Prof. Yanlong Tai | Machine Learning | Best Researcher Award

Posted on 25/03/2025 by ScienceFather

Prof. Yanlong Tai | Machine Learning | Best Researcher Award

Prof. Yanlong Tai, shenzhen institute of science and technology, China academic of science, China

Prof. Dr. Yanlong Tai is a distinguished researcher and professor in the field of smart sensing and flexible electronics. He is the Principal Investigator of the Smart-Sensing-Lab (SM-SE Lab.-SIAT) and serves as the Head of both the SIAT-UAEU International Smart Sensing & Energy Joint Lab and the SIAT-Fudan University (Zhuhai) Joint Innovation Center. Currently, he is a Full Professor at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), China, and a Joint Professor at the University of Science & Technology Shenzhen. Dr. Tai earned his Ph.D. from Fudan University, China (2009-2012), and was a visiting student at OHM University, Germany (2011-2012). He also holds Bachelor’s and Master’s degrees from Anhui University (2001-2008). His professional journey includes extensive research experience across multiple international institutions. He served as a Postdoctoral Researcher at University of California, Davis, USA (2012-2013), Fraunhofer ENAS, Chemnitz, Germany (2013-2014), and KAUST, Saudi Arabia (2014-2017). He later worked as a Research Scientist at Masdar Institute, UAE (2017-2019) before joining SIAT as a Professor in 2019.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award – Prof. Dr. Yanlong Tai

Prof. Dr. Yanlong Tai is an outstanding researcher and innovator, making him a highly suitable candidate for the Best Researcher Award. His extensive experience, leadership roles, and impactful research in smart materials, energy harvesting, and wearable electronics position him as a global leader in advanced sensing technologies.

🎓 Education

Ph.D. (2009 – 2012) – Fudan University, China
Visiting Student (2011 – 2012) – OHM University, Germany
Bachelor & Master Degree (2001 – 2008) – Anhui University, China

💼 Work Experience

Professor (2019 – Present) – Shenzhen Institutes of Advanced Technology (SIAT), CAS, China
Research Scientist (2017 – 2019) – Masdar Institute, United Arab Emirates
Postdoc Researcher (2014 – 2017) – King Abdullah University of Science and Technology (KAUST), Saudi Arabia
Postdoc Researcher (2013 – 2014) – Fraunhofer ENAS, Chemnitz, Germany
Postdoc Researcher (2012 – 2013) – University of California, Davis, USA

🏆 Achievements, Awards & Honors

📌 Principal Investigator of Smart-Sensing-Lab (SM-SE Lab.-SIAT)
🏅 Head of SIAT-UAEU International Smart Sensing & Energy Joint Lab
🏅 Head of SIAT-Fudan University (Zhuhai) Joint Innovation Center
🎖️ Full Professor at SIAT, CAS, Shenzhen, China
🎖️ Joint Professor at the University of Science & Technology, Shenzhen

Publication Top Notes:

Pretreatment mitochondrial priming correlates with clinical response to cytotoxic chemotherapy

CITED:663

Study of surface modification of nano-SiO2 with macromolecular coupling agent (LMPB-g-MAH)

CITED:154

A highly sensitive, low-cost, wearable pressure sensor based on conductive hydrogel spheres

CITED:152

Fabrication of paper-based conductive patterns for flexible electronics by direct-writing

CITED:142

Preparation, characterization and reaction mechanism of a novel silver-organic conductive ink

CITED:92

Double‐Twisted Conductive Smart Threads Comprising a Homogeneously and a Gradient‐Coated Thread for Multidimensional Flexible Pressure‐Sensing Devices

CITED:78

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Posted on 10/02/2025 by ScienceFather

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Mr. Fangzhou Lin, Hong Kong University of Science and Technology, Hong Kong

Fangzhou Lin is a Ph.D. researcher in Civil Engineering at the Hong Kong University of Science and Technology (HKUST), specializing in deep learning, machine vision, construction robots, and multimodal data fusion. He holds a Bachelor’s degree in Civil Engineering from Fuzhou University (2015-2019) and a Master’s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Lin’s research focuses on the integration of artificial intelligence and robotics in construction automation, with applications in fire safety inspection, resource management, visual measurement, and quality assessment. His work has been published in leading journals such as Automation in Construction, Computer-Aided Civil and Infrastructure Engineering, and Advanced Engineering Informatics. He has contributed to multiple cutting-edge studies on robotic systems for construction site management, vision-based measurement techniques, and reinforcement learning-based scheduling for electric concrete vehicles. As an emerging scholar in construction automation and AI-driven inspection technologies, Fangzhou Lin actively collaborates on multi-disciplinary research projects to enhance efficiency, safety, and sustainability in the built environment. His contributions to automated reality capture, rebar positioning, and construction robotics are shaping the future of intelligent construction and infrastructure development.

Professional Profile:

SCOPUS

Suitability of Fangzhou Lin for the Best Scholar Award

Fangzhou Lin is an outstanding early-career scholar with a strong background in deep learning, machine vision, construction robotics, and multimodal data fusion within the field of civil engineering. His academic trajectory, research productivity, and innovative contributions make him a compelling candidate for the Best Scholar Award. Below is a detailed assessment of his suitability based on key criteria.

🎓 Education

2015.09 – 2019.06 | Fuzhou University – Bachelor’s Degree in Civil Engineering
2019.09 – 2022.06 | Southeast University – Master’s Degree in Structural Engineering
2022.09 – Present | Hong Kong University of Science and Technology – Ph.D. in Civil Engineering

🏗️ Work & Research Experience

Expertise in: Deep learning, machine vision, construction robots, multimodal data fusion
Published in top journals such as Automation in Construction and Computer-Aided Civil and Infrastructure Engineering
Conducting research on:
- 🔥 Fire Safety Inspection using AI-driven visual inspection
- 🤖 Robotics for Construction Management with multi-task planning and automatic grasping
- 🏗️ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
- 🎯 Vision-based Monitoring for assembly alignment of precast concrete bridge members

🏆 Achievements & Awards

Published multiple high-impact journal papers 📚
Lead researcher on innovative construction technology projects 🔍
Contributed to advanced AI-driven automation for civil engineering 🤖
Research works under review in prestigious engineering journals 🏅
Collaborated with leading experts in civil engineering and robotics 🤝

Publication Top Notes:

Efficient visual inspection of fire safety equipment in buildings

Masoud DANESHTALAB | deep learning | Best Researcher Award

Posted on 28/12/202428/12/2024 by ScienceFather

Prof. Masoud DANESHTALAB | deep learning | Best Researcher Award

Prof. Masoud DANESHTALAB, Mälardalen University, Sweden.

Masoud Daneshtalab, Ph.D., Docent, Full Professor
Masoud Daneshtalab is a globally recognized scholar and Full Professor at Mälardalen University (MDU), Sweden. With over two decades of academic and professional excellence, he has made significant contributions to computer science and engineering, specializing in dependable systems, AI, and hardware/software co-design. A prolific researcher with an H-index of 35 and over 5,100 citations, Dr. Daneshtalab is included in the prestigious World’s Top 2% Scientists Ranking. He serves as the Scientific Director of Fundamental AI at MDU and collaborates internationally, holding adjunct professorships and contributing to cutting-edge research initiatives.

Professional Profile:

Google Scholar

Suitability of Masoud Daneshtalab for the Best Researcher Award

Dr. Masoud Daneshtalab is a highly suitable candidate for the “Research for Best Researcher Award,” based on his exceptional academic achievements and professional contributions. Here are the key reasons

Education

🎓 Academic Journey

Docent (2018): Qualified in Computer Science and Electronics, Mälardalen University, Sweden.
Ph.D. (2008–2011): Information and Communication Technology, University of Turku, Finland. Dissertation: Adaptive Implementation of On-Chip Networks under Prof. Hannu Tenhunen.
M.Sc. (2004–2006): Computer Engineering, University of Tehran, Iran. Thesis: Low Power Methods in Network-on-Chips under Prof. Ali Afzali-Kusha.
B.Sc. (1998–2002): Computer Engineering, Shahid Bahonar University of Kerman, Iran.

Experience

💼 Professional Contributions

Scientific Director (2024–Present): Fundamental AI, Mälardalen University, Sweden.
Full Professor (2020–Present): Innovation, Design & Engineering, MDU.
Adjunct Professor (2019–Present): Computer Systems, Tallinn University of Technology, Estonia.
Previous Roles: Associate Professor at MDU (2016–2020), EU Marie Curie Fellow at KTH Royal Institute of Technology (2014–2016), Lecturer at the University of Turku (2011–2014), and Researcher at the University of Tehran (2006–2008).

Research Interests

🔬 Key Areas

Optimization and robustness in deep learning models.
HW/SW co-design and heterogeneous computing.
Dependable systems, memory architectures, and interconnection networks.
Cutting-edge projects include sustainable AI, federated learning, and reliable autonomous systems.

Awards

🏆 Recognitions

Best Paper Awards: IEEE ECBS (2019), IEEE MCSoC (2018), and multiple HiPEAC Paper Awards (2013–2017).
Research Grants: Marie Skłodowska-Curie Fellowship (2014), Nokia Foundation (2009), and others.
Top Reviewer: IEEE Transactions on Computers (2013).
Fellowships: GETA, Helsinki University of Technology (2008–2011).

Publications

A review on deep learning methods for ECG arrhythmia classification

CITIED: 490

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

CITIED: 147

Routing algorithms in networks-on-chip

CITIED: 136

Smart hill climbing for agile dynamic mapping in many-core systems

CITIED: 125

EDXY–A low cost congestion-aware routing algorithm for network-on-chips

CITIED: 124

Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems

CITIED: 122

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Posted on 18/11/2024 by ScienceFather

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Mr. Seyed matin malakouti, University of Rijeka, Croatia

Seyed Matin Malakouti is an accomplished electrical engineer and researcher specializing in control systems engineering and machine learning. He completed his Master of Science in Electrical Engineering from the University of Tabriz, Iran, after earning his Bachelor’s degree from Isfahan University of Technology. His research spans various applications of machine learning, including wind power generation prediction, heart disease classification using ECG data, and solar farm power generation forecasting. Seyed’s work has resulted in several high-impact publications in prestigious journals, with his research on wind energy and machine learning techniques receiving significant citations. He has also been involved in cutting-edge projects such as predicting global temperature change and advancing renewable energy solutions. In recognition of his contributions, Seyed has received multiple awards, including the Best Researcher Award at the International Conference on Cardiology and Cardiovascular Medicine in 2023, and nominations for Best Paper and Best Researcher Awards in other international conferences. Additionally, he actively contributes to the scientific community as a peer reviewer for numerous journals in the fields of artificial intelligence, environmental sciences, and electrical engineering.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Seyed Matin Malakouti is a highly qualified and accomplished researcher in the field of Electrical Engineering, specializing in Control Systems, Machine Learning, and Data Science. His impressive academic background includes a Master’s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training 🎓

2020 – 2022: M.Sc. in Electrical Engineering – Control System Engineering, University of Tabriz, Iran
2014 – 2019: B.Sc. in Electrical Engineering, Isfahan University of Technology, Iran

Awards & Honors 🏆

2023: Best Researcher, International Conference on Cardiology and Cardiovascular Medicine
2023: Nominated for Best Paper Award, International Research Awards on Mathematics and Optimization Methods
2024: International Young Scientist Awards, Best Researcher Category

Technical Skills 🛠️

Machine Learning 🤖
Data Science 📊
Programming Languages: MATLAB, Python 💻

Peer Review Activities 🧐

Seyed has reviewed articles for prestigious journals, such as:

IEEE Access
Artificial Intelligence Review
BMC Public Health
Environmental Monitoring and Assessment 🌱

Publication top Notes:

Machine learning and transfer learning techniques for accurate brain tumor classification

ML: Early Breast Cancer Diagnosis

Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Discriminate primary gammas (signal) from the images of hadronic showers by cosmic rays in the upper atmosphere (background) with machine learning

Estimating the output power and wind speed with ML methods: A case study in Texas

Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award

Posted on 27/08/2024 by ScienceFather

Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award

Assoc Prof Dr. Izabela Rojek, Kazimierz Wielki University, Poland

Dr. Izabela Rojek is a prominent academic and researcher serving as the Head of the Department of Data Processing Methods and Tools and the Dean of the Faculty of Computer Science at Kazimierz Wielki University in Bydgoszcz, Poland. She holds the qualifications of Ph.D., D.Sc.Eng., and Associate Professor. Dr. Rojek’s research is centered on engineering sciences, specifically in Technical Informatics, Telecommunications, and Mechanical Engineering. Her extensive scientific output includes five books, 190 articles and chapters in monographs, and over 6000 points in the Ministry of Science and Higher Education (MNiSW) ranking, with a Hirsch index of 18 (Web of Science and Scopus) and 20 (Google Scholar). She has been recognized with 15 national and international awards, including four UKW Rector’s Awards and three foreign medals for outstanding inventions. Dr. Rojek’s contributions extend to 20 grants and innovation projects, and she actively participates in the Manufacturing Engineering Committee of the Polish Academy of Sciences, where she chairs the Manufacturing Digitisation Section.

Professional Profile:

ORCID

Suitability for Best Researcher Award:

Izabela Rojek is an exemplary candidate for the Best Researcher Award due to her outstanding contributions to the field of engineering sciences, particularly in Technical Informatics and Telecommunications. Her extensive publication record, high citation metrics, and significant involvement in national and international research projects highlight her impact on the field. Her leadership roles and innovative research further demonstrate her exceptional qualifications for this award.

Education:

Ph.D. in Engineering Sciences from Kazimierz Wielki University
D.Sc.Eng. (Doctor of Science in Engineering)
Associate Professor (Assoc. Prof.)

Work Experience:

Kazimierz Wielki University, Bydgoszcz
- Head of the Department of Data Processing Methods and Tools
- Dean of the Faculty of Computer Science

Additional Roles and Experience:

Member of the Manufacturing Engineering Committee of the Polish Academy of Sciences
Chair of the Manufacturing Digitisation Section of this Committee
Participation in the implementation of the IFS Applications IT system, including solution design, data migration, and training material preparation

Research and Contributions:

Authored 5 books and over 190 articles and chapters in monographs
Achieved over 6000 points in MNiSW (Polish Ministry of Science and Higher Education) evaluation
Total Impact Factor (IF) above 120
Hirsch index: h=18 (573 citations, Web of Science), h=18 (717 citations, Scopus), h=20 (1097 citations, Google Scholar)
Involved in 20 grants and innovation projects and 10 research topics
Recipient of 15 national and international awards, including 4 UKW Rector’s Awards and 3 foreign medals for outstanding inventions

Publication top Notes:

Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Use of Machine Learning to Improve Additive Manufacturing Processes

Review of the 6G-Based Supply Chain Management within Industry 4.0/5.0 Paradigm

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

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

Posted on 06/08/2024 by ScienceFather

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 🏆🎖️

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
2007.5
Chinese Academy of Sciences KC Wong Work Incentive Fund
2004.3
The Excellent Presidential Scholarship of Chinese Academy of Sciences, 2004

WORKSHOP AND PRESENTATION 🎤📅

Biweekly workshop: “Air pollution and exposure modeling” (2015-present, University of Southern California, California, USA)
Invited presentation: “GCN-assisted U-Net for segmentation of OCT images” (Bay area data science workshop, Mar. 27, 2021)
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

Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award

Posted on 01/08/2024 by ScienceFather

Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award

Ms. Hind MEZIANE, ACSA Lab, Faculty of Sciences, University Mohammed First, Oujda, Morocco

Hind Meziane is a dedicated researcher and Ph.D. candidate in Computer Science at the ACSA Laboratory, Department of Mathematics, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco. Her academic journey began with a Baccalaureate in Science (Science Mathematics Option B) from Mehdi Ben Berka High School in Oujda in 2012. She then pursued higher education at Mohammed Premier University, obtaining a DEUG in Mathematics and Computer Science (2012-2014), a LICENSE in Mathematics and Computer Science (2014-2016), and a Specialized Master’s in Computer Engineering with Honors (2017-2019).

Professional Profile:

ORCID

SCOPUS

GOOGLE SCHOLAR

Summary of Suitability for Best Scholar Award

Hind Meziane is a highly accomplished researcher whose work primarily focuses on the security of Internet of Things (IoT) systems. She is currently pursuing a Ph.D. in Computer Science at Mohammed Premier University and has an impressive academic background, including a specialized master’s degree in Computer Engineering and a bachelor’s degree in Mathematics and Computer Science. Her research contributions are well-documented through various publications in reputable international journals and conference proceedings.

🎓 Education:

2019-Present: Doctorate (PhD) in Computer Science at Mohammed Premier University, Faculty of Sciences, Oujda.
2017-2019: Specialized Master in Computer Engineering, with Honors, at Mohammed Premier University, Faculty of Sciences, Oujda.
2014-2016: LICENSE in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
2012-2014: DEUG in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
2011-2012: Baccalaureate in Science, Mathematics Option B from Mehdi Ben Berka High School, Oujda.

Publication top Notes:

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

A Comparative Study for Modeling IoT Security Systems

Modeling IoT based Forest Fire Detection System with IoTsec

A Study of Modelling IoT Security Systems with Unified Modelling Language (UML)

Classifying security attacks in iot using ctm method

Internet of Things: Classification of attacks using CTM method

Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award

Posted on 25/07/2024 by ScienceFather

Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award

Prof. Changgyun Kim, Department of Artificial Intelligence & Software/Samcheok,South Korea

Changgyun Kim is an esteemed academic and researcher associated with Kangwon National University, Department of Artificial Intelligence & Software, and Dongguk University’s Industrial Engineering department in South Korea. His research expertise spans deep learning, healthcare, and data mining. He has made significant contributions to the field, including developing AI-based systems for detecting betting anomalies in sports, diagnosing tooth-related diseases using panoramic images, and creating models for obesity diagnosis using 3D body information. His work is published in renowned journals such as Scientific Reports, Annals of Applied Sport Science, JMIR Medical Informatics, Sensors, Sustainability, the International Journal of Distributed Sensor Networks, and Applied Sciences. Dr. Kim’s notable projects include establishing IoT-based smart factories for SMEs in Korea and developing web applications for obesity diagnosis using data mining methodologies. His extensive research portfolio underscores his commitment to advancing AI applications in various domains

Professional Profile:

ORCID

Education

No specific details about Changgyun Kim’s educational background are provided in the provided information. To give a more comprehensive overview, details such as degrees obtained, institutions attended, and fields of study would be needed.

Work Experience

Dongguk University: Jung-gu, Seoul, KR
- Department: Industrial Engineering
- Position: Not specified in the provided information.
Kangwon National University
- Department: Artificial Intelligence & Software
- Position: Not specified in the provided information.

Publication top Notes:

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Detectability of Sports Betting Anomalies Using Deep Learning-based ResNet: Utilization of K-League Data in South Korea

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values

Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology

Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea

Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award

Posted on 13/07/2024 by ScienceFather

Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award

Prof. Ming-Hsiang Su, Data Science, Soochow University, Taiwan, Taiwan

Ming-Hsiang Su is an esteemed assistant professor in the Department of Data Science at Soochow University in Taipei, Taiwan. He earned his Ph.D. in Computer Science and Information Engineering from National Chung Cheng University and has an impressive academic background with an M.S. in Management Information Systems from National Pingtung University of Science and Technology and a B.S. in Computer Science from Tunghai University. His research expertise includes spoken dialogue systems, personality trait perception, speech emotion recognition, and speech signal processing. Before his current role, Ming-Hsiang conducted postdoctoral research at National Cheng Kung University and served as a lecturer at multiple institutions, including National Pingtung University of Science and Technology and National Chung Cheng University. His professional journey also includes a stint as an R&D engineer at Cino Group. His work in deep learning, natural language processing, and emotion and personality perception has significantly contributed to advancements in speech signal processing.

Professional Profile:

ORCID

🎓 Education

Ph.D. in Computer Science and Information Engineering, National Chung Cheng University
M.S. in Management Information Systems, National Pingtung University of Science and Technology
B.S. in Computer Science, Tunghai University

💼 Work Experience

Assistant Professor (August 2020 – Present)
Department of Data Science at Soochow University, Taipei, Taiwan
Postdoctoral Fellow (August 2013 – July 2020)
Department of Computer Science and Information Engineering (CSIE) at National Cheng Kung University, Tainan, Taiwan
Lecturer (June 2013 – July 2013)
Skill Evaluation Center of Workforce Development Agency, Ministry of Labor, Taichung City, Taiwan
Lecturer (February 2012 – January 2013)
Department of Management Information Systems at National Pingtung University of Science and Technology, Pingtung, Taiwan
Lecturer (September 2006 – January 2013)
Department of Mathematics at National Chung Cheng University, Chiayi, Taiwan
R & D Engineer (August 2003 – September 2004)
Cino Group, Taipei, Taiwan

Ming-Hsiang Su’s career reflects his dedication to advancing the field of computer science, particularly in speech and signal processing, through a blend of academic excellence and practical research. 🌟