I am an Associate Professor of Computer Science at the New Jersey Institute of Technology, where I also serve as Associate Chair of Graduate Studies. My research has been supported by the National Science Foundation, including an NSF CAREER award, and recognized with best paper awards at ICALP 2017 and ICCAD 2022.
I earned my PhD in Computer Science from Carnegie Mellon University. Before NJIT, I served on the faculty at the University of Puerto Rico–Río Piedras and held visiting appointments at ICERM and the Simons Institute for the Theory of Computing.
With Gary Miller and Richard Peng, I helped develop subgraph preconditioners that yielded an asymptotically optimal algorithm for planar graphs and a near-optimal algorithm for the general case. The work was later featured in the Research Highlights section of Communications of the ACM.
A complementary line of work, based on "super-graph" preconditioners, focused on practical performance. The Combinatorial Multigrid Solver (CMG) remains one of the fastest available solvers for graph Laplacians and supports applications including hypergraph partitioning in electronic design automation. This contributed to a Best Paper Award at ACM/IEEE ICCAD 2022.
I introduced the method of algebraic fingerprints, enabling faster exact and parameterized algorithms for a range of hard problems. The framework drove major advances on problems such as Hamiltonian Cycle and, in joint work with Andreas Björklund and Petteri Kaski, led to record-setting algorithms recognized with a Best Paper Award at ICALP 2017.
A comprehensive, self-contained introduction to machine learning, presented through an open-access collection of Jupyter Notebook-based lectures.