Shravan Nayak
Applied Science Intern at Mila - Quebec Artificial Intelligence Institute
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolShravan Nayak is a PhD researcher and applied science intern at Mila with eight years of experience bridging production software engineering and cutting-edge multimodal AI research. He has shipped user-facing features at Microsoft PowerPoint and later pivoted to building datasets and alignment methods—creating CulturalVQA and MID-Space to probe and improve cultural awareness and pluralistic preferences in vision-language and diffusion models. His work combines large-scale dataset engineering (ServiceNow Research) with model evaluation and novel metrics, revealing notable performance gaps across regions and model classes. He also contributes practical deep learning implementations—e.g., CNNs and ResNet50 transfer learning for image tasks—demonstrating both research rigor and hands-on ML engineering. Based in Montreal, he focuses on robustness, inclusivity, and interpretability in multimodal systems, often uncovering non-obvious cultural and deployment vulnerabilities that drive more equitable model design.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Université de MontréalBachelor of Technology Electronics Engineering, Bachelor of Technology Electronics Engineering at Indian Institute of Technology (Banaras Hindu University), Varanasi
Github Skills (13)
mask-rcnn10
keras10
computer-vision10
transfer-learning10
faster-rcnn10
machine-learning10
tensorflow10
python10
resnet9
data-structure7
algorithms6
data-structures6
algorithm6
Programming languages (6)
Github contributions (5)
ZoranPandovski/al-go-rithms
Oct 2018 - Oct 2018
:musical_note: Algorithms written in different programming languages - https://zoranpandovski.github.io/al-go-rithms/
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 1 PR in 1 day
Contributions summary:Shravan implemented Convolutional Neural Networks (CNNs) for image classification tasks using Keras within a deep learning context. They added CNN models for the MNIST Fashion dataset, achieving a reported accuracy of 0.92. Furthermore, the user integrated transfer learning using ResNet50 on a custom dataset, demonstrating an understanding of model architectures and optimization techniques.
pythoncomputer-scienceprogramming-languagejavascriptalgorithms-and-data-structures
deeplsd/Merkel-Podcast-Corpus
May 2022 - Jul 2022
Contributions:3 commits, 1 push, 1 comment in 2 months
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