Omer Tariq | Cryptographic Accelerators | Best Researcher Award

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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

 

Lidan Wang | Chaotic Cryptography | Best Researcher Award

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Prof. Lidan Wang | Chaotic Cryptography | Best Researcher Award

Supervisor at Southwest University, China

Summary:

Prof. Lidan Wang is a distinguished professor and doctoral supervisor in the College of Artificial Intelligence at Southwest University in Chongqing, China. She earned her B.E. degree in Automatic Control from Nanjing University of Science and Technology in 1999 and her Ph.D. in Computer Software and Theory from Chongqing University in 2008. Furthering her academic journey, she completed post-doctoral research at Chongqing University in 2012. Prof. Wang’s research focuses on artificial intelligence, particularly in the areas of artificial neural networks, neural morphological systems, memristor devices and systems, chaotic systems, and nonlinear circuit design. She has led over 20 significant research projects, including those funded by the National Key R&D Program and the National Natural Science Foundation of China.

 

Profile:

Education:

Prof. Lidan Wang earned her Bachelor of Science degree in Electrical Engineering from Nanjing University of Science and Technology, China, in 1996. She pursued her Master’s degree and Ph.D. in Electrical Engineering at Chongqing University, China, completing her Ph.D. in 2008. Additionally, Prof. Wang conducted post-doctoral research at Chongqing University from 2012 to 2014, further advancing her expertise in the field of artificial intelligence and neural networks.

Professional Experience:

Prof. Lidan Wang began her academic career as an Associate Professor at Southwest University, Chongqing, China, serving from 2008 to 2012. She was promoted to Professor in 2013, a position she continues to hold. In addition to her role at Southwest University, Prof. Wang has gained international experience through various visiting professorships, including at Imperial College London, Nanyang Technological University, Texas A&M University at Qatar, and the University of Tasmania. Her leadership extends beyond teaching and research, as she currently serves as the deputy director of the Chongqing Key Laboratory of Brain-like Computing and Intelligent Control, Secretary General of the Chongqing Artificial Intelligence Society, and Director of the Chongqing Young Science and Technology Leaders Association.

Research Interests:

Prof. Lidan Wang’s research interests lie primarily in the realm of artificial intelligence, with a strong focus on artificial neural networks and neural morphological systems. Her work explores memristor devices and systems, chaotic systems, and nonlinear circuit design. Prof. Wang is particularly engaged in advancing the understanding and application of these technologies, aiming to develop innovative solutions and systems that integrate these complex components. Her research contributes significantly to the fields of artificial intelligence and neural network technologies.

Skills:

Prof. Lidan Wang possesses advanced skills in artificial intelligence, encompassing artificial neural networks and neural morphological systems. She is proficient in the design and implementation of memristor devices and systems, as well as in the analysis and development of chaotic systems and nonlinear circuits. Her expertise extends to leading and managing research projects, having successfully undertaken numerous high-profile projects including National Key R&D Program subprojects and various funding initiatives. Additionally, Prof. Wang has a strong background in patent development and academic publishing, contributing to her distinguished reputation in the scientific community.

Conclution:

Given her exceptional research contributions, extensive publication record, numerous awards, and significant leadership roles, Prof. Lidan Wang is a highly deserving candidate for the Best Researcher Award. Her work not only advances the field of artificial intelligence but also inspires and influences the global research community.

Publication Tob Noted:

Memristor-based cellular nonlinear/neural network: design, analysis, and applications

  • Authors: S. Duan, X. Hu, Z. Dong, L. Wang, P. Mazumder
  • Journal: IEEE Transactions on Neural Networks and Learning Systems
  • Volume: 26, Issue 6
  • Pages: 1202-1213
  • Year: 2014
  • Citations: 297

Electronic nose feature extraction methods: A review

  • Authors: J. Yan, X. Guo, S. Duan, P. Jia, L. Wang, C. Peng, S. Zhang
  • Journal: Sensors
  • Volume: 15, Issue 11
  • Pages: 27804-27831
  • Year: 2015
  • Citations: 296

Exponential stability of complex-valued memristive recurrent neural networks

  • Authors: H. Wang, S. Duan, T. Huang, L. Wang, C. Li
  • Journal: IEEE Transactions on Neural Networks and Learning Systems
  • Volume: 28, Issue 3
  • Pages: 766-771
  • Year: 2016
  • Citations: 159

A novel memristive Hopfield neural network with application in associative memory

  • Authors: J. Yang, L. Wang, Y. Wang, T. Guo
  • Journal: Neurocomputing
  • Volume: 227
  • Pages: 142-148
  • Year: 2017
  • Citations: 158

Memristor model and its application for chaos generation

  • Authors: L. Wang, E. Drakakis, S. Duan, P. He, X. Liao
  • Journal: International Journal of Bifurcation and Chaos
  • Volume: 22, Issue 08
  • Article Number: 1250205
  • Year: 2012
  • Citations: 147

Volatile and nonvolatile memristive devices for neuromorphic computing

  • Authors: G. Zhou, Z. Wang, B. Sun, F. Zhou, L. Sun, H. Zhao, X. Hu, X. Peng, J. Yan, …
  • Journal: Advanced Electronic Materials
  • Volume: 8, Issue 7
  • Article Number: 2101127
  • Year: 2022
  • Citations: 129

Resistive switching memory integrated with amorphous carbon-based nanogenerators for self-powered device

  • Authors: G. Zhou, Z. Ren, L. Wang, J. Wu, B. Sun, A. Zhou, G. Zhang, S. Zheng, S. Duan, …
  • Journal: Nano Energy
  • Volume: 63
  • Article Number: 103793
  • Year: 2019
  • Citations: 126

Artificial and wearable albumen protein memristor arrays with integrated memory logic gate functionality

  • Authors: G. Zhou, Z. Ren, L. Wang, B. Sun, S. Duan, Q. Song
  • Journal: Materials Horizons
  • Volume: 6, Issue 9
  • Pages: 1877-1882
  • Year: 2019
  • Citations: 123

Capacitive effect: An original of the resistive switching memory

  • Authors: G. Zhou, Z. Ren, B. Sun, J. Wu, Z. Zou, S. Zheng, L. Wang, S. Duan, Q. Song
  • Journal: Nano Energy
  • Volume: 68
  • Article Number: 104386
  • Year: 2020
  • Citations: 118

 

Phenias Nsabimana | Cybersecurity And Cryptography | Best Researcher Award

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Mr. Phenias Nsabimana, Cybersecurity And Cryptography, Best Researcher Award

Phenias Nsabimana at Ghent University, Rwanda

Mr. Phenias Nsabimana is a professional in the field of nutrition, food sciences, and education. He holds a Ph.D. in Nutrition and Food Sciences from Ghent University (USA), a Master of Science (MSc) in Food Science from Washington State University, and a Bachelor’s degree in Chemistry with Education from the National University of Rwanda. Mr. Nsabimana is also a Lecturer and Ph.D. student at the College of Agriculture, Animal Science, and Veterinary Medicine at the University of Rwanda. He is a member of the Belgian Nutrition Society, the Rwandan Society of Food Science and Technology (RFST), and the Washington State University alumni association. His current interests include the application of data science in Food Science and Nutrition, promoting healthy eating to prevent malnutrition and non-communicable diseases, formulating healthy foods, and ensuring food safety and environmental sanitation.

Profile:

Education:

Ph.D. in Nutrition and Food Sciences:

  • Institution: Ghent University (USA), Belgium
  • Duration: From 12/02/2018 to Present
  • Expected Qualification: Ph.D. (to be awarded by December 2024)

Master of Science (MSc) in Food Science:

  • Institution: Washington State University, USA
  • Duration: From 03/18/2011 to 05/12/2013
  • Title Awarded: Master of Science (MSc)

Bachelor’s degree in Chemistry with Education:

  • Institution: National University of Rwanda
  • Duration: From 10/03/2000 to 07/20/2006
  • Title Awarded: Bachelor’s degree (of four years)

Professional Experience:

Mr. Phenias Nsabimana has been working as a Lecturer and Ph.D. student at the College of Agriculture, Animal Science, and Veterinary Medicine at the University of Rwanda since June 2010. His responsibilities include teaching courses related to food science and nutrition, mentoring students in their academic activities, and conducting research focused on community outreach activities.

Research Interest:

  • Application of Data Science in Food Science and Nutrition: Utilizing data science techniques to analyze and improve various aspects of food science and nutrition, such as food quality, safety, and sustainability.
  • Promoting Healthy Eating and Preventing Malnutrition: Investigating strategies and interventions aimed at promoting healthy eating behaviors and addressing nutritional deficiencies to prevent malnutrition-related issues.
  • Development of Functional Foods: Researching the development and evaluation of functional foods that offer health benefits beyond basic nutrition, such as foods fortified with vitamins, minerals, or bioactive compounds.
  • Food Safety and Quality Assurance: Focusing on ensuring food safety standards, implementing quality assurance measures, and developing methods for detecting and mitigating foodborne hazards.
  • Sustainable Food Systems: Exploring sustainable practices in food production, distribution, and consumption to reduce environmental impact and improve long-term food security.
  • Nutritional Epidemiology: Conducting studies to understand the relationship between diet, nutrition, and health outcomes at the population level, including factors influencing dietary patterns and their impact on public health.

Membership:

  • Belgian Nutrition Society: Mr. Phenias Nsabimana has been a member of the Belgian Nutrition Society since 2018, contributing to the field of nutrition science and research.
  • Rwandan Society of Food Science and Technology (RFST): Since 2015, Mr. Nsabimana has been an active member of the RFST, engaging in discussions and activities related to food science and technology in Rwanda.
  • Washington State University Alumni: As a proud alumnus of Washington State University, Mr. Nsabimana maintains his connection to the university and its network of alumni since 2013.

Top Noted Publication: