dr. G. Joseph

Assistant Professor
Signal Processing Systems (SPS), Department of Microelectronics

Expertise: Compressive Sensing, Sparse Signal Processing, Signal Processing for Communication, Linear Dynamical Systems

Themes: Distributed autonomous sensing systems, Signal processing for communication


Geethu Joseph received the B. Tech. degree in electronics and communication engineering from the National Institute of Technology, Calicut, India, in 2011, and the M. E. degree in signal processing and the Ph.D. degree in electrical communication engineering (ECE) from the Indian Institute of Science (IISc), Bangalore, in 2014 and 2019, respectively. She was a Postdoctoral Fellow with the Department of Electrical Engineering and Computer Science, Syracuse University, NY, USA, from 2019 to 2021. She is currently an assistant professor at TU Delft. 


Dr. Joseph was awarded the 2022 IEEE SPS best PhD dissertation award and the 2020 SPCOM best doctoral dissertation award. She is also a recipient of the Prof. I. S. N. Murthy Medal in 2014 for being the best M. E. (signal processing) student in the ECE dept., IISc, and the Seshagiri Kaikini Medal for the best Ph.D. thesis of the ECE dept., at IISc for the year 2019-'20

Dr. Joseph holds 25+ peer-reviewed publications in the fields of signal processing, communications, and control theory. She is a member of the IEEE signal processing society and an active reviewer for major journals and conferences in signal processing, communications, and control theory. Her research interests include statistical signal processing, network control, and machine learning.

EE4740 Data compression: Entropy and sparsity perspectives

Data compression and its connections to information theory and compressed sensing

EE4C03 Statistical digital signal processing

A second course on digital signal processing: random signals, covariances, linear prediction, spectrum estimation, optimal filtering, Wiener and Kalman filters, LMS and RLS algorithm

Education history

EE4560 Information theory

(not running) Source and channel coding

Signal processing for environment-aware radar

In future, cars will exploit multiple radars towards autonomous driving. Before this becomes a reality, several challenges will have to be solved.

  1. Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO
    Yanbin He; Geethu Joseph;
    In ICASSP,

  2. Output Controllability of a Linear Dynamical System with Sparse Controls
    Geethu Joseph;
    IEEE Transactions on Control of Network Systems,
    2022. DOI: 10.1109/TCNS.2022.3188484

  3. Stabilizability of Linear Dynamical Systems Using Sparse Control Inputs
    Chandrasekhar Sriram; Geethu Joseph; Chandra R. Murthy;
    IEEE Transactions on Automatic Control,

  4. Sparsity-aware Bayesian inference and its applications
    Joseph, Geethu; Khanna, Saurabh; Murthy, Chandra R; Prasad, Ranjitha; Thoota, Sai Subramanyam;
    In Handbook of Statistics,
    Elsevier BV, 2022.

  5. State Estimation of Linear Systems With Sparse Inputs and Markov-modulated Missing Outputs
    Geethu Joseph; Pramod K. Varshney;
    In European Signal Processing Conference,

  6. Near-field Focusing Using Phased Arrays With Dynamic Polarization Control
    Nitih Jonathan Myers; Yanki Aslan; Geethu Joseph;
    In European Signal Processing Conference,

  7. Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers
    Saikiran Bulusu; Geethu Joseph; M. Cenk Gursoy; Pramod K. Varshney;
    In Neurips,
    2022. (S. Bulusu and G. Joseph have equal contribution).

BibTeX support

Last updated: 15 Dec 2022