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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
From Symptoms to Solutions: Mastering Systematic Root Cause Analysis
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In today’s complex operational environments, problems are inevitable, but their recurrence doesn’t have to be. Whether you’re managing manufacturing processes, academic research projects, service delivery systems, or hospitality operations, the ability to identify and address root causes rather than just symptoms can mean the difference between temporary fixes and lasting solutions.
Freshwater Ecology
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I am writing this post to introduce how interesting is the world of ecology. Specifically I want to share my experiences with the “Fresh Water Ecology” course at OSU, which has shown me a completely different perspective of looking at nature. How? lets get into the details.
Growing amount of pesticide/weedicide use in agriculture across the world
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Increasing human population across the world has raised demands for food productions. And this is exerting pressure on both agriculture as well as meat industries. The growing food demand has subsequntly increased demands for required input materials, like seeds, fertilizers (including pesticide and weedicides).
Data Science in Ecology
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Let me introduce a part of ecological studies which uses various modeling methods used and now prominent/useful in data-driven modeling. This blog is simply to motivate, why data science has become a widely used tool in ecological studies. I will discuss the pros of such modeling techniques and try to set baselines with various terminologies used in the domain.
portfolio
Passion to Engineer Something!!
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Undergrad Adventures: Formula Student Competitions 
Colorful Corvallis
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Blooming Beauties: colored memories I can not ever leave behind!! 
Nature that Inspires..
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Beauty & Complexity!! What a balance .. 
Hiking & fun nothing else.
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Hiking 
projects
NL2SQL: LLM-Powered Natural Language to Database Query Engine
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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
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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
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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
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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
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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
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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
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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
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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.