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Aniirudh Ramesh

Aniirudh Ramesh

AI researcher and deep tech entrepreneur focused on complex systems

ani-ramesh
Knoxville, Tennessee
Joined January 2025

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Summary

Aniirudh is a deep tech entrepreneur and researcher dedicated to developing AI-powered solutions for real-world complex problems. He co-founded ASTERS, a startup focused on creating decision support systems for active-shooter scenarios, and has been accepted into the UChicago Transform AI accelerator. His work often involves optimizing network dynamics and information flow. aniramesh+1
With a strong academic background, Aniirudh is pursuing a Ph.D. in Systems & Controls Engineering with minors in Statistics and Data Science at the University of Tennessee, Knoxville. His research encompasses Artificial Intelligence, Algorithms, Decision Theory, Operations Research, and Discrete Optimization, leading to highly cited publications on topics like active-shooter egress optimization and agricultural disease classification. aniramesh+2
Aniirudh demonstrates a commitment to mentorship and community engagement, serving on the Student Leadership Council for SENTRY (a DHS Center of Excellence), acting as a Student Ambassador for the Anderson Center for Entrepreneurship and Innovation, and mentoring numerous students in STEM research. He also volunteers as a pen pal for 'Letters to a Pre-Scientist,' inspiring young adults in scientific careers. aniramesh+1
He has a proven track record of grit and hustle, co-authoring multiple research papers as an undergraduate and securing a direct PhD admission. His achievements include winning a competitive grant from a DHS Center of Excellence, successfully spinning off research into a startup, and gaining industry experience at Dow and the startup-VC ecosystem at Dorm Room Fund. aniramesh+1

Work

Education

Writing

An Optimized Evacuation Plan for an Active-Shooter Situation Constrained by Network Capacity

January 1, 2025

This paper demonstrates an NHSMDP-based naive algorithm that provides an optimized egress plan for evacuees during active-shooter incidents, aiming to reduce casualties and time spent in the shooter's line of sight by leveraging prior building knowledge and real-time shooter location updates.

Favicon imagescholar.google.com

The effectiveness of naive optimization of the egress path for an active-shooter scenario

January 1, 2023

Demonstrates the effectiveness of a non-homogeneous semi-Markov-Decision-Process (NHSMDP) based naive algorithm for providing optimized egress plans during active-shooter incidents, showing significant reduction in casualties and exposure time compared to intuitive responses.

Favicon imagepmc.ncbi.nlm.nih.gov

Tomato crop disease classification using pre-trained deep learning algorithm

January 1, 2018

Focuses on the application of pre-trained deep learning algorithms for the accurate classification of diseases in tomato crops, which is crucial for early detection and mitigation in agricultural production.

Favicon imagescholar.google.com

Disease classification in maize crop using bag of features and multiclass support vector machine

January 1, 2018

Explores the use of bag of features and multiclass support vector machines for classifying diseases in maize crops, contributing to automated plant disease detection.

Favicon imagescholar.google.com

Grape crop disease classification using transfer learning approach

January 1, 2018

Investigates a transfer learning approach for classifying diseases in grape crops, showcasing advancements in applying deep learning techniques to agricultural challenges.

Favicon imagescholar.google.com

Hobbies

Aniirudh enjoys spending time outdoors, particularly hiking in natural areas like the Great Smoky Mountains. aniramesh+1

He is formally trained in Indian classical music and plays the violin. aniramesh+1