Harshwin Venugopal
Senior AI Engineer at Huawei Technologies Canada Co., Ltd.
Old Toronto, Ontario, Canada
Summary
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Senior🎓
Top SchoolHarshwin Venugopal is a Senior AI Engineer with 10 years of experience building and productionizing machine learning systems, currently focusing on time series forecasting for systems at Huawei Technologies Canada. He blends academic rigour from IISc and the University of Waterloo with practical experience leading ML development at Migrations.ml, where he delivered financial forecasting, backtesting, and explainable-AI research. His strengths include end-to-end pipelines, performance optimization, synthetic data generation (VAE/GANs), and handling class imbalance with active learning approaches. Comfortable across research and production, he has a background in speech and computer vision research and a track record of mentoring and operationalizing models in high-stakes domains. An interesting through-line in his career is applying signal-processing and systems thinking—from energy and audio projects to financial and systems forecasting—to squeeze more predictive value from noisy, real-world data.
10 years of coding experience
2 years of employment as a software developer
Bachelor’s Degree, Electrical and Electronics Engineering, 8.42, Bachelor’s Degree, Electrical and Electronics Engineering, 8.42 at PES institute of technologyMaster of Engineering - MEng, Systems Design Engineering (SYDE), Master of Engineering - MEng, Systems Design Engineering (SYDE) at University of Waterloo
Github Skills (21)
mining7
medical6
svm6
classify6
pattern6
support-vector-machine5
keras5
fuzzy5
image-retrieval5
retrieval5
classification5
convolutional-neural-networks5
vector4
machine-learning4
data-mining4
Programming languages (1)
Github contributions (5)
Harshwin/Wide-Residual-Newtork-
May 2019 - Nov 2020
Medical image classification is essential to store and retrieve large amount of data. Also to help doctors assisting instantaneously. Various approaches are provided in literature. Very good results are obtained using fuzzy inference system, some data mining techniques. But the best published results are using Local binary pattern(LBP) and Support vector machine(SVM). In this paper, we try to use a deep learning approach to classify medical images. A technique called WRN( Wide Residual Network) is implemented and tested using Image Retrieval in Medical Applications(IRMA) dataset.
Contributions:4 commits, 3 pushes, 1 branch in 1 year 6 months
pythonmedical-imagedata-miningfuzzy-inference-systemresidual
Harshwin/Speech-enhancement-DNN
Jun 2017 - Jul 2020
Contributions:25 pushes, 1 branch in 3 years 1 month
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