Looking for PhD students to start in Fall 2027
I am looking for 1-2 PhD students to join my research group in Fall 2027. PhD students in my group are required to have a
strong interest and background in the design and analysis of algorithms, including a background in graph theory
and discrete probability.
I have worked with my recent PhD students in one of two areas.
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Distributed Graph Algorithms.
The goal of research in this area is designing efficient algorithms for graph problems (e.g., All Pairs Shortest Paths,
Minimum Spanning Tree, Maximal Independent Set, etc.) in various models of distributed computing (e.g., LOCAL, CONGEST, MPC, etc.)
or showing that such efficient algorithms do not exist. How "efficiency" is exactly measured depends on the specific model
of computation, but in general efficiency in distributed computing is measured by the number
of communication rounds, the total number of bits communicated, etc.
For students starting off in this area, additional background in distributed or parallel algorithms, randomization and probabilistic analysis,
communication complexity, and information theory woud be a definite plus, but is not a requirement.
If you are interested, take a look at my DBLP page
for recent papers that will give you a sense of my research focus in this area.
Also take a look at recent graduate level courses I have taught on Distributed Algorithms and
Randomized Algorithms.
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Computational Epidemiology.
I am also part of the Computational Epidemiology group, which is a
an inter-disciplinary collaboration of researchers based in the Departments
of Computer Science, Biostatistics, and Internal Medicine at the University of Iowa.
Our group has a particular interest and expertise in the mathematical modeling of the spread of
Healthcare Associated Infections (HAIs), e.g., C.Diff, MRSA, C. auris, etc,.
In our work, we model disease-spread as a stochastic diffusion process over graphs, which in turn allows us to model
surveillance, vaccination, and other disease mitigating interventions
as combinatorial optimization problems on graphs. The focus of my work in this area is on designing scalable approximation algorithms for
such optimization problems and evaluating their solutions on real-world healthcare datasets.
Students interested in applying their algorothmic expertise to problems in healthcare would be well-suited to work in ths area.
My current algorithmic work is supported by the Algorithmic Foundations program at the National Science Foundation
with grants for research on distributed message complexity and distributed random sampling.
To understand the process of applying to the PhD program at the University of Iowa, Department of Computer Science, take
a look at our Graduate Programs webpage.
In any case, feel free to write to me with a current CV.