Data Science · Machine Learning · Vibe Coding

Finding the non-trivial
in complexity.

Hi, I’m Abhishek. I turn raw and messy datasets into robust AI models that becomes production-grade framework.

Abhishek Mall
01 ABOUT
I

make difficult data
useful.

Developing methods for low-quality, high-dimensional datasets that break off-the-shelf tools.

  • AI : 3D Modeling, Representation Learning, Feature Extraction, and Continuous Optimization.
  • Systems : High-throughput Processing, Big-data Handing and End-to-End Pipelines.

Give me an ambiguous problem, petabytes of noisy data, and a high-stakes question — that’s where I build my best systems.

Python PyTorch SQL GenAI HPC / GPU
0 images analyzed
0 projects lead
0 citations
02 EDUCATION

Built on first principles.

Physics taught me how to reduce complex systems to what matters. Computation taught me how to scale that thinking.

2020 — 2025 Hamburg, Germany
DOCTORATE

PhD
Physics

Universität Hamburg
International Max Planck Research School for Ultrafast Imaging & Structural Dynamics

THESIS

Unraveling Heterogeneity in X-ray Single Particle Imaging

2016 — 2020 Mumbai, India
DUAL DEGREE

MS
Physics

MTech
Materials Science

Indian Institute of Technology - Bombay

THESIS

Design and Optimization of Nanophotonic Metasurface using Deep Learning

2013 — 2016 New Delhi, India
UNDERGRADUATE

BS
Physics

Hansraj College, University of Delhi

FOCUS

Physics, Mathematical Methods and Computational Foundations

03 EXPERIENCE

From complex questions
to simple solutions.

I own the full arc: framing the question, designing the method, implementing the pipeline, scaling computation and communicating the result.

08/2025 — 06/2026
POSTDOCTORAL RESEARCHER · HAMBURG

Max Planck Institute for the Structure and Dynamics of Matter

Developed large-scale deep-learning workflows for 3D reconstruction from millions of noisy 2D X-ray diffraction images, integrating image processing, reconstruction and computational analysis into automated, reproducible pipelines.

Deep Learning3D ReconstructionImage ProcessingAutomation
10/2020 — 07/2025
PH.D. RESEARCHER · HAMBURG

Max Planck Institute for the Structure and Dynamics of Matter

Designed and deployed an end-to-end scientific image-processing pipeline for 1.2 PB of X-ray diffraction data. Developed scalable Python methods for image representation, feature extraction, classification, search, optimization and 3D reconstruction of heterogeneous datasets.

PythonMachine LearningOptimizationHPC / GPUScientific Imaging
07/2020 — 09/2020
ML RESEARCHER · REMOTE

Syracuse University

Built a deep-learning solution for real-time classification of photon-counting data comprising more than 100 million samples, and optimized the inference workflow for near-real-time processing.

Deep LearningClassificationLow-Latency Inference
02/2019 — 06/2020
RESEARCH ASSISTANT · MUMBAI

Indian Institute of Technology Bombay

Developed a generative deep-learning tool for data-driven nanoscale materials design and prediction, replacing computationally intensive workflows with ML-based inference and automated design optimization.

Generative AIDeep LearningOptimizationNanophotonics
2017
RESEARCH INTERN · PARIS

Laboratoire de Physique des Solides

Created a computational scattering model to study nanoscale self-assembly and interaction dynamics.

ModelingNumerical Methods
04 PROJECTS

Proof,
not promises.

Research-grade problems approached with production-minded thinking.

01 UNSUPERVISED ML · LARGE-SCALE DATA

Finding structure in millions of diffraction images

Bayesian and machine-learning pipelines for classifying, embedding and characterizing large X-ray diffraction datasets to reveal heterogeneity and structural change.

Python Machine Learning Representation Learning HPC / GPU
View project →
02 OPTIMIZATION · IMAGING

Optimization without reliable gradients

Noise-robust computational methods for nonlinear holographic imaging and reconstruction problems where conventional gradient-based optimization becomes unreliable.

Optimization Scientific Imaging Numerical Methods Reconstruction
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03 DEEP LEARNING · DESIGN

Learning to design nanophotonic devices

Deep-learning frameworks for fast forward and inverse design of nanophotonic metasurfaces, combining neural prediction with automated design optimization.

Deep Learning Generative Design Optimization Nanophotonics
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Have a difficult data problem?

Let’s make it
understandable.

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