Omer Tariq | Cryptographic Accelerators | Best Researcher Award

Mr. Omer Tariq | Cryptographic Accelerators | Best Researcher Award

Ph.D. Candidate at Korea Advanced Institute of Science and Technology, South Korea

Mr. 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 over seven years of experience in Digital ASIC Design, Embedded Systems, and Hardware Design, Mr. Tariq has demonstrated a strong capability in developing and deploying innovative machine learning solutions using tools like TensorFlow, TensorRT, and PyTorch. His professional journey includes significant roles at the National Electronics Complex and the National Space Agency of Pakistan, where he led projects in SoC/RTL design, satellite imaging payload systems, and autonomous robotics. He is an accomplished researcher with several publications in prestigious journals, and his work is recognized for its impact on AI and robotics. Mr. Tariq holds a Bachelor of Science in Electrical Engineering from the University of Engineering and Technology, Taxila, and is actively seeking roles that will allow him to further contribute to the fields of AI and machine learning.

Profile:

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

 

Oluwafemi Oke | Cybersecurity And Cryptography | Best Researcher Award

Mr. Oluwafemi Oke, Cybersecurity And Cryptography, Best Researcher Award

Oluwafemi Oke at Near East University, Cypus

Oke Oluwafemi is a highly motivated and skilled Ph.D. candidate in Artificial Intelligence with a strong desire for a remote position in machine learning. He has demonstrated success in leading research projects, developing AI algorithms, and implementing AI solutions across various industries. Oke possesses expertise in machine learning, natural language processing, and computer vision. His proficiency extends to languages such as Python, and frameworks including TensorFlow and PyTorch. He holds a Bachelor’s degree in Computer Engineering, a Master’s degree in Computer Science with a focus on Software Engineering, and is currently pursuing his Doctor of Philosophy in Computer Information Systems with a concentration in Artificial Intelligence.

Education:

Babcock University

  • Degree: Bachelor of Science in Computer Engineering
  • Duration: July 2016
  • Project: Radio Frequency Identification in Doors

Babcock University

  • Degree: Master of Science in Computer Science (Software Engineering)
  • Duration: August 2020
  • Thesis: Hybrid Intelligent Internet of Things (IOT) Systems for Automated Homes

Near East University

  • Program: Doctor of Philosophy (PhD)
  • Duration: March 2021 – Present

Profile:

Professional  Experience:

AI Engineer, Cadbury Plc, 2015

  • Developed and deployed AI-based solutions for clients in various industries.
  • Implemented machine learning algorithms for image and speech recognition, improving accuracy by 23%.

AI Consultant, Corporate Affairs Commission, 2017

  • Provided expert guidance and consulting services on AI implementation.
  • Conducted workshops on machine learning and deep learning.
  • Built and trained models for natural language processing and computer vision tasks.

Data Scientist, NEU Cardiac Centre, 2020

  • Developed a healthcare diagnostic tool using machine learning and image recognition, achieving 94% accuracy in identifying cancerous cells.
  • Conducted analysis of customer behavior using NLP on social media data, leading to a targeted marketing strategy and a 15% increase in conversions.

Machine Learning Engineer, Harvest, 2022

  • Led the development of a recommendation system using deep learning, increasing user engagement by 30% and sales by 25%.
  • Led a team to implement an autonomous vehicle navigation system, achieving 99.5% accuracy in real-world scenarios.

Research Interests:

Mr. Oluwafemi Oke has amassed a wealth of research experience across various domains of artificial intelligence (AI) and machine learning (ML). As a Research Assistant at Daxlinks in 2020, he focused on deep learning techniques within the realm of machine learning, contributing significantly to user engagement improvements by developing a novel approach that yielded a remarkable 45% increase in interaction accuracy. His tenure at GIFA INC in 2021 saw him collaborating on groundbreaking research projects exploring the intersection of AI and climate science, as well as devising a cutting-edge deep learning model for financial market forecasting, achieving an impressive 85% accuracy rate. Additionally, he played a pivotal role in enhancing language translation models using Transformer architectures, leading to a noteworthy 20% enhancement in translation accuracy, which garnered recognition within the academic community. Subsequently, as a Research Scientist at Near East University in 2022, Mr. Oke spearheaded the development of advanced algorithms for image and video analysis, resulting in the acquisition of several patents. Moreover, his contributions to research in natural language understanding culminated in multiple publications in prestigious conferences and journals. Notably, Mr. Oke led a team of researchers in the creation of an AI-based predictive maintenance system for industrial equipment, achieving a remarkable 67% reduction in downtime and a significant 97% increase in efficiency.

Publications:

Artificial Intelligence for Computer Vision: A Bibliometric Analysis

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]

Brain-Computer Interfaces: High-Tech Race to Merge Minds and Machines

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]

The Impact of Artificial Intelligence in Foreign Language Learning Using Learning Management Systems: A Systematic Literature Review

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]