Status: open to technical & research roles

Arman Khan

Developer Data Engineer Embodied AI

CS honors grad who builds full-stack software, designs end-to-end data pipelines, and does embodied-AI research. I like problems that span the stack: turning raw data into a usable system, and a research idea into something that runs at scale.

Origin story

About

I'm a developer and data engineer with embodied-AI research experience. I build full-stack software, design end-to-end data pipelines, and turn research ideas into things that run at scale, most recently a robotic-manipulation dataset pipeline in MSU's ACTION Lab.

My capstone, delivered to Henry Ford Innovations, was a full-stack healthcare platform: Angular 18 + Node/Express + Microsoft SQL Server, a 3-tier role hierarchy, and an audit-logging layer I owned end to end. Across my work the throughline is the same: take something ambiguous, make it concrete, ship it, measure it.

Right now I'm open to entry-level software engineering roles where I can keep working across the full stack, from data layer to interface.

The Record
UniversityMichigan State
DegreeB.S. Computer Science
HonorsHonors college
MinorsCog Sci · CMSE
Dean's List7 / 8 semesters
PublicationsIEEE · agentic AI
Field work

Selected Work

Box Office Revenue Analysis & SIR Forecasting

CMSE 202 · Individual Data Science Researcher · Sep to Dec 2024

A 40-year, 7,643-film study of what drives box-office success, then a model to predict it. Built an end-to-end analysis pipeline (correlation matrix, multi-decade trend viz, log-scale outlier handling), then adapted the SIR epidemiological model to simulate how a film's popularity spreads through an audience, solving the ODE system with SciPy and validating against 140+ days of real daily revenue.

PythonpandasNumPySciPyMatplotlibSeaborn
Writeup ↗
Box office SIR forecasting plot

Customer Portal Mobile UI & NFC Prototype

DayDream Technologies · SDE Intern (team of 4) · Oct to Dec 2024

Production mobile UI for a client's customer portal, plus an NFC tap-to-transfer prototype. I built a themed bottom-tab navigator across 9 screens with internationalized labels in 3 languages, resolved a macOS→Windows build blocker and onboarded 2 developers, then prototyped the NFC concept in React Native and validated it on physical hardware.

React NativeTypeScriptReact Navigationreact-i18nextNFC
Internship
Customer portal home screen Customer portal profile screen Customer portal notifications screen

Last.fm Music Recommender

Personal Project · Solo Developer · Jan to May 2025

A collaborative-filtering recommender built solo on live API data, not a toy dataset. I owned every layer: ingesting 50 users' listening history via the Last.fm API, transforming 2,085+ unique tracks into structured DataFrames, building a sparse user-track matrix, computing a 50×50 cosine-similarity matrix, and writing a parameterized k-NN collaborative filter to surface unheard songs.

PythonpylastpandasNumPyscikit-learn
GitHub ↗
Last.fm recommender output

compute-job-metrics

Building
Personal Project · Solo Developer · 2026

A serverless job-tracking service modeling HPC-style scheduling, with a persistent scheduler daemon. Built cloud-native on AWS: Lambda compute, API Gateway, DynamoDB persistence, and infrastructure defined in CDK, with CI/CD through GitHub Actions. Carries my HPC batch-scheduling background into a cloud-native design.

AWS LambdaAPI GatewayDynamoDBCDKGitHub Actions
GitHub ↗
compute-job-metrics API: posting and querying jobs via terminal
Published & in progress

Research

Agentic AI framework diagram
Published · IEEE

Agentic AI for Mission Adaptation

A Distributed Cognition Framework for Air Force Operations · InCIT 2025, Phuket

Co-authored an IEEE-published paper proposing an agentic AI framework for combat triage and mission adaptation, grounded in distributed cognition. It fuses multimodal data from drones, wearables, and battlefield sensors to assess injury severity, threat, and mission status in real time.

The system pairs survival-regression and triage-consistency models trained on large-scale EMS data (NEMSIS) with reinforcement-learning agents that simulate UAV coordination, evacuation timing, and dynamic route adaptation under adversarial conditions, surfacing explainable recommendations for medics, pilots, and commanders.

Authors: Majumdar, Kirkley, Basu Mallik, Khan, Basu · DOI 10.1109/InCIT66780.2025.11276050
Embodied AI · Data Engineering

Data Engineering for Downstream Model Training

MSU ACTION Lab · Undergraduate Research Assistant

In MSU's ACTION Lab, I built the data engine behind a robotic-manipulation learning effort. I wrote a Python pipeline using SAM2 to propagate frame-0 annotations through full videos, running segmentation as batch jobs on the HPC cluster.

122,320
segmentation masks
>30×
faster on HPC
361
episodes

The dataset feeds the lab's downstream model training: the precise, large-scale ground truth that hand-labeling couldn't produce.

SAM2PythonCUDAHPC

Up, up
let's build something.

Open to entry-level software engineering roles and research collaborations. The fastest way to reach me is email.