Darin Tsui
Hi there! My name is Darin. I’m a Ph.D. candidate and NSF GRFP fellow at the Georgia Institute of Technology studying Electrical and Computer Engineering and Bioengineering. I previously received my BS degree at the University of California San Diego in Bioengineering.
I currently work with Dr. Amirali Aghazadeh in the AI ML & Information Group, where I develop scalable tools for generative models in the biosciences. I am currently supported by the NSF Graduate Research Fellowship. My research focuses on:
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Leveraging mechanistic interpretability frameworks, such as cross-layer transcoders and sparse autoencoders, for circuit discovery, protein design, and extracting biological mechanisms from protein language models.
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Extracting higher-order interactions in models by developing algorithms at the intersection of signal processing, coding theory, and combinatorics.
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Developing principled generative models for high-throughput cryo-electron microscopy (cryo-EM), autoregressive protein generation, and variant effect prediction.
My background is highlighly interdisciplinary. I recently accepted an internship offer with Generate:Biomedicines to conduct research in protein structure and cryo-EM structure determination. I have also previously interned at Surgalign (acquired by Xtant Medical) validating medical imaging deep learning models.
Outside of research, I co-chair Bioengineering Day at Georgia Tech, which aims to highlight interdisciplinary research. Previously, I served as President of the IEEE student branch and helped found the IEEE EMBS chapter at UC San Diego.
News
| May 12, 2026 | I’m happy to announce I’ve accepted an internship at Generate:Biomedicines this summer working at the intersection of protein structure and cryo-EM structure determination with Axel Levy and Max Baranov! |
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| Apr 30, 2026 | ProtoMech has been accepted to ICML! ProtoMech uncovers computational pathways in protein language models and brings us one step closer to understanding how they work. |
| Feb 21, 2026 | Our work on leveraging generative AI, as well as sparse priors, for high-throughput cryo-EM acquisition has been accepted to CVPR! |
| Oct 02, 2025 | I gave a guest lecture in ECE 8803–Generative and Geometric Deep Learning–on explainability and mechanistic interpretability in AI. You can find the recording of the lecture here. |
| Sep 24, 2025 | My work on adapting sparse autoencoders for low-N protein engineering into the NeurIPS AI4Science workshop. |