Katkuri Aditya Vardhan Reddy

I'm an incoming MS in Machine Learning student at Carnegie Mellon University.

Before starting my MS, I was a Research Engineer - Intern at Ola Krutrim, India's first AI unicorn, where I worked on AI agents, moderation, model evaluations, and DPO.

Before joining Kurtrim, during my undergraduate studies, I worked with 6 research labs spanning 4 countries, researching a variety of problems. To date, I have worked on 6 research papers and 2 theses.

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 six months working on my undergraduate thesis at Harvard University in the Shafiee Lab, supervised by Prof. Hadi Shafiee. I'm deeply grateful for the funding and mentorship I received during this period. My research focused on 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. My research there focused on few-shot object detection and in-sensor computing.

I'd love to chat about AI and meet interesting people, feel free to reach out for a virtual coffee chat!

Email  /  LinkedIn  /  LeetCode  /  Github  /  Google Scholar  /  YouTube

• 2025-08 Career Update: I will be joining Carnegie Mellon University for an MS in Machine Learning!
• 2024-11 Launched my YouTube channel: Aditya Katkuri.
• 2024-11 Awarded Department Gold Medal and Honours Degree.
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2024-08 Joined Ola Krutrim as a Research Engineer - Intern.
2024-08 Started as a visiting student researcher (remote) with King Abdullah University of Science and Technology, with funding.
2024-01 Started as a research trainee at Harvard University with funding, to conduct my undergraduate thesis.
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 Technology 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 multi-modal, multi-agent systems—especially how they integrate with users and with each other to enable more intelligent, interactive, and helpful 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.