dedalus

A Python implementation of LDFI.

Official CLI for the Dedalus platform.

Official TypeScript SDK for the Dedalus platform.

Under the supervision of Prof. Matthew Browning, I was tasked to study the creation of a spectral solver for Boussinesq approximated fluid dynamics and then, using Dedalus, to study the effects of rotation and stratification on turbulent convection.

A provenance tree generator for recursive Datalog with negation (and Dedalus).

Source code and data for the paper "Data Assimilation in Large Prandtl Rayleigh-Bénard Convection from Thermal Measurements" by A. Farhat, N. E. Glatt-Holtz, V. R. Martinez, S. A. McQuarrie, and J. P. Whitehead.

An iOS voice assistant that actually gets things done—offering smarter email, notes, search, and personalized actions using GPT-4, voice commands and real-time sync.

Codebase for the joint final project of MATH 704 and MATH 801, completed during the first year of my PhD in Mathematics at UW–Madison.

Official Go SDK for the Dedalus platform

Interactive water-world simulator: full-sphere shallow-water ocean physics (Dedalus v3) rendered on a CesiumJS globe. Drag a moon, change rotation, watch tides, currents and anomalies respond in real time. FastAPI + React/TypeScript, zarr data contract, live anomaly detection.

A stand-alone translator for Dedalus programs.

Project for the datathon organized by Dedalus #Data4Health that we participated in and got the #2 place.

An input generation platform for testing distributed protocols.

Dedalus simulation scripts for active hydrodynamic theory of euchromatin and heterochromatin.

An oracle for distributed system failure preemption and avoidance.

EDA

Aerospace simulation portfolio using OpenFOAM, SU2, FEniCSx, PyFR, FiPy and Dedalus for CFD, FEA and scientific computing projects.

Pseudo-spectral 2D Kolmogorov-flow solver (Dedalus 3) + compact PyTorch U-Net learning next-step vorticity prediction, with physical-parameter conditioning.

LaTeX source file for my Computer Science Thesis "Clinical Data Management Processes and Predictive Machine Learning Models Development for Diagnosis and Rehabilitation in the Cardiovascular Domain", which spans over 100 pages. Research was conducted in collaboration with the multinational company Dedalus