Robert J. Webber
I am Ph.D. student advised by Jonathan Weare
at the Courant Institute of Mathematical Sciences.
You can reach me at rw2515 [AT] nyu [DOT] edu.
I research Monte Carlo methods with applications in physics and chemistry.
I work to identify sources of error, prove error bounds, and improve Monte Carlo accuracy.
To find out more, check out my curriculum vitae
or take a look at the projects below.
Discovering patterns in time series data
- The ``variational approach to conformational dynamics" (VAC)
is a popular approach for analyzing Monte Carlo simulation data.
VAC identifies a set of slowly decorrelating functions of the data
that approximate eigenfunctions of the Markov transition operator.
I established the convergence of VAC estimates
and derived asymptotic error bounds
- Recently, we extended VAC to make it more robust
for applications with limited data.
The new approach, integrated VAC (IVAC), is easy to tune
and works well with neural network approximation spaces.
- Collaborators: Douglas Dow, Chatipat Lorpaiboon, Erik Thiede,
and Jonathan Weare.
Rare event sampling
- It is challenging to study statistics
of tropical cyclones because
simulations are expensive and intense tropical cyclones are rare.
I developed an efficient new rare event sampling algorithm
and applied the algorithm to study intense tropical cyclones
- Many rare event sampling schemes use resampling.
Resampling can lead to high or low error levels,
and there is not a good understanding of why this occurs.
In response to this problem,
I developed new mathematics to identify minimal variance resampling schemes [arXiv]
- The rapid intensification process in tropical cyclones is mysterious.
I helped develop an algorithm that identifies the most likely pathway leading to rapid intensification in a high-resolution weather model
- Collaborators: David Plotkin, Morgan O'Neill,
Jonathan Weare, and
Identifying the ground-state quantum system
You can hear me talk (virtually or in person) at
- It can be expensive to compute the ground-state energy
for many-electron systems,
particularly systems that are strongly correlated.
I helped design a Monte Carlo scheme for
computing the ground-state energy
that produced efficiency gains of up to a thousand [online publication].
- We recently wrote a second paper that further improved the efficiency of the computational approach
- Collaborators: Samuel Greene, Jonathan Weare, and Timothy Berkelbach.