Past CoE-funded Projects (2022) - Center of Excellence in Data Science and Artificial Intelligence

Immersitech – Development of a Framework for the Evaluation of Spatial Audio System Performance

Mark Bocko, Distinguished Professor, Electrical and Computer Engineering Professor, Physics and Astronomy
Director, Center for Emerging and Innovative Sciences (CEIS) at University of Rochester, is working with Rochester based Immersitech, Inc. to develop a framework to evaluate the performance of spatial sound reproduction. This research combines representations of human peripheral auditory system response and low-level processing of binaural data in the brainstem with learning networks to infer auditory scenes from given acoustic stimuli. The framework provides a tool to evaluate and guide the development of spatial sound reproduction systems.

Velan Studios – Causal Modeling Methods for Big Data: Optimizing Gaming Design Decisions Using Data Science

Causal modeling represents an active area of research for determining causal relationships when a randomized controlled experiment is not available. The RPI team has developed extensive experience building causal models using micro-level data. Jason Kuruzovich (left) and Kristin Bennett (right) will be worked with Velan Studios to apply methods to the context of answering business relevant questions on engagement outcomes in gaming that are also relevant for theory development. Questions may involve training, skill matching, in a skill-based collaborative / competitive game Knockout City. The analytics capabilities will be further incorporated as infrastructure for future games launched by Velan Studios and available to startups funded through Velan Ventures.

LightTopTech – Microscope design for Gabor-domain optical coherence microscopy of the brain and organoids powered by automated image processing and feature extraction

Headshot Photo of Jannick Rolland Jannick Rolland will be working with LighTopTech Corp. to develop a microscope for Gabor-Domain Optical Coherence Microscopy (GDOCM) and related image processing tools will be developed in this project, enabling commercialization of pre-clinical and clinical GD-OCM for brain tissue and organoid characterization.

Kitware – Identify Hidden Biases of AI Algorithms via Human-Machine Collaboration

AI algorithms, especially deep learning, learn biases from data; thus, it is urgent and vital to identify and mitigate biases in these algorithms. However, previous bias identification pipelines rely on human experts to conjecture potential biases (e.g., gender, color, etc.). This approach might work for limited domains and problems but have no chance to scale when considering general AI deployment; humans alone cannot realize all the biased attributes, e.g., identifying biases for a bridge classifier trained on ImageNet. Kitware, Inc. has contacted Chenliang Xu, Associate Professor in the Department of Computer Science and Wilmot Distingshied Professor 2021-2023 at the University of Rochester, to develop novel, scalable human-machine collaborative systems to assist humans in discovering hidden biases in any image classifiers.

Trendly – Few-Shot learning for Fine-Grained Object Recognition

Trendly, Inc. worked with Jiebo Luo on issues with fine-grained object recognition. Prof. Luo will be exploring few-shot learning for fine-grained object recognition by utilizing contrastive learning to extract a discriminative representation of objects to facilitate learning from few examples. The concepts will be verified by a high-precision model for luxury bag authentication and generalized to other fine-grained object recognition tasks such as fine art, artifacts, and jewelry.

L3Harris – Scalable Architectures and Increased Capacity for Bi-stable Resistively-coupled Ising Machine – BRIM

Dr. Michael Huang (right) and Dr. Zeljko Ignjatovic (left) worked with L3Harris to explore two scalable architectures (a microchip and a multi-processor types) based on a previously proposed CMOS-compatible Ising machine will be explored and characterize to address issues using an Ising machine.

IBM – Learning to Localize Sources of Network Diffusion

PI Researcher: Gonzalo Mateos Buckstein

Learning to Localize Sources of Network Diffusion: We propose a deep learning solution to the inverse problem of localizing sources of network diffusion. Invoking graph signal processing (GSP) fundamentals, the problem boils down to blind estimation of a diffusion filter and its sparse input signal encoding thesource locations. While the observations are bilinear functions of the unknowns, a mild requirement on invertibility of the filter enables a convex reformulation that we solve via the alternating-direction method of multipliers (ADMM). We unroll and truncate the novel ADMM iterations, to arrive at a parameterized neural network architecture for Source Localization on Graphs (SLoG-Net), that we train in an end-to-end fashion using labeled data. This way we leverage inductive biases of a GSP model-based solution in a data-driven trainable parametric architecture, which is interpretable, parameter efficient, and offers controllable complexity during inference. By advancing innovative machine learning technologies to tackle data science problems encountered with sensor, information, social, and brain networks, this university-industry collaboration is primed to generate economic and broader societal impacts.

D3 – Efficient radar imaging and machine learning for concealed object detection

Panos Markopoulos of RIT worked with D3Engineering to develop solutions for efficient radar imaging and machine learning towards concealed object detection. This project will focus imaging on MIMO radar technology, intelligent radar sensing/imaging , and thereto tailored machine learning

Flaum Eye Institute – 3D eye imaging and machine learning strategies to improve cataract surgery

Cataract surgery is the most often performed surgery in any hospital of the world (28 million/year). However, the process by which the intraocular lens to replace crystalline lens is selected relies on limited anatomical information and rudimentary formulas. Susana Marcos worked with the Flaum Eye Institute at the University of Rochester will attempt to propose the use of 3-D quantitative optical coherence tomography images and machine learning approaches to obtain an accurate expression of the estimated lens position based on the pre-operative anterior segment anatomy and full crystalline lens shape. This method will improve the refractive outcomes of cataract surgery, increasing patient satisfaction and reducing the burden of refractive error correction.

Monro Inc. – Developing Analytics for Optimizing the Customized Discount Provided by Salesforce for Automobile Service

Yufeng Huang (left) and Mitchell Lovett (right) worked with Monro Inc. will study how to help salesforce provide better customized discount to consumers.

Leveraging data from a large automobile service company, this project will use business analytics methods and develop a structural econometric model to characterize consumer demand and bargaining with salesperson. They will use the model to predict the market outcome under different interventions to salespersons, including setting a monthly discount quota, or completed forbidding discount.

Pfizer – Using Neural Network and Genetic Algorithm to Optimize Laser Surface Functionalization for Biomedical Applications

Chunlei Guo, worked with Pfizer in developing advanced materials for biomedical applications, including preserving fluidic drug delivery and increasing delivery accuracy. Pfizer is a leading producer of COVID-19 vaccines and the economic values of solving these issues are immeasurable. We plan to incorporate our pioneered superhydrophobic surfaces to Pfizer applications to alleviate the aforementioned issues. The experimental procedure of producing superhydrophobic surfaces is complex and time-consuming. In this project, we will develop a neural network and genetic algorithm to optimize these fabrication parameters to speed up the process and achieve the optimized surface property for biomedical applications.