Prof. Hsin-Yuan Chen | AI Awards | Outstanding Scientist Award

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Prof. Hsin-Yuan Chen | AI Awards | Outstanding Scientist Award

Prof. Hsin-Yuan Chen, Zhejiang University, Taiwan

H.Y. J. Chen is an accomplished researcher with expertise spanning multiple fields including bioengineering, materials science, and guidance system technologies. Holding a Web of Science ResearcherID (JSL-7102-2023) , Chen has an impressive H-index of 58, with over 11,000 citations accumulated from works published between January 2000 and March 2024. Some of Chen’s notable contributions include studies on biochar anodes for lithium-ion batteries, computational fluid dynamics (CFD) analysis of cormorant takeoff mechanisms, and innovations in van der Waals semiconductor photodetectors. Chen’s interdisciplinary work also extends into preprints and collaboration on machine learning applications in conformal field theories.

Professional Profile:

Scopus

Suitability Summary for Research for Outstanding Scientist Award

Researcher: H.Y. J. Chen

Summary:

H.Y. J. Chen stands out as a highly suitable candidate for the Research for Outstanding Scientist Award due to his exceptional contributions and interdisciplinary expertise across multiple scientific domains. Chen’s research spans bioengineering, materials science, and guidance system technologies, showcasing a profound impact on these fields.

🎓Education:

H.Y. J. Chen is an accomplished researcher with expertise in bioengineering, materials science, and guidance system technologies. Chen earned both his Master’s and Bachelor’s degrees, as well as a Ph.D., from Zhejiang University, Hangzhou, China.

Publication Top Notes:

  • Protective Effects of an Oligo-Fucoidan-Based Formula Against Osteoarthritis Development via iNOS and COX-2 Suppression Following Monosodium Iodoacetate Injection
    • Citations: 0
  • Hinokitiol Inhibits Breast Cancer Cells In Vitro Stemness-Progression and Self-Renewal with Apoptosis and Autophagy Modulation via the CD44/Nanog/SOX2/Oct4 Pathway
    • Citations: 1
  • Alleviating 3-MCPD-Induced Male Reproductive Toxicity: Mechanistic Insights and Resveratrol Intervention
    • Citations: 1
  • Hinokitiol as a Modulator of TLR4 Signaling and Apoptotic Pathways in Atopic Dermatitis
    • Citations: 1
  • Integrating Explainable Artificial Intelligence and Blockchain to Smart Agriculture: Research Prospects for Decision Making and Improved Security
    • Citations: 7

 

 

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

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Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award 

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:

Education

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

 

 

Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award

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Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award 

Mr. Lianfa Li, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China 

Dr. Lianfa Li is a distinguished Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences. Since August 2017, he has been at the forefront of innovations in data science and machine learning, with a particular focus on remote sensing and air pollution modeling to study exposure and health effects. Dr. Li’s academic journey began with a Bachelor of Science in Resources, Planning, and Management from Nanjing University in 1998, followed by a Ph.D. in Geographical Information Science from the Institute of Geographical Sciences and Natural Resources Research at the Chinese Academy of Sciences in 2005. His career includes significant roles such as Associate Professor at the Chinese Academy of Sciences, Postdoctoral Scholar and Associate Specialist at the University of California, Irvine, and Research Associate at USC’s Department of Preventive Medicine.

Professional Profile:

 

ORCID

 

Summary of Suitability for the Top Researcher Award

Lianfa Li, PhD, currently a Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences, is an exemplary candidate for the Top Researcher Award. His extensive background in data science and machine learning, particularly in the realm of remote sensing and air pollution exposure, positions him as a leader in his field. Below are the reasons why Dr. Li is suitable for this prestigious award:

EDUCATION 🎓📚

  • PhD in Geographical Information Science (June 2005)
    Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
    Advisor: Prof. Jinfeng Wang
  • Bachelor of Science in Resources, Planning and Management (Aug 1998)
    Nanjing University, Nanjing, Jiangsu Province, China
    Advisor: Prof. Yunliang Shi

ACADEMIC EMPLOYMENT 🏛️💼

  • Senior Research Associate, Lead Data Scientist (Aug 2017-Present)
    Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA
    Leading innovations in data science and machine learning, and the modeling efforts in remote sensing and air pollution (exposure and health effects)
  • Research Associate (Aug 2017-July 2014)
    Department of Preventive Medicine, University of Southern California, Los Angeles, CA
  • Associate Specialist (June 2013-June 2014)
    Program in Public Health, University of California, Irvine, CA

HONORS AND AWARDS 🏆🎖️

  1. 2010.6
    The paper about Bayesian risk modeling (Risk Analysis, 30(7), 1157-1175) selected for a media outreach campaign in 2010 by Society for Risk Analysis
  2. 2007.5
    Chinese Academy of Sciences KC Wong Work Incentive Fund
  3. 2004.3
    The Excellent Presidential Scholarship of Chinese Academy of Sciences, 2004

WORKSHOP AND PRESENTATION 🎤📅

  1. Biweekly workshop: “Air pollution and exposure modeling” (2015-present, University of Southern California, California, USA)
  2. Invited presentation: “GCN-assisted U-Net for segmentation of OCT images” (Bay area data science workshop, Mar. 27, 2021)
  3. Invited presentation: “Enhancing semantic segmentation with contextual information” (Bay area data science workshop, Dec. 07, 2019)

Publication top Notes:

Geocomplexity Statistical Indicator to Enhance Multiclass Semantic Segmentation of Remotely Sensed Data with Less Sampling Bias

Multiscale Entropy-Based Surface Complexity Analysis for Land Cover Image Semantic Segmentation

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Encoder–Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation

Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation