Alec Bhamani

Data science student at UW–Madison

I build predictive models, machine-learning systems, and practical automation tools. I’m especially interested in work that connects technical analysis to a real decision.

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Alec Bhamani
Open to 2026–27 data and ML internships

Selected projects

A few things I’ve built, from retail prediction and language models to tools for everyday work.

Predictive modeling · Team of four

Crystal Farms & Cub Foods promotion model

Finding promotional windows where discounts could improve profitability—not just lift unit sales.

PythonPredictive modelingSales analytics
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Technical details
My contribution
Collaborated on the Python modeling workflow and the analysis of historical retail sales data.
Approach
Converted historical sales patterns into predicted promotional windows the team could compare and present.
Result
First-place project at the UW–Madison Tech Exploration Lab.
Language models · Independent build

Decoder-only GPT training stack

A custom PyTorch GPT-2 training stack covering data preparation, pretraining, chat tuning, evaluation, and export.

PyTorchCausal attentionKV cacheNLP
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Technical details
Build
Implemented a decoder-only transformer with causal attention, tied embeddings, checkpoint recovery, and KV-cache generation.
Engineering
Added GPT-2 tokenization, resumable pretraining, assistant-only chat tuning, perplexity evaluation, and a Hugging Face export path.
Verified scale
The checked-in configurations calculate to 7.27M, 51.24M, and 124.48M parameters.
Repository workflow README
  1. prepare_data.py Local text or FineWeb-Edu → GPT-2 BPE → train.bin and val.bin
  2. train.py Resumable decoder-only pretraining → gpt_model.pt
  3. finetune_chat.py JSONL conversations with assistant-token-only loss → gpt_chat.pt
  4. eval.py Perplexity evaluation, then Hugging Face and Ollama-compatible export tooling

Presets calculated from model.py

PresetLayers × widthHeadsContextParams
tiny4 × 12842567.27M
small8 × 512851251.24M
gpt212 × 768121024124.48M
Agent tooling · Independent build

skillmap

A Python CLI that indexes installed agent skills and returns only the tools relevant to the current work context.

PythonKnowledge graphsRetrievalCLI
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Technical details
Problem
As skill catalogs grow, overlapping descriptions make it harder for an agent to select the right capability.
Approach
Discover skills, extract stable concepts, build a graph, and rank a scoped neighborhood for each work query.
Verification
Added redirection-aware ranking, incremental updates, diagnostics, and a 55-test regression suite.
A real scoping run Diagnostic source

skillmap scope "manage worktrees and terminals from the command line"

orca-cli Matched on: terminal, worktree

1 of 5 installed skills surfaced

55 / 55
tests passed
8 / 8
curated queries ranked first
3 / 3
adversarial queries ranked first
1.8 / 5
skills surfaced per query

Fresh local diagnostic against current main · 5 installed skills · July 29, 2026

Personal finance · Work in progress

Pace

A paycheck-first budgeting app that calculates how much to spend, save, and invest from variable income.

TypeScriptReactD1Plaid
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Technical details
Build
Developing a full-stack TypeScript app with a paycheck-planning engine and D1-compatible persistence.
Logic
Converts pay frequency and monthly category limits into per-paycheck spending, savings, and investable amounts.
Integration
Supports Plaid Sandbox account linking with encrypted access-token handling on the server.
Paycheck plan calculation Implementation
Typical take-home paycheck$2,000.00
Per-paycheck spending cap− $1,200.00
Savings contribution− $200.00
Available to invest$600.00
$1,200Spend
$200Save
$600Invest

Illustrative Sandbox values · calculation mirrors the current planning engine in app/page.tsx

MadData 2025 · Team hackathon

BadgerBot course recommender

A natural-language course search prototype for exploring UW–Madison classes by topic, credits, and completed prerequisites.

PythonFAISSMiniLMFlask
Explore the team repository
Technical details
My contribution
Built the initial Flask interface and project setup as part of the team; the current retrieval implementation reflects contributions across the repository.
Approach
Embeds 380 catalog records with MiniLM, retrieves candidates with FAISS, and filters by credits and prerequisite gaps.
Output
Returns up to five course cards with course details, credits, and missing prerequisites.
Prototype recommendation Retrieval source
Query

3 credit course about data visualization

Completed: COMP SCI 220, COMP SCI 320
  1. COMP SCI 565
    Introduction to Data Visualization3 credits · Missing prerequisites: None
  2. COMP SCI 765
    Data Visualization3 credits · Missing prerequisites: None
  3. STAT 436
    Statistical Data Visualization3 credits · Missing prerequisite: STAT 240

Prototype output regenerated from the committed FAISS index; ranking percentages intentionally omitted.

Additional product experiments and coursework are available on GitHub.

Browse all repositories

Tools and methods

Tools matter most in context. These are the methods I’ve used across projects, coursework, and consulting work.

Model development

Python, PyTorch, regression, clustering, model evaluation, and experiment design.

Data & retrieval

Pandas, NumPy, R, FAISS, statistical analysis, feature preparation, and ranking.

Product delivery

Flask, APIs, Git, Java, workflow automation, technical documentation, and team collaboration.

Experience and education

Consulting, service leadership, and project delivery alongside my studies at UW–Madison.

Jan 2026–Present

Associate Consultant · Krahnborn

Migrated roughly 557 client accounts into Salesforce, built reusable AI skills for internal delivery, and developed a configurable sync that consolidates service tickets across client organizations.

Aug 2024–Jan 2026

Bar Manager & Catering Staff · Wisconsin Union

Promoted into leadership and coordinated staff for high-volume university venues and events.

2020—2022

Jr. Project Engineer · Blue Lynk

Delivered 20+ interactive document projects for public and private clients while meeting cloud-based quality standards.

Get in touch

I’m open to data science, machine-learning, and analytics internship conversations for 2026–27.

Email Alec View résumé