Biomathematical Sciences Program

The goal of this program is to create educational structures that help Five College students become scientifically multilingual in fields of life and quantitative sciences by providing the means for each student to trace their own intentional pathway across the disciplines.

The daunting complexities of biological phenomena from neurological development to ecosystem carbon fluxes require development of new modeling and analytic approaches. Sorting through potential mechanisms and patterns to develop testable hypotheses requires collaboration between life science investigators and mathematicians, statisticians, and computer scientists.

Cutting edge life science research increasingly involves such collaborations, but researchers are too often stymied by the different languages of their disciplines. The goal of this program is to create educational structures that help Five College students become scientifically multilingual in fields of life and quantitative sciences by providing the means for each student to trace their own intentional pathway across the disciplines.

The Five College Certificate in Biomathematical Sciences is currently available to students at Amherst, Hampshire, Mount Holyoke and Smith Colleges, and courses in the program are available to all students enrolled at one of the five campuses through the Five College Course Interchange.

On This Page

Nicole DelRosso working in a lab

Engaged Alumni

Learn how Hampshire College alum Nicole DelRosso engaged with Biomathematical Sciences while a student at the Five Colleges!

dorothy wrinch in the smith mobile

Dorothy Wrinch: The First Biomathematician in the Valley

The Four College Biomathematics Program, begun in 2011, allows the colleges of the Pioneer Valley to work together across many scientific disciplines. This, however, is not the first biomathematics collaboration in the college consortium. The first experiment with intercollege cooperation in any field began 70 years earlier, with the appointment of Dorothy Wrinch in 1941 to teach courses on molecular biology at Amherst, Mount Holyoke, and Smith colleges. In her words, the effort was “the first of its kind to be given in any center of higher education.”

Certificate Requirements

The Five College Certificate in Biomathematical Sciences is currently available to students enrolled at Amherst, Hampshire, Mount Holyoke and Smith Colleges.

A minimum of six courses and a research experience are required. In addition to an introductory and capstone biomathematical sciences courses, students are expected to take courses complementing their major. Students with majors in the quantitative sciences (mathematics, statistics, computer science, engineering, and physics) are expected to take courses in the life sciences (biology, neuroscience, biochemistry, chemistry, etc.) and students with majors in the life sciences are expected to take courses in the quantitative sciences for the certificate. A biomathematical science research experience is also an essential component of the certificate, to provide an experience outside the classroom.

In summary, to qualify for the Five College Certificate in Biomathematical Sciences, students must complete the following:

  • One gateway course: an entry level biomath course to introduce current research questions and foundational skills
  • 4 courses in the life sciences if you are majoring in a quantitative science, or 4 courses in the quantitative sciences if you are majoring in a life science
  • A capstone course in biomathematical or biostatistical methods or an honors thesis in a biomathematical sciences topic
  • A research experience of one summer (or equivalent) with a team of life and mathematical science mentors

The gateway course should contain some basic programming skills and examples in biology. Courses that students have used include Frontiers in Biomathematics (Smith, IDP 170), Modeling in the Sciences (Smith, MTH 205), and Mathematical Modeling (Amherst, Math 140). At least 2 of the 4 courses that complement the major should be upper level courses. Hybrid courses, e.g., computational biology, bioinformatics, biostatistics, can count toward either life sciences or quantitative sciences. To explore the certificate program, please contact your campus certificate advisor or the current program director.

Courses

Spring 2022 Biomathematics Courses

01
4.00

Lee Spector

MW 01:30 PM-02:50 PM

Amherst College
COSC-452-01-2122S
lspector@amherst.edu

Evolutionary computation techniques harness the mechanisms of biological evolution, including mutation, recombination, and selection, to build software systems that solve difficult problems or shed light on the nature of evolutionary processes. In this course students will explore several evolutionary computation techniques and apply them to problems of their choosing. The technique of genetic programming, in which populations of executable programs evolve through natural selection, will be emphasized.

Requisite: COSC 112. Limited to 20 students. Preference given to Computer Science majors. Spring semester: Professor Spector.

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Tanya L. Leise

TTH 03:00 PM-04:20 PM; M 03:00 PM-03:50 PM

Amherst College
MATH-140-01-2122S
tleise@amherst.edu

Mathematical modeling is the process of translating a real world problem into a mathematical expression, analyzing it using mathematical tools and numerical simulations, and then interpreting the results in the context of the original problem. Discussion of basic modeling principles and case studies will be followed by several projects from areas including biology and the social sciences (e.g., flocking and schooling behavior, disease spread in populations, generation of artificial societies). This course has no requisites; projects will be tailored to each student’s level of mathematical preparation. Four class hours per week, with occasional in-class computer labs.

Limited to 24 students. Spring semester. Professor Leise.

Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Ryan P. McShane

TTH 01:00 PM-02:20 PM

Amherst College
STAT-225-01-2122S
rmcshane@amherst.edu

This course is an introduction to nonparametric and distribution-free statistical procedures and techniques. These methods rely heavily on counting and ranking techniques and will be explored through both theoretical and applied perspectives. One- and two-sample procedures will provide students with alternatives to traditional parametric procedures, such as the t-test. We will also investigate correlation and regression in a nonparametric setting. A variety of other topics may be explored in the nonparametric setting including resampling techniques (for example, bootstrapping), categorical data and contingency tables, density estimation, and the one-way and two-way layouts for analysis of variance. The course will emphasize data analysis (with appropriate use of statistical software) and the intuitive nature of nonparametric statistics.

Requisite: STAT 111 or STAT 135. Limited to 24 students.  Professor McShane. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Pamela B. Matheson

MWF 09:00 AM-09:50 AM

Amherst College
STAT-230-01-2122S
pmatheson@amherst.edu

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Shu-Min Liao

MWF 11:00 AM-11:50 AM

Amherst College
STAT-230-02-2122S
sliao@amherst.edu

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Amy S. Wagaman

TTH 08:30 AM-09:50 AM

Amherst College
STAT-231-01-2122S
awagaman@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Amy S. Wagaman

TTH 11:30 AM-12:50 PM

Amherst College
STAT-231-02-2122S
awagaman@amherst.edu

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Justin Baumann

TTH 01:45PM-03:00PM

Mount Holyoke College
116782
jbaumann@mtholyoke.edu
The statistics sections of biology articles have become so technical and jargon-filled that many biologists feel intimidated into skipping them or blindly accepting the stated results. But how can we ask relevant questions or push the boundaries of knowledge if we skip these sections? Using lectures, data collection, and hands-on analysis in R, this course will connect statistics to biology to help students develop a gut instinct for experimental design and analysis. We will explore sampling bias and data visualization and review methods and assumptions for the most common approaches with examples from current biological literature and our own data.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
0.00

Justin Baumann

M 01:30PM-04:20PM

Mount Holyoke College
116783
jbaumann@mtholyoke.edu

02
0.00

Justin Baumann

W 01:30PM-04:20PM

Mount Holyoke College
116784
jbaumann@mtholyoke.edu

01
4.00

Maria Gomez

MW 11:30AM-12:45PM

Mount Holyoke College
116401
magomez@mtholyoke.edu
Chemists have always been interested in understanding patterns in their data. The scientific method uses observations to create theories and models to understand physical phenomena. Data science algorithms allow us to find unexpected patterns in chemical data. New chemical theories can be developed using a combination of data from either experiment or simulation, algorithms and physical insight. This class uses the case method providing three challenge problems to find hidden chemical rules from large chemical data sets through algorithms and physical insight. There will be lectures on the physical/chemical problems, the data sets, and the possible algorithms to consider before the teams of students tackle these problems. The teams will write papers on their findings and use the peer review process to improve their papers.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Shruti Biswal

MW 01:45PM-03:00PM

Mount Holyoke College
116934
sbiswal@mtholyoke.edu
Have you ever used Google's image search tool and wondered how the search results were found? Why is it so difficult for a computer to "see" as we do? Computer scientists are actively researching how to approach this challenge of "computer vision." This course will introduce the fundamentals of applied computing using computer vision as a motivating theme. Students will learn foundations of programming (in the Python programming language) before working with computational tools more independently.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Melody Su

T 01:45PM-03:00PM;TH 01:45PM-04:20PM

Mount Holyoke College
116900

Prospect Hall 037;Prospect Hall 039

msu@mtholyoke.edu
This intermediate-level course presents a hands-on introduction to robotics. Each student will construct and modify a robot controlled by an Arduino-like microcontroller. Topics include kinematics, inverse kinematics, control-theory, sensors, mechatronics, and motion planning. Material will be delivered through one weekly lecture and one weekly guided laboratory. Assignments include a lab-preparatory homework, guided lab sessions, and out-of-class projects that build upon the in-class sessions. Students have access to the Fimbel Maker and Innovation lab for fabricating and demonstrating their robots.
Instructor Permission: Permission is required for interchange registration during all registration periods.

01
4.00

Melody Su

TTH 11:30AM-12:45PM

Mount Holyoke College
116890
msu@mtholyoke.edu
How does Neflix learn what movies a person likes? How do computers read handwritten addresses on packages, or detect faces in images? Machine learning is the practice of programming computers to learn and improve through experience, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodology, and Bayesian probabilistic modeling. Students will learn to program in MATLAB or Python and apply course skills to solve real world prediction and pattern recognition problems. Programming Intensive.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Tori Day

MWF 08:30AM-09:45AM

Mount Holyoke College
116879
day22v@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Tori Day

MWF 10:00AM-11:15AM

Mount Holyoke College
116881
day22v@mtholyoke.edu
Topics include differential and integral calculus of functions of several variables.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Samantha Kirk

MWF 08:30AM-09:45AM

Mount Holyoke College
116888
skirk@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Chassidy Bozeman

MWF 11:30AM-12:45PM

Mount Holyoke College
116889
cbozeman@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Lidia Mrad

MW 03:15PM-04:30PM;F 03:15PM-04:05PM

Mount Holyoke College
116891
lmrad@mtholyoke.edu
Topics include elements of the theory of matrices and vector spaces.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Timothy Chumley

TTH 11:30AM-12:45PM

Mount Holyoke College
116897
tchumley@mtholyoke.edu
Dynamical systems are mathematical models that evolve with time -- for example, the population of a species in an ecosystem or the price of a financial asset. This course will focus on discrete-time models where one iterates a single variable function and follows the evolution of points in its domain. Our aim will be to study the qualitative, long-term behavior of these models by developing mathematical theory and doing simulation. Topics will include periodicity, bifurcations, chaos, fractals, and computation.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Pramesh Subedi

MWF 10:00AM-11:15AM

Mount Holyoke College
116907
psubedi@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Laurie Tupper

MWF 01:45PM-03:00PM

Mount Holyoke College
116909
ltupper@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

03
4.00

Pramesh Subedi

MWF 03:15PM-04:30PM

Mount Holyoke College
116911
psubedi@mtholyoke.edu
This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Laurie Tupper

MWF 11:30AM-12:45PM

Mount Holyoke College
116913
ltupper@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

02
4.00

Marie Ozanne

MWF 01:45PM-03:00PM

Mount Holyoke College
116916
mozanne@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Amy Nussbaum

TTH 10:00AM-11:15AM

Mount Holyoke College
116918
anussbau@mtholyoke.edu
The methods taught in traditional statistics courses are based on assumptions that are often not satisfied by real data sets. In this course we will learn about approaches that require fewer assumptions, known as nonparametric methods. After taking this course, students will be able to examine assumptions for different approaches to statistical inference, compare nonparametric statistical tests such as sign and Wilcoxon tests to their parametric equivalents, and implement non-parametric approaches using R. In addition, the course will incorporate computational techniques for statistical analysis, including simulation, permutation tests, and bootstrapping.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Ileana Streinu

M W F 1:20 PM - 2:35 PM

Smith College
CSC-205-01-202203

Ford 345

istreinu@smith.edu
MTH 205-01, CSC 205-01
Offered CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific topics, organized as case studies, span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Prerequisites: MTH 112 or MTH 114. CSC 111 recommended. Enrollment limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Ileana Streinu

M W F 1:20 PM - 2:35 PM

Smith College
MTH-205-01-202203

Ford 345

istreinu@smith.edu
MTH 205-01, CSC 205-01
Offered CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific topics, organized as case studies, span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Prerequisites: MTH 112 or MTH 114. CSC 111 recommended. Enrollment limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

01
4.00

Luca Capogna

TU TH 2:45 PM - 4:00 PM

Smith College
MTH-364pd-01-202203
lcapogna@smith.edu
Partial differential equations allow us to track how quantities change over multiple variables, e.g. space and time. This course provides an introduction to techniques for analyzing and solving partial differential equations and surveys applications from the sciences and engineering. Specific topics include Fourier series, separation of variables, heat, wave and Laplace’s equations, finite difference numerical methods, and introduction to pattern formations. Prerequisite: MTH 211, MTH 212, and MTH 264 strongly recommended) or MTH 280/281, or permission of the instructor. Prior exposure to computing (using Matlab, Mathematica, Python, etc.) will be helpful.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.

Contact Us

2021-22 Program Director and Hampshire College Certificate Advisor:

Sarah HewsAssociate Professor of Mathematics, Hampshire College & Visiting Assistant Professor of Mathematics & Statistics, Amherst College

Amherst College Certificate Advisor:

Tanya Leise, Brian Boyle Professor in Mathematics and Computer Science, Amherst College

Smith College Certificate Advisor:

Christophe GoléProfessor of Mathematics & Statistics, Smith College

Mount Holyoke College Certificate Advisor:

Martha HoopesProfessor of Biological Sciences, Mount Holyoke College

Five College Staff Liaison:

Ray Rennard, Director of Academic Programs