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Nice To Meet You

Hey, I'm Dev Shah

Hey, I'm Dev Shah and I'm a 19 year old Machine Learning researcher based in Toronto. I’m currently a ML researcher working in Medical Imaging x AI lab under Dr. Tyrrell; looking to integrate Artificial Intelligence into a clinical setting to improve the diagnosis process. I’m also studying Computer Science at the University of Toronto. My ultimate aspiration is to make a significant and lasting positive impact on the world, with the hope of touching the lives of billions. While I acknowledge that achieving this goal may be a long and challenging journey, it is one that I am wholeheartedly committed to pursuing. Check out my portfolio and skills below! I will be posting updates on my website and throughout my newsletters, make sure to subscribe below! Stay tuned :)

Portfolio

EDUCATION.

2022

COMPUTER SCIENCE UNDERGRADUATE AT UNIVERSITY OF TORONTO

Data Analysis, Software Development, Object-Oriented Programming, Mathematics, Data Structures & Algorithms
2021

INNOVATOR AT THE KNOWLEDGE SOCIETY

Exponential Technologies, Real-World Skills, People & Leadership, Character & Mindset, Consulting & Advisory, Artificial Intelligence
2020

INTERNATIONAL BACCALAUREATE PROGRAM

Critical Thinking, Complex Problem Solver, Engaging Global Challenges

MY EXPERIENCES.

2023

Machine Learning Engineer -- Interactions LLC.

Created a LLM driven avatar using NVIDIA’s Omniverse platform and Audio2Face interface for enhancing customer service. Leveraged a tech stack including Python, PyTorch, AWS, Docker, GRPC, Hugging Face, and NVIDIA Audio2Face.
2022

Machine Learning Researcher -- University of Toronto

Developing a massive machine learning model with contrastive learning for feature extraction and diagnosis of knee ultrasounds.
2022

Research Intern -- Interac

Sensing the market through solid research and analysis of trends and technologies that could directly and indirectly, impact the future of financial services.

MY SKILLS.

Data Analysis

Ability to analyze and interpret complex data
Software Development

Proficient in developing software using various programming languages and tools
Object-Oriented Programming

Strong understanding of object-oriented programming concepts and design patterns
Mathematics

Strong mathematical skills including calculus, linear algebra, and statistics

MY TOOLS.

Programming Languages

Python, Java, JavaScript, C++, HTML/CSS, SQL
Applied Machine Learning

Random Forest, XGBoost, Decision Tree, Fast Fourier Algorithm, Tensorflow, pandas, NumPy, Matplotlib, NLTK, SciKit, SkLearn
Machine Learning Frameworks

UNET, Neural Networks (CNN, RNN), Natural Language Processing, Transformers, LLMs
Tools

Git, Tensorflow, VS Code, Visual Studio, PyCharm, Jupyter Notebook, Google Colab, Power Bi

Featured Projects.

Featured Project 1

LLM Driven Avatar

Developed a LLM driven avatar using NVIDIA’s Omniverse platform and Audio2Face interface, integrating cutting-edge Machine Learning libraries. This solution significantly improved installation-related customer service, resulting in a 47% reduction in support ticket escalation and a 32% decrease in installation process duration. Leveraged a tech stack including Python, PyTorch, AWS, Docker, GRPC, Hugging Face, and NVIDIA Audio2Face to create a state-of-the-art avatar for enhancing customer service. Successfully delivered a robust system that demonstrated its efficiency by achieving a 92% customer satisfaction rate based on post-implementation surveys.

Demo Link

Featured Project 2

Nuclei Segmentation with UNET Framework.

Based on the infamous UNET for biomedical imaging paper, I built the UNET architecture from scratch. Collected data from Kaggle of pictures of Nuclei. The images were acquired under a variety of conditions and vary in the cell type, magnification, and imaging modality (brightfield vs. fluorescence). Using Python and SkLearn, preprocessed the images by applying various image enhancement techniques, such as contrast stretching, and minmaxscalar. Adjusted hyperparameters, such as learning rate, batch size, and number of epochs, using TensorFlow’s built-in tools for hyperparameter tuning. Model trained with over 96% accuracy.

Github Link