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Akash Chohan

Intelligent sensing. Embedded AI. Experimental systems.

I explore the intersection of hardware

and intelligence. Building systems that

sense, process, and decide in real time.

From quantum sensing setups to

edge deployed models, I design

experiments that work in the real world.

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About

Building systems at the edge of sensing and intelligence.

I am an MSc Data Science student at the University of Surrey, with a foundation in engineering and a focus on intelligent sensing systems. My work sits at the intersection of experimental hardware, applied machine learning, and real-time decision systems.

Over the past few years, I have developed experience across a range of technical domains: from designing ODMR-based quantum sensing setups involving RF electronics and optical systems, to building edge-deployed neural networks for resource-constrained environments. I am drawn to problems that require integrating hardware and software tightly—systems where understanding the physics matters as much as the algorithms.

I am currently exploring opportunities in PhD research, industry R&D, and technical studentships that allow me to contribute to experimental systems, applied physics, or embedded intelligence. I value work that is rigorous, collaborative, and grounded in real-world constraints.

Research

Technical interests spanning hardware, algorithms, and systems.

Quantum Sensing & Spectroscopy

ODMR-based magnetometry, NV-center physics, RF and optical system integration, signal acquisition and processing for precision measurement.

Embedded & Edge AI

On-device inference, model compression and quantization, real-time processing on MCUs and edge accelerators, latency-constrained deployment.

Multimodal Sensor Fusion

Combining heterogeneous sensor streams—RF, optical, inertial, thermal—for robust perception in complex environments.

Hardware–Software Co-Design

Designing systems where physical constraints inform algorithmic choices—from sensor interfaces to custom data pipelines.

Signal Processing & Time-Series

DSP fundamentals, spectral analysis, filtering, feature extraction from noisy sensor data, real-time streaming pipelines.

Experimental System Design

Prototyping scientific instruments, lab automation, data acquisition systems, and reproducible experimental workflows.

Projects

Selected work across sensing, embedded systems, and data.

Research Project

ODMR-Based Quantum Magnetometry System

Designed and assembled an optically detected magnetic resonance setup for NV-center magnetometry. Integrated RF generation, optical excitation, and lock-in detection to measure magnetic field variations at microtesla sensitivity. Work included PCB design for signal conditioning and Python-based data acquisition.

Quantum SensingRF SystemsSignal ProcessingPython
Technical Project

Real-Time Edge Inference Engine

Built a low-latency inference pipeline for deploying quantized neural networks on ARM Cortex-M microcontrollers. Optimized memory layout and implemented custom operators to achieve sub-10ms inference on classification tasks. Benchmarked against TensorFlow Lite Micro.

Edge AIEmbedded SystemsC/C++TinyML
Technical Project

Multimodal Sensor Fusion Platform

Developed a data fusion framework combining IMU, LiDAR, and camera streams for robust state estimation. Implemented extended Kalman filtering and tested on mobile robotics platforms. Focused on handling sensor dropouts and timing misalignment.

Sensor FusionRoboticsROSPython
Open Source

Spectral Analysis Toolkit for Lab Data

Created a modular Python library for processing spectroscopy data—FFT analysis, peak detection, baseline correction, and automated report generation. Used in active research for characterizing optical and RF spectra from experimental setups.

Signal ProcessingScientific PythonData Pipelines
Skills

Tools and technologies I work with regularly.

Languages & Core

PythonC/C++TypeScriptMATLABSQLBash

Machine Learning

PyTorchTensorFlowScikit-learnONNXTensorRTHugging Face

Embedded & Edge

ARM Cortex-MTensorFlow Lite MicroCMSIS-NNFreeRTOSArduinoRaspberry Pi

Signal Processing

NumPy/SciPyDSP FundamentalsFFT/Spectral AnalysisKalman FilteringLock-in Detection

Hardware & Lab

RF SystemsOptical SetupsPCB Design (KiCad)OscilloscopesDAQ SystemsLabVIEW

Data & Infrastructure

PandasPostgreSQLDockerGitLinuxAWS/GCP
Education

Academic background and qualifications.

Msc Data Science

Data Science

University of SurreyGuildford, Surrey

2025 – 2026

Focus on machine learning, statistical inference, and data systems. Dissertation research on embedded AI and sensor data processing.

Bsc Computer Science

Computer Science & Engineering

Guru Nanak Dev UniversityAmritsar, Punjab

2022 – 2025

Core coursework in programming, electronics, signal processing, and Software Engineering. Final year project on Optically Detected Magnetic Resonance (ODMR).

Three Year Diploma

Computer Science & Engineering

Mehr Chand Polytechnic CollegeJalandhar, Punjab

2019 – 2022

Foundation in technical skills and practical engineering applications.

Experience

Industry and collaboration experience.

Research Collaborator

Surrey AI Imaging Ltd

2026 – Present

Contributing to applied research on imaging systems and AI-driven analysis pipelines. Working on data processing workflows and model evaluation for real-world deployments.

Random Self Researcher

Independent

2024 – Present

Alongside formal projects, I independently explore topics across photolithography, neurotechnology, biosciences, semiconductor devices, psychology, and socio-technical systems. This work is self-directed and focused on building conceptual understanding rather than publication.

Research Assistant

GNDU Quantum Lab

2024 – 2025

Designed and built a modular, low-cost ODMR quantum sensing system using Silicon Carbide (SiC). The setup integrates a custom RF synthesizer, temperature-controlled laser excitation, optical fluorescence detection, and software lock-in amplification (via Red Pitaya). Using 808 nm optical excitation and RF spin manipulation, I demonstrated room-temperature ODMR and detected spin resonances around ~70 MHz. The system is modular, scalable, and suitable for research and quantum education.

Vision

What I am working towards.

I am actively seeking PhD positions and research roles where I can contribute to experimental systems that bridge sensing hardware and intelligent algorithms. My interests lie in environments where rigorous engineering meets frontier science—whether that is developing next-generation sensor systems, deploying AI at the edge, or building instrumentation for fundamental research.

I am also open to R&D roles in industry that emphasize technical depth: applied data science, scientific software development, and embedded system design. The common thread is work that is hands-on, technically demanding, and has real-world impact.

If you are working on something at this intersection and are looking for a collaborator, I would be glad to hear from you.