Hi, I'm Vinay
Machine Learning Engineer
Artificial Intelligence | Data Science | Software Development | Virtual Reality | Freelance
About
My Introduction
I'm a passionate & proactive human with strong expertise & experience of 4+ years in Machine/Deep Learning, Software Development, Computer Vision & Web Development. I am currently working as research assistant at The University of Texas at Dallas. Self-motivation, attention to detail, innovative problem solving, strong analytical skills, articulate communication & creativity are the things that drive me to achieve great results. If you're looking to hire, collaborate, or require any assistance, leave me an email
Skills
My technical & miscellaneous skillsAI & Data Science
3+ Years XPComputer Vision
92%Image Processing
90%Probability & Statistics
85%Data Analytics & Visualization
80%Frameworks & Libraries
90%Programming
4+ Years XPPython
95%C
90%C#
90%SQL
95%Computing
3+ Years XPMicrosoft Azure
85%Amazon Web Services
85%GPU & Distributed Computing
90%Virtualization
85%Front End
2+ Years XPHTML
90%CSS
85%JavaScript
75%BackEnd
2+ Years XPPython - Flask
95%Node JS
80%Misc
4+ Years XPGit
90%Linux
90%DevOps
75%Software Development
85%Leadership
95%Communication
90%Time Management
98%Experience
My journey in the academic & professional frontMS - Biomedical Engineering
The University of Texas at Dallas, RichardsonB.Tech - Biomedical Engineering
D Y Patil University, IndiaClass XII
Maths, Physics, Chemistry, Biology, Information Technology | Maharashtra State Board, IndiaResearch assistant
The University of Texas at DallasSoftware Engineer
EduvanceTechnical Lead
WeCan Educational Organization - startupTrainee - Intern
Maquet Getinge GroupServices
Services that I offer as a freelancerArtificial Intelligence
Software
Development
Training &
Mentoring
Portfolio
My works, projects & contributions
Hyper-spectral image segmentation of head and neck squamous cell carcinoma
Developed and optimized a Wavelet CNN on an HPC to segment hyper-spectral images, training it to leverage spatial & spectral features for multi-class segmentation of head & neck cancers (100+ patients). Achieved 0.74 IoU on unseen testing data.
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3D Unet-based cardiac segmentation of neonatal mouse
Developed and tuned a 3D Unet to segment neonatal mouse hearts from a 1000+ image stack, preprocessing and augmenting the cardiac light-sheet data. Achieved 0.84 IoU in accurately segmenting three distinct cardiac structure.
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Emotion Recognition Tool
Developed machine learning system to classify human emotions in real-time video. Trained convolutional neural networks on FER-2013 dataset with 35,000 facial images. Achieved 82% accuracy in detecting 7 emotions using GPU-optimized TensorFlow/Keras framework.
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Pima Indian Diabetes Prediction
Developed random forest classifier and performed exploratory data analysis then engineered features from dataset of 768 patients. Employed perceptron model optimized through hyperparameter tuning and achieved 89% accuracy on unseen test data.
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Tech Mahindra Project - Truck Fuel Efficiency Prediction
Developed neural network model to predict truck fuel efficiency using real-world telemetry data. Extracted throttle input readings from sensor streams to correlate with MPG measurements. Employed regression algorithms achieving 85% accuracy in MPG estimates.
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Research
My research & publications4D Light-sheet imaging and interactive analysis of cardiac contractility in zebrafish larvae
Journal: APL Bioengineering, Volume 7, Issue no. 2 (2023) | Progress: PublishedLink: https://doi.org/10.1063/5.0153214
A machine learning based nucleus segmentation and cell tracking to enable quantification of cellular dynamics across myocardium in 4D. Integrated parallel computing to apply algorithms for image analysis and reconstruction, improving efficiency by 10x.
Interpretation of Autism Spectrum Disorder through Artificial Intelligence
Available at: IJRASET | Progress: PublishedLink: https://www.ijraset.com/fileserve.php?FID=31480
A multi-cascaded neural network model integrated with emotion analysis for automated assessment of autism symptoms. Extracted facial landmarks and attributed patterns in emotional outputs to identify behavioral biomarkers in patients for early autism detection.