StemForAll
2026
Organizers:
Alex Iosevich (UR), Stephen Kleene (UR) and Azita Mayeli
(CUNY)
Registration
Link
Deadline: July 26, but please register sooner!
StemForAll2026 program dates: July 27, 2026 - August 7, 2026
Schedule of the program
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: Karam Aldahleh (Oxford), Jacob
Canel (Penn State), Jiajun Du (UR), Sam Ferguson (NYU), Vishal
Gupta (UR), Gabe Hart (UR), Alex Iosevich (UR), Joshua Iosevich
(RIT), Alhussein Khalil (MIT), Steven Kleene (UR), Neeraja Kulkarni
(UR), Tran Duy Anh Le (UCSD), Alex Liu (UR), Azita Mayeli (CUNY)
Svetlana Pack (Penn State), Alice Quillen (UR), Akshay Sant (UR),
Steven Senger (Missouri State), Liz Tatum (UR), John Werren (UR),
Kunko Zou (UR), and Yan Zou (UR)
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: There are 9 projects running this year.
Projects T1, T2, T3 and T4 are theoretical projects, though each has
an applied component. Projects A1, A2 and A3 are applied, though
each has a theoretical component. Project P1 is physics, and project
B1 is biology, though both are related to a number of ideas pursued
by other research groups.
In addition, three mini-courses will take place in Landers
Auditorium during the first week of the program: Signal recovery (A.
Iosevich), Python programming (to be determined), and Representation
theory with applications to signal recovery (K. Aldaleh).
(T1) 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.
This year, we are going to run three research subgroups on Signal
Recovery. The non-abelian recovery group, run by Karam Aldahleh,
Alex Iosevich, Azita Mayeli, Alhussein Khalil, and Akshay Sant is
going to explore signal recovery in the
finite non-commutative setting (NC) where
representation theory is going to play a key role. The fourier ratio
and graph recovery group (G), run by Sam Ferguson, Vishal
Gupta, Alex Iosevich, Joshua Iosevich, is going to investigate recovery problems on graphs. The
Parseval frames recovery group (P), run by Alex Iosevich,
Tran Duy Anh Le, and Azita Mayeli is going to explore explore uncertainty principles and signal recovery in
the context of Parseval frames. The three subgroups
will meet in Hylan 201 and Hylan 202.
Project supervisors: Karam Aldehleh, Sam Ferguson, Vishal
Gupta, Gabe Hart, Alex Iosevich, Joshua Iosevich, Azita Mayeli,
Akshay Sant, and Tran Duy Anh Le
Participants: Gideon Afriyie G1 (UR), Karam Aldahleh
NC1 (UR), Kenneth Fei NC2 (UR), Gabe Hoag NC3
(UR), Suleman Khan P1 (Baruch), Misha Jindal P2
(UR), Allhussein Khalil NC4 (UR), Julian King NC4
(Geneseo), Austin O'Connor P3 (UR), James-Lucius
Okenwa G2 (UR), Gus Smith G3 (UR), Ben Song G4
(Pittsford Highschool), Suva Parvin Srithe G5 (UR), Erdem
Togay P4 (UR), Fiona Zhang G7 (UR), Yiyi Zhao P5
(UR)
Location: Hylan 201, Hylan 202, and Hylan 909
Background reading: Will be continually made available on
the Discord channel.
(T2) Analyzing the Fractal Dimension Distribution of
EEG Data to Classify Seizures: Real world data is replete
with fractal behavior, highly geometrically complex patterns that-
in a meaningful sense- have a fractional dimension. These
patterns are, by nature, hard to approximate in finite data and as
such are underexploited by standard data analysis techniques.
However, it is possible to learn a lot about the distribution
underpinning data using fractal analysis - for example, past
research has indicated the electroencephalogram (EEG) recordings of
brain waves have different fractal dimensions when in an epileptic
vs. nonepileptic regime, suggesting that measuring fractal behavior
could be useful for detecting seizure activity in EEG data.
We plan to approach this problem with the following goals:
i) Develop more sensitive mathematical tools for estimating the
fractal dimension distribution of real world time series data
ii)Apply these tools to identify seizures by detecting changes in
the fractal dimension distribution over a sliding time window, and
iii) Compare our methods to the industry standard multi-fractal
analysis methods.
Project supervisors: Svetlana Pack and Jacob Canel
Participants: Grace Brandt (UR), Josie Elliston (UR), Oscar
Jackson (UR), Aashi Jindal (unaffiliated), Ethan Kang (UR), Nadia
Lach-Hab (UR, Shana Kester (UR), Pitambar Pandey (UR), Farid Rohan
(UR), Claire Strobel (UR), Jin Yuan (UR), Lior Zendel (UR)
Location: Hylan 203
Background reading: The definitions of fractal dimension and
dimension distribution that we are working with are expressed in the
language of measure theory. We do not expect anyone to come in with
any measure theory background, however we did prepare a document
that includes a crash course in all the measure theory and fractal
analysis you need to understand the motivation behind our fractal
analysis technique. We highly encourage you to look through at least
Section 1 of this document over the coming weeks if you have time.
If you want to do even more supplemental reading to get comfortable
with the notion of fractal dimension, Hausdorff dimension, that we
will talk about, we suggest you to read sections 3.1, 3.2, 3.3
in Falconer. These materials will be available on Discord .pdf
format.
(T3) Fixed Points of the Conjugate in the Steenrod Algebra: The
Steenrod algebra is an algebraic object with many interesting
connections to topology. The Steenrod algebra comes with a great
deal of intricate structure: it has its own versions of addition and
multiplication, as well as more novel operations. In particular, the
Steenrod algebra comes equipped with an automorphism called the
conjugate, that is, a special function from the Steenrod algebra to
itself.
A more familiar example of an algebraic object with a simpler
conjugate is the complex numbers: the complex numbers come equipped
with addition and multiplication, as well as the complex conjugate.
The fixed points of the complex conjugate are the real numbers,
because the complex conjugate of any real number is itself.
Since the Steenrod algebra has a more complicated structure than the
complex numbers, the fixed points of its conjugate are not known.
The goal of this project is to investigate and identify the fixed
points in small portions of the Steenrod algebra. This is a good
project for students interested in algebra, combinatorics, or
topology, and no specific background is required.
Project supervisor: Elizabeth Tatum
Participants: Ishika Jahaly (U of Mauritius), Logan Singh
(UR), Showmee Zhou (UR)
Location: Hylan 206
Background reading: Will be continually made available on
the Discord channel.
(T4) 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: Evan Balder (RIT), Yoonseo Han (UR), Zoe
Juergensen (UR), Ethan Luvisia (UR), Rohan Khanna (UR), Braden Lenn
(UR)
Location: Hylan 1011
Background reading: Will be continually made available on the
Discord channel.
(A1) 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: Vishal Gupta, Gabe Hart, and Alex
Iosevich
Location: Hylan 1106A
Participants: John Anastasi (UR), Zherui Cao (UR), Yinuo
Feng (UR), Jun Hung (UR), Arjun Kanani (UR), Naomi Kim (UR), Michael
Koyfman (highschool), Bowen Li (UR), Linh Nguyen (UR), Kunxu Song
(UR), Anh Tran (UR), Zesheng Yu (UR), Emily Yun (UR), Yining Zha
(UR), Yichen Zhang (UR), Owen Zhao (UR)
Background reading: Will be continually made available on the
Discord channel.
(A2) 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: Wenzan Fan (UR), Jiarui Feng (UR), Sanjay Makam
(RIT), Alex Phillips (UR), Armaan Sapra (UR), Zihao Wang (UR),
Zhongtian Zhai (UR), Daiming Zhou (UR), Kunko Zou, Yan Zou
Location: Hylan 1106B
Background reading: Will be continually made available on the
Discord channel.
(A3) Financial Mathematics: Real-Time “Hot Search” Topic
Rankings for Predictive Asset Pricing and Event-Driven RL
Financial Trading: This project examines whether
platform-curated hot-search / trending topic rankings generate
short-horizon attention and information shocks that predict returns,
volatility, and liquidity. An end-to-end, reproducible pipeline is
developed to (i) continuously ingest ranked hot topics via
APIs/snapshots, (ii) map topics to tradable objects
(stocks/industries/indices) via entity linking and candidate
ranking, (iii) infer structured semantics (direction, horizon,
confidence, type) using an NLP pipeline, and (iv) train a
risk-aware, event-driven RL trading policy (SMDP/event-triggered)
under realistic constraints and transaction costs. Expected outcomes
include rigorous event-study and cross-sectional tests, a
leakage-safe evaluation framework with open-source code, and a
real-time monitoring dashboard that produces time-stamped
topic-to-asset signals for paper trading and research auditability.
Python, probability, linear algebra, time series, and asset pricing
is highly strictly required; familiarity with NLP, ML, and RL is
preferred but not required.
Project supervisor: Jiajun Du
Participants: Polina Chub (UR), Xingyan Jin (UR), Hanzhang Li
(UR), Jingyi Li (UR), Ilia Lukinov (UR)
Location: Hylan 1101
Background reading: Will be continually made available on the
Discord channel.
(P1) 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: Benjamin Akararatovic (UR), Nicolas
Cannella (NYU)
Location: Hylan 1104
Background reading: Will be continually made available
on the Discord channel.
(B1) 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: Sam Garcia (UR), Nathan Carpenter-Holmes,
Ziqing Li (UR), Riya Vaish (UR), Sinan Yang(UR)
Location: Hutchinson 473
Background reading: Will be continually made available on the
Discord channel.