About Ayesha

I'm a Ph.D. candidate with 6 years of research experience related to machine learning, software development, and data science. While I have been a student of the Case Western Physics Department, my independent research has relied on these skills as I tackle various questions in the realm of computational biophysics. I plan to defend my thesis this Spring 2025!

In my free time I've performed with The Cleveland Orchestra at Carnegie Hall, ran two 5k races, played four leagues of soccer, and adopted two pets. I have future plans to finally serve as the Dungeon Master for my long term D&D group.

Research areas

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    Machine Learning Analysis for Microscopy

    Created a robust machine learning analysis for analyzing cell adhesion dynamics in microscopy data.

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    Data Profiling

    Profiled data for over 2 billion Centers for Medicare & Medicaid Services claims.

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    Question-Answering for Knowledge Graphs

    Implemented vector databases to help users query knowledge graph by interacting with a chat bot.

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    Measuring Uncertainty for Neural Network Predictions

    Compared the efficacy of algorithms intended to detect new classes of objects.

Résumé

Experience

  1. Data Science Intern, Noblis

    May 2022 — August 2022, June 2023 — February 2024

    Profiled data for over 2 billion Centers for Medicare & Medicaid Services claims Developed a fraud, waste and abuse detection algorithm Improved performance of semantic search model to develop SPARQL queries Developed model testing framework for semantic search model Explored question answering systems with vector databases

  2. Research Assistant, Case Western Reserve University

    May 2020 — Present

    Developed machine learning algorithm for processing images taken with a novel microscopy technique Developed algorithm to track cell dynamics over time using a high-performance computing cluster Collaborated with engineers to make models accessible and robust Explored visualization methods for neural network training loss landscapes

  3. Deep Learning Research Intern, Rochester Institute of Technology

    May 2018 — August 2018

    Implemented deep neural networks for machine learning Developed testing framework to compare various open-set algorithms in deep neural networks

Education

  1. Case Western Reserve University

    Ph.D. Physics (Expected Sring 2025)

    Research focus: machine learning applications in biophysics; visualization of neural network loss landscapes. Awarded NSF Graduate Research Fellowship for support of a research-based doctoral degree at an accredited institution.

  2. St. Edward's University

    B.S., Mathematics

    Dean's list, McKemie Scholar, CAMP Scholar, Minor in Physics

Top skills

  • Python:

    Expert, 6 years experience

  • Machine Learning:

    Expert, 6 years experience

  • Natural Language Processing:

    Expert, 2 years experience

  • Cloud Computing:

    Expert, 4 years experience

Publications

3. Goreke, U., Gonzales, A., Shipley, B., Man, Y., Wulftange, W., An, R., Hinczewski, M., & Gurkan, U. A. (Under Revision for Nature Communications). Motion Blur Microscopy. bioRxiv Preprint.

2. Praljak, N., Shipley, B., Gonzales, A., Goreke, U., Iram, S., Singh, G., Hill, A., Gurkan, U.A. & Hinczewski, M. (2020). A Deep Learning Framework for Sickle Cell Disease Microfluidic Biomarker Assays. Blood, 136, pp.15-16. DOI,PDF.

1. Roady, R., Hayes, T. L., Kemker, R., Gonzales, A., & Kanan, C. (2020). Are open set classification methods effective on large-scale datasets?. Plos one, 15(9), e0238302. DOI,PDF.