available · spring 2026
m.s. computer science · uicaug 2024 · may 2026
01 /  archit rathod

Bridging scalable data engineering with transparent, responsible AI. Specializing in Python, TypeScript, React, and PyTorch.

ar@archit.dev — zsh
~ $
focusML fairness · distributed systems
stackpy · ts · pytorch · gcp
gsocopenstreetmap · 2025
scroll · selected work below
PyTorchNext.jsFastAPIPostgreSQLKubernetesBigQueryNeo4jTerraformGCPDockerTypeScriptOpenStreetMapCausal InferenceCounterfactualsGSoC · 2025PyTorchNext.jsFastAPIPostgreSQLKubernetesBigQueryNeo4jTerraformGCPDockerTypeScriptOpenStreetMapCausal InferenceCounterfactualsGSoC · 2025
02 /  featured work

Tools that prove the thesis.

Four projects at the intersection of scalable systems and responsible AI — a VS Code fairness debugger, a galaxy morphology explainability study, a causal-graph fairness library on PyPI, and a year of published research.

open source· case study

FairLint-DL

A deep-learning-based fairness debugger that ships as a VS Code extension. It wraps a custom PyTorch DNN around a two-phase gradient-ascent counterfactual search, cutting auditing time by 40% via real-time causal analysis.

PyTorchVS Code APITypeScriptCausal DAGsGradient AscentCounterfactuals
auditing time−40%
fairness metrics12 supported
counterfactual samples2,048 / run
fairlint-dl · vscode-extension · v0.4.2
READY
Model · Credit-scoring DNN
Gradient-ascent counterfactual search
inputdense₁dense₂softmax
$ fairlint audit --model=credit_dnn --protected=age
→ loading checkpoint · 4.2M params · 3 hidden layers
→ phase 1: gradient ascent on latent perturbations ε ∈ [−0.3, 0.3]
→ phase 2: counterfactual search · n=2048 synthetic samples
! edge case detected: age∈[36,50] × income∈[>100k]
done in 1.2s · 40% faster than baseline
Counterfactual Matrix
Approval probability
<30k
30–60k
60–100k
>100k
18–25
0.82
0.79
0.84
0.88
26–35
0.81
0.80
0.83
0.87
36–50
0.78
0.76
0.74
0.69
50+
0.80
0.82
0.85
0.89

Click Run Fairness Audit to sweep 2,048 counterfactual samples and surface the discriminatory edge case.
interactive demo · click the audit button to run

galaxy-xai · morphology-classifier · v1.0
READY
Galaxy Type
Architecture
XAI Method

Select a galaxy type, architecture, and XAI method. Run the explainer to overlay attention heatmaps and see faithfulness scores.

interactive demo · select method + architecture, then run
research· explainability

Galaxy Morphology XAI

A systematic evaluation of post-hoc explainability for astronomical AI — comparing Grad-CAM, LIME, Integrated Gradients, and GradientSHAP across four CNN architectures on Rubin LSST-scale galaxy surveys. No single method wins universally; the right choice depends on architecture, dataset, and the faithfulness criterion the scientist prioritizes.

PyTorchCaptumGrad-CAMLIMEGradientSHAPRubin LSST
best accuracy96.1% (ResNet-18)
best deletion AUC0.32 (Grad-CAM)
best insertion AUC0.89 (GradientSHAP)
images evaluated10,758

open source · pypi

relfair

Relationship-aware counterfactual fairness testing. Propagates protected-attribute interventions through a causal DAG so flips stay on the data manifold — detecting 3–4× more discrimination than naive flipping on Adult, ACS, and German Credit.

PythonCausal DAGsCounterfactualsLL 144PyPI
detection lift
+27 pp
datasets
3
tests
62
view project →
research · multi

Research Publications

Three peer-reviewed venues. Ascend.ai at ICDSA 2024, Automated Disaster Image Classification at ICSISCET 2023, Multi-Agent Simulators for Social Networks at NeurIPS 2023 MASec.

NeurIPS 2023ICDSA 2024ICSISCET 2023
venues
3
best accuracy
95%
co-authors
12+
03 /  experience

Five roles. One throughline.

Research labs, open-source foundations, and a simulation company — all asking the same question: how do we build systems that stay accountable at scale?

Research Assistant

aug 2025 — present
Urban Transportation Center · UIC
  • Architected Python pipelines processing 8.5M+ OD pairs (300GB+ data).
  • Containerized routing engines via Docker across 2,926 zones.
  • Engineered Next.js/FastAPI freight toolkit tracking crash metrics across 285+ municipalities.
PythonDockerNext.jsFastAPIOSMnx

Open-Source Software Engineer · GSoC

may 2025 — aug 2025
OpenStreetMap Foundation
  • Developed RESTful API via FastAPI and PostgreSQL/PostGIS for real-time road closures.
  • Built React map interfaces with OpenLR encoding.
  • Automated CI/CD via GitHub Actions.
FastAPIPostgreSQL/PostGISReactLeafletOpenLR

Research Assistant

feb 2025 — may 2025
UIC
  • Constructed geospatial analysis pipelines (OSMnx) to map Chicago's road network.
  • Engineered graph-based cycle detection models to identify traffic congestion zones.
OSMnxNetworkXGeoPandasShapely

Research & Web Engineer

mar 2023 — jul 2024
SimPPL
  • Designed Next.js/FastAPI ethical-AI platform.
  • Scraped 2,300+ Stormfront threads into BigQuery.
  • Led data team analyzing 80M+ YouTube comments for misinformation — cut analysis time by 30%.
  • Built Neo4j graph visualizers (20K+ nodes).
Next.jsFastAPIBigQueryNeo4jPython

AI Engineer

dec 2023 — jun 2024
DIRL · Boston University
  • Led 14 engineers building a gamified virtual marketplace via React.
  • Platform ran behavioral simulations with 2,000+ participants and autonomous LLM agents.
React.jsLLM AgentsBehavioral Sim
04 /  skills matrix

A working vocabulary.

Dense, terminal-style listing — because engineering starts with the primitives you can actually reach for at 2am.

Languages
08 entries
PythonTypeScriptJavaScriptC++SQLRustHTMLCSS
Frameworks
07 entries
React.jsNext.jsNode.jsFastAPIFlaskPyTorchTensorFlow
Cloud / DevOps
06 entries
GCPAWS (EC2, Lambda)DockerKubernetesCI/CDTerraform
Databases
06 entries
PostgreSQLBigQueryMongoDBNeo4jRedisMySQL
claude code

But what are skills without Agent Skills?

A collection of custom Claude Code skills I use daily — with copyable SKILL.md files so you can drop them straight into your own setup.

view skills
m.s. computer science
University of Illinois Chicago · Aug 2024 — May 2026
NLPData ScienceAlgorithmic FairnessResponsible AI
b.e. information technology · tsec · university of mumbai
Thadomal Shahani Engineering College · Feb 2021 — May 2024
Machine LearningComputer NetworksData MiningImage ProcessingBusiness Intelligence
05 /  research

Written. Reviewed. Built.

3 peer-reviewed publications + 4 technical reports across explainable AI, responsible ML, disaster response, social networks, and causal inference.

2026
Technical Report

Galaxy Morphology XAI

Deep learning classifiers are now standard tools for automated galaxy morphology classification at the scale demanded by next-generation ast

Gargi Sathe ·Archit Rathod
2025
Technical Report

LLM Toxicity Mitigation

Large Language Models (LLMs), when trained on web-scale corpora, inherently absorb toxic patterns from their training data. This leads to "t

Mokshit Surana ·Archit Rathod ·Akshaj Kurra Satishkumar
2025
Course Project

YouTube Misinformation Detection

YouTube serves as a major conduit for viral, multilingual political narratives, particularly during global conflicts. This project investiga

Archit Rathod ·Srinath Ganesh ·Vishaal Dayashanker ·Harsh Shelke ·Vignesh Pathak
2024
Technical Report

HTE Benchmarking

This research focuses on benchmarking heterogeneous treatment effect (HTE) estimation algorithms in networked environments to enhance our un

Archit Rathod ·Mokshit Surana ·Gargi Sathe
2024
ICDSA 2024

Ascend.AI

The proposed approach embarks on an intricate and comprehensive exploration of advanced and innovative technologies to enhance interview ski

Archit Rathod ·Gargi Sathe ·Siddh Shah ·Kumkum Saxena
2023
NeurIPS 2023

Multiagent Social Simulators

Multiagent social network simulations are an avenue that can bridge the communication gap between the public and private platforms in order

Aditya Surve ·Archit Rathod ·Mokshit Surana ·Gautam Malpani ·Aneesh Shamraj ·Sainath Reddy Sankepally ·Raghav Jain ·Swapneel S Mehta
2023
ICSISCET 2023

CNN Disaster Classification

Natural disasters act as a serious threat globally, requiring effective and efficient disaster management and recovery. This paper focuses o

Archit Rathod ·Veer Pariawala ·Mokshit Surana ·Kumkum Saxena
peer-reviewed technical reportGoogle Scholar
06 /  let's talk

Hire a builder who questions the model.

I'm actively interviewing for summer / full-time roles in AI infrastructure, fairness tooling, and applied ML. Let's find a problem worth debugging together.

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