Persönlicher Status und Werkzeuge

Rana Ali Amjad

Rana Ali Amjad

Rana Ali Amjad
Research Assistant

Institute for Communications Engineering
Theresienstrasse 90
Building N4, 3. Floor
D-80333 Munich

Room: N3409
Phone: +49 89 289-23494
Email: ranaali.amjadnoSpamAllowed@tum.de

Research Interests

  • Statistical Machine Learning.
  • Information Theory.
  • Secrecy.
  • Coding Theory.
  • Algorithms and Discrete Mathematics.

Education

  • PhD Student at LNT, TUM since January 2014
  • MSCE 2011-2013, TUM
  • BSc Electrical Engineering and Computer Science, University of Engineering and Technology, Lahore, Pakistan

Available Theses


Master Thesis - Designing codes for secret key generation and extracting the secret bits in left over hash lemma
The source model of secret key generation deals with the idea of Alice and Bob generating a key in a distributed manner from correlated observations. This key must be kept secret from an evesdropper. In this internship/thesis the student will start by looking at a simpler model which corresponds to the left over hash lemma. The student will build on some preliminary work done by me to design codes for the extraction of left over hash in a simple setting. After this the student will extend the work to design codes for distributed secret key generation for the source model.
Supervisors: Rana Ali Amjad

Forschungspraxis or MSCE Internship - Code design for Physical Layer Security
Wiretap channel represents the basic setup for physical layer security. It has been extensively studied in the last four decades and the fundamental limits of communication for this channel are known in a wide variety of scenarios. Nevertheless the only explicit code construction that can achieve wiretap secrecy capacity uses Polar codes. Designing codes for secrecy involve the combined design of codes for reliability and channel resolvability. In 2015 a new coding scheme for channel resolvability was introduced by Amjad and Kramer. The aim of this internship is to combine this channel resolvability code with existing channel codes in order to design wiretap code.
Supervisors: Rana Ali Amjad

Forschungspraxis or MSCE Internship - Code Design for Secret Key Generation/ Left over Hash Lemma
The source model of secret key generation deals with the idea of Alice and Bob generating a key in a distributed manner from correlated observations. This key must be kept secret from an evesdropper. In this internship/thesis the student will start by looking at a simpler model which corresponds to the left over hash lemma. The student will build on some preliminary work done by me to design codes for the extraction of left over hash in a simple setting. After this the student will (if time permits) extend the work to design codes for simple cases of distributed secret key generation for the source model.
Supervisors: Rana Ali Amjad

Theses in Progress


Sirine Ammar: Bachelor Thesis - Community Detection in Dramatic Plays
We are given the scene configuration of a dramatic play in the form of a matrix that links characters to scenes. A matrix entry is 1 if the corresponding character is present in the corresponding scene and 0 otherwise. Based on this matrix and on the generalized formulation of information-theoretic co-clustering by Clemens Blöchl, we want to cluster characters into meaningful groups and compare the results with those obtained from text analysis.
Supervisors: Bernhard Geiger, Rana Ali Amjad

Mohamed Nabil Babai: Bachelor Thesis - How many clusters?
In this thesis, we investigate heuristics for determining the number of clusters in cluster analysis. We place focus on popular clustering techniques such as k-means and spectral clustering, but we will also summarize heuristics for hierarchical methods. After doing a literature survey, small Matlab experiments will illustrate the results.
Supervisors: Bernhard Geiger, Rana Ali Amjad

Kairen Liu: Master Thesis - Information Theoretic Analysis of Neural Networks
Various types of neural networks have gained a lot of attention in recent years and have found numerous practical applications with impressive results. Albeit their success, their behaviour is not very well understood mathematically. The aim of this thesis is to approach the topic from an information theoretic perspective and see if one can use insight from information and coding theory to analyze/design neural networks for specific applications.
Supervisors: Rana Ali Amjad, Bernhard Geiger

Emna Ben Yacoub: Forschungspraxis (12 ECTS) - M-Type Approximation of Hidden Markov Models
In this research project, we replace transition and observation probability matrices of hidden Markov models (HMMs) by matrices where each entry is an integer multiple of integer M (i.e., is "M-type"). The problem is an immediate extension of approximating finite-length probability vectors by M-type vectors.

The Viterbi algorithm can be used to infer the state sequence from the observation sequence, given that the algorithm has knowledge of the transition and observation matrices. If, instead of the true matrices, the algorithm has knowledge only of their M-type approximations, this will lead to an increase in error probability. We try to find a connection between a probabilistic divergence measure between the true and the M-type model (e.g., Kullback-Leibler divergence rate, matrix norms, etc.) and this increase in error probability.
Supervisors: Bernhard Geiger, Rana Ali Amjad

Awards

  • Walter Gademann Prize for best Master thesis in Department of Electrical, Electronic and Computer Engineering at Technical University of Munich, Germany.
  • Achievement Award for outstanding performance in Master in Communication Engineering degree at Technical University of Munich,  Germany.
  • Naeem Shafi Gold Medal for best performance(Communications Major) in Bachelors of Electrical Engineering and Computer Science degree at University of Engineering and Technology, Lahore Pakistan.
  • Academic Color Holder in High School for best performance over the span of 4 years.
  • Gold Medal for best performance in High School State Examinations.

Publications

2017

2016

2015

2014

2013

2012

Master Thesis