Current CoE-funded Projects

PI Researcher: Wayne Knox

Development of a new Intraoperative Instrument Using AR/VR technology: We propose to continue and expand our recently started research project on using multispectral polarization technology for the detection of nerves and to learn how to incorporate it into a VR/AR/MR technology as a new surgical aid. This device could help to avoid accidental nerve cuts during routine surgery. The project will develop a research prototype for use in live human surgical clinical trials. We will test different modes of image processing such as artificial intelligence and methods of data science to determine the best modes of operation according to the surgeon’s requirements.

PI  Researchers:
Michael Huang (left)
Tong Geng (right)

Ising Machine Learning: When the Ising Model and the Ising Machine Meet Machine Learning: The drastically increasing computational cost of Machine Learning (ML) has brought concerns on environmental sustainability and challenges in its practical deployment. Inspired by the recent success of using the Ising model and the Ising machines to solve NP optimization problems with orders-of magnitude speedup and energy reduction, this project will investigate how to leverage the Ising model’s strong expressivity and the Ising machine’s extraordinary computational speed in learning problems to create a new ML paradigm, Ising Machine Learning (IML). IML has the potential to enable the sustainable development of ML by significantly reducing the computational cost of current ML modelities with increased accuracy.

PI Researcher: Chenliang Xu

Machine learning often achieves good average performance by exploiting unintended cues in the data. For instance, when backgrounds are spuriously correlated with objects, image classifiers learn background as a rule for object recognition. This phenomenon—called “shortcut learning”—undermines the robustness and fairness of AI algorithms. The objective of this proposal is to build robust and fair AI algorithms against multiple shortcuts. In contrast to prior arts holding the single-shortcut assumption, we propose a new framework to identify and mitigate multiple shortcuts. Our work can broadly benefit many research fields, e.g., algorithmic fairness and robustness, data-centric AI, and representation learning.

PI Researcher: Pablo Postigo

Optical filters are one of the key products in optics. Nevertheless, there are still some limitations that affect the optical performance of optical filters, like angle of incidence or the operative spectral ranges. Pablo Postigo and IDEX Health Science, LLC conducted research to develop computational methods based in AI to design new optical filters with enhanced properties, like higher resistance to the angle of incidence, a wider transmission/ reflection band or a higher number of spectral bands. Furthermore, the use of a filter design procedure based on AI may open new paths for the development of filters with other enhanced properties like polarization selectivity.

PI Researcher: Andreas Savakis

Domain Adaptation using Vision Transformers: Domain adaptation aims to overcome the distribution shift between the target domain used for testing and the source domain used for training. We propose to utilize Vision Transformers as an alternative to convolutional features for fine-grained domain adaptation. Vision transformers, inspired by natural language processing, offer excellent performance due to their attention mechanism, and are promising for fine-grained classification. We propose to incorporate vision transformers in the source hypothesis transfer framework and apply our fine-grained adaptation method on the rare planes dataset, which includes synthetic data for training and measured data for testing.

PI Researcher: Chunlei Guo 

Neural Network assisted Femtosecond Laser Fabrication of Anti-bacterial Surfaces: The PI’s lab has been working with Pfizer in developing materials for biomedical applications. Pfizer plans to launch a new project in developing anti-bacterial surfaces to reduce the stringent requirements for equipment and vaccine container sterilization. Pfizer is a leading producer of COVID-19 vaccines. The economic values of solving these issues are immeasurable. We plan to incorporate our recently developed anti-bacterial surfaces to alleviate the aforementioned issues. The experimental procedure of producing anti-bacterial surfaces is complex and time-consuming. In this project, we will develop a neural network and genetic algorithm to optimize and speed up these fabrication processes.

PI Researcher:
Ling Wang

This project aims to develop a label-free and automated cell classification method for pharmaceutical research using phase imaging and deep learning techniques. The proposed approach leverages the strengths of multiple deep learning models and ensemble learning techniques to improve the accuracy and efficiency of cell classification. The project targets phase contrast images, which are label-free and provide detailed information about cell morphology and dynamics. The proposed method has the potential to benefit pharmaceutical research by providing an accurate and efficient tool for cell classification, aiding in drug discovery, toxicity testing, and other aspects of pharmaceutical research that require precise characterization of cell types and behaviors.

PI Researcher: Yunbo Zhang

This project aims to extract tacit knowledge from experienced machinists and transfer it to the next generation of machinists. We will collect multimodal data from experienced machinists through an experimental set for tool wear determination in machining; develop machining Learning methods for tacit knowledge extraction consist of: behavior data fusion based on an integrated Bayesian hierarchical and infinite hidden Markov model, and identifying useful tacit knowledge patterns; and integrate tacit knowledge into the XR system. The project’s ultimate goal is to transfer the developed XR training system to our industry partner, AA Tech, for their training of new machinists.