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Bachelor of Technology in Computer Science & Engineering

• GPA: 9.23/10.00
• Capstone Project: Sector-Specific Stock Recommender Systems
• Developed a hybrid system combining deep learning forecasting and a fine-tuned LLM for sector-specific stock recommendations.

Software Engineer

• Developing a POC using supervised ML for automated calibration of ISO 8583 data elements.
• Identifying misconfigured data elements responsible for transaction failures.
• Innovation: Prati Yodha (July 2025 - Sept 2025). Proposed a generative-AI agent for automating the champion-challenger MLOps lifecycle. Recognized as a Technical Innovation by Visa IP Committee.

Semester Intern

• Designed and prototyped RCA Copilot, a multi-agent system for code search, metric analysis, and incident pattern mining.
• Collaborated with senior engineers to demonstrate feasibility for automated incident triaging in production.

Software Engineering and ML Intern

• Developed a scalable embedding-based database comparison tool to validate a custom data replication solution, efficiently handling∼10M-record tables.
• Proposed a novel framework leveraging embeddings and recognized with a Technical Innovation Award by the VISA IP Team for impactful innovation.

Sector-Specific Stock Recommender Systems

• Fine-tuned Llama-3.1-8B-Instruct with QLORA on a curated financial news corpus to generate sector-specific stock recommendations.
• Proposed a hybrid framework combining price forecasting, sentiment analysis, and performance indicators.

Explainable and Interpretable Isolation Forest

• Proposed a hybrid model integrating Isolation Forest with Decision Trees, combining efficient anomaly identification with rule-based explanations.
• Demonstrated strong detection performance across multiple datasets with human-interpretable insights.

AML Classification in Blockchain via GCN Hybrids

• Developed hybrid models combining Graph Convolutional Networks (GCNs) with PNN, WNN, and RBFN architectures.
• Achieved superior detection accuracy on the Elliptic dataset compared to stand-alone GCN baselines.

Deep Reinforcement Learning for Financial Forecasting

• Investigated RL approaches (DDPG, PPO, RDPG) for stock market forecasting against baseline ML models.
• Designed a Spark Streaming framework for real-time financial time series analysis.

• Best Paper Award: ICDMAI 2025 (Won for 2 papers)
• Technical Innovation Award: Visa IP Committee (Prati Yodha)

• Amazon ML Challenge: Ranked 65th nationally (Top 5% of 2000+ teams)
• Prof. MRD Scholarship Award: Top 5% among 1000+ students at PES University
• Technical Innovation Award: Visa IP Committee (Samata)

• Winner: Karnataka State Police Hackathon (150+ teams)
• First Runner-up: HACK'E'LTH Hackathon, GE HealthCare (100+ teams)

• JEE Mains: Secured 98.8 percentile (among 1.2 million candidates)