About

I’m Kanav Atre, a Purdue senior in Data Science & Applied Statistics. I like to work across the stack, but have a strong inlcination in Machine Learning, Computer Vision and databases .

In my most recent internship, I built a real-time KPI pipeline with Neon, ClickHouse, and Postgres. I was also responsible for deploying and maintaining a data migration and archival infrastructure for fast, accurate and most up-to-date analytics. I also worked a bit on the frontend integration of supply-chain specific charts and built executive-facing dashboards and demos for prospective customers.

I also have a strong undergraduate research project in pedestrian detection and behavior analysis using large-scale image datasets to study changes in pedestrian patterns before and after the COVID-19 pandemic.

Purdue • Data Science & Applied StatisticsGraduation: Dec 2026Interested in: data-intensive systems, full-stack, ML tooling
Kanav Atre

Skills & Technologies

A snapshot of what I reach for most often.

Languages

PythonJavaSQLC++JavaScriptTypeScript

Frontend

ReactNext.jsTailwind CSSFramer MotionHTMLCSS

Backend

Node.jsFastAPIPostgreSQLRedisClickHouseNeonDB

Data

Pandasscikit-learnPyTorchTensorFlowOpenCVKerasHugging Face

Infra

DockerAWSGitHub ActionsLinux

Tools

GitVimVS Code

Experience

Jun 2025 — Aug 2025

Software Engineer Intern

ketteQ

Built real-time KPI pipelines across Neon, ClickHouse, and PostgreSQL; delivered interactive dashboards (bias charts, treemaps, stacked bars) used by analysts and leadership.

  • Real-time analytics pipeline (Neon → ClickHouse/Postgres)
  • Executive-facing KPI dashboards + demos for prospective customers
  • Data migration + archival infrastructure to keep analytics fast

Aug 2023 — Jun 2024

Undergraduate Researcher

Purdue University

Pedestrian detection and behavior analysis project using large-scale image datasets to study changes in pedestrian patterns before and after the COVID-19 pandemic.

  • Mentored by Prof. Carla Zoltswoski and Prof. Edward J. Delp
  • Analyzed a dataset of 50,000+ images to study pre- and post-COVID pedestrian behavior
  • Built models using KNNs, linear classifiers, and neural networks for feature extraction and accuracy improvement

Aug 2022 — Jan 2023

Data Science Intern

Renzoe Box

Worked on computer vision and data pipelines for RenzoeMatch, improving color classification accuracy and powering product recommendation systems.

  • Improved the RenzoeMatch model by combining 3-point color matching with LAB-space feature extraction and brightness normalization for more reliable tone classification
  • Engineered web-scraping pipelines using BeautifulSoup to collect structured product data from dynamic websites
  • Built automated data ingestion workflows that directly powered RenzoeMatch’s recommendation engine