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Education

Carnegie Mellon University, Pittsburgh, PA, United States

Master of Science - MS, Information Systems (Machine Learning specialization)

2024 - December 2025

Grade: 4.0/4.0

Relevant Coursework:

• Deep Learning (PhD)
• Advanced Natural Language Processing
• Statistics
• Machine Learning in Production
• Unstructured Data Analytics
• Data Science and Big Data
• Distributed Systems for Information Systems Management
• Object Oriented Programming with Java
• Database Management
• Corporate Startup Lab
• Digital Transformation
• Decision Making Under Uncertainty
• Managing Disruptive Technologies
• Economic Analysis
• Organizational Implementation and Design

Activities and societies:

• Graduate Research Assistant
• Lead for Women in Business (Tepper School of Business)
• Society of Women Engineers
• Girls who Code

Anna University, Chennai, India

Bachelor of Engineering - BE, Computer Science

2018 - 2022

Grade: First Class with Distinction

Relevant Coursework:

• Object Oriented Programming
• Design and analysis of algorithms
• Linear Algebra and Partial Differential Equations
• Probability and Random Processes
• Operating Systems
• Cloud Computing
• Soft Computing
• Database Management Systems
• Principles of Management

Activities and societies:

• Undergraduate researcher
• Department Coordinator of Vulcans ( Cultural club)
• RETWEET (Inter-college Cultural Team)
• Department Head of Youth Red Cross
• Member of the Department Design Team

Experiences

Download my Resume - resume

Graduate Research Assistant ( Inference strategies & LLM Reasoning) - Carnegie Mellon University - January 2025 — Present Pittsburgh, USA

• Conducted research in Vector Database Integration, Semantic Knowledge Graphs, Chain-of-Thought Decomposition, and Multi-hop Reasoning architectures to enhance structured data retrieval & self-supervised learning in foundation models
• Engineered advanced prompt scaffolding techniques with parameter-efficient fine-tuning (PEFT) & implemented reasoning trace evaluation frameworks incorporating retrieval-augmented prompting, contrastive learning, optimizing emergent reasoning & causal inference capabilities in multimodal LLMs while enhancing interpretability and cognitive alignment
• Developed novel metrics integrating latent space analysis & reasoning path extraction for measuring coherence & factuality in collaborative problem-solving scenarios, resulting in 28% improvement in task completion efficiency and 45% reduction in hallucination rates

Machine Learning Engineer Intern - Bank of New York - January 2025 — Present Pittsburgh, USA

• Pioneered human-in-the-loop (HITL) reinforcement learning from human feedback (RLHF) for optimizing conversational flows, implementing automated prompt engineering and red-teaming capabilities that reduced hallucination rates by 43% while ensuring responsible AI deployment
• Deployed knowledge distillation and parameter-efficient transfer learning techniques for scalable, personalized, multi-channel deployment across 9 core AI capabilities: Content Generation, Anomaly Detection, Decision Reapplication, Code Modernization, Virtual Assistance, Data Migration, Scenario Creation, Prediction/Classification, and Unstructured Data Q&A
• Accelerated enterprise-wide AI adoption by developing specialized upskilling for transformer architecture expertise (GPT, BERT, T5), zero-shot learning techniques, and semantic kernel integration, resulting in comprehensive deployment of vector database integration capabilities and $4.2M in annual cost savings

Full Stack Developer - Zoho Corporation - August 2022 – Present Chennai, India

• Part of the Manage Engine - Log360 Cloud OD Team Resolved numerous bugs and optimized code, resulting in a 150% performance improvement in a module.
• Developed a feature serving as a framework for all application modules, demonstrating advanced knowledge of generalization and scalability.
• Contributed to a product serving millions of customers, directly impacting their experience through developing a key feature.

Programmer Analyst Trainee Intern - Cognizant Technology Solutions - January 2022 – August 2022 Chennai, India

• Part of the Artificial Intelligence and Analytics (AIA) - Informatica Cloud Team.
• Used Informatica software to design data storage systems to provide clients with faster insight into their business.
• Analyzed and tested Data warehousing techniques, ETL tools. Created various mappings using Infomatica Powercenter,IICS.

Undergraduate Researcher - SRM Easwari Engineering College - May 2021 – December 2022 Chennai, India

• Research intern under Prof. KPK Devan, Dept of Computer Science and Engineering:

• Worked on a fake tweet detection system that detects fake tweets related to COVID 19
• Extracted tweets related to COVID 19 using tweepy and the twitter API
• Developed an LSTM based model to detect fake tweets using NLP

Summer Research Intern - NIT Calicut - June 2021 – July 2021 India Research intern under Prof. Prabu Mohandas, Dept of Computer Science and Engineering:

• Worked on finding solutions for the challenging task of Music Instrument Recognition. Developed a model that labels unlabeled audio files so manual annotation can be avoided.
• A Convolutional neural network and K nearest neighbors classifier framework were implemented to identify the musical instrument present in a monophonic audio file and the performance of the two models are compared.
• The model performed well with excellent result of 99.17% accuracy for the Convolutional Neural Network and 97% accuracy for the k-nearest neighbor architecture. Worked on finding solutions for the challenging task of Music Instrument Recognition. Developed a model that labels unlabeled audio files so that manual annotation can be avoided.

Research Intern - Indian Institute of Information Technology Design & Manufacturing - May 2020 – May 2021 India

-Research intern under Dr. Sivaselvan B, Dept of Computer Science and Engineering:

• Explored state-of-the-art deep learning based approaches for detecting forged facial images and videos. Delved into the evaluation metrics commonly used for assessing the performance of detection algorithms, highlighting their strengths and limitations.
• Presented a comparative study of four popular deep learning architectures, DenseNet, MobileNet, Xception, and EfficientNet, for the detection of face forgery.
• Conducted a comprehensive evaluation and comparison, to provide valuable insights for researchers and practitioners seeking to leverage deep learning models for face forgery detection.