Mr. Omer Tariq, Korea Advanced Institute of Science and Technology, KAIST, South Korea
Omer Tariq is a Ph.D. candidate at the Korea Advanced Institute of Science and Technology (KAIST), specializing in efficient and privacy-preserving deep learning for AIoT and autonomous systems. With a strong foundation in digital ASIC design, embedded systems, and hardware design, Omer has over seven years of experience in developing and deploying innovative machine learning solutions using TensorFlow, TensorRT, and PyTorch. His research includes advanced robotics software systems, autonomous navigation, and state-of-the-art motion planning algorithms. He has led teams in high-performance SoC/RTL design and verification at the National Electronics Complex, Pakistan, and contributed to satellite imaging systems at SUPARCO. Omer holds a BSc in Electrical Engineering from the University of Engineering and Technology, Taxila, and has published several papers in prominent journals. His technical skills are complemented by a range of certifications in machine learning, data science, and digital signal processing.
Professional Profile:
Summary of Suitability for Best Researcher Award
Omer Tariq is a Ph.D. candidate specializing in efficient and privacy-preserving deep learning for AIoT and Autonomous Systems. His work is highly relevant to current technological advancements and addresses significant challenges in machine learning, robotics, and autonomous systems. His research includes:
Korea Advanced Institute of Science and Technology (KAIST)
Doctor of Philosophy (Ph.D.) in Computer Science
May 2021 – July 2025
- Majors: Machine Learning & AI
- CGPA: 3.74/4.3
- Coursework: Programming for AI, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, Intelligent Robotics, Human-Computer Interaction, Artificial Intelligence and Machine Learning, Technical Writing for Computer Science, Advanced Machine Learning, IoT Datascience
University of Engineering and Technology (UET), Taxila
Bachelor of Science in Electrical Engineering
Nov 2010 – July 2014
- CGPA: 3.25/4.0
- Thesis: Computer Vision-Assisted Object Detection and Control Framework for 3-DoF Robotic Arm
- Area: Microelectronics, Control Systems, and Advanced Computer Architecture
Work Experience
Department of Industrial & Systems Engineering (ISysE), KAIST
Research Assistant
Nov. 2023 – March 2024
- Designed and developed the electronics and power management module for the DAIM-Autonomous Mobile Robot, enhancing operational efficiency and reliability.
- Engineered advanced robotics software systems for autonomous navigation and task execution.
- Implemented state-of-the-art robot motion planning, mapping, and localization (SLAM) algorithms to improve real-time navigation accuracy.
National Electronics Complex, Pakistan (NECOP)
Engineering Manager & Team Lead
Apr. 2019 – Sep. 2022
- Led verification and validation of high-performance SoC/RTL designs, ensuring system performance and reliability.
- Spearheaded RTL development and optimization for high-performance IC designs, including logic synthesis, DFT, scan chain insertion, formal verification, and static timing analysis.
- Managed the use of Synopsys and Cadence EDA tools for front-end and back-end digital IC design processes.
National Space Agency, Pakistan (SUPARCO)
Assistant Manager
Oct. 2014 – Apr. 2019
- Designed and developed satellite imaging payload systems for national satellite missions.
- Engineered high-speed, multi-layer PCB designs and conducted signal/power integrity simulations for satellite systems.
- Developed embedded systems for the Satellite Ku-Band Positioning Unit, enhancing communication and positioning capabilities.
Publication top Notes:
2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation
TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection
Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations
DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors