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MY PORTFOLIO

Nadina O. Zweifel, PhD

Projects

Statistical characterization of tactile scenes in three-dimensional environments reveals filter properties of somatosensory cortical neurons


Natural scenes statistics have been studied extensively using collections of natural images and sound recordings. These studies have yielded important insights about how the brain might exploit regularities and redundancies in visual and auditory stimuli. In contrast, natural scenes for somatosensat...
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Stack
  • Python
  • C++
  • MATLAB

A dynamical model for generating synthetic data to quantify active tactile sensing behavior in the rat


As it becomes possible to simulate increasingly complex neural networks, it becomes correspondingly important to model the sensory information that animals actively acquire: the biomechanics of sensory acquisition directly determines the sensory input and therefore neural processing. Here, we exploi...
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Stack
  • C++
  • Python
  • MATLAB

Defining “active sensing” through an analysis of sensing energetics: homeoactive and alloactive sensing


The term “active sensing” has been defined in multiple ways. Most strictly, the term refers to sensing that uses self-generated energy to sample the environment (e.g., echolocation). More broadly, the definition includes all sensing that occurs when the sensor is moving (e.g., tactile stimuli obtain...
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Stack
  • C++
  • Python

Skills

I've worked with a range of tools and programming languages for data analysis, data visualization, signal processing, and machine learning.


  • Data Analysis

    Experience with
    Matlab libraries as well as SciPy and Pandas.


  • Data Visualization

    Experience with
    Seaborn, Matplotlib, and Illustrator.


  • Machine Learning

    Experience with
    Tensorflow/Keras, PyTorch, and Scikit-Learn

About Me

Starting off as an audio engineer, I acquired real work life experience in a demanding field that requires problem solving skills and flexibility as well as a clear head under short term pressure. I’ve expanded my practical engineering skills with an undergraduate degree in Systems Engineering and a Master and PhD degree in Biomedical Engineering. The choice of projects throughout my undergraduate and graduate studies let me deepen my knowledge in signal processing and data analysis and discover my great interest in computational problems. My most recent research focused on simulation and modeling as well as machine learning.

Curriculum Vitae

Awards

Academic Excellence Award in Engineering

2016

Research Progress Award in Neural Engineering

2020