Majors

Courses

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CSCI 005: Introduction to Computer Science (3)

Introduction to elements of computer science. Students learn computational problem-solving techniques and gain experience with the design, implementation, testing, and documentation of programs in a high-level language. In addition, students learn to design digital devices, understand how computers operate, and learn to program in a small machine language. Students are also exposed to ideas in computability theory. The course also integrates societal and ethical issues related to computer science.

CSCI 005GR: Introduction to Biology and Computer Science (3)

This course introduces fundamental concepts from the Core course CSCI 005 using biology as the context for those computational ideas. Students see both the intellectual and practical connections between these two disciplines and write computer programs to explore biological phenomena. Biology topics include the basics of biochemistry, the central dogma, population genetics, molecular evolution, metabolism, regulation, and phylogenetics. Computer science material includes basic data types and control structures, recursion, dynamic programming, and an introduction to automata and computability. This course fulfills the computer science Core requirement at Harvey Mudd College. It does not fulfill the Harvey Mudd biology Core requirement.

CSCI 035: Computer Science for Insight (3)

This course extends CSCI 005 in developing software-composition skills. Pairing lectures and lab sessions, the experience will deepen foundations in algorithms and data structures, introduce machine learning and its mindset, weigh tradeoffs between human- and machine-efficiency, and build sophistication in software, both assembling existing software packages and from-scratch design. Students will deploy and assess computing projects of their own design – with substantive application beyond CS itself – as the course’s final capstone. The course continues in the language of CSCI 005 and especially encourages computing efforts which contribute to fields of interest beyond CS, whether academic or extracurricular.

CSCI 042: Principles and Practice of Computer Science (3)

Accelerated breadth-first introduction to computer science as a discipline for students (usually first-year) who have a strong programming background. Computational models of functional and object-oriented programming. Data structures and algorithm analysis. Computer logic and architecture. Computability. Extensive practice constructing applications from principles, using a variety of languages. Successful completion of this course satisfies the CSCI 005 Core requirement and CSCI 060 coursework.

CSCI 049: Special Topics in Computer Science (1.53)

Computer Science seminar on a special topic of general interest to the broader HMC and 5C community. Cannot be taken for Computer Science major elective credit.

CSCI 060: Principles of Computer Science (3)

Introduction to principles of computer science: Information structures, functional programming, object-oriented programming, grammars, logic, correctness, algorithms, complexity analysis, and theoretical limitations. Those who have completed CSCI 042 cannot take CSCI 060.

CSCI 070: Data Structures and Program Development (3)

Abstract data types including priority queues and dynamic dictionaries and efficient data structures for these data types, including heaps, self-balancing trees, and hash tables. Analysis of data structures including worst-case, average-case and amortized analysis. Storage allocation and reclamation. Secondary storage considerations. Extensive practice building programs for a variety of applications.

CSCI 081: Computability and Logic (3)

An introduction to some of the mathematical foundations of computer science, particularly logic, automata, and computability theory. Develops skill in constructing and writing proofs, and demonstrates the applications of the aforementioned areas to problems of practical significance.

CSCI 105: Computer Systems (3)

An introduction to computer systems. In particular, the course investigates data representations, machine level representations of programs, processor architecture, program optimizations, the memory hierarchy, exceptional control flow (exceptions, interrupts, processes and Unix signals), performance tuning, caches and virtual memory, system-level I/O, networking, and basic concurrent programming. These concepts are supported by a series of hands-on lab assignments.

CSCI 111: Domain-Specific Languages (3)

This course explores how to design a new programming language. In particular, we’ll focus on “Domain-Specific Languages”— languages designed for people who want to use a computer to perform a specialized task (e.g., to compose music or query a database or make games). Through readings, discussions, and programming, we’ll investigate why and how you would create a domain-specific language. The course also features a project that asks you to propose, design, and implement your own domain-specific language.

CSCI 121: Software Development (3)

Introduction to the discipline concerned with the design and implementation of software systems. The course presents a historical perspective on software development practice and explores modern, agile techniques for eliciting software requirements, designing and implementing software architecture and modules, robust testing practices, and project management. Student teams design, develop, and test a substantial software project.

CSCI 123: Computing Practices, Projects, and People (3)

This course dives into the technical and professional skills necessary to plan, execute, document, and present computational projects beyond a classroom. A central focus of the course is a team-based project to develop a tutorial for an existing software tool or API. A variety of exercises will help explore and build literacy in common tools and workflows in a professional computing environment. Additionally, students will discuss human-human interactions around negotiation, conflict management, peer review of both code and written work, and ethical decision-making.

CSCI 124: Interaction Design (3)

This course introduces students to issues in the design, implementation, and evaluation of human-computer interfaces, with emphasis on user-centered design and graphical interfaces. In this course, students learn skills that aid them in choosing the right user interaction technique and developing an interface that is well-suited to the people for whom it is designed.

CSCI 125: Computer Networks (3)

Principles and analysis techniques for internetworking. Analysis of networking models and protocols. Presentation of computer communication with emphasis on protocol architecture.

CSCI 131: Programming Languages (3)

A thorough examination of issues and features in language design and implementation including language-provided data structuring and data-typing, modularity, scoping, inheritance, and concurrency. Compilation and run-time issues. Introduction to formal semantics.

CSCI 132: Compiler Design (3)

The design and implementation of compilers. Topics include elegant theoretical results underlying compilation techniques, practical issues in efficient implementation of programming languages, and bit-level interactions with operating systems and computer architectures. Over the course of the semester, students build a working compiler.

CSCI 133: Database Systems (3)

Fundamental models of databases: entity-relationship, relational, object-oriented. Relational algebra and calculus, query languages. Data storage, caching, indexing, and sorting. Locking protocols and other issues in concurrent and distributed databases.

CSCI 134: Operating Systems: Design and Implementation (3)

Design and implementation of operating systems, including processes, memory management, synchronization, scheduling, protection, file systems, and I/O. These concepts are used to illustrate wider concepts in the design of other large software systems, including simplicity; efficiency; event-driven programming; abstraction design; client-server architecture; mechanism vs. policy; orthogonality; naming and binding; static vs. dynamic, space vs. time, and other trade-offs; optimization; caching; and managing large code bases. Group projects provide experience in working with and extending a real operating system.

CSCI 137: File Systems (3)

Computer storage and file systems. Characteristics of nonvolatile storage, including magnetic disks and solid-state memories. RAID storage. Data structures used in file systems. Performance, reliability, privacy, replication, and backup. A major portion of the course is devoted to readings selected from current research in the field.

CSCI 140: Algorithms (3)

Algorithm design, analysis, and correctness. Design techniques including divide-and-conquer and dynamic programming. Analysis techniques including solutions to recurrence relations and amortization. Correctness techniques including invariants and inductive proofs. Applications including sorting and searching, graph theoretic problems such as shortest path and network flow, and topics selected from arithmetic circuits, parallel algorithms, computational geometry, and others. An introduction to computational complexity, NP-completeness, and approximation algorithms. Proficiency with programming is expected as some assignments require algorithm implementation.

CSCI 142: Complexity Theory (3)

Brief review of computability theory through Rice’s Theorem and the Recursion Theorem followed by a rigorous treatment of complexity theory. The complexity classes P, NP, and the Cook-Levin Theorem. Approximability of NP-complete problems. The polynomial hierarchy, PSPACE-completeness, L and NL-completeness, #P-completeness. IP and Zero-knowledge proofs. Randomized and parallel complexity classes. The speedup, hierarchy, and gap theorems.

CSCI 144: Scientific Computing (3)

Computational techniques applied to problems in the sciences and engineering. Modeling of physical problems, computer implementation, analysis of results; use of mathematical software; numerical methods chosen from: solutions of linear and nonlinear algebraic equations, solutions of ordinary and partial differential equations, finite elements, linear programming, optimization algorithms, and fast Fourier transforms.

CSCI 145: Advanced Topics in Algorithms (1.5)

The objective of this course is to explore sophisticated algorithm design and analysis techniques that are generally not taught in a first algorithms course. The course addresses topics such as graph matching, competitive analysis of online algorithms, matroid theory, and approximation algorithms and schemes.

CSCI 151: Artificial Intelligence (3)

This course presents a general introduction to the field of Artificial Intelligence. It examines the question: What does (will) it take for computers to perform human tasks? It presents a broad introduction to topics such as knowledge representation, search, learning and reasoning under uncertainty. For each topic, it examines real-world applications of core techniques to problems which may include game playing, text classification and visual pattern recognition.

CSCI 152: Neural Networks (3)

Modeling, simulation, and analysis of artificial neural networks and their relation to biological networks. Design and optimization of discrete and continuous neural networks. Back propagation and other gradient descent methods. Hopfield and Boltzmann networks. Unsupervised learning. Self-organizing feature maps. Applications chosen from function approximation, signal processing, control, computer graphics, pattern recognition, time-series analysis. Relationship to fuzzy logic, genetic algorithms, and artificial life.

CSCI 153: Computer Vision (3)

Computational algorithms for visual perception. Students will develop applications that acquire, process and interpret still images and image streams. The course will cover representations of color, shading, texture and shape along with stereo and motion analysis, object recognition and approaches for three-dimensional representation. Applications include robotics, human perception and the use of large image databases.

CSCI 155: Computer Graphics (3)

This course is an introduction to the major concepts in modern computer graphics. Students will become familiar with the technical challenges posed by the capture, display, and generation of digital images. Important concepts such as the role of specialized hardware, trade-offs in physical realism and rendering time, and the critical reading and analysis of graphics literature will be introduced.

CSCI 158: Machine Learning (3)

Machine learning is concerned with the study and development of systems that learn patterns in data. This course introduces the most common problems in the field and the techniques used to tackle these problems, with a focus on supervised and unsupervised learning. Concepts include mathematical foundations and algorithmic approaches.

CSCI 159: Natural Language Processing (3)

An introduction to the fundamental concepts and ideas in natural language processing, sometimes called computational linguistics. The goals of the field range from text translation and understanding to enabling humans to converse with robots. We will study language processing starting from the word level to syntactic structure to the semantic meaning of text. Approaches include structured and statistical methods, as well as exploration of current natural language research. Students will build and modify systems and will use large existing corpora for validating their systems.

CSCI 181: Computer Science Seminar (13)

Advanced topics of current interest in computer science.

CSCI 183: Computer Science Clinic I (3)

The Clinic Program brings together teams of students to work on a research problem sponsored by business, industry, or government. Teams work closely with a faculty advisor and a liaison provided by the sponsoring organization to solve complex real-world problems. Students are expected to present their work orally and to produce a final report conforming to professional publication standards. CSCI 183 HM and CSCI 184 must be taken consecutively in the same academic year to count toward the major.

CSMT 183: Computer Science and Mathematics Clinic I (3)

Team project in joint computer science and mathematics, with corporate affiliation. CSMT 183 and CSMT 184 must be taken consecutively to count toward the major.

CSCI 184: Computer Science Clinic II (3)

The Clinic Program brings together teams of students to work on a research problem sponsored by business, industry, or government. Teams work closely with a faculty advisor and a liaison provided by the sponsoring organization to solve complex real-world problems. Students are expected to present their work orally and to produce a final report conforming to professional publication standards. CSCI 183 and CSCI 184 HM must be taken consecutively in the same academic year to count toward the major.

CSMT 184: Computer Science and Mathematics Clinic II (3)

Team project in joint computer science and mathematics, with corporate affiliation. CSMT 183 and CSMT 184 must be taken consecutively to count toward the major.

CSCI 186: Computer Science Research and Independent Study (0.53)

A research or development project under computer science faculty supervision. No more than 3 units can count toward major elective credit.

CSCI 189: Programming Practicum (1)

This course is a weekly programming seminar, emphasizing efficient recognition of computational problems and their difficulty, developing and implementing algorithms to solve them, and the testing of those implementations. Attention is given to the effective use of programming tools and available libraries, as well as to the dynamics of team problem-solving. No more than 3 credits can count toward the major elective requirement.

CSCI 195: Computer Science Colloquium (0.5)

Oral presentations and discussions of selected topics, including recent developments in computer science. Participants include computer science majors, Clinic participants, faculty members, and visiting speakers. No more than 2.0 credits can be earned for departmental seminars/colloquia. All majors welcome.

MATH 019: Single and Multivariable Calculus (4)

A comprehensive view of the theory and techniques of differential and integral calculus of a single variable together with a robust introduction to multivariable calculus. Topics include limits, continuity, derivatives, definite integrals, infinite series, Taylor series in one and several variables, partial derivatives, double and triple integrals, linear approximations, the gradient, directional derivatives and the Jacobian, optimization and the second derivative test, higher-order derivatives and Taylor approximations, line integrals, vector fields, curl, divergence, Green’s theorem, and an introduction to flux and surface integrals.

MATH 055: Discrete Mathematics (3)

Topics include combinatorics (clever ways of counting things), number theory, and graph theory with an emphasis on creative problem solving and learning to read and write rigorous proofs. Possible applications include probability, analysis of algorithms, and cryptography.

MATH 062: Introduction to Probability and Statistics (3)

Sample spaces, events, axioms for probabilities; conditional probabilities and Bayes’ theorem; random variables and their distributions, discrete and continuous; expected values, means and variances; covariance and correlation; law of large numbers and central limit theorem; point and interval estimation; hypothesis testing; simple linear regression; applications to analyzing real data sets. Possible additional topics include ANOVA, multiple regression, and logistic regression.

MATH 073: Linear Algebra (3)

Theory and applications of linearity, including vectors, matrices, systems of linear equations, dot and cross products, determinants, linear transformations in Euclidean space, linear independence, bases, eigenvalues, eigenvectors, and diagonalization. General vector spaces and linear transformations; change of basis and similarity. Additional Topics.

MATH 082: Differential Equations (3)

Modeling physical systems, first-order ordinary differential equations, existence, uniqueness, and long-term behavior of solutions; bifurcations; approximate solutions; second-order ordinary differential equations and their properties, applications; first-order systems of ordinary differential equations. Applications to linear systems of ordinary differential equations, matrix exponential; nonlinear systems of differential equations; equilibrium points and their stability. Additional topics.

MATH 119: Advanced Mathematical Biology (3)

Further study of mathematical models of biological processes, including discrete and continuous models. Examples are drawn from a variety of areas of biology, which may include physiology, systems biology, cancer biology, epidemiology, ecology, evolution, and spatiotemporal dynamics. (Crosslisted as BIOL 119)

MATH 131: Mathematical Analysis I (3)

This course is a rigorous analysis of the real numbers and an introduction to writing and communicating mathematics well. Topics include properties of the rational and the real number fields, the least upper bound property, induction, countable sets, metric spaces, limit points, compactness, connectedness, careful treatment of sequences and series, functions, differentiation and the mean value theorem, and an introduction to sequences of functions. Additional topics as time permits.

MATH 171: Abstract Algebra I (3)

Groups, rings, fields, and additional topics. Topics in group theory include groups, subgroups, quotient groups, Lagrange’s theorem, symmetry groups, and the isomorphism theorems. Topics in Ring theory include Euclidean domains, PIDs, UFDs, fields, polynomial rings, ideal theory, and the isomorphism theorems. In recent years, additional topics have included the Sylow theorems, group actions, modules, representations, and introductory category theory.

MATH 198: Undergraduate Mathematics Forum (1)

The goal of this course is to improve students’ ability to communicate mathematics, both to a general and technical audience. Students will present material on assigned topics and have their presentations evaluated by students and faculty. This format simultaneously exposes students to a broad range of topics from modern and classical mathematics. Required for all majors; recommended for all joint CS-math majors and mathematical biology majors, typically in the junior year.

MATH 199: Mathematics Colloquium (0.5)

Students will attend weekly Claremont Math Colloquium, offered through the cooperative efforts of the mathematics faculty at The Claremont Colleges. Most of the talks discuss current research in mathematical sciences and are accessible to undergraduates. No more than 2.0 credits can be earned for departmental seminars/colloquia.

ENGR 079: Introduction to Engineering Systems (4)

An introduction to the concepts of modern engineering, emphasizing modeling, analysis, synthesis, and design. Applications to chemical, mechanical, and electrical systems. A course materials fee, payable to the HMC Department of Engineering, applies. No textbook purchase required.

BIOL 023: Biology Laboratory (1)

Application of molecular biology techniques to problems in human genetics, bioengineering, and environmental sensing.

BIOL 046: Introduction to Biology (3)

Topics in ecology, evolution, molecular genetics, and computational biology.

BIOL 054: Experimental Biology Laboratory (1)

Investigations in physiology, biochemistry, ecology, molecular biology, and other areas of experimental biology.

BIOL 101: Comparative Physiology (3)

The general aim will provide a broad introduction to comparative physiology. Students will learn the links between cellular & molecular mechanisms, organ systems, and organismal function in animals. Students will examine the relationship between structure and function in biology. During the process, students will be introduced to the diversity of animals and the scientific tools used to make physiological measurements.

BIOL 108: Ecology and Environmental Biology (3)

Principles of organization of natural communities and ecosystems, including population dynamics, species interactions, and island biogeography. Modern experimental and mathematical approaches to ecological problems. Application of ecological principles to conservation biology, climate change, and other environmental impacts.

BIOL 109: Evolutionary Biology (3)

Evolutionary mechanisms, including natural selection, population genetics, speciation, and macroevolutionary processes. Modern methods of phylogenetic reconstruction. History of biological diversity and the fossil record.

BIOL 113: Molecular Genetics (3)

Molecular description of gene function in both prokaryotic and eukaryotic cells, including DNA, RNA, and protein structure; DNA replication; transcription and translation; and gene regulation.

MCBI 118A: Introduction to Mathematical Biology (1.5)

An introduction to the field of mathematical biology. Continuous and discrete mathematical models of biological processes and their analytical and computational solutions. Examples may include models in epidemiology, ecology, cancer biology, systems biology, molecular evolution, and phylogenetics.

MCBI 118B: Introduction to Computational Biology (1.5)

An introduction to the field of computational biology. Algorithms for phylogenetic inference and computational methods for solving problems in molecular evolution and population genetics.

BIOL 119: Advanced Mathematical Biology (3)

Advanced study of mathematical models of biological processes, including discrete and continuous models. Examples are drawn from a variety of areas of biology, which may include physiology, systems biology, cancer biology, epidemiology, ecology, evolution, and spatiotemporal dynamics. (Crosslisted as MATH 119)

BIOL 154: Biostatistics (3)

Statistical techniques for analyzing biological data, including parametric, nonparametric, and randomization methods. Statistical aspects of experimental design with an emphasis on analyzing data collected in BIOL 054.

BIOL 188: Advanced Computational Biology (3)

Computational algorithms and methods used in the study of genomes. Lectures, discussions, and computer laboratory exercises.

BIOL 191: Biology Colloquium (0.5)

Oral presentations and discussions of selected topics including recent developments. Participants include biology majors, faculty members, and visiting speakers. Required for junior and senior biology majors. No more than 2.0 credits can be earned for departmental seminars/colloquia.

MCBI 199: Joint Colloquium for the Mathematical and Computational Biology Major (0.5)

Students registered for joint colloquium must attend a fixed number of colloquium talks during the semester in any field(s) related to their interests. The talks may be at any members of The Claremont Colleges or a nearby university and may be in any of a wide array of fields including biology, mathematics, computer science and other science and engineering disciplines including bioengineering, cognitive science, neuroscience, biophysics, and linguistics. Students enrolled in the joint colloquium are required to submit a short synopsis of each talk that they attend. No more than 2.0 credits can be earned for departmental seminars/colloquia.

CHEM 024: Chemistry Laboratory (1)

Applications of thermodynamics, equilibria, electrochemistry, structure/property relationships, synthesis and spectroscopy.

CHEM 042: Chemistry in the Modern World (4)

Chemistry plays a powerful role in addressing an array of current and future global and societal challenges. This course examines contemporary applications of chemistry to describe innovative advances in such areas as energy, medicine, technology, materials, to name a few. These applications illustrate such fundamental concepts as molecular and electronic structure in dictating chemical and physical properties; intermolecular forces, phase behavior, thermodynamics, electrochemistry, kinetics, and equilibria. Lecture and individual and group exercises conducted in class are used as a context for introducing chemistry principles.

PHYS 023: Special Relativity (1.5)

Einstein’s special theory of relativity is developed from the premises that the laws of physics are the same in all inertial frames and that the speed of light is a constant. The relationship between mass and energy is explored and relativistic collisions analyzed. The families of elementary particles are described and the equivalence principle developed.

PHYS 024: Mechanics and Wave Motion (4)

Classical mechanics is introduced beginning with inertial frames and the Galilean transformation, followed by momentum and momentum conservation in collisions, Newton’s laws of motion, spring forces, gravitational forces and friction. Differential and integral calculus are used extensively throughout. Work, kinetic energy and potential energy are defined, and energy conservation is discussed in particle motion and collisions. Rotational motion is treated, including angular momentum, torque, cross-products and statics. Other topics include rotating frames, pseudoforces and central-force motion. Simple harmonic and some nonlinear oscillations are discussed, followed by waves on strings, sound and other types of waves, and wave phenomena such as standing waves, beats, two-slit interference, resonance and the Doppler effect.

PHYS 050: Physics Laboratory (1)

This course emphasizes the evidence-based approach to understanding the physical world through hands-on experience, experimental design, and data analysis. Experiments are drawn from a broad range of physics subjects, with applications relevant to modern society and technology.

PHYS 051: Electromagnetic Theory and Optics (3)

An introduction to electricity and magnetism leading to Maxwell’s electromagnetic equations in differential and integral form. Selected topics in classical and quantum optics.

PHYS 052: Quantum Physics (3)

The development and formulation of quantum mechanics, and the application of quantum mechanics to topics in atomic, solid state, nuclear, and particle physics.

PHYS 054: Modern Physics Laboratory (1)

Classical experiments of modern physics, including thermal radiation and Rutherford scattering. Nuclear physics experiments, including alpha, beta and gamma absorption, and gamma spectra by pulse height analysis. Analysis of the buildup and decay of radioactive nuclei.

PHYS 064: Mathematical and Computational Methods for Physicists (3)

This course combines mathematical and computational methods that are useful for studying physical systems. Topics include: Linear algebra, Hilbert spaces, the eigenvalue problem and numerical algorithms for solving problems in linear algebra, including various modes of decomposition; Fourier series and transforms, convolution, correlation and numerical methods using fast Fourier transforms; computer simulation methods based on integrating coupled differential equations and also using pseudorandom numbers, including Monte Carlo methods; partial differential equations, separation of variables, Laplace and Poisson equations in various dimensions, the wave equation, and numerical approaches to solution.

PHYS 084: Quantum Information (3)

Quantum computation and communication. Fundamentals of discrete-state quantum mechanics as appropriate for quantum information science. Possible topics include universal logic gates for quantum computing, quantum computing algorithms, quantum error correction, quantum cryptography and communication, adiabatic quantum computing, and hardware platforms for quantum computation and communication.

PHYS 111: Theoretical Mechanics (3)

The application of mathematical methods to the study of particles and of systems of particles; Newton, Lagrange, and Hamilton equations of motion; conservation theorems; central force motion, collisions, damped oscillators, rigid body dynamics, systems with constraints, variational methods.

PHYS 116: Quantum Mechanics (3)

The elements of nonrelativistic quantum mechanics. Topics include the general formalism, one-dimensional and three-dimensional problems, angular momentum states, perturbation theory and identical particles. Applications to atomic and nuclear systems.

PHYS 117: Statistical Mechanics and Thermodynamics (3)

Classical and quantum statistical mechanics, including their connection with thermodynamics. Kinetic theory of gases. Applications of these concepts to various physical systems.

PHYS 133: Electronics Laboratory (1)

An intermediate laboratory in electronics involving the construction and analysis of rectifiers, filters, transistor and operational amplifier circuits.

PHYS 134: Optics Laboratory (2)

A laboratory-lecture course on the techniques and theory of classical and modern optics. Topics of study include diffraction, interferometry, Fourier transform spectroscopy, grating spectroscopy, lasers, quantum mechanics and quantum optics, coherence of waves and least-squares fitting of data.

PHYS 170: Computational Methods in Physics (2)

Advanced techniques in computational physics including high performance computing using parallelization (both CPU- and GPU-based ), machine learning and neural networks, and metaprogramming.

PHYS 193: Physics Clinic (3)

Team projects in applied physics, with corporate affiliation.

PHYS 194: Physics Clinic (3)

Team projects in applied physics, with corporate affiliation.

PHYS 195: Physics Colloquium (0.5)

Oral presentations and discussions of selected topics, including recent developments. Participants include physics majors, faculty members, and visiting speakers. Required for all junior and senior physics majors. No more than 2.0 credits can be earned for departmental seminars/colloquia.

PHYS 199: Senior Thesis in Physics (13)

Original experimental or theoretical investigations in physics undertaken in consultation with a faculty member. Projects may be initiated by the student or by a faculty member. Present faculty research areas include astrophysics, biophysics, optics, solid-state and low-temperature physics, general relativity, quantum mechanics, particle physics, geophysics, and soft matter physics. Students are responsible for an oral presentation on progress and plans in the first half of the thesis research.

CORE 079: STEM & Social Impact: Climate Change (3)

In this course our focus is to prepare Harvey Mudd students for the lifelong challenge of fostering “a clear understanding of the impact of their work on society.” We will use climate change as an opportunity to explore the impact of our work on society. There are four primary components of that exploration: critical analysis of the social context of STEM, the expansion and application of concepts from the core to understand this social-technical problem, collaborative projects that promote positive change in the world, and communicating our project designs and professional choices. Plenary sessions will explore topics such as environmental justice, earth system science, the relation between expertise and power, policy processes, data science, community engagement, multidisciplinary collaboration, impactful careers, and science communication. Individual sections will explore particular climate-related issues in greater depth. Final team projects will challenge students to apply these concepts in proposals for climate solutions.