Rensselaer is America’s oldest technological research university, offering bachelor’s, master’s, and doctoral degrees in engineering, the sciences, information technology and web science, architecture, management, and the humanities, arts, and social sciences.

The university offers degrees from five schools: Engineering; Science; Architecture; Humanities, Arts, and Social Sciences; and the Lally School of Management; as well as an interdisciplinary degree in Information Technology and Web Science.

Rensselaer offers more than 145 programs at the bachelor’s, master’s, and doctoral levels. Students are encouraged to work in interdisciplinary programs that allow them to combine scholarly work from several departments or schools. The university provides rigorous, engaging, interactive learning environments and campus-wide opportunities for leadership, collaboration, and creativity.

Driven by talented, dedicated, and forward-thinking faculty, Rensselaer has dramatically expanded the research enterprise by leveraging our existing strengths and focusing on five signature research areas: biotechnology and the life sciences; energy and the environment; computational science and engineering; nanotechnology and advanced materials; and media, arts, science, and technology.

The Institute is especially well-known for its success in the transfer of technology from the laboratory to the marketplace so that new discoveries and inventions benefit human life, protect the environment, and strengthen economic development.

Rensselaer Polytechnic Institute

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Wilkes Medal

William H. Wiley 1866 Distinguished Faculty Award

National Academy of Science : Elected Foreign Member

Computer Society of IEEE : Member

Computer Science Journal : Editorial Board Member

Computational Methods in Science and Technology : Editorial Board Member

Analyzing and predicting success of professional musicians

Resource-Mediated Consensus Formation

Modelling epidemic spread in cities using public transportation as a proxy for generalized mobility trends

Polarization and tipping points

Characterizing Topics in Social Media Using Dynamics of Conversation

Optimizing Edge Sets in Networks to Produce Ground Truth Communities Based on Modularity

Dr. Szymanski's research interests span from network science, with an emphasis on social networks and computer wireless and sensor networks, to analysis and design of distributed algorithms, simulation of computers, networks and biological/ecological phenomena. His projects include spread of opinion and other dynamic processes on networks, communities in social networks, and large-scale parallel and distributed computing and simulation. 

Dr. Szymanski developed a novel Mutual Exclusion algorithm and led the development of the GANXIS and SpeakEasy community detection algorithms, the SENSE simulator for sensor network simulation, and VOGUE and Conceptor-based innovative HMM models.

Computer Wireless

Computation & Information Technology

Computer Networks

Network Science

Energy and the Environment

Social Networks

Sensor Networks

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Claire and Roland Schmitt Distinguished Professor of Computer Science; Professor of Physics, Applied Physics, and Astronomy; Director, Network Science and Technology Center

People tend to think of the arena of politics as being driven by human decision and emotions, and therefore unpredictable. But network scientists like Boleslaw Szymanski, a computer science professor at Rensselaer Polytechnic Institute, have found that the country’s political activity – from American society’s ever-growing partisan divide to its grappling with the spread of misinformation online – can be explained by abstract and elegant models.

These models provide insights — and even answers — to a number of  pressing questions: Is increasing access to information driving us apart? Can an entrenched minority ultimately prevail? Could structural changes be made that insulate us from misinformation and reduce the polarization that divides us? 

Szymanski studies the technical underpinnings of our choices, how we influence one another, and the impact of the algorithms we rely upon to navigate a growing ocean of information. His work has yielded fascinating insights, including research on  how a committed minority will overcome less determined opposition and the development of a parameter to determine what drives polarization in Congress. Through his research on the influence of minority opinions, Szymanski found that when just 10 percent of the population holds an unshakable belief, it will ultimately be adopted by the majority of the society. 

“When the number of committed opinion holders is below 10 percent, there is no visible progress in the spread of ideas. It would literally take the amount of time comparable to the age of the universe for this size group to reach the majority,” said Szymanski, a computer science professor at Rensselaer Polytechnic Institute. “Once that number grows above 10 percent, the idea spreads like flame.”

In his present work, Szymanski is researching tools for measuring the level of polarization in specific news sites, search engines, and social media services, and developing remedies, like algorithms that offer better data provenance, detect misinformation, and create internal consistency reasoning, background consistency reasoning, and intra-element consistency reasoning tools. 

“Informed citizens are the foundation of democracy, but the driving interest of big companies that supply information is to sell us a product,” Szymanski said. “The way they do that on the internet is to repeat what we showed interest in. They’re not interested in a reader’s growth — they’re interested in the reader’s continued attention.”  

With the political environment becoming increasingly bitter and dubious information becoming ever more prevalent, Szymanski is available to discuss his research on polarization, disinformation, and the power of a committed minority.



Network Science Offers Key Insights into Polarization, Disinformation, and Minority Power

Risks that threaten modern societies form an intricately interconnected network, so it is important to understand how risk activations in distinct domains influence each other. We study the global risks network defined by World Economic Forum experts. Risks are modeled as Cascading Alternating Renewal Processes (CARP) with variable intensities driven by hidden values of exogenous and endogenous failure probabilities. We use maximum likelihood evaluation to find the optimal model parameters based on the expert assessments and historical status of each risk. This approach enables us to analyze risks that are particularly difficult to quantify, such as geo-political or social risks in addition to more quantitative risks such as economic, technological and natural.

In the talk, we describe model dynamics and discuss how to use the model to provide quantitative means for measuring interdependence and materialization of risks in the network. We also talk about limits of the predictability of the system parameters from historical data and model ability to recover hidden variable. Then, we describe how the network evolved recently by comparing steady state which would be reached if the risks were left unabated at different time points. Finally, we also analyze the model resilience and optimal control. Our findings elucidate the identity of risks most detrimental to system stability at various points in time. The model provides quantitative means for measuring the adverse effects of risk interdependence and the materialization of risks in the global risk network.

Boleslaw Szymanski "Failures, Dynamics, Evolution and Control of the Global Risk Network"

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