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
grad.STEM.for.All@gmail.com
Please read the rest of the page and then....
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.
Description: 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.
