The Srivastava Group (Signal Science Lab) focuses on developing fundamental data processing methods that interface between biophysical techniques and biological systems to extract high temporal and spatial resolution information, enabling and aiding various spectroscopic, microscopic and imaging methods to study biological systems in native (or native-like) environments.
The structure, dynamics and function of a biomolecule or tissue play a key role in determining disease mechanisms, knowledge of which is essential for early diagnosis, drug development and effective treatment. Despite major advancement, physical methods lack sufficient sensitivity and resolution to conduct biological studies in native conditions, primarily to due to presence of experimental noise. Our focus is to develop and deploy signal processing techniques that can precisely localize and remove noise from signals, especially when noise is dominant.
Key areas of activity include:
1) characterization of signal and noise information based on their distinct properties.
2) theoretical development of novel data representations (and subsequently noise thresholds) to uniquely identify/separate experimental signal from noise based on their distinct characteristics.
3) development of novel regularization methods to obtain "locally" optimized solutions for mathematically ill-posed inverse problems, and obtain quantifiable relationship between inputs and outputs.
4) designing advanced signal quality measurement techniques
5) incorporation of the data processing methods on various biophysical techniques such as ESR, NMR, MRI and cryo-EM, among others.
Google Scholar: Madhur Srivastava