StemForAll
2026
Organizers:
Alex Iosevich (UR), Stephen Kleene (UR) and Azita Mayeli
(CUNY)
Registration
Link
Last update: Saturday, December 27, 2025
StemForAll2026 program dates: July 27, 2026 - August 7, 2026
Introduction: Welcome to StemForAll2026 summer workshop. All
the interested Rochester area students are welcome to participate.
The registration process is only used to assign the students to
suitable projects. The main idea behind the workshop is to share the
research we are doing with undergraduate students for the purpose of
familiarizing them with research methods and techniques. Quite often
research papers result from these discussions, but the main emphasis
is on learning and the creative process.
Program instructors: Vishal Gupta (UR), Gabe Hart (UR), Alex
Iosevich (UR), Steven Kleene (UR), Svetlana Pack (Penn State),
Steven Senger (Missouri State), Curt Signorino (UR), Kunko Zou (UR),
and Yan Zou (UR) (more names coming soon)
History of the program: StemForAll has been running at the
University of Rochester since 2018. In one form or another this
program has existed at the University of Rochester and University of
Missouri since 2001. Many of its participant have since obtained
Ph.Ds in mathematics and related fields and have become successful
researchers. The links to the previous programs, including
StemForAll2025 can be found here.
Rochester StemForAll location and time: StemForAll2025 in the
Rochester area is going to take place in July/August 2026 in the
Hylan Building on the University of Rochester campus.
Structure of the workshop: StemForAll2026 is going to consist
of a wide variety of projects, listed below, involving the
interaction of pure and applied mathematics, statistics, physics and
data science. Many of the projects are strongly related, which
should lead to a considerable amount of interaction. We are going to
have a blast!
StemForAll2026 Projects:
Signal Recovery: The basic problem
in signal recovery is to send a finite signal via its Fourier
transform, with some of the Fourier values missing, and recover the
signal exactly if a reasonable set of assumptions is satisfied. In
practical situations, the values of the original signal are missing.
the assumptions are on the Fourier coefficients, and the recovery is
approximate, not exact. This has significant applications in
industrial data science. In this project we are going to explore
both pure and applied aspects of signal recovery. The applied and
pure directions will be explored together, with significant daily
interactions between the research groups. Connections with AI and
Quantum Information Theory will also be explored.
The topics covered this year will include: i) Signal recovery and
image reconstruction, ii) Imputation of time series and complexity
of times series, and iii) Signal recovery and quantum information
theory. Most likely, we are going to run these as three separate
projects, but there will be constant communication between the
groups.
Project supervisors: Vishal Gupta, Gabe Hart, Alex Iosevich,
and Tran Duy Anh Le
Participants: Suleman Khan (Baruch), Julian King (Geneseo),
Fiona Zhang (UR),
Background reading: coming soon
Medical data: We are going to work with large swaths of
medical data, including EEG, seizures and others, and look for
identifiable patterns using neural network analysis and more
elementary statistical techniques. Connections with the theory of
exact signal recovery will also be explored.
Project supervisor: Svetlana Pack
Participants: coming soon
Background reading: coming soon
Stability of solutions of ordinary differential equations
arising from geometry: (description coming soon)
Project supervisor: Stephen Kleene
Participants: coming soon
Background reading: coming soon
Sales modeling: We are going to build and test neural
network models with economic indicator regressors to effectively
predict future sales in retail. A variety of neural network models
will be built using tensorflow, keras, facebook prophet and others.
The second major part of this project is using generative AI for
parameter tuning and inventory optimization. Theoretical aspects of
this problem will be considered as well. Connections with the theory
of exact signal recovery will also be explored.
Project supervisors: Alex Iosevich
Participants: Ilia Lukinov (UR)
Background reading: coming soon
Quantum random walks and quantum random circuits: A
quantum walk is a quantum mechanical equivalent of a classical
random walk. A popular type of quantum random walk involves discrete
and iterated local unitary transformations for quantum states that
are connected by a graph. In contrast, quantum random circuits are
composed of a series of local unitary transformations that are
sampled independently according to the Haar measure. Quantum walks
and random circuits have many applications including in quantum
computing, quantum simulation, condensed matter physics and for
demonstrating quantum advantage.
We will explore the properties of more general classes of iterated
quantum transformations on quantum states connected by a graph that
include both random and fixed unitary components and possibly
classical components such as state initialization. Much of our
exploration is likely to involve calculations in python, though we
will also try to gain understanding analytically.
Project supervisor: Alice Quillen
Participants:
Background reading: coming soon
Wolbachia: Understanding the Great Pandemic: Wolbachia
are among the most common and widespread bacteria on our planet,
infecting from 40-60 percent of all invertebrates. As such,
they represent one of the great pandemics in the history of life.
They are transmitted both vertically through eggs, and horizontally
between species, and are able to jump between very different
species. Wolbachia manipulate reproduction in their hosts. They can
convert males into females, induce reproduction without mating, and
kill sons while leaving daughters alone. They can also induce
protection against viruses. As such, they are being
investigated as a method to reduce spread of harmful human viruses
vectored by invertebrates. One of the most common effects of
Wolbachia is an induction of a sperm-egg incompatibility (sperm from
infected males prevent uninfected eggs from developing (called
cytoplasmic incompatibility, or CI). This mechanism provides a
“drive” to the Wolbachia, but only once they exceed a threshold
frequency in the population, determined by the level of their cost
to the infected host. Given this situation, the key question
is “How do Wolbachia invade and become established in new species,
thus explaining their widespread distribution”. One school of
thought is that they must be beneficial. However, there are
problems with this interpretation. Our goal is to explore
models for the invasion of CI Wolbachia into new species, that
consider different relevant parameters. Examples include population
simulations with finite size populations, local population
structure, resource competition, inbreeding, and stochastic
sampling, We are looking for students who are interested in the
junction between computational biology and mathematics.
Attached is a review article on Wolbachia, and a pdf of a
presentation given by John Werren at the International Meeting on
Wolbachia, which lays out some of the arguments for exploring
alternative models for Wolbachia dynamics.
Skill Set Required: Students who have experience in computer
programming using languages such a R and/or Python are desired, as
well as an interest in biological phenomena.
Project supervisor: John H. Werren
Participants: coming soon
Background reading: coming soon
Numerical solutions of PDE/PDE arising in geometry:
Mean curvature flow is a second order geometric evolution equation
on surfaces that can be thought of as the negative gradient
flow for the area functional. To decrease the value of a function
most efficiently, one moves in the direction of the negative
gradient of the function. Thus, to decrease the area of a surface
most efficiently, one deforms it according to the mean curvature
flow. The process is non-linear and exists for small times scales
and almost always develops singularities in the surface, which are
modeled on "self shrinkers", surfaces which shrink under the
flow. Exact solutions to the flow, especially embedded ones, are of
central interest to the field. In this project, the group will
numerically approximate a family of immersed--the surfaces
self-intersect--rotationally symmetric self shrinkers for the
mean curvature flow in in euclidean three space constructed by
Stephen J. Kleene and Niels Martin Moller in 2010. If it can be
shown that the members of the are non-degenerate, they can be
desingularized and new families of embedded self shrinkers can be
shown to exist.
Project supervisor: Stephen Kleene
Participants: Braden Lenn (UR)
Background reading: coming soon
Useless neurons: Not every neuron in an artificial
neural network contributes equally to its performance. In fact, many
neurons can be removed—or pruned—with little or no loss in accuracy.
When done carefully, pruning can greatly reduce the computational
cost of the inference stage, making models faster and more
efficient. For this reason, a variety of methods have been developed
to identify neurons that are relatively unimportant or redundant. In
this group, we will use ResNet-18 (and possibly ResNet-50) as a case
study to evaluate the effectiveness of different pruning techniques.
We will also examine the theoretical ideas behind these methods and
explore the possibility of developing new pruning criteria.
Familiarity with Python and multivariable calculus is preferred but
not strictly required.
Project supervisor: Kunko Zou and Yan Zou
Participants: coming soon
Background reading: coming soon.