Katkuri Aditya Vardhan Reddy

I'm a Research Engineer - Intern at Ola Krutrim, working on Indic LLMs and AI Agents.

Before joining Kurtrim, during my undergraduate studies, I worked with 6 research labs across 4 countries, researching a variety of problems, including multi-instance learning, diffusion conditioning, domain generalization, few-shot object detection, in-sensor computing, physics-informed machine learning, and more. To date, I have worked on 6 research papers and 2 theses. Of these, 3 papers have been published in reputed journals, 2 are currently under review, and the remaining 3 will not be made public at this time (please feel free to contact me for copies).

I graduated with a Department Gold Medal, earning a Bachelor's (Honours) in Aeronautics with a minor specialization in Data Science from Manipal Institute of Technology, India.

In 2024, I spent a wonderful time working on my undergraduate thesis for 6 months at Harvard University in the Shafiee Lab, supervised by Prof. Hadi Shafiee. I am deeply grateful for the funding I received during this period. During this time, I researced multi-instance learning, diffusion conditioning, and ensemble models for human fertility applications.

My longest research experience, spanning 15 months, was with Carnegie Mellon University in the Xu Lab, supervised by Prof. Min Xu. I worked remotely with the lab, which allowed me the flexibility to contribute over such an extended period. During this time, I researched few-shot object detection and in-sensor computing.

I'd love to chat about AI and meet interesitng people. If you're around Bengaluru, feel free to reach out for a cup of coffee!

Email  /  LinkedIn  /  Google Scholar  /  Github  /  YouTube

• 2024-11 Launched my YouTube channel: Aditya Katkuri.
• 2024-11 Awarded department gold medal and honours degree.
• 2024-08 Joined Ola Krutrim as a Research Engineer - Intern.
• 2024-08 Started as a visiting student researcher (remote) with King Abdullah University of Sincence and Technology, with funding.
• 2024-01 Started as a research trainee at Harvard University with funding, to conduct my undergradaute thesis.
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2023-12 Awarded the ACHIEVER scholarship for this year.
2023-11 Received the Diamond Jubilee award for this year.
2023-10 Received partial fellowship to attend the IndoML 2023, held at IIT Bombay.
2023-03 Secured World Rank 4 and Asia Rank 1 by AeroMIT team in SAE AeroDesign East.
2023-06 Started as a summer research intern at Indian Institute of Delhi with funding, to conduct my summer thesis.
2022-12 Awarded the ACHIEVER scholarship for this year.
2022-11 Started as a research intern (remote) with National University of Singapore.
2022-11 Received the Diamond Jubilee award for this year.
2022-10 Started as a research intern (remote) with Carnegie Mellon University.
2022-08 Chosen as the President of IE Aerospace technical club.
2022-04 Secured World Rank 2 and Asia Rank 1 by AeroMIT team in SAE AeroDesign West.
2022-03 Chosen as the Head of Research and Development of AeroMIT student team.
2022-01 Secured National Rank 3 by our team in CAD CLASH.

My current research interests lie at the intersection of NLP and multi-modal machine learning, specifically focusing on enabling advanced personal assistants and AI agents through natural language understanding, computer vision, and contextual AI.

We propose a deep learning-based intrusion detection system (IDS) to enhance network security against sophisticated cyber-attacks. Using the NSL-KDD dataset, the system integrates Recursive Feature Elimination (RFE) and a Decision Tree classifier for feature optimization, followed by evaluation of various deep learning models, including CNN-LSTM.

We provide a systematic review of deep learning-based computer vision approaches for autonomous UAV applications across four domains: sensing, landing, surveillance, and search and rescue. By analyzing recent Scopus-indexed studies, we highlight trends, challenges, and emerging opportunities, emphasizing the growing role of AI-driven computer vision in UAV technologies.

We propose an ensemble AI-based framework for predicting live birth rates by integrating patient characteristics, IVF cycle outcomes, and embryo imaging data. This approach shifts from traditional embryo-centric predictions to comprehensive cycle-based forecasting, offering improved tools for personalized counseling and decision-making in family planning.

I explored computer vision and machine learning techniques to enhance clinical decision-making in IVF procedures. Specifically, I worked on dIffusion models, multi-instance learning models, and CNNs. Conducted in collaboration with Boston’s Brigham and Women’s Hospital, this research aims to improve live birth outcomes through AI-driven solutions.

I explored a Physics-Informed Neural Network (PINN) for predicting dependent shoulder and elbow torque, combining physics-based modeling with CNN architectures. Using surface EMG signals from upper arm muscle groups, the approach bridges data-driven and physics-informed methods, offering insights into musculoskeletal modeling for human assistive robotics.