Grad STEM for All

Overview: The goal of Grad Stem For All is to empower and further inspire all interested students from colleges and universities in Western New York area to pursue advanced degrees in STEM fields, particularly mathematics and statistics. We are especially looking to involve groups that have traditionally been underrepresented in STEM, including women and underrepresented minorities. Our goal is to provide these students with the information, skills and training needed to succeed in graduate school, academic careers, and industry. The two components of the program are coursework and mentoring. During the summer, faculty from local universities will be teaching a series of mini-courses that will enhance the undergraduate education the students receive at their liberal arts institution. These mini-courses are meant to be a preview of graduate coursework and provide students with a deeper background in mathematics and/or statistics. Each student participant will also be paired with an experienced faculty mentor from an area college or university, who can provide support and guidance throughout the student's college career and beyond. By having an identified mentor, students will more readily be able to get involved in research projects, learn about graduate schools, get advice on the application process, and learn more about life in academia in general. We intend for the mentor/student relationship to be long lasting, inspiring students to pursue advanced coursework and careers in STEM field.

Interested individuals are encouraged to contact us at

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Organizers: This program is organized by three faculty at the University of Rochester: Drs. Joseph Ciminelli and Alex Iosevich from the Department of Mathematics, and Dr. Sally Thurston from the Department Biostatistics and Computational Biology. Outreach for the program is organized by Dr. Cheri Boyd from the Department of Mathematics at Nazareth College. Critical to the sucess of these efforts are the cooperation from faculty from the School of Mathematics from Rochester Institute of Technology, and other faculty and graduate students from these departments. We hope to expand this list to cover every college and university in the Rochester area that teaches mathematics and statistics courses.

Mentoring: Every participant in our program will be assigned to a mentor, who will assist them with course selection, advise them on additional readings and help them seek out graduate and professional opportunities. The participant will be able to maintain phone and email contact with the mentor, in addition to regular meetings.

Mini-courses: The first wave of summer courses will be offered in June, 2018. Students will be responsible for their housing and transportation to the program. Each mini-course will run Monday through Friday for a two-week period. One mathematics and one statistics min-course will be offered June 4-15, and a second mathematics and statistics mini-course will be offered June 18-29. The formal coursework for each min-course will be concentrated in the mornings, with the afternoons dedicated to mentoring activities and small group problem solving. Each day will begin with a 45 minute lecture starting at 9 a.m., followed by a 30 minute discussion period. The second 45 minute lecture will begin at 10:15 a.m.., followed by another discussion period until noon. After lunch, a one hour problem solving session with graduate assistants will be followed by a mentoring session at 2 p.m.. We expect the wrap up each day's activities by 3.30 p.m.. A detailed description of the mini-courses follows below.

Description of the mini-courses:

Mini-course 1 June 4-15
Instructor: Daniel Birmajer
Title: Introduction to functional programming with Haskell
Prerequisites: Some programming experience and basic knowledge of logic and mathematical structures would be helpful.

:  The mini-course offers an introduction to the functional programming paradigm using Haskell as the programming language. We will cover the close connection between mathematical reasoning and the Haskell syntax. This leads to a clear implementation of mathematical concepts in a computer, and the possibility of applying logic and induction to the testing of correctness of computer programs. Along the way we will write many interesting programs, implement several algorithms, learn about higher order functions, algebraic and recursive data types and lazy evaluation. These methods will allow us to handle infinite data structures.

Mini-course 2 June 4-15, 2018
Instructors: Joseph Ciminelli, Alexis Zavez and Valeriia Sherina
Graduate assistants: Jiatong Sui
Title: Bayesian inference with application to social network modeling
Prerequisites: Two semesters of calculus.

Description: Social media is a prominent aspect of our society. Individuals are connected to each other and share relationships if it be via social media website or more traditionally through inter-personal friendships. It has thus become important to be able to model a social network and the relationships that exist between people. By representing the network with certain statistical properties, we can suggest new relationships, target marketing efforts, and understand the social trends that alter societal norms.

In order to model such social network relationships, we must first develop an understanding of Bayesian inference. With the Bayesian approach to inferential methods, we use data to update parameter estimates. In doing so, we are able to incorporate new information and use this to readjust our understanding of model parameters. We will fully develop a foundation in Bayesian inference, discussing posterior distributions, conjugate priors, hierarchical models and a brief introduction to Markov chain Monte Carlo methods. We then end the course by framing current challenges in social network models within the Bayesian framework.

Mini-course 3 June 18-29, 2018
Instructor: Alex Iosevich and Steven Kleene
Graduate assistants: Charlotte Aten, Nikolaos Chatzikonstantinou, Dionel Jamie and Emily Windes
Title: Optimization from the ground up
Prerequisites: Two semesters of calculus.

Description: In the first week we shall review the basic concepts of first and second year calculus. We shall then apply these concepts to basic optimization problem in one variable and then move on to their higher dimensional variants using Lagrange Multipliers. Towards the end of the first week we shall introduce some theoretical notions like continuity and convergence of functions. During the second week of the course we shall discuss more elaborate optimization problems like the iso-perimetric inequality, which says that the shape in the plane that has the largest area given a fixed perimeter is the disk. In the process we shall develop a variety of tools that will take us close to the cutting edge of this subject matter.

Mini-course 4 June 18-29, 2018
Instructor: Ernest Fokoue and Matt McCall
Graduate assistants: David Burton
Title: Statistical knowledge and information discovery with R
Prerequisites: Two semesters of calculus (required). Prior exposure to R, or Python, or Matlab, or C, or C++, or C# would be desirable. Knowledge of basics of matrix algebra would be useful, as would familiarity with basic probability and statistics.

Description: Upon completing this course, the student will have learned some of the foundational concepts and tools of statistical machine learning used for knowledge and information discovery in practical data science applications. The course will use hands on examples in a vignette style to touch on elements of statistical text analysis, machine learning methods for audio analysis and elements of image processing. For each of the topics covered, pointers to potential advanced research at the graduate level will be provided. All the practical sessions will be with R.