# Rana Ali Amjad

## 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 |

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. |
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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

- Amjad, Rana Ali:
**Variable-to-Fixed Length Resolution Codes for Approximate Random Number Generation**.*XV International Symposium on Problems of Redundancy in Information and Control Systems*, Sep 2016 - Amjad, Rana Ali:
**Error Exponent and Achievable Rates for Probabilistic Amplitude Shaping**.*LNT Tagung*, Aug 2016 - Geiger, B. C. and Amjad, R. A.:
**Hard Clusters Maximize Mutual Information – Some Results and an Open Problem**.*Internal LNT Workshop*, Aug 2016 - Amjad, Rana Ali:
**Information Theoretic Clustering**.*European School of Information Theory (ESIT)*, Apr 2016

### 2015

- Amjad, R.A.; Kramer, G.:
**Channel resolvability codes based on concatenation and sparse linear coding**.*IEEE Int. Symp. Inf. Theory (ISIT)*, Jun 2015 - Amjad, Rana Ali:
**Sparse and Concatenated Codes for Channel Resolvability**.*Summer School on Information Processing for Large Networks*, Jun 2015 - Amjad, Rana Ali:
**Low Complexity Codes for Channel Resolvability**.*Euopean School of Information Theory (ESIT)*, Apr 2015 - Amjad, Rana Ali:
**Coding for Channel Intrinsic Random Extraction**.*17th Joint Conference on Communications and Coding (JCCC)*, Mar 2015 - Amjad, Rana Ali:
**Algorithms for Distribution Matching and Resolution Coding**.*SP Coding and Information School*, Jan 2015

### 2014

- Amjad, Rana Ali:
**A Learning Perspective of Context Tree Weighting**.*Machine Learning in Communication*, Sep 2014 - Böcherer, G.; Amjad, R. A.:
**Informational Divergence and Entropy Rate on Rooted Trees with Probabilities**.*IEEE Int. Symp. Inf. Theory (ISIT)*, Jun 2014 - Amjad, Rana Ali:
**Coding Theorems and Algorithms for Simulation of Discrete Memoryless Sources**.*Euopean School of Information Theory (ESIT)*, Apr 2014

### 2013

- Bai, Q; Amjad, R. A.; Nossek, J.A.:
**Average Throughput Maximization for Energy Harvesting Transmitters with Causal Energy Arrival Information**.*IEEE Wireless Communications and Networking Conference*, IEEE, 2013, 4232-4237 - Böcherer, G.; Amjad, R. A.:
**Fixed-to-Variable Length Resolution Coding for Target Distributions**.*IEEE Information Theory Workshop (ITW)*, Sep 2013 - Amjad, R. A.; Böcherer, G.:
**Fixed-to-Variable Length Distribution Matching**.*IEEE Int. Symp. Inf. Theory(ISIT)*, Jul 2013