^-1/d) for SequOOL, a state-of-the-art optimistic optimization algorithm. Our approach is competitive across a wide range of numerical benchmarks. Additionally, we introduce weight quantization in a large language model as a novel task for black-box optimization.
@inproceedings{sunkara2025adaptive, title = {Adaptive Partitioning Schemes for Optimistic Optimization}, author = {Sunkara, Raja and Tripathy, Ardhendu}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, series = {Proceedings of Machine Learning Research}, volume = {267}, pages = {58029--58064}, year = {2025}, publisher = {PMLR}, }

ECML-PKDD

No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects

Raja Sunkara, and Tie Luo

In Joint European conference on machine learning and knowledge discovery in databases, 2022

Bib

@inproceedings{sunkara2022no,
  title = {No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects},
  author = {Sunkara, Raja and Luo, Tie},
  booktitle = {Joint European conference on machine learning and knowledge discovery in databases},
  pages = {443--459},
  year = {2022},
  organization = {Springer},
}