Welcome to RPI CS! Theory Group Professors: Mark Goldberg Associate Professors: Daniel Freedman, Mukkai Krishnamoorthy, Malik Magdon- Ismail, Bulent Yener. Assistant Professors: Elliot Anshelevich, Sanmay Das. Petros Drineas (Assistant Professor)

2 Overview (My) Research Agenda Massive Datasets A glimpse of my work Future directions (Theory Group?) Teaching Agenda Shameless advertising We offer all the courses that you need in order to get a solid background in TCS. We use really cool and sometimes hard math in our courses!

3 Massive Datasets appear everywhere Just a few examples:  The World Wide Web. Too much junk, but Google usually saves the day!  Document Databases. IBM estimated that its databases contained more than 3 ¢ 10 8 documents.  AT&T call-detail records. These include calling number, called number, time of day, length of call for 260 Million calls/day.

4 Queries on Massive Datasets What kind of queries do we want to perform on Massive Datasets?  World Wide Web: search for Jaguar, but separate the web pages that refer to the animal from the ones that refer to the car.  Document Databases: find documents that are similar to a given document.  AT&T call-detail records: distinguish business accounts from residential accounts.  MANY, MANY, MANY MORE EXAMPLES/QUERIES! Definitely more research is necessary. A quote of Christos Papadimitriou: “CS might not have produced an implant chip that increases our intelligence, but we produced Google, which increases our IQ just as efficiently.”

5 Why is it difficult? 5 things to remember about Massive Datasets:  They are not readily accessible.  They are stored in secondary memory and we only have sequential access over the data.  We do not want to read the data too many times! Equivalently, we want to make a small number of passes over the data.  Sometimes, we are only allowed to make one pass over the data (streaming model).  Even worse, in certain applications we are not even allowed to read the full data once (sublinear algorithms). Imagine sorting 1 trillion numbers, stored in secondary memory (no random access there!). Thus, no QuickSort, MergeSort, etc. How do you do it fast? Not surprisingly, it is not easy! The Massive Dataset paradigm motivates looking for new algorithms, new complexity classes, new lower bounds, new proof techniques, etc.

6 My work In many applications data appear as large matrices We can make a few “passes” through the matrices. We can create and store a small “sketch” of the matrices in RAM. Computing the “sketch” should be a very fast process. Finally, discard the original matrix and work with the “sketch”. 1.A “sketch” consisting of a few rows/columns of the matrix is adequate for efficient approximations. 2.We draw the rows/columns randomly, using adaptive sampling; e.g. we pick rows/columns with larger elements with higher probability.

7 Applications: Data Mining We are given m (>10 6 ) documents and n (>10 5 ) terms describing the documents. Database An m-by-n matrix A (A ij shows the frequency of term j in document i). Every row of A represents a document. Queries Given a new document x, find similar documents in the database (nearest neighbors).

8 Applications (cont’d) Key observation: The exact value x T · d might not be necessary. 1.The feature values in the vectors are set by coarse heuristics. 2.It is in general enough to see if x T · d > Threshold. Two documents are “close” if the angle between their corresponding vectors is small. So, x T ·d = cos(x,d) is high when the two objects are close. (We assume that the vectors are normalized.) A·x computes all the angles and answers the query.

9 Approximating Matrix Multiplication Goal Proximity problem: identify all document-document matches in the database. Given an m-by-n matrix A, approximate the product A·A T. Idea 1.Pick s columns of A to form an m-by-s matrix S. 2.(discard A) Approximate A · A T by S · S T.

10 The algorithm It only requires storage of a few columns of A. Faster than full matrix multiplication. We can prove error bounds for this algorithm! We can use similar ideas to tackle various other matrix operations.

11 Future Directions Extract structure from data in the form of tensors (multi-dimensional arrays) instead of matrices (two-dimensional arrays). Think of the evolution of the graph (vertices are users and edges denote the exchange of an message) over time. This can be modelled as a three-dimensional array. Can we find communities of users in this time-evolving graph? Extract non-linear structure from data. A subtle point is that most of the existing techniques only extract linear structure from the data. Can we devise efficient algorithms to extract non- linear structure from massive datasets?

12 Teaching Agenda Graduate Courses offered by Theory Group members in the last few years … Topics in Computational Geometry (Freedman) Graph Theory (Goldberg) Computability and Complexity (Goldberg) Machine and Computational Learning (Magdon-Ismail) Introduction to Computational Finance (Magdon-Ismail) Random Graphs and The WEB-graph (Goldberg, Moorthy, Yener) Network Security (Yener) Network Flows and Linear Programming (Drineas, Yener) Randomized Algorithms (Drineas) Machine Learning (Das) Advanced Algorithm Design (Anshelevich)

13 Enjoy your (theory) classes!