Esha Manchanda

Doctoral Researcher

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Hi! I am Esha, a first year PhD student at the Lee Kong Chian School of Medicine, Nanyang Technological University. I previously completed my Masters in Biomedical Data Science at Nanyang Technological University and graduated with a Bachelors from Ashoka University with a Gold Medal in Computer Science.

I am passionate about improving human health through the confluence of technology and medicine. My research interests lie at the intersection of neurobiology, immunology, data science and machine learning.

My PhD research is supervised by Dr Tianrong Yeo and Professor Andrew Tan, and focuses on the analysis of spatial and single-cell omics in multiple sclerosis.

Beyond my research, I love playing badminton, running, solo travelling and learning korean!


Selected Previous Work

  1. Data-Efficiency with Comparable Accuracy: Personalized LSTM Neural Network Training for Blood Glucose Prediction in Type 1 Diabetes Management

    Esha Manchanda, Jialiu Zeng, Chih Hung Lo
    Publication

    Automated insulin delivery systems are being developed for type one diabetes (T1D) management. However, two people can have completely different blood glucose dynamics under identical exogenous conditions, and current systems do not account for the unique disease patterns of a single individual. Therefore, in this work, we investigate a personalised blood glucose prediction model to address the inherent inter-patient variability in people with T1D.

  2. Dysregulation of the Metabolic-Inflammatory Axis in Progressive Multiple Sclerosis

    Esha Manchanda, Eka Saipuljumri, Jialiu Zeng, Chih Hung Lo
    Presented as a talk and poster at SSBBI & Neuroscience Singapore 2024
    Poster

    Metabolic impairments are shown to be implicated in the pathogenesis of neuroinflammatory diseases such as MS, and therapies aimed at enhancing metabolic functions could potentially attenuate neuroinflammation and open new avenues for treatment of progressive MS. In this study, we investigated the role of mitochondrial, autophagic, and lysosomal dysfunction in progressive MS, exploring their interconnected roles in driving neuroinflammation and neurodegeneration.

  3. Exploring the Epstein-Barr Virus-Multiple Sclerosis Link Through A Mathematical Model of Immune Response Dynamics
    Manuscript

    In my undergraduate thesis, I explored the relationship between the Epstein-Barr Virus (EBV) and Multiple Sclerosis. Through a mathematical modeling approach, my work studies the potential link between EBV and MS, closely examining the immune system's response to EBV infection. It investigates the role of T-cell exhaustion in uncontrolled EBV lytic activity in B cells, leading to chronic inflammation and potentially the development of MS.

  4. Model Agnostic Meta Learning For Disease Prediction From Metagenomic Data
    Project Report

    Over time I have developed a growing interest in the gut microbiome and its impact on health. I worked on a project employing model-agnostic meta-learning techniques aimed at predicting diseases through the targeted analysis of fecal gut microbiome data.

    5. Teaching

    I really enjoy teaching, and inspired by my many great teachers, I adopt a constructivist teaching style—where I focus on guiding students to construct knowledge rather than passively absorb information.

    Teaching Assistantships

    Workshops Conducted

    • IEEE Ashoka PySci Computational Biology Workshop (November 2023)
      [Hands-on exercise]
    • Computer Science Society Tools for Web Scraping (October 2021)
      [Code]