Signal Science Lab at Cornell

Community-focused Research

Investigations into biomedical research require synergy between its three pillars: physical methods (that captures information), biological systems (that provides information), and data processing methods (that interprets information). While development in the physical methods and biological systems have made significant advancement in the past decades, the third pillar of data processing approaches, which threads the first two pillars, is lagging.

The Signal Science Lab is leading efforts to develop fundamental data processing methods and advanced computational workflow to extract high-fidelity signals with excellent spatial and temporal resolution from physical measurements.

Wavelet Denoising

We are developing signal processing techniques based on wavelet transforms that can precisely localize and remove noise from signals

Inverse Problems and Uncertainty Analysis

We are developing a “localized” approach to solve ill-posed mathematical inversion problem and determine uncertainty in physical methods, from which indirect information is obtained from experiments

Biomedical/Biophysical Applications

We are designed advanced data processing workflow 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.

News and Updates

Henry wins the Learning Initiatives Undergraduate Research Award from Cornell Engineering

December 23, 2020

Henry Zheng is selected for the Engineering Learning Initiatives Undergraduate Research Award with funding from Cornell University! The award facilitates connections and provides opportunities for undergraduate students who are motivated to pursue undergraduate research during their time at Cornell. Henry will use the award to support his research project in the Signal Science Lab. Money and Henry seems to be good friends!

Madhur to give Invited Talk at ENC 2021 on Wavelet Denoising

November 1, 2020

Madhur Srivastava has been invited to give a talk at the 62nd Experimental Nuclear Magnetic Resonance Conference (ENC) to be held virtually from April 12-14, 2021. He will be presenting his work on wavelet denoising to improve Signal-to-Noise Ratio (SNR) in experimental magnetic resonance signals via data processing methods.

More information: https://www.enc-conference.org/ProgramAbstracts/InvitedSpeakers/tabid/191/Default.aspx

Madhur gives Invited Talk at Vaibhav Summit 2020 organized by Indian Government

October 11, 2020

Madhur Srivastava gives an invited talk in the (Virtual) Global Summit of Non-Resident Indians and Indian researchers called Vaibhav Summit organized in October 2020. The Summit is aimed at developing mechanisms for involving Indian Diaspora working in top universities, R&D organisations across the world, to further enhance the knowledge-base of Indian Research and Academic Institutions. He presented his research work to boost computational biology research in India.

Henry wins the SRC Undergraduate Research Program Award from Siemens

October 1, 2020

Henry Zheng is selected for the SRC Undergraduate Research Program (URP) Award with funding from Mentor, A Siemens Business! Apart from fame, he is also getting monetary reward! Undergraduate Research Program (URP) is an academic year-long program which encourages participation from student candidates that are U.S. citizens and diverse in gender and background. The program combines a structured undergraduate research experience with industry mentorship.

William wins the Cornell Presidential Research Scholarship

July 28, 2020

William Bekerman is selected as the Hunter R. Rawlings III Cornell Presidential Research Scholar! Apart from fame, he is also getting monetary reward!

Journal of Physical Chemistry highlights SVD work

June 4, 2020

The Virtual Issue of J. Phys. Chem. on new tools and methods has highlighted a paper by Madhur Srivastava and Jack Freed. The paper is among 25 papers selected by the editorial team that was published in J. Phys. Chem. over the past two years. Srivastava and Freed developed a “localized” approach to solve ill-posed mathematical problems and calculated the uncertainty within the solution for Electron Spin Resonance Spectroscopy. The method has led to many publications in high impact journals on structural determination of biomolecules.