Current CoE-funded Projects - Center of Excellence in Data Science and Artificial Intelligence

The CoE proudly sponsors the research of faculty and partner companies. The Center’s funding allows researchers to produce innovative and visionary approaches to data science in a variety of areas and industries. We currently are funding these partnerships and are looking forward to seeing the results of the research and the positive economic impact that it will have on New York State and the world of data science.

Assessment of Elasticity and Transmissivity of Crystalline Lens in Response to LIRIC Treatment

Headshot Photo of Jannick Rolland PI Researcher: Jannick Rolland and Kevin Parker

This project aims to develop a robust algorithm to accurately estimate shear wave speed (SWS) in elastography of the eye within a multi-frequency wave field. This will be employed to determine the effect of Clerio Vision’s laser treatment (“laser induced refractive index change”, LIRIC) upon the elasticity and transmissivity of the human crystalline lens. The motivation is to (1) advance clinical mapping of the biomechanical properties of the eye, and (2) restore the elasticity and transmissivity the human crystalline lens to reverse presbyopia. Computational models and animal lenses will be employed to determine accuracy and any impact on elasticity and transmissivity.

Chip – Fiber Alignment Assisted by Deep Learning

PI Researcher: Jaime Cardenas

We propose to develop a novel fiber array to chip alignment system using deep learning and computer vision for automating the alignment process for a fiber array.

Development of a Low-Cost, Low-Power Integrated Machine Health Monitoring Sensor

PI Researcher: Michael Heilemann

This project, in conjunction with ADVIS Inc. is focused on developing a device to cost-effectively bring machine health monitoring to a broad spectrum of Department of Defense (DoD) assets (vehicles, pumps, etc.), where the implementation of conventional monitoring systems is cost prohibitive. To meet size (~1 in.3) and power consumption (battery life of ~3 years) requirements, the device utilizes low-power embedded machine learning (ML) models trained on data acquired by a vibration sensor. Spectral features extracted from a recorded signal are used to train an embedded ML model to perform tasks such as the detection of anomalies and faults in mechanical systems.

Dynamical-System-Powered GL: When Hardware Dynamical System Meets Graph Learning

PI Researchers: Tong Geng

During the first year of CoE support, PI has demonstrated that Ising Machine with proper augmentation can be used to efficiently solve simple graph learning problems and outperforms GNNs in terms of inference speed and accuarcy. The resulting new graph learning paradigm is dubbed “IML”. This year, this project aims to improve IML through co-design of Hamiltonian, hardware architecture, and training algorithm and develop “DS-GL”, that effectively harnesses nature’s power within flexible, self-trainable, real-valued hardware Dynamical Systems to solve real-world Graph Learning problems. DS-GL aims to achieve fast training, high accuracy, and low inference response samultaneously.

Machine Learning to Produce Controllable Surface Nanopatterning

PI Researcher: Chunlei Guo

Surface nanopatterning has a range of applications, such as creating superhydrophobic glass without compromising its transparency. The Guo lab will work with AlchLight on developing machine learning for controllable laser surface nanopatterning. The project will focus on developing machine learning in searching for optimal processing parameters, which can speed up the parameter search speed from weeks to hours. Optical property and surface morphology for the processed samples will also be characterized, which will be used to feedback machine learning.

Multispectral Polarimetric Imaging of Nerves

PI Researcher: Wayne Knox

We have made significant progress in imaging nerves using polarized light sensing at different wavelengths. Starting in the visible spectral range and using raw or frozen chicken and now progressing to human cadaver nerve imaging, we have developed new methodologies of image processing to highlight the nerves to stand out from the surrounding tissues. We propose to expand our project to include a FIH (First in Human) surgical study which will present new data science challenges with image motion, and also to push into the near infrared for better tissue penetration, and to develop an endoscopic version of our technology.

Toward Speaker-Specific Voice Spoofing Countermeasures

Headshot Photo of Zhiyao Duan PI Researcher: Zhiyao Duan

This is a continuation of last year’s CoE project which aimed to develop and deploy noise-resilient voice spoofing countermeasures to IngenID’s automatic speaker verification (ASV) engines. This new project aims to develop a new paradigm of spoofing countermeasures, specifically, speaker-specific countermeasures, to fit to each speaker’s characteristics more accurately. This is to respond to the highly realistic deepfakes generated by rapidly developing generative AI technology (e.g., voice cloning from ElevenLabs), which has been shown to successfully fool state-of-the-art speaker-generic voice spoofing countermeasures. By designing speaker-specific passive and active countermeasures, we will significantly improve the accuracy and robustness of countermeasures.