Sanjana M.

About Sanjana M.

My Ph.D. research in Physics is involved with experimental research in Bio-sensing and Bioelectronics. In this regard, I have developed great interest in the process of fabrication of state-of-the-art devices for sensing, electrochemical studies, sensing at single-molecule level and post-sensing data analysis through sophisticated statistical methods. In my research I have been working on experiments hands-on that involves me designing of devices on AutoCAD, working in the cleanroom for top-down fabrication of our Quantum NanoElectroPore (Q-NEP) chips using a variety of methods like Photolithography, Reactive Ion Etching, metal deposition, etc. Post-fabrication I perform characterization of chips using Scanning Electron Microscope, assembly of chips with microfluidic systems and automation of fluid flow through pressure control systems, wire bonding on custom made PCB boards, electrical characterization using I-V and CV measurements in solution environment using DIY preamplifier digitized using ITC-18. Moreover, my interest in device fabrication and optimization has motivated me to study electrochemistry in details and develop a new recipe for electrochemical deposition of Au that has successfully established high-throughput stable and reversible electrochemical deposition on quantum tunneling electrodes. Furthermore, I have also successfully performed sensing of single-molecules of DNA with ionic and tunneling current measurements. Besides, electrical sensing, I also perform plasmon based sensing of single-molecules of protein with surface-enhanced Raman spectroscopy (SERS) using the Q-NEP as a platform. Post collecting sensing data, I perform statistical analysis for model fitting. The very first exposure to Bayesian statistics happened through a side-project where I developed my own Gibbs\' sampling with Markov Chain Monte Carlo simulation code that performs Bayesian analysis of images of cells under external AC EF stimulation. The sampler worked as a great framework for analyzing digital images and performs forward modeling of intensity data and inverse modeling of model parameters and learns about their probability distributions. Based on the techniques I have learned developing the code, I have further learned and worked with various other Bayesian methods such as Gaussian Mixer Model (GMM), AutoStepfinder, etc for statistical analysis of data.

Experience

1. Ltd Module Development Engineer

   Intel

   Nov 2024 — Present · US

Education

• University of Calcutta

  Bachelor of Science (B.Sc.), Physics(Honours)

  2011 — 2014

• B D Memorial Institute

  High School/Secondary Certificate Examinations

  2003 — 2011

• Indian Institute of Technology (Indian School of Mines), Dhanbad

  Master of Science (MSc), Applied Physics

  2014 — 2016

• Arizona State University

  Doctor of Philosophy - PhD, Physics