I am a PhD student in Machine Learning at Johns Hopkins University, under advisors Randal Burns and Alan Yuille. My research focus is scalable and high performance Machine Learning, Fluid Dynamics and Medical Imaging methods. Member of the Institute for Data Intensive Engineering and Science (IDIES) hands-on Graph Data Science.

Publications

Scaling Orthogonal Matching Pursuit to High Performance CPUs & GPUs - arXiv - GitHub Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts - BiorXiv
Edge-Parallel Graph Encoder Embedding - arXiv - GitHub
Analysis of Inertial-Range Intermittency in Forward and Inverse Cascade Regions in Isotropic Turbulence - arXiv
Analysis of Energy Cascade and Buoyancy Loss in a Publicly Accessible Stable Atmospheric Boundary Layer Dataset - harvard.edu

Projects

Utilizing model pruning and Tensor parallelism to scale Evolutional Deep Neural Network Dall-E Image Generator for Google Docs, Sheets and Slides (Deprecated) Documentation
Deploy Scientific Datasets to Multi-Disk Systems
Alice in Wonderland BERT-based chatbot - Cool Interactive project
Hierarchical Time Series Analysis
Visualize Turbulence Data
Tools for Working with Graphs
Hand Gesture Recognition
Malaria Detection & Segmentation

Talks

DeepMind's EEML Albania 2023

Articles

ChatGPT can’t count, and that’s a problem
Hands-On Quickstart to Training Large Language Models
To Compress or Not to Compress — A Zarr Question
Plot Most Important Nodes in a Graph with NetworkX and MatPlotLib
More CPU cores is seldom better, and here’s why
The Reasons Behind Numpy’s Speed are Often Misunderstood - Part 2
Efficiently Querying Large Scientific Data Using Zarr’s partial decompress
The Reasons Behind Numpy’s Speed are Often Misunderstood - Part 1
Parallelize Graph Computations Using Ligra Framework’s EdgeMap Interface

Other Cool Experience

Hands-on Experience with the 494th most powerful computer in the world!