Department Research Overview
- Research Interests
- Research Labs
- What Does Research Mean to Me? (Our Faculty Speak)
- Machine Learning and ECE: Made for Each Other
- Patents and the ECE Department
- Office of Technology Licensing & Industry Relations Innovation Website
Featured Research Papers
- Federated Bandit
- Combining Two Computer Performance Laws
- Cost-Aware Reinforcement Learning
- Paving the Way to Infrared Beam Steering with CMOS Circuits
- Autonomous Conflict Resolution for Smart City Services
- Novel Wireless Communication Technology for Networking on the Fly
|Prof. Fang Luo|
Prof. Fang Luo joined the Electrical and Computer Engineering Department a short time ago and has established a very active research program on the next generation of power electronics. This effort has been greatly enhanced by Prof. Luo teaming up with the Spellman High Voltage Electronics Corp., a regional manufacturer of custom high voltage power systems.
|Left to right: Prof. Peng Zhang, Prof. Xin Wang, and Prof. Yue Zhao|
Photovoltaic electric power generation is becoming more and more common and its use is likely to grow as time goes on. However operators of electric power systems with a large photovoltaic component have a hard time predicting the behavior of the power systems. There is an urgent need for effective and accurate transient (i.e. short time frame) and dynamic (as it happens) simulation methods for power systems with high photovoltaic penetration. A team involving Stony Brook faculty Prof. Peng Zhang, Prof. Yue Zhao and Prof. Xin Wang, and post-doc Yifan Zhou recently received a major US Dept. of Energy award to fulfill this need.
Stony Brook University professor Peng Zhang, a SUNY Empire Innovation professor in the Department of Electrical and Computer Engineering, is leading a statewide team of collaborators in developing “AI-Grid,” an artificial intelligence-enabled, autonomous grid designed to keep power infrastructure resilient from cyberattacks, faults and disastrous accidents.
The work is part of the National Science Foundation’s (NSF) Convergence Accelerator Program, which supports and builds upon basic research and discovery that involves multidisciplinary work to accelerate solutions toward societal impact.
Prof. Emre Salman and doctoral candidate Ivan Miketic recently published a unique obfuscation technique to make digital computer chips more resistant to reverse engineering. Why is this important? One of the key security issues for chip design companies is reverse engineering. Reverse engineering involves several physical attacks to the chip to regenerate the circuit netlist. The “netlist” is the description of a circuit including the gates, inputs, outputs and their interconnections. Once the netlist is obtained, counterfeit designs that are not authentic can be fabricated. This is typically referred to as Intellectual Property (IP) theft. Reverse engineering poses a significant economic risk to the semiconductor industry due to lost profits and reputation. It also presents a considerable risk to consumers and private data.
Continually increasing carbon dioxide concentrations in the atmosphere have already led to changes in the climate as well as the acidification of the oceans. This increased acidity of the oceans is analogous to a slow motion “spill” of acid. And just like we clean up after oil spills, we need to clean up this acid spill as well.
The approach of ECE’s Prof. Matthew Eisaman and a team of researchers, called SEA MATE, which stands for Safe Elevation of Alkalinity for the Mitigation of Acidification Through Electrochemistry, uses carbon-free electricity and electrochemistry to effectively pump this excess acid out of the ocean and then sells the acid for useful purposes. This acid removal restores the ocean chemistry such that the remaining ions in the ocean react with atmospheric carbon dioxide, safely locking it up for 10,000 – 200,000 years as oceanic bicarbonate. So the net effect of SEA MATE is the reversal of ocean acidification along with the net removal of carbon dioxide from the atmosphere.
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?
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.
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.
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.
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 .