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AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
judge-from-scratch
April 28, 2026 – Present
Generate data, train, evaluate, and deploy a specialized LLM judge — explained step by step.
View Projectreval-collector
April 2, 2026 – Present
LangGraph pipeline that generates evidence-grounded political bias test cases for REVAL. Searches real sources with Tavily, scores case quality with an LLM judge, and exports directly to REVAL's eval format.
View Projectreval
March 16, 2026 – Present
Fact-aligned benchmark for evaluating political and ideological bias in LLMs. Uses counterfactual pairs, LLM-as-judge scoring, and a ground-truth taxonomy rather than symmetry assumptions. Bedrock / Anthropic / OpenAI / MiniMax / Ollama providers. US + India today; UK, Germany, Brazil planned.
View ProjectIRC-Reference-Tools
January 15, 2026 – Present
Reference toolset for IRC (Integrated Rack Controller)
View ProjectiDRAC-MCP-Reference-Server-and-Tools
December 4, 2025 – December 13, 2025
iDRAC-MCP-Reference-Server-and-Tools
View ProjectUniversityofNewHavenChatbot
September 24, 2024 – November 13, 2025
UniversityofNewHavenChatbot — GitHub repository
View ProjectiDRAC-Telemetry-Reference-Tools
January 22, 2021 – Present
Reference toolset for PowerEdge telemetry metric collection and integration with analytics and visualization solutions.
View ProjectLSTM-footballMatchWinner
June 18, 2018 – August 29, 2022
This repository contains the code for a conference paper "Predicting the football match winner using LSTM model of Recurrent Neural Networks" that we wrote
View ProjectonlineNewsPopularity
April 22, 2018 – April 22, 2018
Implementation of two research papers to find the best algorithm that predicts the popularity of a news article. Done for the course of "Data Mining"
View ProjectinfluentialUserDetection
October 16, 2017 – October 16, 2017
Determines the influence of a twitter user by calculating the user's spread of communication with the help of retweets.
View ProjectCultural Fit Analysis
The candidate's projects are predominantly personal and academic, indicating a strong drive for self-learning and exploration in data science and machine learning. However, the lack of professional experience or team-based projects makes it difficult to assess cultural fit in a corporate environment. The diversity of projects, including those involving Go and JavaScript, suggests a broad technical curiosity.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are concise but do not provide insight into collaboration, problem-solving approaches, or communication style.