Passion to Engineer Something!!
Published:
Undergrad Adventures: Formula Student Competitions 
Posts by Collection
portfolio
Colorful Corvallis
Published:
Blooming Beauties: colored memories I can not ever leave behind!! 
Nature that Inspires..
Published:
Beauty & Complexity!! What a balance .. 
Hiking & fun nothing else.
Published:
Hiking 
projects
NL2SQL: LLM-Powered Natural Language to Database Query Engine
Published:
Multi-stage LLM pipeline achieving 91% accuracy that converts plain-English questions into optimized SQL queries, cutting query fulfillment from days to under 3 seconds. Built during AI/ML internship at Seagate Technology.
Hybrid HAB: Digital Twins for Predicting Harmful Algal Blooms
Published:
Digital twin models that predict dangerous harmful algal blooms before they happen, combining watershed simulation, hydrodynamic modeling, and machine learning. Funded by U.S. Army Corps of Engineers.
GEPC: Making AI Decisions Understandable Through Part-Based Explanations
Published:
Scalable Explainable AI framework achieving 84% label-transfer accuracy across 158 ImageNet categories, reducing annotation effort by 85%. Transforms opaque neural network predictions into human-readable part-based explanations.
C-Star: Predicting unresolved variables in dynamical systems
Published:
Universal Dynamic Equations (UDEs) that uncover hidden drivers of ecosystem collapse from observable data alone—delivering superior early-warning capability for regime shifts with less data than conventional methods.
publications
A Simple Method of Solution For Multi-label Feature Selection
Published in IEEE International Conference on Electrical, Computer and Communication Technologies, 2019
The study proposes a two-step algorithm for multi-label classification (MLC). It first decomposes the output label space into lower dimensions, then applies feature selection in the reduced space. This approach efficiently handles high-dimensional datasets and reduces computational load.
Recommended citation: Valadi, Jayaraman K., Prasad T. Ovhal, and Kunal J. Rathore. "A simple method of solution for multi-label feature selection." 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. https://ieeexplore.ieee.org/document/8869493
RA2Vec: Distributed Representation of Protein Sequences with Reduced Alphabet Embeddings
Published in Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB 2020), 2020
Protein function identification has become an important task due to the rapid growth of sequenced genomes. This work introduces RA2Vec (Reduced Alphabets to Vectors), a novel approach for protein sequence representation that combines reduced amino acid alphabets with Word2Vec-style embeddings. We map protein sequences to reduced alphabets based on hydropathy and conformational similarity, then apply skip-gram models to create distributed vector representations that capture both biochemical properties and sequential context.
Recommended citation: Wijesekara, R. Y., Lahorkar, A., Rathore, K., & Valadi, J. K. (2020). RA2Vec: Distributed representation of protein sequences with reduced alphabet embeddings. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (pp. 1-1). https://dl.acm.org/doi/10.1145/3388440.3414925
Generating Part-based Global Explnations via Correspondence
Published in IJCAI 2024 Workshop on Explainable Artificial Intelligence (XAI), 2024
Deep learning models are notoriously opaque. Existing explanation methods often focus on localized visual explanations for individual images. Concept-based explanations, while offering global insights, require extensive annotations, incurring significant labeling cost. We propose an approach that leverages user-defined part labels from a limited set of images and efficiently transfers them to a larger dataset. This enables the generation of global symbolic explanations by aggregating part-based local explanations, ultimately providing human-understandable explanations for model decisions on a large scale.
Recommended citation: Rathore, K., & Tadepalli, P. (2025). Generating Part-Based Global Explanations Via Correspondence. arXiv preprint arXiv:2509.15393. https://drive.google.com/file/d/1_1LySYzjUUow22PV4M9FScRstdFIzk89/view
Predicting Unobserved Driver of Regime Shifts in Social-Ecological Systems with Universal Dynamic Equations
Published in EcoEvo Arxiv, 2025
Ecosystems around the world are anticipated to undergo regime shifts as temperatures rise and other climatic and anthropogenic perturbations erode resilience. Forecasting these nonlinear ecosystem dynamics can help stakeholders prepare for rapid changes. One major challenge is that regime shifts can be difficult to predict when driven by unobserved factors—such as illegal fishing from a fishery or unreported poaching in a game reserve. This paper advances scientific machine learning methods, specifically universal dynamic equations (UDEs), to identify changes in an unobserved bifurcation parameter and predict ecosystem regime shifts.
Recommended citation: Rathore, K. J., Buckner, J. H., Meunier, Z. D., Esquivel, J. A., & Watson, J. R. (2025). Predicting unobserved driver of regime shifts in social-ecological systems with universal dynamic equations. https://ecoevorxiv.org/repository/view/11165/
talks
Poster: Explanations via Minimal regions and Semantic Correspondence
Published:
The objective of this poster presentation was to articulate our research endeavors in the realm of Explainable Artificial Intelligence to industry professionals, and to illustrate potential applications thereof. The central premise was to underscore the importance of generating human-comprehensible and interpretable explanations through sub-symbolic representations of images, such as components of an object within an image, which serve as inputs to a Deep Neural Network (DNN) model.
Poster: Exploring relational AI in terms of reducing Social Isolation
Published:
This poster was produced in a group efforts for understanding and evaluating learning from our coursework in Ethics in AI class.
Poster: Discriminative Global Explanations via Part Attributes
Published:
This poster presentation served as a platform for me to articulate our research concepts and advancements in the field of Explainable Artificial Intelligence to industry professionals. Subsequently, I elaborated on the discriminative global explanations and the methodology we are employing to generate such explanations, along with their potential benefits to the industry.
XAI@IJCAI: Generating Part-Based Global Explanations Via Correspondence
Published:
We proposed a methodology for generating global explanations using correspondence techniques. The focus was on delivering human-comprehensible verbal explanations, articulated as part-based expressions, derived from the input image to the Deep Neural Network classifier.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.