Harvey Mudd College Biomedical Engineering

Biology Major Programs

Goals and Learning Outcomes

The focus of Harvey Mudd’s biology program is on preparing students for professional practice in diverse areas related to biology. Following graduation the majority of our students go on to PhD programs in the life sciences or into the workforce in technical fields. With these outcomes in mind, our curriculum is structured to provide students with the general technical and communication skills required to succeed in a broad range of scientific settings. Upon graduation from our program we expect HMC students to:

  • Understand fundamental principles of biology and how we know what we know.
  • Appreciate the breadth of biology, its interfaces with other disciplines, and its impact on society.
  • Be able to take intellectual and practical ownership of their work and demonstrate maturity and responsibility as scientists.

To achieve these broad goals effectively, we believe all biology majors should be able to:

  1. understand and effectively communicate the foundational scientific principles and findings in biology;
  2. read and critically interpret the primary scientific literature;
  3. formulate hypotheses and plan and execute experiments to test those hypotheses;
  4. understand the use of and be able to apply quantitative methods to interpret biological data;
  5. communicate results in writing using conventional scientific formats;
  6. communicate results orally through formal presentations and by leading and participating in discussions;
  7. synthesize ideas from multiple sources into a literature review or research proposal;
  8. demonstrate teamwork and leadership skills;
  9. demonstrate an understanding of how biology relates to current issues in the world.

The biology curriculum includes five primary components, each of which addresses a subset of these learning outcomes:

Core lecture courses. All biology majors take a required set of lecture courses designed to cover the fundamental principles of biology and to instill an appreciation for the breadth of biology. Each of these core courses also begins to introduce students to primary literature in the field (learning outcomes 1, 2, 9).

Laboratory courses. All biology majors take a required Introductory Laboratory course and at least two upper level laboratories. Laboratory courses are designed to serve learning outcomes 3, 4, 5, 6 and 8.

Seminar courses. All biology majors are required to complete at least one seminar-style course, defined as a course in which material is covered primarily by discussion of readings from the primary literature and students give substantial oral presentations and write a significant synthetic paper. Seminar courses address learning outcomes 2, 6, 7 and (often) 9.

Colloquium. Weekly research presentations by invited speakers from other institutions increase the breadth of biological topics to which students are exposed, encourage critical thinking, and model professional communication skills, serving learning outcomes 1, 2, 6 and 9.

Senior thesis. As seniors, all biology majors put learning outcomes 1-9 into practice by completing a year-long independent research project or team clinic project.

Major Requirements

A biology major must successfully complete the following courses:

Biology Core

  •  BIOL054 HM – Experimental Biology Laboratory
  •  BIOL154 HM – Biostatistics
  •  BIOL101 HM – Comparative Physiology
  •  BIOL108 HM – Ecology and Environmental Biology
  •  BIOL109 HM – Evolutionary Biology
  •  BIOL113 HM – Molecular Genetics
  •  CHEM056 HM – Organic Chemistry I
  •  CHEM058 HM – Organic Chemistry I Laboratory
  •  CHEM105 HM – Organic Chemistry II

Biology Electives

Eleven credits of advanced biology, selected by the student and advisor. Electives must include:

At least two Harvey Mudd laboratory courses, selected from:

  •  BIOL103 HM – Comparative Physiology Laboratory
  •  BIOL110 HM – Experimental Ecology Laboratory
  •  BIOL111 HM – Molecular and Cellular Biology Laboratory
  •  BIOL184 HM – Biochemistry Laboratory

and

One Harvey Mudd seminar-style course (requiring student presentations and reading from the primary literature), some examples include:

  •  BIOL121 HM – Marine Ecology
  •  BIOL129 HM – Topics in Human Evolution
  •  BIOL183 HM – Topics in Physiology
  •  BIOL185 HM – Special Topics in Biology (most offerings)
  •  BIOL189 HM – Topics in Biochemistry and Molecular Biology (most offerings)

Related non-biology technical courses may be substituted for advanced biology courses with permission of the department. With prior departmental permission, up to two credits of Biology 197 may count as Biology Electives.

  •  BIOL197 HM – Directed Reading in Biology

Colloquium

Four semesters of Biology Colloquium:

  •  BIOL191 HM – Biology Colloquium

The colloquium requirement is waived for any semester during which a student is away on a study abroad program.

Harvey Mudd Computer Science

The Harvey Mudd College Department of Computer Science graduated its first class in 1992. Since then, the department has grown to its current size of eighteen tenure-track faculty, several visiting or adjunct faculty, and five staff members.

In addition to the Computer Science Major, the department supports the Joint Major in Computer Science and Mathematics and the Mathematical and Computational Biology Major. There are currently approximately 80 students total per graduating class in these three majors.

The department works closely with our sister departments at Pomona and Claremont McKenna Colleges and our courses draw students from all five Claremont Colleges and the Claremont Graduate University.

The department seeks to provide students with a strong foundational background blending experimentation, theory, and design. Our graduates are equally well-prepared for work in industry and graduate school. The capstone Clinic Program provides students with a year-long software design project and our active Research Program involves approximately 50 students in research, funded by grants and gifts from the National Science Foundation, Howard Hughes Medical Institute, and the Rose-Hills and Baker Foundations.» Learn More» Learn More

Department News

  • [September 2, 2021] Along with Professor George Montañez, students Eric M. Weiner ’21,  Aaron Trujillo ’21, Abtin Molavi ’21 published a paper titled “Hyperparameter Choice as Search Bias in AlphaZero” in the 2021 IEEE International Conference on Systems, Man, and Cybernetics. The paper looks at how the choice of a hyperparameter in the deep reinforcement learning system AlphaZero acts as a biasing mechanism, allowing human input into what has been argued to be a learning system that lacks human input.
  • [August 16, 2021] Along with Professors Yi-Chieh (Jessica) Wu and Ran Libeskind-Hadas, student Matthew LeMay ’21 published a paper titled “The Most Parsimonious Reconciliation Problem in the Presence of Incomplete Lineage Sorting and Hybridization is NP-Hard” in the Workshop on Algorithms in Bioinformatics (WABI) 2021. Matthew presented the paper.
  • [August 16, 2021] Along with Professors Yi-Chieh (Jessica) Wu and Ran Libeskind-Hadas, student Matthew LeMay ’21 published a paper titled “A Polynomial-Time Algorithm for Minimizing the Deep Coalescence Cost for Level-1 Species Networks” in the IEEE/ACM Transactions on Computational Biology and Bioinformatics. 
  • [July 16, 2021] Along with Professor George Montañez, students Amani Maina-Kilaas ’23, Cynthia Hom ’23, Cindy Lay CMC ’22 and Kevin Ginta (Biola University, ’21) will present a paper titled “The Hero’s Dilemma: Survival Advantages of Intention Perception in Virtual Agent Games” at the 2021 IEEE Conference on Games.
  • Along with Professor George Montañez, students Jonathan Hayase ’20, Julius Lauw  ’20, Dominique Macias ’19, Akshay Trikha ’21 and Julia Vendemiatti ’21 published a paper titled “The Futility of Bias-Free Learning and Search.” Learning algorithms are machines that turn data resources into predictions. Their paper shows that unless algorithms do this conversion in a biased way, predisposing their predictions toward predetermined outcomes, they cannot predict any more accurately than random guessing. The paper proves that finding a good bias for a given problem is difficult, when searching among any set of data resources that on average isn’t itself positively biased. These results apply to machine learning algorithms, AI systems, genetic learning algorithms, and many other forms of search and optimization.
  • Mara Downing, Chris Thompson and Professor Lucas Bang had their paper titled “Automatically Solving Deduction Games via Symbolic Execution, Model Counting, and Entropy Maximization” accepted at the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Strategy Game Workshop. Mara and Chris designed a DSL for expressing a class of puzzles called “deduction games”, implemented a symbolic execution engine for it using an automated theorem prover, and then wrote and entropy maximizer that outputs the steps of game solution. The main takeaway is that you can give their system the source code of a game and it will then automatically solve the game, playing it in real time.
  • Professor Geoff Kuenning co-authored the paper “Graphs Are Not Enough: Using Interactive Visual Analytics in Storage Research”, which appeared in the Usenix HotStorage ’19 Workshop. The paper presents a visualization tool that helps system designers and experimenters explore the vast number of possibilities available (often millions or more) when configuring storage systems. The tool makes it easy to “zero in” on the parameters that have the most impact on performance in a chosen situation, so that an analyst can quickly find the best settings for a given environment.
  • The International Conference on Automated Planning and Scheduling (ICAPS) has accepted three papers co-authored by Professor Jim Boerkoel with students working in his HEATLab (the Human Experience & Agent Teamwork Lab).  The papers are:  “Quantifying Degrees of Controllability in Temporal Networks with Uncertainty”, written with Shyan Akmal ’19, Savana Ammons ’20, and Maggie Li ’19;  “Measuring and Optimizing Durability Against Scheduling Disturbances”, written with Joon Lee ’20 and Viva Ojha ’19; and “Reducing the Computational and Communication Overhead of Robust Agent Rescheduling”, written with Jordan Abrahams ’19 and co-author Jeremy Frank. The papers will be presented at the ICAPS conference in Berkeley, CA in Summer 2019.