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@ -6,43 +6,41 @@ University](https://www.cornell.edu).
**I am always looking for good students!**
Previously, I was a professor in the [Department of Computer
Previously, I was an assistant professor in the [Department of Computer
Sciences](https://www.cs.wisc.edu/) at the [University of
Wisconsin--Madison](https://www.wisc.edu), and a postdoc at the [Department of
Computer Science](https://www.cs.cornell.edu/) at [Cornell
University](https://www.cornell.edu/), and in the [Programming Principles,
Logic, and Verification Group](http://pplv.cs.ucl.ac.uk/welcome/) at the
[University College London](https://www.ucl.ac.uk/). I was a graduate student in
the [Department of Computer Science](https://cis.upenn.edu) at the [University
of Pennsylvania](https://www.upenn.edu).
[University College London](https://www.ucl.ac.uk/). I obtained my PhD from the
[Department of Computer Science](https://cis.upenn.edu) at the [University of
Pennsylvania](https://www.upenn.edu).
I am funded by the National Science Foundation, the Office of Naval Research,
and Facebook Research.
## Research Interests ##
I design methods to **formally verify** that programs are correct, especially
programs that use **randomization**. Such programs can be easy to show correct
on paper, but surprisingly challenging for computers to analyze. Accordingly,
my research blends ideas from two classical areas of computer science:
**randomized algorithms** from theoretical computer science (**TCS**) and
**formal verification**.
I design methods to **formally verify** that **algorithms** are correct. I am
especially interested in programs satisfying quantitative guarantees, or
properties from mathematical or scientific applications.
Drawing inspiration from how humans reason about randomized algorithms, we can
build simpler and more automated verification techniques. In the past, I've
applied this approach to properties like **accuracy**, **incentive
compatibility**, Markov chain **mixing**, and various notions of **algorithmic
stability**.
A particular focus of my work has been [**differential
A particular focus of my work has been verifying programs that use
**randomization**. Such programs can be easy to show correct on paper, but
surprisingly challenging for computers to analyze. Drawing inspiration from how
humans reason about randomized algorithms, we can build simpler and more
automated verification techniques. In the past, I've applied this approach to
properties like **accuracy**, **incentive compatibility**, Markov chain
**mixing**, various notions of **algorithmic stability**, and [**differential
privacy**](https://en.wikipedia.org/wiki/Differential_privacy), a rigorous
definition of privacy that is currently under extensive study.
I have investigated a variety of formal methods---such as [**type
definition of privacy. I have developed a variety of [**type
systems**](https://en.wikipedia.org/wiki/Type_system) and [**program
logics**](https://en.wikipedia.org/wiki/Hoare_logic)---to verify that programs
are differentially private.
logics**](https://en.wikipedia.org/wiki/Hoare_logic) to verify that programs are
differentially private.
From a more traditional algorithms perspective, I am also interested in applying
differential privacy to optimization, machine learning, and mechanism design.
More broadly, I am interested in verifying all kinds of programs and properties
with rich mathematical structure, such as continuous-time systems, programs with
symmetries, economic mechanisms, and most recently, algorithms from numerical
analysis and applied mathematics.
## Teaching ##
- **Data Structures and Functional Programming (CS 3110)**: [F23](https://www.cs.cornell.edu/courses/cs3110/2023fa/) [S23](https://www.cs.cornell.edu/courses/cs3110/2023sp/) [S22](https://www.cs.cornell.edu/courses/cs3110/2022sp/)