2025 to 2026 Project Index

Portfolio

Selected builds across ML systems, networking tools, optimization, and software engineering.

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14 Projects
4 Build Types
3 Network Systems
Latest build window July 2026

Held Constant, an evidence first portfolio backtesting research application.

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Held Constant portfolio backtesting application

Held Constant · Portfolio Backtesting Without Blind Spots

Jul 2026

FastAPI · SQLAlchemy · PostgreSQL · yfinance · Chart.js

PythonFastAPISQLAlchemy PostgreSQLyfinanceChart.js Backtesting
Details

Research application for building weighted portfolios, retrieving dividend adjusted historical prices, and comparing results against market, gold, bond, savings, or inflation benchmarks. It supports buy and hold strategies plus monthly, quarterly, and annual rebalancing.

The engine models transaction costs, reports risk and attribution metrics, and uses a moving block bootstrap for confidence intervals. A 52 test suite checks metric calculations, invariants, benchmark compounding, data ingestion, and API behavior.

Bubble Tea POS

Bubble Tea POS · Inventory & Ordering

Aug 2025 to Dec 2025

JavaFX · PostgreSQL · AWS · Node.js · React

JavaJavaFXPostgreSQL JDBCAWSNode.js Express.jsReactVite Bootstrap
Details

Complete point of sale platform with customer ordering, staff roles, and inventory management. Built a normalized PostgreSQL schema with triggers, transactional order processing, and audit logs. Implemented a responsive JavaFX UI with role based access, receipt printing, and admin dashboards.

Backend uses a DAO layer over JDBC, batch inserts, and parameterized queries for safety and speed. Deployed on AWS with environment configs and GitHub Actions for CI build checks.

AURORA compute recommendation

AURORA · ML Compute Calibration & Recommendations

Feb 2026

Next.js · FastAPI · Docker Sandbox · Profiling

FastAPINext.jsDocker ProfilingMLOps Tooling
Details

Full stack prototype that turns a calibration run into a defensible compute decision. Users upload an ML repo, run a sandboxed job in Docker, and AURORA captures runtime, logs, and profiling signals to estimate scaling and recommend options with clear reasoning.

Produces reproducible run bundles with a Dockerfile, scripts, and README. Optional GenAI assistance supports quick tuning and diagnosis.

StreamSense Reactor dashboard

StreamSense Reactor · Real Time Streaming KPIs

2025

Redpanda (Kafka) · Node.js · PostgreSQL · React

RedpandaKafkaNode.js PostgreSQLReact
Details

Production style event driven analytics pipeline that ingests high volume service events, streams them through Redpanda with Kafka compatibility, computes rolling KPIs, persists results to PostgreSQL, and visualizes health in a near real time React dashboard.

Built as a clean multiservice repo with containerized local infrastructure and a streaming worker for aggregation and durable KPI writes.

InsightBoard analytics dashboard

InsightBoard · Internal Analytics Dashboard

2025

React · Express · PostgreSQL · JWT/RBAC

ReactExpressPostgreSQL JWTRBAC
Details

Internal tool that turns raw event level data into business KPIs: revenue, activity, daily trends, and event type breakdowns. Strict role based access control gives admins platform metrics while users see only their own.

Node and Express backend with JWT auth and enforced RBAC, backed by PostgreSQL with indexed time series queries. React keeps the dashboard clean and readable.

Fast traceroute hop timing diagram

Fast Traceroute · Parallel ICMP Path Discovery

Jan 2026 to May 2026

C++ · Winsock · Raw ICMP · Async DNS · CSCE 612

C++Raw SocketsICMP Async DNSBatch Tracing
Details

Implemented a Windows traceroute tool that sends ICMP probes with controlled TTL values, resolves hop names asynchronously, and prints ordered path results with RTT timing and error handling. Includes raw socket mode plus a Windows ICMP Helper API fallback.

Batch mode traces many targets with worker threads and a topology aware smart mode that reuses stable prefixes. The public repo includes an academic integrity notice.

Reliable UDP sliding window diagram

Reliable UDP Sender · Sliding Window Transport

Jan 2026 to May 2026

C++ · UDP · RTT/RTO Estimation · Fast Retransmit · CSCE 612

C++UDPSliding Window RTOFast Retransmit
Details

Built a custom reliable sender over UDP with connection setup, packet sequencing, ACK processing, sender window flow control, retransmission timers, RTT/RTO updates, duplicate ACK fast retransmit, and graceful FIN close.

The sender coordinates producer and background ACK threads to keep the transfer window full while tracking throughput and checksums. This originated from CSCE 612 coursework; the public repo includes an academic integrity notice.

DNS crawler packet inspection visualization

DNS Packet Inspector · Raw UDP Resolver Client

Jan 2026 to May 2026

C++ · DNS Wire Format · UDP · Name Compression · CSCE 612

C++DNSUDP Packet ParsingPTR/A Records
Details

Implemented a command line DNS client that builds raw DNS queries, sends them to a chosen resolver, and decodes answers, authority records, and additional records.

Supports A and PTR lookups, compressed name parsing, retries, timeouts, and sockets across platforms. The public repo includes an academic integrity notice.

NaviAI maritime route visualization

NaviAI · Maritime Routing with Risk

Jan 2025 to May 2025

ML Engineer · Semifinalist, Mays AI Competition 2024

PythonNetworkXTransformers StreamlitPandas
Details

Multi objective routing that balances time, fuel, and operational risk. The engine combines live conditions with a news derived risk score to adjust edge weights in real time. A Transformer forecaster and PPO support routing under uncertainty.

A Streamlit UI compares fastest, cheapest, and safest routes with short LLM rationales. Cached data, reproducible configs, scenario replays, and runtime monitoring support analysis.

VisionQuery semantic search UI

VisionQuery · Text to Image Semantic Search

2025

FastAPI · CLIP · Prometheus/Grafana

FastAPICLIPVector Search PrometheusGrafana
Details

Backend first semantic search MVP that embeds images and text in the same CLIP vector space and returns ranked results with cosine similarity. Exposes clean endpoints for ingestion, search, health checks, and metrics.

Uses a simple in memory vector store, containerized development with Docker Compose, and basic observability dashboards.

Monte Carlo risk analysis

Monte Carlo Risk Analysis · SPY Simulation

Dec 2025

Quant/ML · Simulation · Risk Metrics

PythonNumPyPandas Monte CarloVaR/CVaR
Details

Built a simulation based risk engine using log returns and distribution diagnostics. Runs Monte Carlo paths to estimate VaR, CVaR, drawdowns, and scenario stress tests.

Modular data loaders and plotting utilities support quick experiments and parameter sweeps.

ViT vs CNN benchmark results

ViT vs CNN · Small Data Benchmark

Dec 2025

ML Engineer · Reproducible Benchmarks

PyTorchViTCNN ReproducibilityMetrics
Details

Controlled benchmark comparing a compact CNN to a Vision Transformer under limited data regimes. Tracked scaling behavior across dataset fractions, seeds, and augmentations; exported metrics and checkpoints for reproducible comparisons.

Includes training curves, calibration checks, and error analysis to show when transformers win and when they do not.

Style transfer output

Style Converter · VGG19 NST

2025

PyTorch · VGG19 · Neural Style Transfer

PyTorchVGG19SWE
Details

Classic VGG19 neural style transfer with a streamlined UI. Users tune content and style weights, TV loss, and iteration budget. A coarse to fine schedule and multiscale Gram features produce crisp textures.

Includes deterministic seeds and progress snapshots. Runs on CPU or GPU, with large images tiled to keep memory bounded.

Based on "A Neural Algorithm of Artistic Style" →

Fake review detector overview

Fake Review Detector · Adaline from Scratch

2025

Python · NumPy · NLP · Interpretable Classifier

PythonNumPyNLP Gradient DescentInterpretability
Details

Transparent fake review classifier that separates CG (computer generated) from OR (organic) reviews. It stays lightweight and explainable without opaque embeddings.

Uses manually designed features such as length, suspicious phrase count, and uppercase word count, then trains an Adaline model with batch gradient descent.