Doctor of Philosophy Programme (Communications, Signal Processing and Learning)

PhD Program (last date:
11 April 202420 April 2024)  MTech Programs (last date: 08 April 2024)
Research Areas
We conduct research in a variety of aspects of Communications, Signal Processing and Machine Learning, including
 Theoretical Foundations: This includes applied mathematics, algorithm design, deriving bounds on the performance of various communications, signal processing, and machine learning systems.
 Applied Research: This includes careful design, implementation, simulation and validation of algorithms, development of machine learning systems for speech, vision, communications and networking.
 Systems Development: This includes design and implementation of communications and signal processing algorithms on hardware (e.g., SDR and FPGA), prototype and proofofconcept systems development (autonomous vehicles and 5G6G communication systems), and validation.
The candidates are encouraged to go through the individual faculty profiles to know more about the current research areas of the CSPL group. The research problems pursued by the group primarily use the following mathematical tools
 Linear algebra/matrix theory
 Probability theory, random variables and random processes
 Convex and nonconvex optimization
 Real and complex analysis
Alumni
Our PhD alumni are currently located in toptier academic and research institutions, including University of Pittsburgh, IIT Indore, IIT Mandi, NIT Warangal, VIT, Qualcomm, and Samsung Research.
PhD Admissions
 August admissions: online applications typically open during midMarch to midApril
 January admissions: online applications typically open during November
NonEC/EE students, including those with mathematics or computer science backgrounds, are also eligible to apply for the PhD program. Please see the EE PhD admissions brochure for complete details regarding various modes of admission, stipend, eligibility, etc.
Written Test and Interview
The admission process consists of the following stages. The written test and interview are typically conducted on the same day in offline mode in IIT Hyderabad campus — the test in the morning session (about 90 minutes) and the interview of the shortlisted candidates (about 20 minutes) in the afternoon session. However, depending on logistical constraints, the interviews may extend to the next day.
The written test and the interview are designed to examine the mathematical and logical reasoning of the candidates. We are looking for students who are willing to learn and improvise. In particular, we are not interested in rote formulabased approaches for solving problems. The candidates are expected to have a clear understanding of the fundamentals. The focus of the written test is on
 Linear algebra
 Probability theory & random variables
 Mathematical aptitude, including but not limited to elementary calculus and geometry
 Their respective areas of interest
 Basics of communications and/or signal processing
 Programming aptitude
Linear Algebra 

Vector spaces, linear independence, basis, dimension 
Inner product and norm 
Matrix algebra, rank, eigenvectors, determinant 
Solving systems of linear equations 
Special matrices (diagonal, triangular, orthogonal, symmetric etc.) 
Probability Theory & Random Variables 

Probability density and distributions 
Combinatorial probability 
Binomial, exponential, normal, Poisson, exponential random variables 
Jointly distributed random variables 
Mean, variance, mode etc. 
Independence, Conditional probability, Bayes theorem 
References
We recommend the following textbooks to prepare for linear algebra and probability theory & random variables.
 Boyd, Stephen, and Lieven Vandenberghe, "Introduction to applied linear algebra: vectors, matrices, and least squares", Cambridge University Press.
 David Lay, “Linear Algebra and its Applications”, Pearson.
 Gilbert Strang, “Linear Algebra and its Applications”, Cengage.
 Erwin Kreyszig, “Advanced Engineering Mathematics”, John Wiley & Sons.
 Bertsekas, Dimitri, and John N. Tsitsiklis, "Introduction to probability", Vol. 1. Athena Scientific.
 Athanasios Papoulis and S. Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, McGraw Hill.
 Sheldon Ross, “A First Course in Probability”, Pearson.