Yogesh Kuchimanchi · Data Scientist

I got into data by building PCs.

I’m Yogesh, a data science graduate student at RIT. Collecting parts got me interested in the numbers behind a decision. These days, that curiosity takes me into machine learning, language, and how things change over time.

A little about me

It started with PC parts.

I like building PCs. Collecting parts meant comparing specs, prices, and performance. Somewhere along the way, I got as interested in those comparisons as I was in the build.

That interest brought me to data science. At RIT, I’ve worked on readmission models, news classification, and research on women’s safety in India. The subject changes, but I still want to understand what the numbers say and what I can build with them.

  1. Compare like with like.

    A benchmark needs context. In my readmission project, I keep patients separate across splits and check how well predicted risks match outcomes.

  2. Make the information usable.

    NewsSnap turns a news classifier into an application where someone can inspect predictions. The training pipeline, API, and interface are all part of the project.

  3. Check again later.

    A model’s inputs can change after training. DriftLab lets me explore what happens to performance and monitoring alerts when they do.

  4. Follow a conversation.

    My capstone looks at how discussions about women’s safety change after an incident and what people return to months or years later.

One question, at different moments

How discourse changes over time.

These windows are measured from the incident, not a verdict. Choose one to explore the research question.

Acute phase

What does the conversation focus on immediately after an incident?

Explore the findings

The crossroads

I want to keep doing both.

I like investigating a question and building something from the answer. As I finish my master’s, I’m looking for work that gives me room to do both.

Featured projects

Selected projects.

Source code, evaluation results, and demos for four projects.

Explore all GitHub projects

Results

Project dashboards

Inspect the available results, then open the demo or source.

Readmission risk

Existing portfolio results

30-day readmission modeling with patient-disjoint splits and probability calibration.

Held-out encounters
10,822
ROC-AUC
0.661
PR-AUC
0.175
Brier score ↓
0.077
Held-out evaluation
ROC-AUC
0.661
PR-AUC
0.175
Brier score
0.077

Scale: 0–1. Higher AUC is better; lower Brier is better. These measure different properties and should not be ranked against each other. Prediction-level data is needed for ROC and calibration curves.

A nod to where it started

Put the parts together.

A small interactive build, with each part representing a step in an ML project.

0 / 4 connected
YK-ML // CASE_01
DATA
FLOW
POWER
Assembly mode

Select a component, then fit it into the matching slot.

Installed applications

Complete the build to unlock the demos.

System locked
01
Application lockedInstall all four components
02
Application lockedInstall all four components
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Application lockedInstall all four components
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Application lockedInstall all four components
01

Clinical Readmission Risk Modeling

A leakage-safe 30-day readmission pipeline with patient-disjoint cohorts, calibration, missingness analysis, and drift checks.

Held-out test: 0.661 ROC-AUC, 0.175 PR-AUC, and 0.077 Brier score on 10,822 encounters.
CatBoostPyTorchCalibrationStreamlit
02

NewsSnap: Transformer News Classification System

A four-class AG News system with a DistilBERT training pipeline, FastAPI service, React dashboard, and explicit inference modes.

Held-out evaluation: 0.870 accuracy, 0.869 macro-F1, and 0.827 MCC on 12,000 articles.
DistilBERTFastAPIReactCI/CD
03

Model Behavior Under Distribution Shift

A deterministic simulator for performance decay, class-prior shift, PSI, approximate KS, and configurable monitoring alerts.

Verified mixed-shift run: ROC-AUC fell from 0.758 to 0.372; max PSI reached 0.608; three alerts fired.
NumPyPandasPSI / KSStreamlit
04

Women-Safety Public Discourse Research

An interactive research dashboard covering 351,501 Reddit and YouTube comments across 16 women-safety cases in India.

Paper accepted at ASONAM 2026. The dashboard separates submitted-paper findings from later model audits.
PythonPandasQwenStatistical testing

The full collection

All GitHub Projects.

Search my public repositories by name or language. This list updates from GitHub.

View GitHub

46 of 46 repositories · Most recently updated first · Refreshing from GitHub…

Python

NewsSnap

Full-stack news aggregator with DistilBERT summarization, React dashboard, CI/CD pipeline, and Docker deployment

Project overview
Python

self healing pipeline

Bounded self-healing data pipeline with LangGraph, strict contracts, isolated repair, durable recovery, and an evidence-based technical report.

Project overview
Python

DataGuard AI

FixMyData is a smart AI-powered application designed to automatically detect and explain data quality issues in CSV files including missing values, outliers, duplicate entries, type mismatches, and more.

Project overview
Python

rag document brain

Retrieval-augmented generation pipeline with sentence-transformer embeddings, ChromaDB vector search, and TinyLlama synthesis

Project overview
Python

ai code reviewer

FastAPI code review server powered by TinyLlama-1.1B with streaming SSE, severity classification, and KV-cache optimization

Project overview
Python

sentiment engine

Real-time sentiment analysis API with DistilBERT, batch processing, and trend visualization via FastAPI and Streamlit

Project overview
Python

restaurant analytics llm

AI-powered restaurant analytics dashboard with natural language SQL queries. Built with Streamlit, SQLite, and LLM integration for intuitive data exploration and insights.

Project overview
Python

SymptomAid AI

Professional AI-powered symptom analysis tool using Ollama and Streamlit. Provides structured medical insights with comprehensive safety disclaimers for educational purposes.

Project overview
Python

stock sentiment dashboard

Real-time dashboard to track public sentiment around stock tickers and compare it with actual price trends using NLP and Yahoo Finance data.

Project overview
Python

MetalVision AI

Advanced Metal Surface Defect Detection System using PyTorch with CNN, Attention Mechanisms, Ensemble Learning, SMOTE, and Cross-Validation

Project overview
Repository

NeuroCanvas

🎨 An AI-powered creative platform that generates art, analyzes emotions, and creates narratives through an intuitive web interface. Built with React, Flask, and multiple AI APIs.

Project overview
Python

NeuroCanvas AI Art Generator

🧠🎨 AI-Powered Emotional Art Generation Platform - Transform emotions into stunning neuromorphic art through advanced multimodal AI analysis with contextual memory and narrative generation

Project overview
JavaScript

time chronicles ai

An immersive AI-powered historical storytelling web application that transports users through different eras with multimodal narratives, user authentication, and interactive features.

Project overview
HTML

ai slide deck generator

AI-Powered Slide Deck Generator - Create professional presentations from text using Google Gemini AI with multiple styles, charts, and speaker notes

Project overview
JavaScript

mycelial memories

An AI-powered exploration of emotional connections through historical letters, visualized as living fungal networks

Project overview

Experience

Where I’ve worked.

01

Graduate Researcher, Data Science

Rochester Institute of Technology

  • Built Python and Pandas pipelines to analyze 351,501 Reddit and YouTube comments across 16 women-safety cases in India.
  • Applied Mann–Whitney U, chi-square, and G-tests; fine-tuned Qwen3.5-9B with LoRA and evaluated 3,000 held-out examples.
  • Authored a paper accepted at ASONAM 2026 and documented model limitations through high-confidence error review.
02

GCCIS Technical Assistant

Rochester Institute of Technology

  • Support 200+ Windows and Linux lab systems, troubleshoot access and software issues, and maintain Excel/VBA reports.
03

Research Assistant, Data Science

Rochester Institute of Technology

  • Refactored Pandas and NumPy scripts and validated PostgreSQL and Excel data for accurate weekly research reporting.

Education

Education.

01

M.S. in Data Science

Rochester Institute of Technology · Rochester, NY

Machine Learning, Deep Learning, Cloud Computing, Big Data Analytics
02

Postgraduate Program in Data Science

Vellore Institute of Technology · India

03

B.C.A. in Computer Applications

New Shores International College · India

Skills

Tools I use.

Languages, modeling libraries, databases, and tools used in my projects.

01

Languages

Python · SQL · R · Java

02

Machine Learning & NLP

PyTorch · scikit-learn · CatBoost · XGBoost · Transformers · LoRA · DistilBERT · FT-Transformer

03

Statistics & Evaluation

A/B testing · Mann–Whitney U · Chi-square · G-test · ROC-AUC · PR-AUC · Calibration · PSI / KS / JS

04

Data & Databases

Pandas · NumPy · SciPy · Spark · PostgreSQL · SQLite · Window functions

05

ML Engineering

FastAPI · Streamlit · React · Docker · GitHub Actions · Automated testing · Git · Excel / VBA

Contact

Get in touch.

I’m looking for data science and ML engineering roles. Email me about a role, a project, or a research collaboration.

kuchimanchiyogesh@gmail.com