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🔭 I’m currently working on Microscopy Image Enhancement, focusing on
Super-Resolution, Denoising, Deconvolution, and Physics-aware Deep Learning. -
🧩 My research explores Transformer-based models, Diffusion models, and State Space architectures for improving microscopic image quality across diverse modalities (e.g., SIM, SMLM).
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🧪 I work at the intersection of Computational Imaging, Deep Learning for Inverse Problems, Scientific ML & Image Analysis
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🧠 Strong foundation in algorithms, compiler design, and systems — bringing a structured, low-level understanding to high-level DL models.
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🌱 Currently expanding into:
- Diffusion-based generative models
- Efficient long-range architectures (e.g., Mamba / SSMs)
- Spatiotemporal modeling
- NLP & Reinforcement Learning (for broader ML depth)
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👯 Open to collaborating on:
- Computer Vision
- Scientific Image Analysis
- Generative Models
- Applied ML research projects
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💬 Ask me about:
- Super-Resolution in Microscopy
- Deep Learning for Inverse Problems
- Transformers vs Diffusion vs State Space Models
- Algorithmic thinking in DL systems
Building models is easy.
Understanding their assumptions, limitations, and inductive biases — that’s where the real work begins.
My Coding Stats:📊 My Github Stats
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⏳ My Wakatime Stats
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🗞️Credits
Comming Soon!!
