Coordinator and organizer: Alex Iosevich ( (University of Rochester) and Azita Mayeli ( (CUNY)

Co-organizers: Charlotte Aten (, University of Denver), Steven Kleene (, University of Rochester), Sevak Mkrtchyan (, University of Rochester), Firdavs Rakhmonov (, Emmett Wyman (, University of Rochester), and Yujia Zhai (, Clemson)

Instructors: Charlotte Aten, Matthew Dannenberg (, University of Rochester), Alex Iosevich, Mandar Juvekar (, Boston University), Steven Kleene, Azita Mayeli, Sevak Mkrtchyan, Anna Myakushina (, University of Rochester), Emmett Wyman, Yujia Zhai

Project supervisors: Charlotte Aten, Matthew Dannenberg, Alex Iosevich, Scott Kirila, Azita Mayeli, Svetlana Pack, Donovan Snyder, Evan Witz, Emmett Wyman, Yujia Zhai.

Computer support: Charlotte Aten, Gabe Hart (gabe, University of Rochester), Alex Iosevich, Mandar Juvekar, and Anna Myakushina

Click here for the program poster!

Final Presentations:

Click here for the description of the projects. Click on the items below for the .pdf files of the final presentations:

Buffon Needle Problem

Systems of Non-Linear Equations

Graphs and Complexity

Modeling Seizures

Time Series and Complexity

Fractal Strutctures in Data Sets

VC-Dimension and Applications

Discrete Neural Nets

Videos of the final presentations:

Afternoon presentations: Time Series and Complexity, Systems of Non-Linear Equations, Fractal Strutctures in Data Sets, Graphs and Complexity

Evening presentations: Modeling Seizures, VC-Dimension and Applications, Discrete Neural Nets, Buffon Needle Problem


This Tripods.StemForAll program will be of a slightly different flavor than the previous three. We are going to combine the material from several advanced undergraduate courses such as Probability, Geometry, Combinatorics and Number Theory and focus on the aspects of the material covered in those classes that are susceptible to an effective analysis using modern big data techniques. As a result, we are going to both deepen our understanding of the subject matter in those courses and develop the skills that are valued in Big Data. In the coming weeks and months, we are going to start posting sketches of the type of problems we are going to consider in this program and links to the study materials, both theoretical and computational.

It is my hope that at least a few undergraduate students would be interested in getting started on the projects outlined below during the 2022-2023 academic year, long before Tripods/StemForAll2023 actually begins.

Dates and locations:

The program is going to run from July 24 until August 4 in Hylan 1106A.

Preliminary schedule:

Week 1:

Combinatorial geometry mini-course: 8 a.m. - 9.30 a.m.

Number Theory mini-course: 10:00 a.m. - 11.30 a.m.

Probability mini-course: 12.30 a.m. -  2.00 p.m.

Geometry mini-course: 2.30 p.m. - 4.00 p.m.

Dinner break: 4.00 p.m. - 7.00 p.m.

Python coding groups: 7 p.m. - 9 p.m.

Week 2:

Research groups meeting on their own, with or without instructors: 8.00 a.m. - 9.00 a.m.

Research groups meeting with project supervisors: 9:00 a.m. - 11:00 a.m.

Lunch break: 11:00 a.m. -1.30 p.m.

Research group meetings with project supervisors: 1.30 p.m. - 3.00 p.m.

Participants working individually and in small groups: 3.00 p.m. - 4.00 p.m.

Dinner break: 4:00 p.m. - 7.00 p.m.

Evening regroup with supervisors: 7.00 p.m. - 8.00 p.m.

Python Coding Groups Information: this link will be continually updated with respect to the python groups. The composition of the groups is going to change several times due to the nature of the assignments. Please check this link often.



William Hagerstrom (University of Rochester) (confirmed)

Gabe Hart (University of Rochester) (confirmed)

Jennifer Kim (Cornell University) (confirmed)

Anuurag Kumar (University of Rochester) (confirmed)

Isaac Li (University of Rochester) (confirmed)

Svetlana Pack (University of Rochester) (confirmed)

Nathan Skerret (University of Rochester) (confirmed)

Lily Stolberg (University of Rochester) (confirmed)

Lily Testa (University of Rochester) (confirmed)

Stephanie Wang (University of Rochester) (confirmed)


Karam Aldahleh (University of Rochester)

Ajax Benander (RIT)

Moez Boussarsar (University of Rochester)

Xiangyi Chen (University of Rochester)

Xiaolu Chen (University of Rochester)

Skye Crocker (University of Rochester)

Colin Hascup (University of Rochester)

Yuesong Huang (University of Rochester)

Joshua Iosevich (RIT)

Andrew Isaacson (RIT)

Wentao Jiang (University of Rochester)

Allihussein Khalil (University of Rochester)

Tran Duy Anh Le (University of Rochester)

Jiaming Lyu (University of Rochester)

Xinyi Liu (University of Rochester)

Peter MacNeil (University of Rochester)

Cooper Orio (University of Rochester)

Yining Qian (University of Rochester) 

Zheling Sheng (University of Rochester)

Scott Sun (University of Rochester)

Jiamu Tang (University of Rochester)

Xianquan Yan (University of Rochester)

Tae Ho Yoo (University of Rochester)

Gus Vietze (University of Rochester)

TJ Weaver (RIT)

Matthew Xie (high school)

Kevin Xue (Notre Dame University)

Jonathan Zhang (University of Rochester)


Combinatorial geometry - we are going to explore counting problems involving points, lines, circles and other geometric objects in the plane and higher dimensional space. In the process, we are going to identify problems that susceptible to computer analysis and generate suitable python code to gain additional insight. Applications of combinatorial geometry to big data will also be discussed and explored.

Instructional team: Alex Iosevich, Mandar Juvekar and Firdavs Rakhmonov

Prerequisites: i) Math 150 or a similar course in discrete mathematics. ii) Good python programming skills.

Probability - we are going to explore a variety of topics in probability theory and investigate them using the tools of modern data science such as neural networks and other computational packages. Applications to applied data science will also be discussed.

Instructional team: Sevak Mkrtchyan, Matthew Dannenberg, and Anna Myakushina

Prerequisites: i) Math 201 at the University of Rochester or equivalent. ii) Good python programming skills.

Geometry - we are going to explore several topics in differential geometry that lend themselves to investigation using computational techniques of modern data science. Connections with the neural networks will be thoroughly explored.

Instructional team: Steven Kleene and Emmett Wyman

Prerequisites: i) Linear algebra and multi-variable calculus. ii) Good python programming skills.

Algebra/Number Theory - we are going to explore some aspects of group theory and elementary number theory and discuss connections with modern data science, in particular, with the theory of neural networks.

Instructional team: Charlotte Aten

Prerequisites: i) Abstract algebra. ii) Linear algebra. iii) Good python programming skills.

Link to a description of possible projects:

Tripods vs StemForAll2023:

If you are a U.S. citizen or a permanent resident, and a student at the University of Rochester, Cornell, or another Rochester area college or a university, you are eligible to apply to this program under the auspices of the National Science Foundation Tripods grant. If you are accepted, you will receive a $1600 stipend. If you are not a citizen or a permanent resident, you are eligible to apply to this program under the auspices of StemForAll2023. Limited financial support is available for StemForAll2023 participants as well.