Aakash Saha
Postdoctoral Research Scientist
Columbia University, New York
Email: as7656@columbia.edu
Postdoctoral Research Scientist
Columbia University, New York
Email: as7656@columbia.edu
Greetings, esteemed reader. I am Aakash Saha, a postdoctoral research scientist in the Honig Lab at Columbia University in New York City. I earned my PhD in Computational Chemistry and Biophysics from the Palermo Lab at the University of California, Riverside, where my research was centered on "Unraveling the Mechanism of Activation and Functioning of CRISPR-Cas12a and CRISPR-Cas9-Conjugated Systems."
During my academic journey, I have honed expertise in molecular dynamics simulations, alchemical free energy perturbation methods, and enhanced sampling techniques to investigate the activation mechanisms of large biomolecular complexes. My doctoral research integrated advanced methodologies such as graph theory and Markov State Models to link the conformational dynamics of CRISPR-Cas systems to their functional roles. Additionally, I employed DFT-based QM/MM studies to elucidate catalytic mechanisms, offering a comprehensive view of their biological processes.
My passion for leveraging computational tools extends beyond CRISPR-Cas systems. During summer internships, I explored machine learning frameworks, including Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), applying them to small molecule parameterization and understanding the permeability of beyond Rule of Five compounds. Prior to my computational pursuits, I gained hands-on experience in laboratory techniques such as animal cell culture, protein purification, and biomaterial fabrication, grounding my research in both experimental and theoretical domains.
Currently, my postdoctoral research focuses on the development of bioinformatics tools to study virus and human protein-protein interactions. I am:
- Establishing a protein-protein interaction (PPI) pipeline between viral and human genomes using Bayesian networks that incorporate structural and non-structural cues.
- Exploring machine learning-based detection of interacting residues to enhance the PPI pipeline.
- Conducting network theory-based interactome studies to infer functional roles of PPIs.
As I continue to delve into the fascinating intersection of computational biology, data science, and bioinformatics, I am eager to connect with professionals and collaborators in these fields. I invite you to explore my profile and discuss potential opportunities for impactful scientific collaboration.
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Feel free to email me for questions, project feedback, or just to say hello.
Resume: Link
CV (Detailed): Link
Email: as7656@columbia.edu