Vamsi Nallapareddy
Doctoral Assistant at EPFL (École polytechnique fédérale de Lausanne)
Ecublens, Vaud, Switzerland
Summary
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Top SchoolVamsi Nallapareddy is a ML researcher and Doctoral Assistant at EPFL with eight years of experience applying deep learning to molecular and protein biology. His PhD work focuses on translation elongation factors in higher eukaryotes, building biological foundation models that bridge sequence-level patterns with functional insights. He has hands-on experience transferring models between academia and experimental contexts, including efforts at UCL to automate CATH superfamily assignments and a visiting project integrating assay meta-data at Cold Spring Harbor Laboratory. Trained in computer science at BITS Pilani, Vamsi combines rigorous engineering skills with domain biology, making him adept at turning noisy experimental data into robust predictive models. An implicit strength is his track record of working closely with leading structural biology labs, suggesting strong collaboration across computational and wet-lab teams.
7 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD Computational and Quantitative Biology, Doctor of Philosophy - PhD Computational and Quantitative Biology at EPFLBachelor of Engineering - BE Computer Science, Bachelor of Engineering - BE Computer Science at BITS Pilani, Hyderabad Campus
Github Skills (16)
mobilenet6
autoencoder6
structural-biology5
high-resolution5
deep-learning4
django-blog4
computer-vision4
chainer4
python4
pytorch3
django-project3
object-detection3
django3
video-streaming1
tensorflow1
Programming languages (3)
Github contributions (5)
vam-sin/CATHe
Nov 2021 - Jun 2022
Contributions:18 commits, 3 pushes, 1 branch in 6 months
vam-sin/deepcys
May 2020 - Mar 2021
A complete Deep Learning solution to predicting the behavior of a given cysteine. The predictions are made using the features from the high resolution protein crystal structures.
Contributions:47 commits, 44 pushes, 1 branch in 10 months
pytorchautoencoderpythoncrystaldeep-learning
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