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Department Research Overview

Research Highlights

 

Machine Learning Methods for Revealing the Wellbeing of Fetuses and for Understanding Consciousness

Petar Djuric

Prof. Petar Djuric, colleagues, and students have been looking at two health related topics with an emphasis on artificial intelligence and machine learning techniques.  Here we look at two very interdisciplinary projects.

The first is “Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean Deliveries.” The main objective of the research is to use recent breakthroughs in machine learning to develop predictive analytics to support and improve the interpretation of electronic fetal monitoring data in the last couple of hours before delivery. 

The second project is “In Search for the Interactions that Create Consciousness.” In this research, Petar and collaborators are looking for the physical footprints of consciousness. They are seeking answers to many questions about its origin and nature. What parts of the brain give rise to consciousness? What are the minimal neuronal mechanisms that are sufficient to generate consciousness?

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Machine Learning and ECE: Made for Each Other

Big data, machine learning (ML) and artificial intelligence (AI) applications are revolutionizing the models, methods and practices of electrical and computer engineering. At the same time, electrical and computer engineering research advances in hardware and software are crucial for all those applications to become a reality. New technology domains, such as smart grids, smartphone platforms, autonomous vehicles and drones, energy efficient systems, wearables and Internet of Things (IoT) tools will unfold; embedded with electrical and computer engineering systems in real world or industry practice.

Click here to read more about the ECE department's research in machine learning


Navy Funds Prof. Yacov Shamash's Research to Enhance Energy Resiliency

Prof. Yacov Shamash

Stony Brook researchers, in collaboration with the University of Massachusetts Lowell, will be investigating ways to make energy generation, storage and system operation more efficient, reliable and resilient, particularly in microgrid settings such as shore-based environments, under a new program funded by the United States Navy Office of Naval Research. The Navy grant, totaling $7.36 million and shared equally between the two institutions, will run through Fall 2022.

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Technology to Help Older Adults Age in Place

Fan Ye

A group of forward looking faculty and students at Stony Brook, including Prof. Fan Ye of the Electrical and Computer Engineering department, is developing an almost magic like sensing technology that can revolutionize the way the health conditions of older adults at home are monitored.

The technology can sense the vital signs and physical activities of multiple people in a room/home using different types of sensors, customized hardware and advanced algorithms. The system is completely non-touch (no wearables such as wrist bands or watches). Deployed in a home, it could detect changes in the residents’ health and provide data and notifications to doctors, nurses, family members and even 911. Thus it can enable the early detection of disease onset and early intervention to prevent severe deterioration. This translates into aging in place with dignity and quality of life.

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It's All About Time (Series!)

Xin Wang

Time series are a statistical workhorse of today’s economy and technology. What is a time series? It is simply a sequence of data indexed by time. Examples of time series are daily stock prices, hourly temperature readings, the pressure readings in an industrial process by the second, and the number of calls per minute in a telephone exchange. In a more general form, it can be a sentence in natural language or a set of processes of a system. As the types of sensing devices grow, there is an increasing demand to model the statistical relationships from a large amount of high-dimensional (i.e. many variables) sequential data. Professor Xin Wang leads a group of PhD students and post-doctoral researchers in Stony Brook’s Electrical and Computer Engineering department who seek to develop fundamental machine learning and data processing techniques to more accurately model time series data, as well as advance the understanding of images and video.

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AI & Bayesian Inference for Complex Systems

Mónica Bugallo

We recently spoke with   Mónica Bugallo  to learn how she uses AI in her research. Bugallo is a professor in the   Department of Electrical and Computer Engineering  in the   College of Engineering and Applied Sciences (CEAS) , Associate Dean for Diversity and Outreach for CEAS and Faculty Director for the   Women In Science and Engineering (WISE) Honors program .

 

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AI Microgrid Researchers Aim to Increase Reliability and Safety of Power Grids

Peng Zhang

Addressing the critical need for more reliable and secure power, a multidisciplinary research team at Stony Brook led by Peng Zhang, SUNY Empire Innovation professor in the Department of Electrical and Computer Engineering, is working to develop and demonstrate techniques for AI-enabled resilient network microgrids (AI-Grids) that will help improve the day-to-day reliability of the power grid and enable easier and faster power restoration after outages.

 

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