Hi, I am
.

Curriculum Vitae
Email
Github
Linkedin
Google Scholar

Latest News
May'24 - Won Campus 3MT Competition @ UMD
Mar'24 - Won Engineering 3MT Competition @ UMD
Aug'23 - Selected a GLEAM fellow @ UMD ECE
Jan'23 - Started PhD program @ UMD ECE
Aug'22 - Grad Summer Intern @ Hughes
Aug'21 - Started MS program @ UMD ECE
I am a PhD student at the University of Maryland, College Park advised by Prof Min Wu where I am specialising in signal processing with a focus on explainability. I am working at the intersection of statistical signal processing and explainable machine learning to solve critical research problems.

I am currently a part of the Media and Security Team led by Prof Wu. In the past, I did my bachelors thesis on designing circularly polarised antennas for 5G systems under the guidance of Prof Asok De. I was previously a graduate signal processing algorithms intern at Hughes Network Systems and briefly worked as an undergrad at Bharat Electronics .

I graduated with a B.Tech (First Class with Distinction) in Electronics and Communication Engineering from Delhi Technological University, Delhi, India in 2021 . For more details, check my CV or hit me up on my email.

Publications

Design Of High Bandwidth Circularly Polarised Antipodal Vivaldi Array for 5G Applications
Anirudh Nakra, Abhijeet Vats, Asok De IEEE INCET'21 | IEEE International Conference on Emerging Technologies
IEEE Paper| Simulation

Projects

XAI techniques for uncovering Spurious Correlations
Anirudh Nakra, Henok Gebeyehu
Report


Comparing Modern v/s Traditional Image Inpainting algorithms
Anirudh Nakra
Report| Code


Solving Underwater SLAM
Anirudh Nakra, Henok Gebeyehu, William Chen
Report


A face classifier and digit recognizer from scratch
Anirudh Nakra
Face Classifier | Report| Code
Digit Recognizer | Report| Code


Efficient implementations of Fixed and Floating point MODEMs
Anirudh Nakra
Report| Code

Teaching

Spring 2023, Fall 2022 & Spring 2022
ENEE439D : Design Experiences in Machine Learning

Fall 2021
DATA 603 : Principles of Machine Learning

Courses Taken

ENEE620 : Random Processes : (A)
ENEE621 : Estimation and Detection Theory : (A)
ENEE630 : Advanced Digital Signal Processing : (A+)
ENEE631 : Digital Image and Video Processing : (A+)
ENEE633 : Statistical Pattern Recognition : (A+)
ENEE662 : Convex Optimisation : (A)
CMSC828L : Deep Learning : (A)
CMSC848D : Explainable NLP : (A)