AI & Bayesian Inference for Complex Systems

Electrical and Computer Engineering Professor Talks About the Tools at the Center of AI Applications

The emerging application of artificial intelligence (AI) to a diverse range of fields has positioned it as a valuable research tool. Stony Brook’s Institute for AI-Driven Discovery and Innovation hosts faculty from a wide variety of disciplines who are advancing  machine learning research.

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.

AI Institute: What experience do you have using AI?
Professor Bugallo: My field of expertise is in statistical signal and information processing and more precisely in the theory and application of Bayesian inference for complex systems. Bayesian inference is a critical building block of AI. This data-mining framework allows use of prior information about a system to obtain the probability of a related event. As a result, this methodology is extremely powerful and can be used in analyzing, inferring and predicting parameters in AI-powered systems, virtual assistants and other variable analytics models.

Bayesian inference, at the core of my research portfolio, is an extremely powerful set of tools for modeling, estimation and forecasting of parameters defining complex systems, which are at the center of big data, machine learning and AI applications. Bayesian models and methods map our understanding of a problem (usually characterized by many unknowns) and process observed data (usually large data sets) into measures related to a particular fact in probabilistic (belief) terms. Therefore, my research agenda on theory and practice of such forceful tools in challenging scenarios can significantly contribute to important AI applications.

AI: How do AI, machine learning, etc. fit into the Electrical and Computer Engineering areas of study and research?
PB: Big data, machine learning and 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.

AI: Do you think that students in your field should also take AI-related classes and expose themselves to more AI-related technology?
PB: AI-related courses are extremely important for researchers and professionals in the fields of signal and information processing, as well as in data science and engineering. There are courses from the computer science perspective as well as from the electrical and computer engineering perspective. Both angles are critical to better understand the foundations of the topics and interrelated concepts, the intricacies that challenge the progress of these new technologies, and the newest advances and tools needed to move forward.

AI: What do you see as future applications of AI and AI-related technology as it applies to your fields of research?
PB: Any applications of AI and AI-related technology can benefit from the theoretical advances in Bayesian inference. Application of some recent advances to autonomous vehicles and to IoT scenarios have already been published and resulted in very promising lines of research. For example, we have addressed problems related to real-time self-tracking in IoT systems, critical for localization of low-cost “smart” tagged objects, or indoor altitude estimation of unmanned aerial vehicles, which is of utmost importance for safe drone navigation. The research agenda in our lab is very broad and generally applicable and there are many challenging AI-related applications that could benefit from the theoretical contributions that result from our work.

AI: Thank you for your time, Professor Bugallo.

About the Researcher

Mónica Bugallo is the Associate Dean for Diversity and Outreach in the College of Engineering and Applied Sciences (CEAS) and a Professor in the Department of Electrical and Computer Engineering at Stony Brook University. She is an Affiliate Faculty member of the Institute for Advanced Computational Sciences (IACS) and the AI Institute.

Her research interests include:

  • Statistical signal processing, with emphasis on Bayesian analysis
  • Monte Carlo methods
  • Adaptive filtering
  • Stochastic optimization 
  • Application of Bayesian inference to communications, biology, ecology, finance, smart antenna systems, target tracking in sensor networks, and vehicle positioning and navigation