Aparajita Kashyap

Aparajita Kashyap

they/she

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients

Published in Nature Scientific Reports, 2023

We leverage JH-CROWN: The COVID Precision Medicine Analytics Platform Registry to identify subgroups of COVID-19 patients who are at high risks for severe disease progression.

Recommended citation: Cowley HP, Robinette MS, Matelsky JK, Xenes D, Kashyap A., Ibrahim NF, Robinson ML, Zeger S, Garibaldi BT, Gray-Roncal W. Using machine learning on clinical data to identify unexpected patterns in groups of covid-19 patients. Scientific Reports, 13(1). February 2023. https://doi.org/10.1038/s41598-022-26294-9 https://www.nature.com/articles/s41598-022-26294-9

Trade-offs in concentration sensing in dynamic environments

Published in Biophysical Journal, 2024

We model a eukaryotic cell sensing a chemical secreted from bacteria and develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time.

Recommended citation: Kashyap A, Wang W, Camley BA. Trade-offs in concentration sensing in dynamic environments. Biophysical Journal, 123(10). May 2024. 10.1016/j.bpj.2024.03.025 https://www.cell.com/biophysj/fulltext/S0006-3495(24)00205-4

talks

Synthesizing Gaps and Priorities for a Justice Informatics Research

Published:

Co-presenter of the Justice Informatics Collaborative’s podium abstract; we presented our principles of justice-oriented biomedical informatics research synthesized from our previous workshop and suggested next steps for various stakeholders interested in incorporating justice into their work.

Machine learning for time-series biomedical data

Published:

I was a guest lecturer for Columbia University’s Machine Learning for Healthcare class. My lecture covered processing of various types of time-series biomedical data and provided an overview of machine learning methods suitable for time-series data (e.g. RNNs, transformers, survival analysis).

teaching

Acculturation to Programming and Statistics

Teaching Assistant, Columbia University, 2023

I was a TA for Acculturation to Programming and Statistics in the Departemnt of Biomedical Informatics at Columbia University. This class is caters to students from a wide variety of mathematical and computational backgrounds and provides tools for navigating the informatics landscape. For this class, I created and delivered weekly lectures and semiweekly lab assignments.

Machine Learning for Healthcare

Teaching Assistant, Columbia University, 2024

I was a TA for Machine Learning for Healthcare in the Departemnt of Biomedical Informatics at Columbia University. This class offers a survey of various machine learning topics, with an emphasis on biological and clinical applications. For this class, I designed weekly homework assignments and mentored students for their final project.