Sayan Nath

Key Achievements:

• Fairness and Accuracy in Machine Learning: Conducted in-depth research on balancing fairness and accuracy in machine learning models by implementing bias mitigation strategies. Focused on developing methodologies that enhance model fairness without compromising performance.
• Collaborated with Dr. Shaina Reza from Vector Institute on Responsible AI initiatives, with a focus on large language models (LLMs). Contributing to the development of ethical and transparent AI systems by leveraging LLMs to understand and mitigate biases.
• Acted as a Teaching Assistant (TA) for undergraduate and graduate courses, providing mentorship and guidance to students.

Key Achievements:

• Setting up problems/quiz for Scaler Topics on Keras & PyTorch.
• Working to create an outline for technical content for Keras.
• Writing different technical content as a blog post for Keras.

Key Achievements:

• Implemented MobileViT in TensorFlow and Keras.
• Used MobileViT's official Pytorch weights to port from Pytorch to TensorFlow.
• Created two notebooks for tutorial purposes i.e Off-the-shelf classification and Fine-tuned notebook.
• Publish all the models in TensorFlow Hub.

Key Achievements:

• Responsible for creating Computer Vision Services to enhance the product.
• Worked on on-device Deep Learning with TensorFlow Lite as well deploying Machine Learning Services in production with AWS Sagemaker.
• Trained models on Google Cloud Platform, reproduced SOTA models from research. papers.

Key Achievements:

• Understand the Tensorflow Lite Task Library.
• Implemented CameraX and remove the usage of fragments with the existing Camera2 and Camera API in Object Detection App.
• Implement Support Library with TensorFlow Lite Interpreter.
• Implemented the Image to BitMap conversion and modify Support Library and Task Library.
• Adding a Bounding Box Function in tflite-support Library.
• Implemented Data Binding.