Dr.Reza Askari Moghadam | Bio Sensor Awards | Best Researcher Award

Dr.Reza Askari Moghadam | Bio Sensor Awards | Best Researcher Award-5093

Dr.Reza Askari Moghadam, Sorbonne Université, France

Reza Askari Moghadam is a distinguished academic and researcher currently serving as a Lecturer at Sorbonne Université in Paris, France, specializing in electronics and mechatronics. He holds a Ph.D. in Electronics from the Iran University of Science and Technology, where he conducted innovative research on intelligent fault detection in RF MEMS, funded by the Iranian Telecommunications Research Center. With over a decade of experience as a Tenured Lecturer at the University of Tehran, Reza has significantly contributed to the fields of sensors, actuators, microfluidics, and artificial intelligence. His extensive teaching background encompasses more than 4,600 hours of instruction across various degree programs, from bachelor’s to doctoral levels. Reza’s research output includes 58 articles in international journals and 59 conference papers, highlighting his active engagement in advancing knowledge in his field. He has also participated in multiple collaborations and projects in Europe, further enriching his academic portfolio. In addition to his research and teaching, he possesses a robust skill set in various software tools, including Python, MATLAB, and COMSOL, which support his ongoing contributions to engineering and technology.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: 

Reza Askari Moghadam is an accomplished academic and researcher in the field of Electronics and Engineering, with a solid track record of teaching, research, and publication. His diverse experiences, educational background, and substantial contributions to the field make him a strong candidate for the Best Researcher Award.

Education

  1. Ph.D. in Electronics
    • Institution: Islamic Azad University (IUST), Tehran, Iran
    • Years: 2001 – 2007
    • Thesis: “Intelligent Detection of Faults in RF MEMS”
    • Funding: Iranian Telecommunications Research Center (ITRC)
  2. Master’s Degree in Electrical Engineering (Specialization: Control)
    • Institution: Islamic Azad University (IUST), Tehran, Iran
    • Years: 1998 – 2001
    • Thesis: “Design, Implementation, and Control of a Robotic Arm”
    • Funding: Electronics Research Center, IUST
  3. Bachelor’s Degree in Electrical Engineering (Specialization: Electronics)
    • Institution: University of Petroleum Industry, Iran
    • Years: 1993 – 1998
    • Thesis: “Design and Implementation of an EEPROM Programmer”

Professional Experience

  1. Lecturer
    • Institution: Campus Pierre et Marie Curie, Sorbonne Université, Paris, France
    • Years: Sep. 2023 – Present
  2. Temporary Teaching and Research Attaché (ATER)
    • Institution: Laboratoire Images, Signaux et Systèmes Intelligents (LISSI), UPEC, France
    • Years: Jan. 2022 – Sep. 2023
    • Notes: Contract renewed in September 2022
  3. Tenured Lecturer
    • Institution: Department of “Mechatronics & MEMS”, Faculty of New Sciences and Technologies, University of Tehran (UT), Iran
    • Years: Sep. 2012 – Jan. 2022

Research Activities

  • Collaborated with LISSI Laboratory, UPEC, France since 2016.
  • Visiting Researcher at Nano Center, University of Southampton, UK (2010, three months).
  • Attended Synchrotron Summer School at Daresbury Synchrotron Laboratory, UK (2004, one month).

Publication top Notes:

Simplified U-Net as a deep learning intelligent medical assistive tool in glaucoma detection

High speed universal NAND gate based on weakly coupled RF MEMS resonators

Microfluidics chip inspired by fish gills for blood cells and serum separation

Theoretical and experimental evaluation of small flow rate ultrasonic flowmeter

Design optimization of a heat-to-cool Stirling cycle using artificial neural network

A novel Gamma-type duplex Stirling system to convert heat energy to cooling power: Theoretical and experimental study

Reza Shokri | Neural Recording | Best Paper Award

Mr. Reza Shokri | Neural Recording | Best Paper Award

PhD at University of Genova, Italy

Reza Shokri, born in July 1992, is an accomplished electrical engineer specializing in integrated circuit design and biomedical applications. With a strong academic background and a passion for innovation, Reza has consistently excelled in his field, demonstrating leadership through both research and teaching roles. Currently pursuing his PhD at the University of Genova, he continues to develop cutting-edge technologies that bridge the gap between engineering and medicine. Reza’s work is characterized by its relevance to neural recording systems, showcasing his commitment to improving healthcare through engineering solutions.

Profile:

ORCID Profile

Strengths for the Award:

  1. Outstanding Academic Performance: Reza has demonstrated exceptional academic achievement, being ranked 2nd in a highly competitive PhD entrance exam in Iran, and 65th among over 30,000 participants in the MSc entrance exam. This reflects both his intellect and dedication to his field.
  2. Diverse Research Experience: His research spans critical areas such as DC-DC converters, low-power biomedical ADCs, and neural recording systems. This breadth showcases his versatility and ability to tackle complex problems in engineering.
  3. Significant Contributions to Publications: Reza has authored and co-authored several noteworthy publications, including articles in reputable journals and conference proceedings. His work on a VCO-based ADC for neural recording applications indicates a strong focus on practical and impactful research.
  4. Teaching and Mentoring: His experience as a teaching assistant at reputable institutions highlights his ability to communicate complex concepts and contribute to the education of future engineers.
  5. Relevant Work Experience: His professional roles in both academic and industrial settings, particularly in designing analog integrated circuits for biomedical applications, demonstrate practical skills and a commitment to applying research in real-world contexts.
  6. Collaborative Research Efforts: Reza has effectively collaborated with multiple researchers and professors, indicating strong teamwork skills and an ability to contribute to multidisciplinary projects.

Areas for Improvement:

  1. Language Proficiency: While Reza has an intermediate level of English, enhancing his proficiency could improve his ability to engage with a broader international audience and contribute to global research discussions.
  2. Broader Impact Assessment: Although his research is innovative, focusing more on the societal and economic impacts of his work could enhance its relevance and applicability.
  3. Networking and Conferences: Increasing participation in international conferences and workshops can provide Reza with more opportunities to present his work, receive feedback, and establish connections with other researchers.
  4. Leadership Roles: Pursuing leadership positions in research groups or committees could help him develop skills in project management and strategic planning.

Education:

Reza began his academic journey in Electrical Engineering at Tabriz University, earning his BSc with a thesis on “Implementation of Digital Pen with an Accelerometer.” He later pursued an MSc at the University of Tehran, focusing on circuit design for neural recording systems. His commitment to furthering his expertise led him to the University of Genova, where he is currently working towards a PhD. His education has equipped him with a solid foundation in both theoretical knowledge and practical skills, essential for addressing complex engineering challenges.

Experience:

Reza’s professional experience spans multiple roles in both academic and industry settings. He currently works as an Analog Integrated Circuit Designer at the University of Tehran, focusing on the design and layout of multipolar waveform stimulators for deep brain stimulation systems. Previously, he served as a Hardware Designer at Niktek Company, where he designed a high-resolution arbitrary waveform stimulator. His experience also includes significant projects on DC-DC converters and automotive control modules, showcasing his versatile engineering skills and commitment to advancing technology in biomedical applications.

Awards and Honors:

Reza has received numerous accolades for his academic and research excellence. He ranked 2nd out of over 1500 participants in the Electrical Engineering PhD Entrance Exam in Iran and achieved 65th among more than 30,000 in the MSc Entrance Exam. These accomplishments reflect his dedication and proficiency in electrical engineering. Reza’s commitment to advancing knowledge in his field has not only earned him recognition but also inspires his peers and future engineers to strive for excellence.

Research Focus:

Reza’s research focuses on the intersection of electrical engineering and biomedical applications, particularly in neural recording and stimulation systems. His work includes the design of low-power, high-performance analog-to-digital converters and DC-DC converters tailored for biomedical applications. He is also exploring quantum phase estimation algorithms, reflecting his innovative approach to addressing modern engineering challenges. Reza’s research aims to enhance medical technologies and improve patient outcomes, contributing significantly to the field of biomedical engineering.

Publication Top Notes:

  • A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications
  • Highly Linear, Digital OTA With Modified Input Stage
  • Multipolar Stimulator for DBS Application with Concurrent Imbalance Compensation
  • A Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications
  • A Buck Converter Based on Dual Mode Asynchronous Pulse Width Modulator

Conclusion:

Reza Shokri possesses the qualifications, research experience, and academic achievements that make him a strong candidate for the Best Researcher Award. His dedication to advancing knowledge in electrical engineering, particularly in biomedical applications, is commendable. By addressing areas for improvement, such as enhancing language skills and increasing networking opportunities, Reza can further amplify his contributions to the field and increase his impact as a researcher. His potential for future innovation and leadership in electrical engineering positions him as a valuable asset to the academic and scientific community.

Mr. Fulin Cai | Patient Monitor Award | Best Researcher Award

Mr. Fulin Cai | Patient Monitor Award | Best Researcher Award 

Mr. Fulin Cai, Arizona State University, United States

Fulin Cai is a dedicated Ph.D. student in Computer Engineering at Arizona State University (ASU) under the supervision of Teresa Wu, with a research focus on deep learning, medical signals, and healthcare. He earned his M.S. in Computer Engineering from ASU with a GPA of 3.86/4.0 in 2023. Prior to this, he completed an M.S. in Management Science and Engineering and a B.S. in Information Management and Information System from Shenzhen University (SZU), ranking high in his class. Fulin’s research has led to numerous publications in prestigious journals such as IEEE Sensors Journal and Frontiers in Physiology, with topics ranging from radar sensing to respiratory function monitoring. He has also presented his work at notable conferences like the Institute of Industrial and Systems Engineers (IISE) Annual Conference.

Professional Profile:

ORCID

Education 🎓

  • Arizona State University (ASU), Tempe, USA
    • Ph.D. Student in Computer Engineering (08/2020 – Present)
    • Supervisor: Teresa Wu
    • Research Interests: Deep Learning, Medical Signals, Healthcare
  • Arizona State University (ASU), Tempe, USA
    • M.S. in Computer Engineering, GPA: 3.86/4.0 (05/2023)
  • Shenzhen University (SZU), Shenzhen, China
    • M.S. in Management Science and Engineering, GPA: 86/100 (Rank 3) (06/2019)
    • Supervisors: Li Li and Xianghua Chu
    • Research Interests: Meta Learning, Reinforcement Learning, Optimization
  • Shenzhen University (SZU), Shenzhen, China
    • B.S. in Information Management and Information System, GPA: 3.56/4.0 (Rank 4) (06/2016)

Teaching Experience 👨‍🏫

  • Arizona State University, Tempe, USA
    • Information Systems Engineering (Spring 2024)
  • Shenzhen University (SZU), Shenzhen, China
    • Lecturer, College of Continuing Education:
      • Management Information System Analysis and Design (03/2017-06/2017)
      • Website Construction and Management (09/2017-12/2017)
      • E-commerce Technology (03/2018-06/2018)
      • Management Information System (03/2019-06/2019)
    • TA, Online Course: Living with Etiquette (03/2017-06/2018)

Working Experience 💼

  • Arizona State University, Tempe, USA (08/2020-Present)
    • Position: Graduate Research Assistant
    • Research Topic: Enhanced representation learning for human biosensing applications
    • Responsibilities:
      • Apply computer vision models to human biosensing applications (e.g., ECG for sleep apnea, radar data for physiological measurement and motion detection).
      • Improve representation learning of DL models from time and frequency perspectives when bio signal is treated as a spectrogram (1-channel image).
  • Huawei Technologies Co., Ltd, Shenzhen, China (07/2019-07/2020)
    • Position: Algorithm Engineer
    • Responsibilities:
      • Implementation of automatic channel selection algorithm.
      • Development of channel simulation software based on NS-3.

Academic Services 📝

  • Journal Reviewer:
    • Computers in Biology and Medicine
    • Biomedical Signal Processing and Control
    • Computers & Industrial Engineering
    • International Journal of Production Research
    • Quality and Reliability Engineering International

Publication top Notes:

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing

Corrections to “STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning” [May 23 10998-11006]

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award

Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award 

Prof. Shing-Hong Liu, Chaoyang University of Technology, Taiwan

Shing-Hong Liu is an esteemed academic and researcher in the field of biomedical engineering and computer science. He obtained his B.S. degree in Electronic Engineering from Feng-Jia University, Taiwan, in 1990, followed by an M.S. degree in Biomedical Engineering from National Cheng-Kung University in 1992. In 2002, he earned his Ph.D. from the Department of Electrical and Control Engineering at National Chiao-Tung University, Taiwan. Since August 1994, Dr. Liu has been actively involved in academia, initially as a Lecturer in the Department of Biomedical Engineering at Yuanpei University, Taiwan. He progressed to become an Associate Professor from 2002 to 2008. Currently, he holds the position of Distinguished Professor in the Department of Computer Science and Information Engineering at Chaoyang University of Technology. Dr. Liu’s research focuses on biomedical signal processing, artificial intelligence applications in mobile health (mHealth), and the design of biomedical instruments. He has been recognized for his contributions, being named one of the World’s Top 2% Scientists in 2020. His research projects have received substantial funding, totaling NT$36,329,914, and he has authored 59 papers in SCI journals.

 

Professional Profile:

ORCID

 

Education:

  • B.S. in Electronic Engineering
    • Feng-Jia University, Taizhong, Taiwan, R.O.C.
    • Year of Completion: 1990
  • M.S. in Biomedical Engineering
    • National Cheng-Kung University, Tainan, Taiwan, R.O.C.
    • Year of Completion: 1992
  • Ph.D. in Electrical and Control Engineering
    • National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.
    • Year of Completion: 2002

Work Experience:

  • Lecturer
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • August 1994 – 2002
  • Associate Professor
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • 2002 – 2008
  • Distinguished Professor
    • Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taiwan, R.O.C.
    • 2020 – Present

Achievements:

Shing-Hong Liu has been recognized as one of the World’s Top 2% Scientists in 2020. His research interests focus on biomedical signal processing, artificial intelligence for mHealth applications, and the design of biomedical instruments. He has successfully led projects with a total budget of NT 36,329,914 and has published 59 papers in SCI journals.

Publication top Notes:

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing

A Wearable Assistant Device for Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing