Ishika Nahar
Available for internships
San Diego, CA

Hello, I'm

Ishika Nahar

Applied AI · Data Science · Machine Learning

I'm a UC San Diego student working across applied machine learning, data science, and AI product development. I've used behavioral, healthcare, conversational, wearable, and biomedical data to train models, evaluate AI systems, and turn technical results into tools people can use. Currently at UC San Diego (B.S. Cognitive Science (Machine Learning & Neural Computation)), graduating july 2027.

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Technical Skills

What I actually use

Grouped by the work I've done — applied ML, AI evaluation, analytics, and scientific data.

Applied Machine Learning

Building and evaluating models on real, messy data.

  • Supervised learning
  • Feature engineering
  • Model evaluation
  • Error analysis
  • Computer vision
  • Behavioral modeling
  • Experiment design

AI Systems & Evaluation

Making AI behavior inspectable, explainable, and trustworthy.

  • LLM tracing
  • AI evaluation
  • Prompt & output analysis
  • FastAPI integration
  • Structured knowledge systems
  • Human-in-the-loop validation

Data & Analytics

Turning data into decisions and clear recommendations.

  • Python
  • SQL
  • Pandas
  • Data visualization
  • Tableau
  • Power BI
  • Excel
  • Cohort & user analysis

Scientific & Healthcare Data

Working fluently with specialized, high-stakes data.

  • Behavioral & pose data
  • Biomedical imaging embeddings
  • Wearable time-series data
  • Healthcare interoperability formats
  • Biological & clinical domain interpretation

Professional Experience

Research, internships, and builds

Roles where I shipped models, evaluations, and product-facing AI systems — with case studies linked where they exist.

Machine Learning Intern

Luveo Health

June 2026 – Present

Extending an AI compliance copilot that turns structured healthcare-compliance results into clear, actionable explanations.

Key achievements

  • Mapped and extended compounding support across domain models, field semantics, API contracts, and tests.
  • Connected a React clinical sandbox to an authenticated FastAPI backend across HL7 v2.5 / FHIR R4 scenarios.
  • Instrumented the LLM explanation layer with Langfuse for end-to-end tracing of prompts, tools, and failures.
Open case study

Data Engineer Research Assistant

UC San Diego Neurobiology Lab (Sternson Lab)

June 2025 – Present

Human-in-the-loop computer-vision pipeline to quantify social observational learning in mice.

Key achievements

  • Trained SLEAP multi-animal pose models on 8,200 annotated frames across three labeling batches.
  • Engineered trajectory features, aligned them with ELAN annotations, and trained XGBoost (0.85 mean ROC-AUC, 5-fold CV).
  • Built raster visualizations and a PyQt review GUI; contributed analyses presented at SfN 2025.
Open case study

Hackathon Teammate — Biomedical Model Evaluation

Owkin Hackathon (K-Scope)

July 2026

1st-place team building a certify(prediction) workflow for pathology foundation-model evidence cards.

Key achievements

  • Built the evidence-card dashboard UI for demoing probe / necessity / specificity results without a live GPU.
  • Collaborated on framing evaluation beyond accuracy with matched-random controls and structured caveats.
Open case study

Product & Content Strategy Intern

Skillsoft Corporation

July 2024 – September 2024

Analyzed CAISY beta conversation and learner-engagement data to inform prompting and audience targeting.

Key achievements

  • Turned conversation logs into product recommendations for prompts and targeting.
  • Built Tableau / Power BI views that made cohort patterns usable for the product team.
Open case study
View Complete Experience Timeline

Let's build the things we wish already existed.

Open to Applied AI, ML, and Data Science internships for Fall 2026 and Summer 2027 — especially teams that care about evaluation, not just demos.

Get In TouchDownload Résumé
inahar@ucsd.edu