• Key Courses: Deep Learning, Computer Vision, Algorithms, Data Science
I'm a Computing Science Undergraduate at the University of Alberta, with a strong focus on Artificial Intelligence and Machine Learning. With experience in Python, Java, Dart, Flutter, React, C++ and Assembly, I enjoy working on complex real-work problems using my prior experience and theoretical knowledge.
Currently, my focus is on ML research and projects involving deep learning, reinforcement learning, and generative models. My passion lies in exploring more advanced AI techniques, solving problems, and expanding my knowledge and experience within the field.
I'm actively seeking internship opportunities in AI/ML to further develop my expertise and collaborate with like minded people. I'm always eager to connect with professionals and researchers to exchange ideas and possible collaboration.
Download ResumeResearch in AI/ML with focus on deep learning, reinforcement learning, and generative models.
Built ML models for analyzing complex BIM data to automate parts of construction planning.
Built a cross‑platform app in Flutter/Dart and implemented digital authentication flows.
Implemented a ViT for CIFAR‑10 image classification and built an image captioning pipeline by pairing a pre‑trained ViT with GPT‑2. Achieved over 80 % test accuracy and bridged vision and language with a BLEU score of 0.06 on Flickr8k.
GitHub RepositoryTrained a U‑Net based model for object detection and semantic segmentation on noisy MNIST digits. Tuned hyper‑parameters on Google Colab, reaching 94 % accuracy, 82 % IoU, and 75 % pixel‑wise precision.
GitHub RepositoryDesigned VAE, DDPM and DDIM models from scratch to generate class‑conditioned FashionMNIST images. Implemented robust training pipelines with model checkpointing and gradient clipping, achieving 86 % classification accuracy on generated samples.
GitHub RepositoryIf you’d like to collaborate or have questions about my work, feel free to reach out via email or connect on LinkedIn.