Muhammad Shoaib Ahmed Siddiqui, Machine Learning Developer in Cambridge, United Kingdom
Muhammad Shoaib Ahmed Siddiqui

Machine Learning Developer in Cambridge, United Kingdom

Member since April 6, 2020
Shoaib is part of the machine learning group at the University of Cambridge as a PhD student. Shoaib has worked on various machine learning (ML) algorithms. Most of these ideas have been published in leading journals and conferences. With his novel research background, he is extremely up-to-date with the recent papers and research going on around the world in ML; this demonstrates Shoaib's capability to integrate cutting-edge research into production.
Muhammad is now available for hire




Cambridge, United Kingdom



Preferred Environment

CUDA, TensorFlow, PyTorch, Linux

The most amazing...

...algorithm that I worked on is regarding data cleansing.


  • Junior Researcher

    2017 - 2021
    German Research Center for Artificial Intelligence (DFKI)
    • Co-supervised bachelor's and master's projects and thesis.
    • Created deep learning systems for automated fault detection in the industry (mainly anomaly detection).
    • Developed table detection and structure recognition systems (mostly published as research papers).
    • Demystified deep learning models for time-series analysis through visualization.
    • Created adversarial examples and defenses for time-series and visual modalities.
    Technologies: Python, CUDA, TensorFlow, PyTorch
  • Research Intern

    2019 - 2019
    NVIDIA Research
    • Developed a principled approach towards defending against attacks through better understanding the root cause of the vulnerability of deep learning models against adversarial attacks.
    • Employed alternate visual image representations inspired by the human visual system to enhance adversarial robustness.
    • Defined large-scale deep learning benchmarks with efficient data loading pipelines.
    • Created attention models and weakly-supervised object localization.
    Technologies: CUDA, PyTorch, Python
  • Technical Project Lead

    2016 - 2017
    RheinMain University of Applied Sciences (HS-RM)
    • Served as the technical project lead for FibeVid project (fish biodiversity estimation by low-cost non-destructive video-based sampling).
    • Developed a deep learning-based segmentation system for real-time monocular object detection and tracking.
    • Led fish classification using cross-layer pooling of deep CNN.
    Technologies: CUDA, TensorFlow, C++, Python
  • Research Assistant

    2015 - 2016
    TUKL-NUST Research \& Development Center (SEECS, NUST)
    • Performed document detection and analysis using OpenCV and Tesseract - ICDAR SmartDoc competition 2015 (7th Position).
    • Provided document detection and classification using convolutional and LSTM networks.
    • Detected and segmented objects using convolutional neural networks.
    • Contributed to fish detection and classification using classical computer vision approaches coupled with deep learning.
    Technologies: TensorFlow, C++, Python


  • Cross-Layer Pooling of CNN Activations

    Cross-layer pooling algorithm (Liu et al. 2015) in C++ (using OpenCV and Caffe) and Python (using TensorFlow). The code uses a pretrained ResNet-152 network for its initialization. Refer to the paper for more details on ResNet (He et al. 2015) [] and [] for details on the cross-pooling method.

  • FCN in TensorFlow

    One of my first implementations of FCN using TensorFlow. Used the most powerful base model of Inception ResNet v2 at that time. The project received some attention on GitHub at that time when there was a dearth of good FCN implementations using TF.

  • TorchBrain

    Implemented a computational model of the natural emergence of pooling layers in the for visual recognition between the retina and the LGN during the development of the human brain. The project was based on a NeurIPS-19 paper on this topic.

  • Deep SVDD

    A deep anomaly detection method inspired by the support vector data description (SVDD) where the feature projection using kernel methods was replaced by a deep network initially trained as an auto-encoder.


  • Languages

    Java, Python, C++, C, C#, JavaScript
  • Libraries/APIs

    NumPy, PyTorch, OpenCV, TensorFlow
  • Platforms

    Linux, CUDA
  • Other

    Image Recognition, Artificial Neural Networks (ANN), Artificial Intelligence (AI), Machine Learning, Deep Learning, Image Processing, Natural Language Processing (NLP), Computer Vision Algorithms, Computer Vision, Time Series, Convex Optimization, Lambda Functions, AWS
  • Tools

  • Paradigms

    Distributed Computing


  • Ph.D. Degree in Information Engineering
    2022 - 2022
    University of Cambridge - Cambridge, UK
  • Master of Science Degree in Computer Science (Intelligent Systems)
    2017 - 2021
    TU Kaiserslautern - Kaiserslautern, Germany
  • Bachelor of Science Degree in Computer Science
    2012 - 2016
    National University of Sciences and Technology - Islamabad, Pakistan


  • Machine Learning
  • Machine Learning: Classification
    APRIL 2016 - PRESENT
  • Machine Learning: Regression
  • Introduction to Game Development
  • Machine Learning Foundations: A Case Study Approach

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