My research interests include developing deep learning algorithms for 3D computer vision problems and creating end-to-end solution pipelines. My long-term goal is to develop a wearable assistant for the visually impaired, helping them navigate the real world.

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<papertitle>Learning Adaptive Lab Evolved Mutational Landscapes: Leveraging LoRA on a Protein Language Model</papertitle>

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<strong>Silba Dowell</strong>,Shivanand Venkanna Sheshappanavar

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<em>Extended Abstract at Women in Computer Vision Workshop, CVPR 2025, Nashville, TN, USA</em>

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<a href="https://github.com/sheshap/sheshap.github.io/blob/master/pdf/cvpr25_poster_template_LoRApLM.pdf">[poster]</a> <br>

<p align="justify"> In adaptive laboratory evolution (ALE), microorganisms are subjected to a range of environmental pressures, such as temperature and radiation, in multiple experimental trials to induce naturally occurring, yet desirable, phenotypes. This approach has paved the way for innovations in protein engineering, yielding improvements in biological properties. However, ALE is constrained by its high demand for intensive resources, including the expertise of highly skilled biologists, costly equipment, and a large volume of biological samples, as well as the continuous oversight required to maintain such experimental rigor. One solution is to leverage protein language models (pLMs) to predict impactful mutations and drive evolutionary trajectories without direct human supervision. In this work, ESM-2 is fine-tuned using Low-Rank Adaptation (LoRA) on Escherichia coli data from the ALEdb database for variant effect prediction.</p>

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