curriculum vitae

Basics

Name Niranjan Sarpangala
Label Postdoctoral Scholar
Email sarpangalan@gmail.com
Url https://nsarpangala.github.io
Summary Computational scientist with a passion for building accurate predictive multi-scale frameworks for soft materials. My work spans custom Brownian dynamics simulations for lipid-membrane cargo transport, spectral solvers for viscoelastic networks, and continuum phase-field models of polymer blends. I am excited to work on computational frameworks that link physics-based methods across scales using machine learning to build efficient multi-scale models and accelerate scientific discovery.

Education

  • 2017 - 2023
    Ph.D.
    University of California Merced
    Physics
    • Non-linear dynamics
    • Computational modeling for biophysics
  • 2014 - 2016
    M.Sc.
    Indian Institute of Technology Bombay
    Physics
    • Advanced statistical mechanics
    • Continuum mechanics
  • 2011 - 2014
    B.Sc.
    Mangalore University
    Physics, Chemistry, Math
  •
    Diploma
    JNCASR Bangalore
    Chemistry

Work

  • 2023 - Present

    Philadelphia, PA

    Postdoctoral Researcher
    University of Pennsylvania
    Center for Soft and Living Matter, Department of Physics. Advisor: Prof. Eleni Katifori.
    • Built a fast spectral solver (graph-Laplacian) for viscoelastic truss networks — more accurate than spring-network methods and faster than FEM — enabling multi-scale mechanical modeling of disordered soft materials. Elastic version published in Soft Matter.
    • Used this solver for inverse design of rod architectures in disordered viscoelastic networks, optimizing thickness distributions to maximize energy dissipation under fixed material cost. The optimization framework supports arbitrary cost functions and mixed Dirichlet and Neumann boundary conditions, and runs via GPU-accelerated adaptive gradient descent (JAX).
    • Found that optimal dissipative networks exhibit correlated disorder with source-weighted gradient-like architecture sensitive to the chemical composition of the base material, with direct implications for soft material design.
    • Developing algorithms for viscoelastic networks that adapt autonomously based on local strains; exploring whether hierarchical architectures emerge as solutions to adaptation, aiming to explain hierarchical structures in biomaterials like bone.
  • 2017 - 2023

    Merced, CA

    Graduate Researcher
    University of California, Merced
    Advisor: Prof. Ajay Gopinathan.
    • Built a custom 3D Brownian dynamics simulation engine for multiple motor-driven cargo transport of lipid membrane-enclosed vesicles — coarse-grained, parallelized, and validated against experimental trajectories.
    • Modeled lipid membrane mechanics at the coarse-grained level to study how membrane fluidity and geometry alter inter-motor coordination, producing experimentally testable predictions with implications for neurodegenerative diseases. Published in PLOS Computational Biology and Biophysical Journal.
    • Built a custom discrete-time, continuous-space Monte Carlo simulation of cargo transport on converging microtubule topologies to extract escape rates as a function of geometry and kinetic parameters. Published in Biophysical Reports.
    • Tracked fluorescence microscopy images of microtubule collision events and collected collision statistics, contributing quantitative analysis to a PNAS publication.
    • Developed an end-to-end pipeline for data-driven cargo transport simulation: extracted cytoskeletal network architectures from fluorescence microscopy images (FibRe Extraction tool), ran Brownian dynamics simulations on resulting geometries, and inferred motor switching rates by matching mean square displacements to experiment. Published in European Physical Journal E.
    • Automated a batch video analysis pipeline (FIJI, OrientationJ) to extract the rotation rate of the nematic order parameter from dense microtubule assemblies driven by molecular motors. Submitted to Small.

Awards

Publications