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

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
Lee Spector
MW 01:30 PM-02:50 PM
SCCEA331
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.
Tanya L. Leise
TTH 03:00 PM-04:20 PM; M 03:00 PM-03:50 PM
SMUD014; SMUD014
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.
Ryan P. McShane
TTH 01:00 PM-02:20 PM
SCCEA331
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.
Pamela B. Matheson
MWF 09:00 AM-09:50 AM
WEBS102
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
Amy S. Wagaman
TTH 08:30 AM-09:50 AM
WEBS102
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.
Amy S. Wagaman
TTH 11:30 AM-12:50 PM
WEBS102
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.
Justin Baumann
TTH 01:45PM-03:00PM
Kendade G06
Justin Baumann
M 01:30PM-04:20PM
Kendade G06
Justin Baumann
W 01:30PM-04:20PM
Kendade G06
Maria Gomez
MW 11:30AM-12:45PM
Clapp Laboratory 206
Shruti Biswal
MW 01:45PM-03:00PM
Clapp Laboratory 218
Melody Su
T 01:45PM-03:00PM;TH 01:45PM-04:20PM
Prospect Hall 037;Prospect Hall 039
Melody Su
TTH 11:30AM-12:45PM
Clapp Laboratory 206
Tori Day
MWF 08:30AM-09:45AM
Clapp Laboratory 407
Tori Day
MWF 10:00AM-11:15AM
Clapp Laboratory 407
Samantha Kirk
MWF 08:30AM-09:45AM
Clapp Laboratory 401
Chassidy Bozeman
MWF 11:30AM-12:45PM
Art 221
Lidia Mrad
MW 03:15PM-04:30PM;F 03:15PM-04:05PM
Art 219;Art 219
Timothy Chumley
TTH 11:30AM-12:45PM
Clapp Laboratory 407
Pramesh Subedi
MWF 10:00AM-11:15AM
Clapp Laboratory 402
Laurie Tupper
MWF 01:45PM-03:00PM
Clapp Laboratory 206
Pramesh Subedi
MWF 03:15PM-04:30PM
Clapp Laboratory 407
Laurie Tupper
MWF 11:30AM-12:45PM
Clapp Laboratory 407
Marie Ozanne
MWF 01:45PM-03:00PM
Clapp Laboratory 402
Amy Nussbaum
TTH 10:00AM-11:15AM
Clapp Laboratory 402
Ileana Streinu
M W F 1:20 PM - 2:35 PM
Ford 345
Ileana Streinu
M W F 1:20 PM - 2:35 PM
Ford 345
Luca Capogna
TU TH 2:45 PM - 4:00 PM
Burton 301
Fall 2022 Biomathematics Courses
Josef G. Trapani
TTH 11:30 AM-12:50 PM
(Offered as BIOL 351 and NEUR 351) This laboratory course will provide a deeper understanding of the physiological properties of the nervous system. We will address the mechanisms underlying electrical activity in neurons, as well as examine the physiology of synapses; the transduction and integration of sensory information; the function of nerve circuits; the trophic and plastic properties of neurons; and the relationship between neuronal activity and behavior. Laboratories will apply electrophysiological methods to examine neuronal activity and will include experimental design as well as analysis and presentation of collected data. Throughout the course, we will focus on past and current neurophysiology research and how it contributes to the field of neuroscience. Three hours of laboratory work per week.
Requisites: BIOL 191 and CHEM 151; PHYS 117 or 124 is recommended. Limited to one lab section with 18 students. Open to juniors and seniors. Fall semester. Professor Trapani.
Josef G. Trapani
W 02:00 PM-05:00 PM
Lee Spector
TTH 02:30 PM-03:50 PM
Machine Learning algorithms allow computers to be taught to perform tasks without being explicitly programmed. This course is an introduction to machine learning and data mining. The course will explore supervised, unsupervised, ensemble and reinforcement learning. Topics may include: decision tree learning, rule learning, neural networks, support vector machines, Bayesian learning, clustering, hidden Markov model learning, and/or deep learning. The material of this course has some overlap with that of Computer Science 241, but it is permissible to take both.
Requisite: COSC-211. Fall Semester: Professor Spector.
Katherine E. Moore
MWF 10:00 AM-10:50 AM; TH 09:00 AM-09:50 AM
The study of vector spaces over the real and complex numbers, introducing the concepts of subspace, linear independence, basis, and dimension; systems of linear equations and their solution by Gaussian elimination; matrix operations; linear transformations and their representations by matrices; eigenvalues and eigenvectors; and inner product spaces. This course will feature both proofs and applications, with special attention paid to applied topics such as least squares and singular value decomposition. Four class hours per week, with occasional in-class computer labs.
Requisite: MATH 121 or consent of the instructor. This course and MATH 271 may not both be taken for credit. Limited to 25 students. Fall and Spring semester: The Department.
Pamela B. Matheson
MWF 09:00 AM-09:50 AM
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 semester: Professors Matheson and Horton.
Pamela B. Matheson
MWF 10:00 AM-10:50 AM
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 semester: Professors Matheson and Horton.
Brittney E. Bailey
TTH 08:30 AM-09:50 AM
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.
Brittney E. Bailey
TTH 11:30 AM-12:50 PM
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.
Amy S. Wagaman
MWF 11:00 AM-11:50 AM
Making sense of a complex, high-dimensional data set is not an easy task. The analysis chosen is ultimately based on the research question(s) being asked. This course will explore how to visualize and extract meaning from large data sets through a variety of analytical methods. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering). Additional methods covered may include factor analysis, dimension reduction methods, or network analysis at instructor discretion. This course will feature hands-on data analysis with statistical software, emphasizing application over theory.
The course is expected to include small group work, interactive labs, peer interactions such as peer review and short presentations, and a personal project, to foster student engagement in the course and with each other.
Requisite: STAT 111 or 135. Limited to 24 students. Fall semester. Omitted 2021-22. Professor Wagaman.
Martha Hoopes
MWF 11:30AM-12:45PM
Martha Hoopes,Molly McCutcheon
M 01:30PM-04:20PM
Clapp Laboratory 008
Molly McCutcheon,Martha Hoopes
T 01:30PM-04:20PM
Clapp Laboratory 008
Martha Hoopes,Molly McCutcheon
W 01:30PM-04:20PM
Clapp Laboratory 008
Molly McCutcheon,Martha Hoopes
TH 01:30PM-04:20PM
Clapp Laboratory 008
Timothy Chumley
MWF 10:00AM-11:15AM
Timothy Chumley
MWF 11:30AM-12:45PM
Samantha Kirk
MWF 08:30AM-09:45AM
Samantha Kirk
MWF 01:45PM-03:00PM
Dylan Shepardson
MWF 03:15PM-04:30PM
Samantha Kirk
MWF 03:15PM-04:30PM
Isabelle Beaudry
MWF 08:30AM-09:45AM
Instructor To Be Announced
MWF 11:30AM-12:45PM
Pramesh Subedi
MWF 10:00AM-11:15AM
Instructor To Be Announced
MWF 01:45PM-03:00PM
Isabelle Beaudry
MWF 11:30AM-12:45PM
Instructor To Be Announced
MWF 10:00AM-11:15AM
Laurie Tupper
MWF 10:00AM-11:15AM
Laurie Tupper
MWF 01:45PM-03:00PM
Rob Dorit
TU TH 10:50 AM - 12:05 PM
Bass 210
Katherine M. Kinnaird
M W 10:50 AM - 12:05 PM
Bass 002
Pau Atela
TU TH 2:45 PM - 4:00 PM
Burton 301
Shiya Cao
M W F 10:50 AM - 12:05 PM
Stoddard G2
Lindsay Poirier
M W 9:25 AM - 10:40 AM
McConnell 404
Jared Joseph
W F 1:20 PM - 2:35 PM; M 1:40 PM - 2:55 PM
Stoddard G2
Ben Baumer
M W F 10:50 AM - 12:05 PM
Sabin-Reed 301
Kaitlyn Cook
M 1:40 PM - 2:55 PM; W F 1:20 PM - 2:35 PM
Sabin-Reed 301
TU TH 10:50 AM - 12:05 PM
Seelye 301
Katherine M. Kinnaird
M W F 9:25 AM - 10:40 AM
Sabin-Reed 301
Contact Us
Amherst College Certificate Advisor:
Tanya Leise, Brian Boyle Professor in Mathematics and Computer Science, Amherst College
Hampshire College Certificate Advisor:
Sarah Hews, Associate Professor of Mathematics, Hampshire College & Visiting Assistant Professor of Mathematics & Statistics, Amherst College (on leave 2021-22)
Mount Holyoke College Certificate Advisor:
Martha Hoopes, Professor of Biological Sciences, Mount Holyoke College
Smith College Certificate Advisor:
Christophe Golé, Professor of Mathematics & Statistics, Smith College
Five College Staff Liaison:
Ray Rennard, Director of Academic Programs