About
Zain Sohail builds the invisible systems behind modern machine learning — the pipelines, catalogs, and data architectures that quietly determine whether a model thrives or collapses under the weight of reality. Today, as an ML Research Engineer at HEAD acoustics, he works where audio, large-scale infrastructure, and intelligent retrieval systems intersect.
The Work of Now
At HEAD acoustics, Zain’s interests in structure and signal converge. His current work threads together a data catalog for unifying scattered datasets, a data lake built for large-scale experimentation, and audio-embedding pipelines that support similarity search, anomaly detection, and rapid evaluation workflows.
The aim is simple, but never small: create foundations that allow machine learning research to move faster, more reliably, and with a deeper understanding of the data that shapes it.
Scientific Roots at DESY
Before industry, Zain worked with data that arrived one photon at a time.
At DESY, he developed deep-learning denoising techniques for multidimensional photoemission spectroscopy — reconstructing structure from inherently noisy measurements using 3D U-Nets, Noise2Noise approaches, and custom experimental pipelines.
He also co-authored the SED Python package, built for processing billion-event photon-streams into the high-dimensional images used in scientific analysis. The work sharpened his instinct for modular, scalable tooling — a theme that echoes throughout his career.
Early Engineering Years
Zain’s early roles grounded him in the realities of real-time systems and simulation environments.
At HörTech, he developed signal processing algorithms for hearing aids and created tooling to bridge MATLAB research prototypes with C++ production chains.
At OFFIS, he worked on autonomous driving simulation, establishing interoperability standards and evaluating complex simulator behavior. These experiences built his sensibility for systems where consistency and precision are not luxuries, but requirements.
Academic Foundation
Zain holds an M.Sc. in Data Science from RWTH Aachen, where he studied optimization, statistical learning theory, and advanced signal and image processing. His master’s thesis at DESY explored denoising for high-dimensional spectroscopy data, blending classical approaches with modern deep learning.
He earned his B.Eng. in Engineering Physics from the University of Oldenburg — a program that wove together quantum mechanics, solid-state physics, electronics, and control systems. A semester at the Technical University of Denmark added a more international, design-oriented engineering perspective.
Research Contributions
Zain has shared his scientific work across:
- DESY Photon Science Users' Meetings
- DPG Spring Meetings
- NanoMat Science Day
- Round Table on Deep Learning @ DESY
Across these projects, a consistent theme emerges: accelerating discovery through better data, better tooling, and better ways of seeing complicated systems clearly.
Current Questions
More recently, his curiosity has shifted toward how meaning forms inside intelligent systems. Semantics, shared language, and ontological structure have become the quiet threads running through his work — the scaffolds that help models interpret the world rather than merely process it.
It’s a direction that blends engineering with a touch of philosophy: building systems that are not just efficient, but coherent.
Across physics labs, research centers, and engineering teams, Zain’s work maintains a single throughline: translating complex data into clear, actionable understanding.
Whether through infrastructure, modeling, or tool design, his aim is the same — to make intelligent systems grounded, reliable, and deeply usable.