[Main contributions] – The primary contributions of this work are (a) extending the SMC-EM technique to include the DH-PSF for three dimensional motion, (b) demonstrating the ability to do accurate localization and parameter estimation of confined motion at low signal and low SBR levels, and (c) providing a quantitative comparison to the standard method of GF-MSD and to other approaches based on optimal estimation across a range of (very low) SBR levels and confinement lengths [More …].
My main work during the internship: explored and implemented machine learning techniques to analyze financial information and predict economic conditions. Linked the Risk, Finance, and Treasury models together and visualized the dependency relationship among models. Conducted quantitative data analysis and model simulation work based on model execution framework (MEF) to do balance sheet forecasting and stress testing across Barclays Group.
I'm grateful for the continuous help and support provided by Barclays colleagues who motivate me to keep learning and making progress!
It's a great honor to be a speaker at the Grace Hopper Celebration this year [More ...]. Meanwhile, I'm excited to be awarded the Grace Hopper Celebration Student Scholarship by Anitab.org. Dare to dream, and dare to make a difference!
[Main contributions] In this work, we describe three modifications to SMC-EM aimed at improving its computationally efficiency and demonstrate it through analysis of simulated SPT data of a particle in a three dimensional confined environment. The first two modifications use approximation methods to reduce the complexity of the original motion and measurement models without significant loss of accuracy. The third modification replaces the previous SMC methods with a Gaussian particle filter combined with a backward simulation particle smoother, trading off some level of generality for improved computational performance. In addition, we take advantage of the improved efficiency to investigate the effect of data length on performance in localization and parameter estimation [More … ]
The above figures show the mathematical simplification on confined motion model and measurement model (If you use the figures, please cite the above paper).