Education:
Mr. Omer Tariq is currently pursuing a Doctor of Philosophy (Ph.D.) in Computer Science at the Korea Advanced Institute of Science and Technology (KAIST), School of Computing, specializing in Machine Learning and AI, with a CGPA of 3.74/4.3. His doctoral coursework includes advanced topics such as Programming for AI, Intelligent Robotics, Human-Computer Interaction, and Advanced Machine Learning. He previously earned a Bachelor of Science (BSc.) in Electrical Engineering from the University of Engineering and Technology (UET), Taxila, with a CGPA of 3.25/4.0. His undergraduate thesis focused on developing a “Computer Vision-Assisted Object Detection and Control Framework for a 3-DoF Robotic Arm,” showcasing his early interest in robotics and advanced computer architecture.
Professional Experience:
Mr. Omer Tariq has accumulated extensive experience across various high-tech sectors. From April 2019 to September 2022, he served as an Engineering Manager and Team Lead at the National Electronics Complex, Pakistan, where he led the verification and validation of high-performance SoC/RTL designs. He oversaw RTL development and optimization for integrated circuits, utilizing tools such as SystemVerilog and UVM. Prior to this, from October 2014 to April 2019, Mr. Tariq worked as an Assistant Manager at the National Space Agency (SUPARCO), Pakistan. In this role, he was instrumental in designing and developing satellite imaging payload systems and high-speed PCB designs, contributing to successful national satellite missions. Currently, Mr. Tariq is a Research Assistant in the Department of Industrial & Systems Engineering at KAIST, where he has been involved in designing and developing the electronics and power management module for the DAIM-Autonomous Mobile Robot. His work includes engineering advanced robotics software systems and implementing cutting-edge SLAM algorithms to enhance real-time navigation accuracy.
Research Interests:
Mr. Omer Tariq’s research interests are centered on advancing deep learning techniques for AIoT (Artificial Intelligence of Things) and autonomous systems, with a particular focus on efficiency and privacy preservation. His work explores the application of state-of-the-art machine learning frameworks such as TensorFlow, TensorRT, and PyTorch to develop innovative solutions that address complex challenges in these fields. Mr. Tariq is particularly engaged in improving robot motion planning, mapping, and localization (SLAM) algorithms to enhance autonomous systems’ navigation accuracy. His research also extends to federated learning approaches for secure AIoT-enabled applications, context-aware indoor-outdoor detection frameworks using smartphone sensors, and privacy-preserving methods in smart card authentication for Non-Fungible Tokens. His extensive work in these areas contributes to both theoretical advancements and practical implementations in machine learning and AI.
Skills:
Mr. Omer Tariq possesses a robust skill set in both software and hardware domains, crucial for his work in advanced machine learning and AI systems. He is proficient in programming languages such as C/C++, Python, SQL, SystemVerilog, and Verilog, enabling him to develop and optimize complex algorithms and systems. His expertise extends to a range of technologies and tools including TensorFlow, PyTorch, CUDA, and various AWS services like EC2, PostgreSQL, SQS, and Lambda. Additionally, he is adept in containerization and orchestration technologies such as Docker and Kubernetes. Mr. Tariq’s skills also encompass digital ASIC design and hardware tools, with experience using Cadence IC Design, Synopsys, Vivado/Vitis, and Altium Designer. This diverse technical knowledge underpins his ability to tackle intricate challenges in machine learning, embedded systems, and digital design.
Conclution:
Mr. Omer Tariq’s combination of academic excellence, professional experience, technical skills, and impactful research makes him a strong candidate for the Best Researcher Award. His contributions to AIoT, Autonomous Systems, and privacy-preserving technologies are not only innovative but also address critical challenges in today’s technological landscape. His achievements reflect a commitment to advancing knowledge and technology, making him deserving of recognition as a leading researcher in his field.
Publication Tob Noted:
- A Smart Card Based Approach for Privacy Preservation Authentication of Non-Fungible Token Using Non-Interactive Zero Knowledge Proof
- Authors: MBA Dastagir, O. Tariq, D. Han
- Published in: 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable
- Citations: 1
- Year: 2022
- Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations
- Authors: O. Tariq, MBA Dastagir, D. Han
- Published in: Electronics 13 (16), 3148
- Citations: Not yet cited
- Year: 2024
- TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection
- Authors: MBA Dastagir, O. Tariq, D. Han
- Published in: IEEE Access
- Citations: Not yet cited
- Year: 2024
- 2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation
- Authors: O. Tariq, D. Han
- Published in: IEEE Access
- Citations: Not yet cited
- Year: 2024
- DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors
- Authors: MBA Dastagir, O. Tariq, D. Han
- Published in: Sensors 24 (16), 5125
- Citations: Not yet cited
- Year: 2024
- HILO: High-level and Low-level Co-design, Evaluation and Acceleration of Feature Extraction for Visual-SLAM using PYNQ Z1 Board
- Authors: MB Akram Dastagir, O. Tariq, D. Han
- Published in: 12th International Conference on Indoor Positioning and Indoor Navigation
- Citations: Not yet cited
- Year: 2022