Manpreet Singh Minhas
Verified Expert in Engineering
Artificial Intelligence Engineer and Developer
Waterloo, ON, Canada
Toptal member since August 17, 2021
Manpreet is a computer vision and deep learning engineer and developer who has taken many AI-based ideas from the research stage to production. He regularly shares his industry and research expertise in deep learning and computer vision by authoring technical articles on Medium's Towards Data Science. Manpreet's industry experience is backed by a master's degree in computer vision and deep learning, he is passionate about technology, and he loves to build cool stuff.
Portfolio
Experience
- Python 3 - 4 years
- Deep Learning - 4 years
- Scikit-learn - 4 years
- PyTorch - 4 years
- Convolutional Neural Networks (CNNs) - 4 years
- OpenCV - 3 years
- Computer Vision - 3 years
- Git - 3 years
Preferred Environment
Python 3, Git, Visual Studio Code (VS Code), Ubuntu, PyTorch, GitHub, Amazon Web Services (AWS)
The most amazing...
...thing I've researched and developed is AnoNet, a weakly supervised, fully convolutional network for anomaly detection in textured surfaces.
Work Experience
Computer Vision and Deep Learning Research Engineer
Fugro
- Developed pavement distress detection and classification from end-to-end in PyTorch. Integrated the algorithms into Fugro's C# application by creating a C++ DLL interface.
- Created bird's-eye-view projection to get a top-down view algorithm and a Python GUI application for processing data from SQL databases. Implemented multi-processing to speed up processing by 50%.
- Incorporated deep learning-based object detection and tracking algorithms to reduce manual processing costs by approximately 35%.
- Automated the testing and deployment of packages by introducing GitHub Actions to the team's workflow.
- Applied text recognition on traffic signs to automate categorization based on the MUTCD standard.
Research Associate
University of Waterloo
- Researched, developed, and published an article titled AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces Using CNNs.
- Wrote and published a CNN-based autoencoder architecture for semi-supervised anomaly detection.
- Created and published a technique for anomaly detection in images using transfer learning.
- Developed and presented defect detection using deep learning from minimal annotations.
Experience
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces Using CNNs
https://arxiv.org/abs/1911.10608Automatic Redaction of Video Recordings Using Deep Learning
Bird's-eye-view Projection to Get a Top-Down View
Semi-supervised Anomaly Detection Using Autoencoders
https://github.com/msminhas93/anomaly-detection-using-autoencodersRoad Crack Segmentation Using Transfer Learning
https://github.com/msminhas93/DeepLabv3FineTuningDefect Detection Using Deep Learning from Minimal Annotations
Education
Master's Degree in Computer Vision and Deep Learning
University of Waterloo - Waterloo, ON, Canada
Bachelor's Degree in Electronics and Telecommunication
University of Mumbai - Mumbai, Maharashtra, India
Certifications
Deep Learning Specialization (Five Courses)
DeepLearning.AI
Skills
Libraries/APIs
PyTorch, TensorFlow, Scikit-learn, OpenCV, Pandas, NumPy, SciPy, Matplotlib
Tools
TensorBoard, Scikit-image, Git, Jupyter, Amazon SageMaker, GitHub
Languages
Python 3, C++, SQL, Python, C#
Paradigms
Anomaly Detection, Siamese Neural Networks, Automated Testing
Platforms
Visual Studio Code (VS Code), Amazon Web Services (AWS), Ubuntu
Storage
Amazon S3 (AWS S3)
Other
Convolutional Neural Networks (CNNs), Deep Learning, Neural Networks, Computer Vision, Image Processing, Object Detection, Machine Learning, Variational Autoencoders (VAEs), Graphical User Interface (GUI), Algorithms, DLL, Artificial Intelligence (AI), Data Science
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