Computer Science

This course examines common data structures and their implementation, including stack, queue, string, binary tree, tree and graph; common retrieval, indexing and sorting methods, including linear table, hash table, inverted file, B-tree and other common retrieval and indexing technologies, insertion sorting, shell sorting, heap sorting, quick sorting, cardinal sorting and other common sorting algorithms and their time and space overhead; common algorithms and their implementation, including divide and conquer, recursion, backtracking, greedy method.

This class addresses topics from network structure and growth to the spread of epidemics. The course studies diverse algorithmic techniques and mathematical models that are used to analyze such large networks, and give an in-depth description of the theoretical results that underlie them. Some topics are random graphs, giant components, power laws, percolation, spreading phenomena, community detection, basic algorithms for network science, lower bounds and advanced algorithms for polynomial-time problems, sampling algorithms, streaming algorithms, sublinear algorithms, and graph partitioning algorithms.

The course assumes basic skills in algorithms and mathematics: familiarity with basic graph algorithms (shortest paths, flows), and basic understanding of NP-completeness. Work with basic probabilities and some integrals in included.

This course introduces the discipline of Bioinformatics to students from both physical science and life science backgrounds. It introduces key biological concepts including the main types of molecules (DNA, RNA, and protein) as well as the cell biological processes involved in their regulation and function in biological systems. Students learn to work with and analyze biological sequences through biological sequence databases, process automation, algorithms, and tools to allow pairwise and multiple sequence alignment, as well as approaches using high-throughput next-generation sequence data.

This course provides theoretical as well as practical introduction to quantum computation. By the end of the course students understand the basics of quantum mechanics, quantum logic and computation, important quantum-algorithms, and work with actual quantum computers and quantum simulators. Covered topics include a basic introduction to quantum mechanics to understand quantum computation, quantum algorithms, Simon's algorithm, the prime factorization algorithm, Grover's search algorithm, mathematical models of quantum computation, their relationships to each other, and to physical systems, and quantum error correcting codes. The exercise component of the course includes a background section on the need for quantum computing and then addresses the following topics: hardware technologies for quantum computers, quantum logic, computation on a quantum computer, and programming on IBM Q.

This is a research internship course offered by Rothberg International School. The course's availability is subject to the availability of suitable academic supervision. Students work in a preapproved organization or research institute for a minimum of 8 hours a week (not including transportation) for a total of 88 hours throughout the semester. Students complete a mid-semester meeting including a report submitted to the Internship Coordinator, time sheets, a one-page reflection summarizing the experience, and a portfolio/research paper. Students are assessed on their hours, reflection and work description assignment, and their portfolio/research paper.

This course offers an introduction to the design, construction, and efficient use of databases, with a focus on relational databases. Topics include: data models; database modeling using the entity/relationship model; relational model; database design; SQL query language; views; integrity; transaction processing.