
Machine Learning Engineer & Full-Stack Developer
Specializing in computer vision and intelligent systems. Currently building permutation-equivariant networks for multi-object tracking at Northeastern University. Transitioned from data science to ML engineering, now exploring rotation-equivariant neural networks and geometric deep learning.
My journey from data science to machine learning research
Master's in Data Science, transitioned from data analytics to ML engineering. My unique background combines data science expertise with cutting-edge machine learning research, allowing me to bridge the gap between domain knowledge and technical innovation.
Graduate student at Northeastern University, working on cutting-edge computer vision research while exploring rotation-equivariant neural networks. Currently developing PENMOT: Permutation-Equivariant Networks for Multi-Object Tracking and researching scalable Gaussian Process approximation methods.
Passionate about quantum mechanics, astrophysics, and the mathematical foundations of deep learning. Currently learning rotation-equivariant CNNs (e2cnn), Vector Neurons architecture, and Oracle Cloud Infrastructure Generative AI. I actively write about statistical and mathematical concepts, exploring the theoretical underpinnings of modern ML techniques.
Academic background and qualifications
Northeastern University
Graduate program focusing on advanced data science methodologies, machine learning, and statistical analysis. Specializing in computer vision research and geometric deep learning applications.
Discover my work, experience, and writings
Professional certifications and specialized training
Oracle
Lakera
Postman
Quantium
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