Fall 2021

ASTRONOMY AND ASTROPHYSICS: This course will trace our knowledge of the Universe from astronomy's ancient roots in naked eye observations of the sky to the twenty first century studies of extrasolar planetary systems, black holes, and cosmology. Initial topics will include: Newton's laws of motion and gravitation, orbits and space travel, and the properties of planets' surfaces, interiors, and atmospheres. The course will then combine atomic and nuclear physics with stellar and galactic astronomy to describe stars, supernovae, black holes, the interstellar medium, galaxies, the creation of the elements, and the evolution of the universe.

MODERN COSMOLOGY: Cosmology is the branch of physics that studies the Universe on its largest scales and endeavors to understand its origin, evolution, and fate. In this course, we will review the key ingredients and the main observations that contributed to our current understanding of the Universe. We will discover that modern cosmology not only provides an explanation of how structures formed during cosmic history, and evolve on large scales, but that it also answers questions about how nature is organized at a fundamental level. Topics to be explored include: the expansion of the Universe, the Big Bang model, the cosmic microwave background, the large-scale structure of the Universe, dark matter and dark energy.

QUANTUM PHYSICS AND RELATIVITY: Quantum physics and relativity underpin much of our modern understanding of the universe. The first part of the course begins with a historical introduction to quantum physics, before moving on to topics such as wave interference, the double-slit experiment, complementarity, the Heisenberg uncertainty principle, the Bohr-Einstein debates, Bohr's atomic model, magnetic monopoles, particle in a box, and zero-point energy. Advanced topics include the two-state quantum system, quantum tunneling, and the Schrodinger equation. The second part will present Einstein's special relativity, including topics such as Galilean relativity, Einstein's postulates, time dilation, length contraction, failure of simultaneity at a distance, Lorentz transformations, space-time, four-vectors, the relativistic Doppler effect, Compton scattering, the Einstein and de Broglie relations, and mass-energy equivalence. A brief interlude to general relativity covers the equivalence principle and gravitational redshift. Students should have completed pre-calculus.

CLASSICAL AND QUANTUM COMPUTING DEVICES: This course will introduce students to various techniques used to create micro-/nano-structures, with an emphasis on devices for classical and quantum information processing. Starting with the pioneering ideas presented by Richard Feynman in his paper "Plenty of room at the bottom", students will learn how those visionary proposals have developed into a discipline undergoing an exponential growth and extremely rapid innovation, particularly CMOS (complementary metal-oxide semiconductor) technology. While the course is usually highly interactive, in light of the pandemic, in-person activities will be replaced with virtual experiences including a visit to see examples of fabrication facilities as well as various metrology/microscopy tools (such as an atomic force microscope) in quantum materials labs on the Columbia campus. Students will have the opportunity to participate in a virtually guided tour and preparation of single atom-thick materials as well as write basic programs to run on IBM's quantum circuit interface. The course will conclude with introductory lectures on quantum mechanics and the physics of solids as it relates to quantum information science and technology while maintaining the focus on the experimental and practical aspects of the discipline.

ORGANIC CHEMISTRY: This course combines lectures, virtual laboratory experiments and demonstrations to provide an introduction to the principles and exciting frontiers of organic chemistry. Students will be introduced to the synthesis of organic compounds and the reaction mechanisms. Lecture topics will include: chemical bonds, structural theory and reactivity, design and synthesis of organic molecules, and spectroscopic techniques (UV-Vis, IR, NMR) for structure determination. Recordings of experiments and follow-up discussions will introduce common techniques employed in organic chemistry and will include: extraction, recrystallization, thin layer and column chromatography, reflux, and distillation.

BIOCHEMISTRY: Living organisms are complex and highly organized. Yet the biological basis of life boils down to complex interactions between molecules and biomolecules. This course will provide a foundation for understanding the chemical basis of biological processes. We will explore how molecules such as DNA, RNA and proteins are made and how their structure confers their function. Students will learn how biochemists clone out a selected gene from the entire genome of any organism, mass-produce protein from the gene, and purify it in order to study its biochemical properties and determine its structure. The course will also cover fundamental metabolic pathways involving the breakdown of carbohydrates, lipids and fatty acids and the crucial biological machines that carry out these processes. Students will be exposed to cutting-edge technologies such as liquid chromatography, mass spectrometry, and metabolomics used to profile the metabolome and analyze metabolic fluxes. Students will also learn how perturbation in molecular processes leads to complex pathologies, and how protein structure, enzyme kinetics, and metabolic activity can be leveraged by biochemists to design novel therapeutic compounds. By the end of the course, students will be asked to present their own ideas on a current innovative research concept and its potential applications.

BIOINFORMATICS: The study of biology is changing rapidly thanks to the advent of DNA sequencing technology. This technique produces so much data that researchers must use tools from computer science, statistics, and physics to make sense of it all, in a new field broadly referred to as bioinformatics. In this course, we will explore diverse topics in bioinformatics ranging from genome wide association studies, to functional cancer genomics, to the human microbiome. Our goal is to showcase how data science can be applied to real-world problems across many areas of biology. Some coding experience will be helpful but is not required.

HUMAN PHYSIOLOGY: This course provides an introduction to the major systems of the human body, including the cardiovascular, respiratory, digestive, endocrine, immune, and nervous systems. Discussions will progress from general system structure to function on a cellular level. An overview of pathology and current research will also be presented.

VOLCANOES: Have you ever wondered what causes a volcanic eruption? How do the tiniest particles contribute to how these eruptions affect the globe? We will examine how scientists use chemical and physical clues to predict an eruption as we zoom up from the nanoscale to the largest supervolcanoes in Earth's history.

UNDERSTANDING EARTH'S CLIMATE SYSTEM AND CLIMATE CHANGE: In this course, students will explore the Earth's climate system. We will learn about the physics of climate, how it affects life on Earth, and how humans are changing it. We will discuss the models and tools used by climate scientists and apply one of these methods on real climate data. Toward the end of the course, we will read from an international climate assessment and consider possible solutions.

TOPOLOGY: This course will give an introduction to topology. Roughly speaking, topology is the study of shape. To a topologist, a square and a circle have the same shape since lengths and angles do not affect shape. We will study properties that can describe and distinguish different shapes (Why does a donut have a different shape than a beach ball?). Using these properties, we will be able to prove things like the fundamental theorem of algebra (every polynomial has a root), Nash's equilibrium theorem, "there is a location on the earth where the wind is not blowing", and more! Other topics include: colorings of maps, the classification of surfaces, homotopy groups, the Ham Sandwich theorem, manifolds, knot theory, and homology groups. We will also see applications of topology to questions in data science, biology, and sociology via topological data analysis. No special mathematical background is required.

DIFFERENTIAL GEOMETRY: Differential geometry is a rich field of mathematics; it is also one of the most important tools in theoretical physics. This course will give an introduction to the fundamental objects of differential geometry; manifolds, differential forms, metrics, bundles, connections, sections, and various forms of geometry which are not "flat". At the end of the course, we will have a short introduction to General Relativity. We will also focus on proof-writing techniques throughout the course. Students should have completed pre-calculus, and some knowledge of calculus will be helpful.

KNOT THEORY: This course will be an introduction to the theory of knots. In science, knot theory has applications in quantum physics, DNA modeling, and even the study of the sun's corona. Mathematical knots differ from their real-world counterparts in that they are closed up - they have no ends. A basic question in knot theory is: from the picture of a knot, can you tell if it can be disentangled? This seemingly innocent question is the beginning of a whole branch inside the mathematical field of topology. No background in math will be assumed but an affinity for diagram drawing is encouraged.

GRAPH THEORY BY EXAMPLE: Graph theory is a new and exciting area of discrete mathematics. Simply put, a graph is just a collection of points joined by certain pairs of these points, yet many real-world problems (i.e. traffic flow, school admissions, scheduling) can be formulated as such. Although many problems in graph theory can be easily stated, these problems often have complex solutions with far reaching implications and applications. Problem solving, class discussions, and student examples will be the major proportion of this course. Rigorous proofs will also be presented in the lecture. In addition to exploring the mathematics of graph theory, we will also see how graph theory arises in fields such as computer science, chemistry, game theory, and many others.

FUN WITH MATHEMATICAL INEQUALITIES: Inequalities can be found throughout all of mathematics-from Euclid's Elements to operations research and financial mathematics. This course will focus on developing problem solving skills, especially in the context of mathematical inequalities. Starting with the Cauchy-Schwarz and AM-GM inequalities, we will (time permitting) touch upon a variety of techniques and topics such as the geometry of squares, convexity, as well as "classical" inequalities such as those of Holder, Hilbert, and Hardy. We will often see connections with other branches of mathematics such as geometry and probability. Class discussions and problem solving will be a major part of the course. No special mathematical background is required, though some knowledge of calculus and some exposure to (or enthusiasm for) proofs will be helpful.

COMPUTER PROGRAMMING IN PYTHON: Students will learn the basics of programming using Python. Topics will include: variables, operators, loops, conditionals, input/output, objects, classes, methods, basic graphics, and fundamental principles of computer science. Approximately half of the class time will be spent working on the computer to experiment with the topics covered. Some previous programming experience will be helpful but is not required.

INTRODUCTION TO ALGORITHMS: This course motivates algorithmic thinking. The key learning objectives are the notions of run-time analysis of algorithms, computational complexity, algorithmic paradigms and data structures. Content will primarily be based on high-school algebra and calculus. A tentative list of topics includes: run-time analysis of algorithms, sorting, searching, hashing, computational complexity and complexity classes, graph algorithms, and dynamic programming. The course will cover real world applications like PageRank (ranking web pages), Maps, hashing in cryptocurrency etc.

EXPLORATIONS IN DATA SCIENCE: In this course, students will carry out a series of explorations in data science to learn about statistical thinking, principles and data analysis skills used in data science. These explorations will cover topics including: descriptive statistics, sampling and estimation, association, regression analysis, etc. Classes will be organized to have a lecture component and a hands-on exploration component each session. In the lecture session, an introductory curriculum on data science will be given. In the exploration session, students will be led through data analysis exercises using the statistical analysis language R. These exercises are designed to use open data, such as NYC open data that contain interesting information about neighborhoods of New York City. No prior programming experience is required.

Columbia University Science Honors Program.