StemForAll
2025
Organizer:
Alex Iosevich (University of Rochester) and Azita Mayeli
(CUNY)
Registration
Form
Registration Deadline: March 31, 2025
Last update: Sunday, March 16, 2025
Program dates: July 28, 2025 - August 8, 2025
Introduction: Welcome to StemForAll2025 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. In 2025, the program in
Rochester will be organized by Alex Iosevich (UR), Steven
Kleene (UR). and Azita Mayeli (CUNY).
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 can be found here.
Expansion: The StemForAll program is expanding in 2025-2026.
In addition to the annual program we have been running in the
Rochester area for several years, analogous programs are going to
run at the following institutions:
Missouri State University (Steven Senger)
Virgina Tech (Eyvindur Palsson)
Ohio State University (Krystal Taylor)
The StemForAll Team consists mathematicians who have committed to
running a two-week StemForAll undergraduate research program at
their home institutions during Summer 2025. The members of the team
are going to create a joint database of research problems and other
research materials, and share those with all the other affiliates.
Rochester StemForAll location and time: StemForAll2025 in the
Rochester area is going to take place in July/August 2025 at a time
and location yet to be determined.
Structure of the workshop: In 2025, StemForAll in Rochester
will be mainly (but not entirely) dedicated to the pure and applied
aspects of signal recovery. The basic problem is to send a signal
via its Fourier transform and then recover this signal from
incomplete information. The pure aspects of this problem touch upon
Fourier analysis, probability theory, information theory, complexity
theory and much more. The applied aspects include back-filling time
series, forecasting, recurring transmissions, and this is just the
beginning. The exact location of the program will be
determined in the coming weeks, but it will be somewhere in the
Rochester area.
Workshop Projects:
Signal recovery themed projects:
i) Exact
Signal Recovery
Project
supervisors: Alex Iosevich and
Azita Mayeli
Research
meeting location: Hylan 909
Project
description: Suppose that a signal of length N is
transmitted via its discrete Fourier transform and some of the
signal is lost in the transmission due to noise or interference.
Under what conditions is it possible to recover the original
signal exactly? This innocent looking question quickly leads to
some interesting techniques and ideas from analysis,
combinatorics and other areas of mathematics. We are going to
investigate these types of questions from both the theoretical
and computational points of view.
Project
participants: Bukhari Fandi
(buxariom@gmail.com), Yujia Hu
(yhu77@u.rochester.edu), Julian King (
jhk2@geneseo.edu), Alhussein
Khalil (akhalil3@u.rochester.edu),
Kelvin Nguyen (knguy43@u.rochester.edu),
Marina
Tilgadas
(mtilgad@u.rochester.edu),
Mingyu
Zhang (mzhang95@u.rochester.edu),
Showmee Zhou (zzhou69@u.rochester.edu)
ii) Fourier Analysis and Recovery of Missing
Values in Times Series
Project supervisors: Will Burstein, Alex
Iosevich, Azita Mayeli, and Hari Nathan
Research meeting location:
Hylan 909
Project
description: In a paper in preparation, the project
supervisors showed that the performance of virtually any
reasonable time series forecasting engine can be improved,
with high probability, by judiciously
filtering out a certain number of small Fourier coefficients at
the end of the forecast. The purpose of this project is to optimize
and streamline this process. We will also make an effort to unify
our approach with the classical techniques of exact signal recovery
that will be explored by the Exact Signal Recovery research group.
Project
participants: William Du
(jdu14@u.rochester.edu), Caitlin O'Leyar
(coleyar@u.rochester.edu), Qianxiang Shen
(qshen11@u.rochester.edu), Kunwar Arpid
Singh (kunwar22@iiserb.ac.in)
iii)
Sampling on Manifolds and
Fourier Uncertainty
Principle
Project supervisors: Alex
Iosevich, Azita Mayeli, and
Steven Kleene
Research
meeting location:
Math Lounge,
9th floor of Hylan
Bldg.
Project
description: In
a paper in preparation, Iosevich,
Renfrew and Wyman
are studying the
problem of how many
random samples are
needed to
reconstruct a
band-limited
function on a
compact Riemannian
manifold without a
boundary. They are
studying the
stability of the
recovery process in
terms of the
smallest singular
values of the
underlying matrix.
In this project, we
are going to conduct
extensive numerical
experiments designed
to get a feel for
this process on
concrete Riemannian
manifolds. We are
also going to study
the Fourier
uncertainty
principle on
Riemannian
manifolds, following
up on a recent paper
on this topic by
Iosevich, Mayeli and
Wyman.
Project
participants:
Nikash Gajate
(ngajate@u.rochester.edu),
Zekuan Guo
(zguo26@u.rochester.edu)
Machine
Learning themed projects:
i) Election Forecasts and Neural Networks
Project supervisors: Alex Iosevich and Hari Nathan
Research meeting location:
11 floor of Hylan Bldg.
Project description: We are going to examine the
polling data preceding the November 5, 2024 Presidential
Election and design neural network models to forecast
the outcome in terms of the popular vote, the number of
electoral votes and the outcome of the election. We
shall then compare the performance of our models against
the actual outcome of the elections.
Project
participants: Anastasia
Chyzh
(Achyzh@u.rochester.edu),
Lyudovik
Spencer
(slyudovy@u.rochester.edu),
David Yen
(dyen3@u.rochester.edu)
ii)
Sales
modeling with
economic
indicators
Project
supervisors: Alex
Iosevich and
TBD
Research
meeting
location: 11th
floor Hylan Bldg.
Project
description:
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.
Theoretical
aspects of
this problem
will be
considered as
well.
Project
participants:
Jingwen
Hu
(jhu62@u.rochester.edu),