Sundeep Rangan

Associate Director NYU WIRELESS

Professor, Electrical & Computer Engineering, NYU Tandon

PHONE: 646.997.3804

EMAIL: srangan@nyu.edu

OFFICE: 370 Jay Street, 9th Fl, Brooklyn, NY 11201

Sundeep Rangan received the B.A.Sc. at the University of Waterloo, Canada and the M.Sc. and Ph.D. at the University of California, Berkeley, all in Electrical Engineering. He has held postdoctoral appointments at the University of Michigan, Ann Arbor and Bell Labs.

In 2000, he co-founded (with four others) Flarion Technologies, a spin off of Bell Labs, that developed Flash OFDM, one of the first cellular OFDM data systems and pre-cursor to 4G systems including LTE and WiMAX.  In 2006, Flarion was acquired by Qualcomm Technologies where Dr. Rangan was a Director of Engineering involved in OFDM infrastructure products. He joined the ECE department at NYU Tandon (formerly NYU Polytechnic) in 2010. He is a Fellow of the IEEE and Director of NYU WIRELESS, an academic-industry research center researching next-generation wireless systems.  His research interests are in wireless communications, signal processing, information theory and control theory.

FALL 2017

EL-GY 9123: Introduction to Machine Learning (Graduate)

This course is an introductory graduate-level machine learning for electrical and computer engineering students. The class covers fundamental algorithms in machine learning including linear regression, classification, model selection, support vector machines, dimensionality reduction and clustering. The material will be developed with computer exercises on real and synthetic data. Applications are demonstrated in audio and image processing, robotic control, and text and web analysis. No prior machine learning experience is required.

EE-UY/CS-UY 4563: Introduction to Machine Learning (Undergraduate)

This course provides a hands on approach to machine learning and statistical pattern recognition. The course describes fundamental algorithms for linear regression, classification, model selection, support vector machines, neural networks, dimensionality reduction and clustering. The course includes computer exercises on real and synthetic data using current software tools. A number of applications are demonstrated on audio and image processing, text classification, and more. Students should have competency in computer programming

PREVIOUS CLASSES

SPRING 2017 – EE-UY 3404, FUNDAMENTALS OF COMMUNICATION THEORY

An introductory undergraduate course covering bandpass signal representations and quadrature receivers; transmit and receive filtering, noise in communication systems; digital modulation schemes, coherent and noncoherent receivers, error probability analysis, coding fundamentals, block and convolutional codes. Course includes a series of MATLAB-based simulation labs.

  • Text (main): Proakis, Salehi, “Communications systems engineering” 2nd edition
  • Syllabus

Fall 2016 – EE-UY 4423, Introduction to Machine Learning

Lecture: Tues, Thurs 11-12:30, JAB 678 Starting Sept 6, 2016
Recitation: Friday 11:30-12:50, RH 211
This course provides a hands on approach to machine learning and statistical pattern recognition. The course will provide an introduction to fundamental algorithms for linear regression, classification, model selection, support vector machines, dimensionality reduction and clustering. The material will be developed with hands on python-based exercises on real and synthetic data. Applications will be demonstrated in audio and image processing, robotic control, and text and web analysis.

Spring 2016 – EE-UY 3404, Fundamentals of Communication Theory

Fall 2015 – EL-GY 6023, Wireless Communications

Spring 2015 – EL-GY 6333, Detection and Estimation

Fall 2014 – EL-GY 6303, Probability and Stochastic Processes

Fall 2014 – EE-UY 2233, Introduction to Probability

Spring 2014 – EL 6013, Principles of Digital Communications

Spring 2014 – EE 3404, Fundamentals of Communication Theory

Fall 2013 – EL 6013, Principles of Digital Communications

Spring 2013 – EL 6303, Probability Theory

Spring 2013 – EL 5013, Wireless Personal Communication Systems

Fall 2013 – EL 6323, Introduction to Wireless Networking

Spring 2012 – EL 6013, Principles of Digital Communications, Modulation and Coding

Spring 2012 – EL 9383, Special Topics in Wireless Networking

Millimeter Wave 5G Wireless

The millimeter wave (mmWave) bands (frequencies roughly above 10 GHz) are a new frontier for wireless communications. The massive bandwidths in these frequencies offer the possibility of new networks with orders of magnitude greater capacity than current systems operating in the highly congested bands below 3 GHz. Due to their enormous potential, mmWave is now being developed as a fifth generation (5G) cellular systems including the 3GPP New Radio effort. My group’s work in this area is done with NYU WIRELESS including the following projects:

Other NYU WIRELESS research can be found on its research page

Approximate Message Passing

Approximate message passing (AMP) and their variants are a powerful class of algorithms for various forms linear inverse problems. The methods are based on graphical models and have the benefit of being computationally scalable and applicable to a wide range of problems including compressed sensing, sparse regression, dictionary learning, matrix completion and estimation in networks. In addition, in certain large random instances, the performance of the methods can be precisely characterized with testable conditions for Bayes optimality, even in non-convex instances.

Read more on Approximate Message Passing

Please see all publications from Sundeep Rangan here

Chris Slezak
Ph. D. candidate, Electrical Engineering
mmWave communications, prototyping, channel dynamics

Sourjya Dutta
PhD, Electrical Engineering
Research Interests: Simulation and Modelling, MAC Layer Design

Nicolas Barati
PhD, Electrical & Computer Engineering
Research Interests: Wirelss Networks, mmWave MAC

Menglei Zhang
PhD, Electrical Engineering
Research Interests: Wireless Communications, Channel Modeling, and Cellular System Design