Dr. Muhammad Bhutta | Neural Interface Awards | Best Researcher Award

Dr. Muhammad Bhutta | Neural Interface Awards | Best Researcher Award

Dr. Muhammad Bhutta, University of UTAH Asia Campus, South Korea

Muhammad Raheel Bhutta is an accomplished academic and researcher in the field of Cogno-Mechatronics Engineering, currently serving as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of UTAH Asia Campus in Incheon, South Korea. He earned his Ph.D. from Pusan National University, where his dissertation focused on the development of a real-time brain signal acquisition system and the classification of various mental tasks. He also holds a Master’s degree in VLSI System Design from Griffith University, Australia, and a Bachelor’s degree in Computer Engineering from COMSATS Institute of Information Technology, Pakistan. With extensive teaching experience across multiple universities, Dr. Bhutta has imparted knowledge in areas such as human-computer interaction, digital systems, and embedded system design. In addition to his teaching role, he actively participates in curriculum development and supervises student projects in fields like mobile app development, robotics, and IoT. His professional journey also includes roles as a project manager and senior project manager in various organizations, where he led teams in designing innovative systems, including security and fire alarm systems. Dr. Bhutta is married and resides in South Korea, contributing significantly to academic research and engineering education.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award: Muhammad Raheel Bhutta

Muhammad Raheel Bhutta is an exemplary candidate for the Best Researcher Award due to his extensive academic background, significant contributions to research, and professional achievements in the fields of cognitive mechatronics and computer engineering.

Education:

🎓 Ph.D. in Cogno-Mechatronics Engineering
Pusan National University, South Korea
(Feb. 2012 – Aug. 2017)
📝 Dissertation: “Development of real-time brain signal acquisition system and classification of different mental tasks”

🎓 Master of Engineering in VLSI System Design
Griffith University, Australia
(Feb. 2004 – Feb. 2005)
📝 Thesis Project: “Implementation of double precision floating point divider and square root”

🎓 B.S. in Computer Engineering
COMSATS Institute of Information Technology, Pakistan
(Sep. 1999 – Aug. 2003)
📝 Final Year Project: “USB communicator core using FPGA”

Professional Experience:

👨‍🏫 Assistant Professor
Department of Electrical and Computer Engineering,
University of UTAH Asia Campus, South Korea
(July 2022 – Present)

  • Courses Taught: Accelerated Object-Oriented Programming, Digital System Design, Discrete Structures
  • Committee Member: Undergraduate Research Opportunity Grants (UROG)

👨‍🏫 Assistant Professor
Department of Computer Science & Engineering,
Sejong University, South Korea
(March 2018 – June 2022)

  • Courses Taught: Human-Computer Interaction, Operating Systems, Algorithms
  • Supervised projects in Mobile App Development, IoT, and Robotics

👨‍🏫 Assistant Professor

Department of Electrical & Computer Engineering,
Center for Advanced Studies in Engineering (CASE), Pakistan
(Sep. 2010 – Feb. 2012)

  • Courses Taught: Embedded System Design, Microprocessor
  • Supervised final year projects and chaired multiple committees

Research Interests:

  • Brain-Computer Interfaces 🧠
  • Mechatronics 🤖
  • Human-Computer Interaction 💻

Achievements:

  • Led winning teams in national and international robotics competitions 🏆
  • Published research in real-time brain signal acquisition 📖

Publication Top Notes

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.

Dr. Junwen Luo | Neural Interface Award | Best Researcher Award

Dr. Junwen Luo | Neural Interface Award | Best Researcher Award

Dr. Junwen Luo, Fudan University, China

Dr. Junwen Luo, MIEEE, MBNA, MIET, is a distinguished expert in neuromorphic computing and brain-machine interfaces (BMI). Currently serving as the Head of the BrainUp Research Lab at NaoluBrain Company in Beijing, Dr. Luo is renowned for his pioneering work in noninvasive BMI technologies and brain-inspired algorithms. He holds a Ph.D. in Microelectronics from Newcastle University, where his research focused on digital neural circuits. His academic journey also includes notable positions at leading institutions such as MIT, Imperial College London, and the City University of Hong Kong.

Professional Profile:

Summary of Suitability for Best Researcher Award:

Junwen Luo has demonstrated exceptional research capabilities in neuromorphic computing and Brain-Machine Interfaces (BMI). With over ten years of experience in both academia and industry, he has made significant contributions to the field, evidenced by his 30+ publications in high-impact journals and conferences such as ICML, TMBE, and Sensors. His work is recognized by leading experts like Tobi Delbruck (ETH) and Kea-Tiong Tang (NTH), highlighting his influence in the domain.

Education

  • Ph.D. in Microelectronics
    Newcastle University, United Kingdom
    November 2010 – November 2014
    Thesis Title: The Digital Neural Circuits: From Ions to Networks
  • Neural Engineering Academy Visitor
    Massachusetts Institute of Technology (MIT), United States
    January 2011 – October 2011
    Project Title: The Mechanisms of Stomatogastric Ganglion Nervous Network
  • M.Sc. in Automation
    Newcastle University, United Kingdom
    September 2009 – September 2010
    Thesis Title: The Fuzzy Logic Control of PMSM Machine
  • B.Sc. in Automation
    Huazhong University of Science and Technology, China
    September 2005 – September 2009
    Thesis Title: The Dynamic Control of Industrial Chemical Interactions

Work Experience

  • Head of BrainUp Research Lab
    NaoluBrain Company, Beijing
    April 2022 – Present

    • Develop noninvasive BMI Brain Touch products from concept to production DVT stage.
    • Develop and release the first dream emotion related EEG dataset for sleep-related products.
      Brain Touch Product
      Dream Emotion EEG Dataset
  • Research Scientist
    Alibaba Group, Sunnyvale
    June 2019 – April 2022

    • Focused on sparse neural network CPU acceleration and brain-inspired algorithm development.
    • Developed GEM5 based memory system modifications for accelerating SPMV/COV operations.
    • Created Spike Gating Flow with few-shot online learning performances for action recognition.
      Spike Gating Flow Code
  • Engineering Lead/Research Associate
    Newcastle University, Newcastle/London
    November 2014 – June 2019

    • Led the Controlling Abnormal Network Dynamics using Optogenetic (CANDO) project, developing an implantable brain chip system for optogenetic control of neural activity.
    • Responsibilities included development of invasive BMI hardware computing architecture and Neural Processor Unit (NPU), bio-inspired brain signal processing algorithms, and system integration.
      CANDO Project
  • Research Assistant
    City University of Hong Kong, Hong Kong
    January 2013 – October 2013

    • Worked on the Real-time Cerebellum Prosthesis project, focusing on the design and development of an FPGA-based artificial cerebellum system with AI computing capabilities.
    • Responsibilities included development of on-chip multi-core learning systems, routers, and bio-inspired cerebellar timing learning algorithms.

Publication top Notes:

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice

 

The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation

 

Emotion Recognition from Few-Channel EEG Signals by Integrating Deep Feature Aggregation and Transfer Learning

 

A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions

 

The SCEEGNet: An Efficient Learning Method for Emotion Recognition Based on the Few Channels