Inaam Ilahi

Technical Project Manager | Data Engineer | Data Scientist

My duties include:

1. Guiding/Managing a team of developers for various projects including CRMs and Queue Management Solutions.
2. Developing Data pipelines.

Research Assistant

I worked under the supervision of Prof. Junaid Qadir. Major part of mu research included applying deep reinforcement learning for solving multiple problems.

Freelancer (Part-time)

I now-and-then do projects on fiverr related to C++ and Natural Language Processing (NLP). I am a level one seller and have got all 5 star reviews till now.

Graduate Research Fellow and Teaching Assistant

I worked as a research assistant under the supervision of Prof. Junaid Qadir and Dr. Umar Janjua. We researched on applying deep reinforcement learning for solving the problem of resourse allocation in dense LoRa networks.

I also worked as a graduate teaching assistant with Mr. Sarfraz Raza for two courses: namely, "Object-Oriented Programming" and "Computing Fundamentals and Programming".

Engineering Intern

I worked on designing and implementing the human machine interfaces for programmable logic controllers applied in industry. Furthermore, I even helped in the internal factory acceptance testing of PLCs before being deployed in industry.

Project Intern

I learned about the basics and some advanced topics of industrial automation.

Examining Machine Learning for 5G and Beyond through an Adversarial Lens

Abstract
Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/RL) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.

Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning (Under Review)

Abstract
The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose LoRaDRL and provide a detailed performance evaluation. We propose a multi-channel scheme for LoRaDRL. We perform extensive experiments, and our results demonstrate that the proposed algorithm not only significantly improves long-range wide area network (LoRaWAN)'s packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption. Most previous works focus on proposing different MAC protocols for improving the network capacity. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500% in terms of PDR compared to learning-based techniques.

LoRaDRL: Deep Reinforcement Learning Based Adaptive PHY Layer Transmission Parameter Selection for LoRaWAN

Abstract
The performance of densely-deployed low-power wide-area networks (LPWANs) can significantly deteriorate due to packets collisions, and one of the main reasons for that is the rule-based PHY layer transmission parameters assignment algorithms. LoRaWAN is a leading LPWAN technology where LoRa serves as the physical layer. Here, we propose and evaluate a deep reinforcement learning (DRL)-based PHY layer transmission parameter assignment algorithm for LoRaWAN. Our algorithm ensures fewer collisions and better network performance compared to the existing state-of-the-art PHY layer transmission parameter assignment algorithms for LoRaWAN. Our algorithm outperforms the state of the art learning-based technique achieving up to 500% improvement of PDR in some cases.

Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning

Abstract
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. Thus, this paper provides a comprehensive survey that discusses emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks. We first cover some fundamental backgrounds about DRL and present emerging adversarial attacks on machine learning techniques. We then investigate more details of the vulnerabilities that the adversary can exploit to attack DRL along with the state-of-the-art countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks for DRL-based intelligent systems.

Research Interests

My research interests include:

• Applying Deep Reinforcement Learning for solving various real-world problems.
• Attacking Deep Reinforcement Learning for achieving adversarial benefits.
• Robustifying Deep Reinforcement Learning against adversarial attacks.
• Natural Language Processing

Other Interests

Apart from research, I have a number of other interests too:

• I love reading books. Some of my favourite books are "5AM Club by Robin Sharma", "Thinking Fast and Slow by Daniel Kahneman", "Permanent Record by Edward Snowden", "Rich Dad, Poor Dad by Robert T. Kiyosaki" and many others.
• I like to walk around in the markets searching for street food.
• I like watching movies and also follow a number of TV shows. Some of my favourite include "Silicon Valley" and "Breaking Bad".
• I like swimming but haven't swam in a long time.

Information Technology University (ITU), Lahore, Pakistan.

My thesis was supervised by Dr. Junaid Qadir. Apart from that, I took the following courses:

• Deep Learning by Dr Mohsen Ali
• Machine Learning by Dr. Ali Ahmed
• Natural Language Processing by Dr. Agha Ali Raza
• Computer Vision by Dr. Mohsen Ali
• Advanced Computer Architecture by Mr. Bilal Hashmi
• Advanced Algorithm Analysis by Dr. Waqas Sultani
• Advanced Operating Systems by Madam. Momina Azam
• Advanced Automata by Dr. Falak Sher

National University of Sciences and Technology (NUST), Islamabad, Pakistan.

My thesis was supervised by Dr. Fahad Mumtaz Malik.

Punjab College, Rahimyar Khan, Pakistan.

SHEIKH ZAYED SCHOOL AND COLLEGE, Rahimyar Khan, Pakistan.