CV

Resume.pdf

Education

• Ph.D in Environmental Sciences and Applied AI, 2026 (expected)
• Masters in Mathematical Modeling and Simulation, Centre for Modeling & Simulation, Pune, 2019
• Bachelors in Automotive Engineering, Pune University, 2017

Work experience

• 2023-2025: Graduate Research Assistant
  • Oregon State University
  • Predicting Critical transition via hybrid data-driven model in Socio-Economic Ecological models.
  • Working with Socio-Environmental Analysis (SEA) Lab on data-driven ecology.
  • Supervisor: Dr. James Watson, CEAOS, OSU
• 2022-2023: Graduate Teaching Assistant
  • Oregon State University
  • Explainable AI for Computer Vision DL models and LLMs, Scientific Machine Learning
  • Working on Explainable AI project, building human-understandable explanations for DL models.
  • Supervisor: Dr. Prasad Tadepalli, EECS, OSU
• Summer 2023: AI/ML Intern
  • Seagate Technology LLC
  • Supervisor: Addishiwot Woldesenbet
  • Led the integration of Generative AI and Large Language Model (LLM) technologies into enterprise applications.
  • Developed and optimized LLM models by prompt engineering to convert natural language inquiries into structured and unstructured database queries. This implementation significantly reduced query time, enhanced usability for users unfamiliar with SQL database, and minimized human errors.
  • Played a key role in the exploration, design, evaluation and implementation of efficient data processing pipelines, thereby improving overall system performance.
• 2019 - 2021: Senior ML Engineer
  • Persistent Systems, Pune, INDIA
  • Supervisor: Dr. Kaustubh Waghmare, Kshitij Deshmukh
  • Time-Series Forecasting and Anomaly Detection: I developed and implemented algorithms for analyzing network traffic across the organization. This involved the use of time-series forecasting and anomaly detection techniques.
  • Data Exploration for Machine Learning Models: I conducted extensive data exploration to assess the feasibility of machine learning models for privilege access management applications. This initiative significantly enhanced our network security protocols.
  • Entity and Link Extraction Models: As part of a large-scale team, I developed models for entity and link extraction. This enabled us to create structured and graphical representations of entertainment news.
  • Real-Time Sentiment Analysis Platform: I created a state-of-the-art text summarization streaming platform. This platform was used for real-time sentiment analysis, leveraging the latest technology.
• Jan-July 2019: ML Intern
  • Persistent Systems, Pune, INDIA
  • Supervisor: Kshitij Deshmukh
  • Organizational Network and Data Analytics Tool: I conducted research and development for a tool designed to analyze organizational networks and data. This project involved visualizing the network and interaction of 10k+ employees.
  • Feedback Summarization and Analytics Tool: I developed a machine learning-based tool for summarizing and analyzing survey feedback. This tool was designed to extract key insights from large volumes of survey data, enabling efficient decision-making for human resources.
  • Chatbot Performance Enhancement: I designed, reconstructed, and implemented a framework to enhance chatbot performance. This involved the use of human feedback learning to continuously improve the chatbot’s responses and overall user experience.
  • Explanation for Deep Learning models: Implemented and evaluated explainable AI techniques for deep learning models trained on medical image data.

Skills

• Programming & Tools: Apache Kafka, Computer Vision, Confluence, CUDA, Data Analysis, Flask, Git, Airflow, Linux/Unix, R, MongoDB, MySQL, NumPy, Postgres, Postman, Pytorch, Natural Language Processing (NLP), Python, MATLAB, Operations Research, HTML/CSS, Excel/Numbers/Sheets, Tensorflow, SQL, Pyspark, Jupyter Notebook, Pytorch, OpenAI API, LangChain
  • Cloud skills: Microsoft Azure, Microsoft Azure AI studio, AWS, REST APIs, API handling
  • Statistics and mathematical modeling
• Soft Skills
  • Inter-Disciplinary Research background,
  • Strong problem-solving & computational skills,
  • Advanced engineering and modeling skills,
  • Software project management,
  • Excellent writing and communication skills,
  • Experience in conducting research, Writing peer-reviewed publications,
  • Monitoring & maintaining deployed models, ensuring scalability, reliability, and performance in production environment.
• Interests
  • Dynamical Systems
  • Data Science/ Data-driven modeling
  • Software & simulation engineering

Certifications

• AWS Cloud Practitioner,
• Foundation of GIS,
• AgAID Digital AgAth0n 2023 - Participant,
• Responsible AI Algorithms Design: LinkedinLearning,
• AI for Marketing: LinkedinLearning,

Publications

Predicting Unobserved Driver of Regime Shifts in Social-Ecological Systems with Universal Dynamic Equations

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.

Generating Part-based Global Explnations via Correspondence

Rathore, K., & Tadepalli, P. (2025). Generating Part-Based Global Explanations Via Correspondence. arXiv preprint arXiv:2509.15393.

RA2Vec: Distributed Representation of Protein Sequences with Reduced Alphabet Embeddings

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).

A Simple Method of Solution For Multi-label Feature Selection

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.

Talks

XAI@IJCAI: Generating Part-Based Global Explanations Via Correspondence

August 15, 2024

Conference workshop at IJCAI 2024 Workshop on Explainable Artificial Intelligence (XAI), virtual

Poster: Discriminative Global Explanations via Part Attributes

April 12, 2023

Talk at The PPI Center Spring 2023 Industry Advisory Board Meeting, University of Colorado

Poster: Exploring relational AI in terms of reducing Social Isolation

March 15, 2023

Talk at PHL-546 Ethic in AI Course project, Oregon State University

Poster: Explanations via Minimal regions and Semantic Correspondence

October 27, 2022

Talk at The PPI Center Fall 2022 Industry Advisory Board Meeting, Tigard, Oregon

Teaching Assistance

• Computer Vision
• Data Structures and Algorithm
• Engineering+: Algorithmic Thinking

Leadership

• Led my team at Formula Student Racing events for 2 consecutive years.