Computer Science 691DA - S-Big Data Algorithms & Applic

Big Data brings us to interesting times and promises to revolutionize our society from business to government, from healthcare to academia. As we walk through this digitized age of exploded data, there is an increasing demand to develop unified toolkits for data processing and analysis. In this course our goal is two fold, (i) to study a subset of topics that build the mathematical foundation of big data processing, and (ii) to learn about applications of big data in important domains of interest. For the former, our plan is to concentrate on the following three topics: Stochastic Optimization, Metric Embedding, and High Dimensional Fourier Analysis. For the later, we will emphasize on the following two areas: Biomedical Applications and Social Network Analysis. This is a one credit seminar course. Depending on the number of students, each student will read 1 theory and/or 1 application paper and present them in the class. The presentation of theory paper needs to be in-depth covering the detailed proofs of main theorems and lemmas. Prerequisites: The students are expected to have strong mathematical foundations, must have basic knowledge of algorithms and probability, and should be able to read and understand papers appearing in top theoretical computer science conferences. Senior undergraduate students meeting these requirements are encouraged to take this course.

Open to CMPSCI graduate students only. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA THE ON-LINE FORM: https://www.cs.umass.edu/overrides.