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Nice To Meet You

Hey, I'm Dev Shah

Hey, I'm Dev Shah and I'm a 20 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

My Career Path

Work Timeline

2024

Full Stack Data Scientist

Immigration, Refugees and Citizenship Canada

Engineered and deployed ML models for text classification, leveraging NLP techniques to optimize email categorization. Utilized AWS and Amazon SageMaker for model training and deployment.

2024

Software & Machine Learning Engineer

Fallyx

Developed a Machine Learning pipeline for fall detection. Using a CNN model to classify falls using 6-axis sensor data and engineered a hybrid Conv1D-LSTM model for time series analysis.

2024

Deep Learning Researcher

University of Toronto Missisauga

Developed a PointNet Deep Learning model which was used for age classification of human pelvic bones. Worked with 3D data and performed pre-processing techniques to enhance the model's robustness and accuracy.

2023

Reinforcement Learning Researcher

Robot Vision & Learning Lab

Assisted in developing a digital twin of a chemistry lab to improve efficiency and safety outcomes. Worked with NVIDIA's Omniverse platform to create body assets and robotics equipment in a simulated virtual environment.

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 EC2, Docker, GRPC, and Hugging Face.

2022 - Present

Machine Learning Researcher

University of Toronto

Developing machine learning models using contrastive learning for feature extraction and diagnosis of knee ultrasounds using advanced algorithms such as the Gaussian Pyramid and Encoder models.

2022

Research Intern

Interac Corp.

Conducted a comprehensive and in-depth market analysis, employing robust research methodologies to examine emerging trends and technologies that bear direct implications on the trajectory of financial services.

2022 - Present

Computer Science Undergraduate

University of Toronto

Studying Data Analysis, Software Development, Machine Learning, Deep Learning, Object-Oriented Programming, Operating Systems, Mathematics, Data Structures & Algorithms.

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, Swift, JavaScript, C, C++, Assembly, TypeScript, SQL
Frameworks & Libraries

React, PyTorch, TensorFlow, Redux, Hugging Face Transformers, Sci-kit, Numpy, Pandas, SkLearn
Developer Tools

Git, VS Code, MongoDB, PyCharm, Jupyter Notebook, Google Colab, Power BI
Cloud & DevOps

AWS, GCP, Azure DevOps, Docker, Sagemaker, Vercel, BentoML

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

EyeSpy; multimodal AI-model to help the visually impaired.

Developed a multi-model AI system to help the visually impaired navigate through crowded spaces. The program uses the user’s iPhone camera (ideally, this would be a set of glasses with a camera for ease of use) to scan their surroundings; sampling images every couple of seconds and passing them to the Detectron2 model. This model analyzes the image, performs object detection, and creates bounding boxes of the objects near the visually impaired individual. The bounding boxes are processed in the backend and converted into simple English descriptions (i.e. there is a chair on the left) by splitting the image into a grid with 5x5 pixel boxes. This English description is fed to Cohere’s LLM and the model provides an in-depth description of how to navigate/proceed forward. This description is fed to the individual in an audio format using Whisper from OpenAI.

Github Link