Brendan Rooney

Brendan Rooney leads impactful research in the field of algebraic graph theory as an Assistant Professor of Mathematical Sciences at the Rochester Institute of Technology. With a keen focus on classic graph theory problems, Brendan delves into areas such as graph coloring, homomorphisms, cliques, and Hamilton cycles, employing algebraic methods to uncover new insights and solutions. His research interests extend to exploring spectral bounds for graph parameters, which involves understanding the limitations and capabilities of graphs through their eigenvalues. Brendan is also fascinated by graphs constructed through algebraic means, seeking to uncover the intricate relationships and properties that arise from such constructions. Additionally, he investigates combinatorially regular graphs, aiming to identify and characterize families of graphs that exhibit regularity in their combinatorial structures. Brendan's work is driven by a desire to advance algebraic tools for studying graphs, thereby enhancing the understanding of combinatorially significant families of graphs. By bridging the gap between algebra and graph theory, he aims to contribute to the development of new methodologies and frameworks that can be applied to a wide range of mathematical and real-world problems. In his role at the Rochester Institute of Technology, Brendan is committed to fostering a collaborative and innovative research environment. He actively engages with students and colleagues, encouraging them to explore the rich interplay between algebra and graph theory. Through his teaching and mentorship, he inspires the next generation of mathematicians to pursue their own inquiries into the fascinating world of graphs. Brendan's dedication to his field is evident in his numerous publications and presentations at national and international conferences. His contributions have not only advanced the theoretical understanding of algebraic graph theory but have also opened up new avenues for practical applications in areas such as network analysis, data science, and computer science.