Software Engineer • AI/ML

Building useful things with Python, Go, React, and Machine Learning

Programmer Analyst at Pasadena City College. I ship portals, APIs, data pipelines, and the occasional model. Currently finishing my M.S. in AI at UT Austin focusing on deep learning and vision-language systems.

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GoReactPythonGraphQLDockerOracle

About

I design and build web applications end-to-end: React frontends, Go/Python backends, and data services that integrate with Oracle and Salesforce. I enjoy turning messy systems into clean APIs and dashboards that people actually use.

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UT Austin • AI/ML Coursework

Graduate coursework covering deep learning, transformers, and NLP with hands-on implementation experience

Transformers & LLMs

  • Built transformer from scratch for character-level classification (98% accuracy on letter counting task)
  • Transformer language model achieving 6.3 perplexity on text8 dataset with causal masking
  • Fine-tuned SmolLM2 (1.7B) using LoRA for unit conversion with supervised fine-tuning (SFT) and rejection sampling fine-tuning (RFT)
  • MLP & Transformer planners for autonomous driving in SuperTuxKart with learned query embeddings
PyTorch Attention LoRA HuggingFace

Computer Vision

  • CNN semantic segmentation with U-Net architecture achieving IoU > 0.75 for road boundary detection
  • Multi-task learning for joint depth estimation (MAE < 0.05) and segmentation
  • Vision-language models (VLM + CLIP) achieving 70%+ accuracy on multi-modal QA tasks
  • Autoregressive image generation using Binary Spherical Quantization (BSQ) with patch-level tokenization
  • CNN planners for end-to-end autonomous driving from raw images to waypoint prediction
CNNs U-Net CLIP Multi-task

Model Optimization

  • Low-precision training with half-precision (FP16) achieving 50% memory reduction
  • LoRA adapters for parameter-efficient fine-tuning on large models
  • 4-bit quantization using block quantization achieving 7x memory reduction
  • QLoRA implementation combining quantization with low-rank adaptation for efficient fine-tuning
Quantization LoRA FP16/4-bit

Classical ML & NLP

  • Sentiment classification with perceptron (74%) and logistic regression (77%) using advanced feature engineering
  • Deep averaging networks with GloVe embeddings achieving 77% accuracy on sentiment analysis
  • Typo-robust inference using spelling correction and prefix embeddings (74% on corrupted text)
  • Fact-checking system using DeBERTa for textual entailment on ChatGPT outputs (83% accuracy)
  • Dataset artifact analysis identifying and mitigating spurious correlations in NLI/QA benchmarks
Feature Eng DeBERTa NLI

Course Information

Deep Learning (CS 342)

MLPs, CNNs, residual networks, image classification, semantic segmentation, depth estimation

Advances in Deep Learning (CS 395T)

Memory-efficient training, quantization, LoRA, autoregressive models, vision-language models, CLIP

Natural Language Processing (CS 388)

Sentiment analysis, transformers, language modeling, textual entailment, dataset artifacts, debiasing

Deep Learning Advances in DL NLP NLP Research Paper

Experience

Programmer Analyst • Pasadena City College

Sep 2021 – Present • Pasadena, CA
  • Led React + GraphQL portal used by 40k+ students and staff.
  • Designed Go middleware for vendor APIs; improved throughput and reliability.
  • Streamlined Oracle data workflows; measurable performance gains.

Software Developer • Cloudsquare

Jan 2020 – Sep 2021
  • Built custom Salesforce apps (Apex, LWC, Visualforce).
  • Integrated Microsoft Graph, CLEAR, Plaid, Experian, Equifax.
  • Prototype: Raspberry Pi + TPU face detection system.

Database Administrator • ADR Services, Inc.

Jul 2018 – Jan 2020 • Los Angeles, CA
  • Improved DB performance by ~60% with indexing and query tuning.
  • Governance policies; BI reporting & data extraction.

Junior System Administrator • CSU San Bernardino

Jun 2017 – Dec 2017
  • Linux/Unix admin; automation via Ansible and Docker.
  • Built Node/Express/Mongo tools for lab operations.

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