Michael D. Stiber, Ph.D.

Professor, Computing & Software Systems Department
Principal Investigator, Intelligent Networks Laboratory
Founding Faculty Member

Research

My research lies at the intersection of complex networks, modeling, high performance simulation, data/metadata/paradata management, and analysis and visualization. I have applied these interests to problems in computational neuroscience, artificial intelligence, cybersecurity, and nonlinear dynamics.

The Intelligent Networks Laboratory develops graph-based simulations of biological neural networks and emergency services communications systems (ESCS; 911 in North America), employing recent advances like Graph Neural Networks (GNNs), Spike-Timing-Dependent-Plasticity (STDP), and GPU simulations.

Teaching

As a founding faculty member here at UWB, and a near-founding-faculty member at HKUST, I have been involved in the development and teaching of a broad cross-section of the computer science core, as well as a range of upper-level undergraduate and graduate electives. Subjects I have taught include: introductory, medium-level, and advanced programming, programming tools, object-oriented design, data structures and algorithms, discrete mathematics, calculus, technical writing for software professionals, computer graphics, computer vision, visualization, multimedia, computer architecture, artificial intelligence, neural networks, generative AI, complex systems, signal processing, and computational thinking.

About

I earned my Ph.D. in Computer Science from UCLA. Before coming to UWB, I was an Assistant Professor at the Hong Kong University of Science & Technology and a Research Assistant Professor at the University of California, Berkeley. I've also been a Visiting Associate Professor at the University of Florida and a Fulbright Scholar in the Institute of Physiology of the Czech Academy of Sciences.

Research.

So what is this mind of ours: what are these atoms with consciousness? Last week's potatoes! They now can remember what was going on in my mind a year ago — a mind which has long ago been replaced.
— Richard P. Feynman

My research group, the Intelligent Networks Laboratory (INL), develops and maintains a high-performance simulation framework, called Graphitti, to enable researchers to develop large-scale and long-duration simulations of graph-based systems. This simulator has been applied to biological nervous systems and next-generation 911 communications.

We also build and maintain Workbench, a system that allows investigators to track simulation code, experiment configurations, and data produced over many iterations of coding and simulation. Once fully developed, this will allow them to be confident that their results are valid and easily identify when changing assumptions alter the results.

We eat our own dog food: we use this framework in our own research in computational neuroscience and critical infrastructure security. Recent projects involve integrating Graph Neural Networks (GNNs) and Spike-Timing-Dependent-Plasticity (STDP) into Graphitti, spatiotemporal bursting in simulated cortical cultures, and migrating complex simulation domains to the GPU for Emergency Services Communication Systems (ESCS).

Teaching.

Artificial Neural Networks & Generative AI

A "nuts and bolts" coverage of the mathematical and algorithmic fundamentals of ANNs, as well as modern Generative AI models taught as a special topics class (CSS 590). Topics include basic neurobiology, linear algebra, optimization, supervised and unsupervised learning, backpropagation in multi-layer perceptrons, competitive and recurrent networks, radial basis functions, learning in deep neural networks, and generative AI architectures.

Signal Computing

How computers can process information from the physical world as digital signals. Topics include physical properties of source information, devices for information capture, digitization, compression, digital media representation, mathematics and algorithms relating to digital signal processing, and network communication. See the Fall 2016 CSS 457 syllabus and schedule.

Data Structures, Algorithms, and Discrete Mathematics

I teach a range of intermediate data structures, algorithms, and discrete mathematics courses, including CSS 342, CSS 343 (See the Winter 2017 syllabus and schedule), and CSS 501 (See the Fall 2019 syllabus and schedule).

Programming Issues With Object-Oriented Languages

This class works as a combination of laboratory and tutorial, to help students investigate and understand some of the deeper and more obscure aspects of software development in C++. Covers language and development/execution environment differences, including data types, control structures, arrays, and I/O; addressing and memory management issues including pointers, references, functions, and their passing conventions; object-oriented design specifics related to structured data and classes.

Computational Neuroscience

Everything we are and do, from the simplest physical act to the most complex and subtle thought, is contained within the three pounds of grey matter we carry within our heads. After millennia of pondering and decades of scientific research, we've only just begun to unlock the nature of how nervous systems accomplish their myriad tasks. This course provides an overview of what we've learned about nervous system function and an understanding of how much we still don't know. Students evaluate the biophysical properties of nervous systems from the point of view of computation, and apply computational tools to simulate and analyze nervous system operation.

Discovery Core: Thinking about Thinking

In this first-year Discovery Core (B CORE) course, students explore the similarities and differences between computer processing and human cognition. By combining perspectives from computer science and neuroscience, the course delves into how brains give rise to minds and how this knowledge influences the development of artificial intelligence. Topics include sensory systems, learning and memory, and the definition of intelligence.

About Me.

I grew up in Philadelphia, reading science fiction and dreaming of someday having a robot army to call my own. I've always been interested in the intersection of engineering and biology. As an undergrad at Washington University in St. Louis, I double-majored in Computer Science and Electrical Engineering. Between undergrad and graduate studies, I worked for Philips and Texas Instruments. In grad school, I had the opportunity to take neuroscience classes in the UCLA Medical School. I was fascinated with the idea of building simulations of living systems and how that could allow researchers to answer questions that could be posed in rigorously mathematical terms — questions that couldn't easily be answered by experiments on living organisms, but could be verified that way.

I have a lot of fun following my nose when it comes to research and working with students in both classroom and research settings. I have served in a number of leadership and administrative roles to support our faculty, staff, and students. I served as Associate Dean of the School of STEM from 2013 to 2019, where I helped build our graduate programs. More recently, I served as Director of Cybersecurity Initiatives (2022-2023) and as Chair of the Computing & Software Systems Division (2023-2025). Throughout, I've cultivated a keen sense of humor and social justice, formed during a childhood listening to George Carlin albums and watching Billy Jack movies.