Data Science · Machine Learning · Vibe Coding
Hi, I’m Abhishek. I turn raw and messy datasets into robust AI models that becomes production-grade framework.
Developing methods for low-quality, high-dimensional datasets that break off-the-shelf tools.
Give me an ambiguous problem, 1.2 PB of noisy images, and a high-stakes question—that’s where I build my best systems.
Physics taught me how to reduce complex systems to what matters. Computation taught me how to scale that thinking.
Universität Hamburg
IMPRS-UFAST
Unraveling Heterogeneity in X-ray Single Particle Imaging
Indian Institute of Technology Bombay
Nanoscience, deep learning and inverse design
Hansraj College
University of Delhi
2020 — NOW
I own the full arc: framing the question, designing the method, implementing the pipeline, scaling computation and communicating the result.
Built ML, Bayesian and optimization methods for large-scale X-ray imaging. Developed end-to-end analysis pipelines for millions of images and scaled computation across GPU and HPC systems.
Developed a variational autoencoder with semi-supervised and transfer learning for robust classification under low photon statistics and noisy experimental conditions.
Engineered deep-learning and genetic-optimization frameworks for fast inverse design of manufacturable nanophotonic devices.
Created a computational scattering model to study nanoscale self-assembly and interaction dynamics.
Research-grade problems approached with production-minded thinking.
Bayesian and machine-learning pipelines that classify, embed and characterize EuXFEL datasets—revealing reaction trajectories and structural changes in biological samples.
A noise-robust derivative-free algorithm for nonlinear holographic imaging problems.
A cyclical framework combining neural networks and genetic algorithms for inverse design.
Have a difficult data problem?