Dr. Agnieszka Niemczynowicz – Machine Learning – Best Researcher Award 

Dr. Agnieszka Niemczynowicz - Machine Learning - Best Researcher Award 

Cracow University of technology - Poland

AUTHOR PROFILE 

ORCID 

EARLY ACADEMIC PURSUITS 🎓

Agnieszka Niemczynowicz began her academic journey in the field of solid-state physics, earning her Ph.D. from the Faculty of Physics and Applied Informatics at the University of Łódź, Poland, in 2014. Her early research laid a strong foundation in the fundamental aspects of physics, equipping her with a deep understanding of physical systems and analytical techniques.

PROFESSIONAL ENDEAVORS 🏢

Upon completing her doctorate, Agnieszka transitioned into academia, taking up the role of Associate Professor at the Cracow University of Technology. She has since been instrumental in bridging the gap between physics and computational sciences, expanding her research horizons to include computational and mathematical methods for analyzing complex data sets across various disciplines.

CONTRIBUTIONS AND RESEARCH FOCUS 🔍

Agnieszka’s research is at the forefront of computational analysis, focusing on multivariate statistics, chemometrics, and deep learning. She has developed advanced statistical and machine Machine Learning learning models that have found applications in diverse fields such as engineering, biology, medicine, and management. Her work is characterized by its interdisciplinary approach, integrating complex data analysis methods into practical applications.

ACCREDITATIONS AND RECOGNITION 🏅

A prolific researcher, Agnieszka has authored around 50 publications in international journals, contributing significantly to her field. Her excellence in research was recognized with the Machine Learning prestigious Doak Award in 2022, highlighting her impactful contributions to the scientific community and her role as a thought leader in computational analysis.

IMPACT AND INFLUENCE 🌍

Agnieszka’s work has had a significant impact on how complex analytical data is interpreted and utilized across various sectors. Her models have improved the accuracy of data-driven Machine Learning decisions in numerous applications, thereby enhancing the efficiency and effectiveness of processes in engineering, biology, medicine, and more.

LEGACY AND FUTURE CONTRIBUTIONS 🔮

Currently leading international research grants, Agnieszka investigates the mathematical foundations of hypercomplex neural networks and their applications. Her ongoing work promises to further unravel the complexities of data analysis, pushing the boundaries of what machine learning and computational methods can achieve. Her legacy lies in her pioneering efforts to integrate advanced mathematical models into practical solutions, ensuring that her influence will be felt across multiple disciplines for years to come.

NOTABLE PUBLICATIONS 

  • Title: A critical analysis of the theoretical framework of the Extreme Learning Machine
    Authors: Irina Perfilieva, Nicolás Madrid, Manuel Ojeda-Aciego, Piotr Artiemjew, Agnieszka Niemczynowicz
    Journal: Neurocomputing
  • Title: Use of physicochemical, FTIR and chemometric analysis for quality assessment of selected monofloral honeys
    Authors: Monika Kędzierska-Matysek, Anna Teter, Mariusz Florek, Arkadiusz Matwijczuk, Agnieszka Niemczynowicz, Alicja Matwijczuk, Grzegorz Czernel, Piotr Skałecki, Bożena Gładyszewska
    Journal: Journal of Apicultural Research
  • Title: Conclusions
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)
  • Title: Current research methods in mathematical and computer modelling of motivation management
    Authors: Agnieszka Niemczynowicz, Radosław Antoni Kycia
    Journal: (Book chapter, not a journal)
  • Title: Introduction
    Authors: Joanna Nieżurawska, Radosław Antoni Kycia, Agnieszka Niemczynowicz
    Journal: (Book chapter, not a journal)

Dr. Tai Fei – Information Engineering – Best Researcher Award

Dr. Tai Fei - Information Engineering - Best Researcher Award

Fachhochschule Dortmund - Germany

Author Profile

GOOGLE SCHOLAR

Early academic pursuits 🎓

Tai Fei began his academic journey with a strong focus on signal processing and sonar technology. from september 2009 to november 2014, he pursued his ph.d. under the guidance of prof. dieter kraus at the university of bremen. his doctoral research was intricately linked to a project that involved the detection and classification of underwater targets using synthetic aperture sonar images. during this time, he collaborated with the signal processing group (spg) at the technical university of darmstadt, where prof. abdelhak zoubir served as his doctoral advisor. tai's academic foundation laid the groundwork for his future expertise in sonar technology and signal processing.

Professional endeavors 🏢

After completing his ph.d., tai fei expanded his research scope by joining the center for marine environmental sciences (marum) at the university of bremen. here, he applied his expertise in sonar technology to marine environmental research, contributing to the field of underwater target detection. his career took a turn towards the automotive industry in 2014 when he joined hella, a global leader in automotive technology. from march 2014 to october 2023, tai served as an expert in automotive radar, further broadening his experience in signal processing and its real-world applications. in november 2023, he transitioned to academia, taking on the role of interim professor at the dortmund university of applied sciences in the field of computer vision and robotics, assuming the responsibilities of prof. jörg theim.

Contributions and research focus 🔬

Tai Tei’s research primarily revolves around signal processing, sonar technology, and radar systems. during his doctoral studies, he contributed significantly to the development of algorithms for the detection and classification of underwater targets using synthetic aperture sonar. his work in this field extended to marine Information Engineering environmental research, where he applied sonar technology to study underwater environments. later, his expertise evolved into automotive radar, where he played a key role in the development of radar systems used in modern vehicles for safety and automation. his current research focus includes computer vision and robotics, where he integrates his deep understanding of signal processing into cutting-edge technological advancements.

Accolades and recognition 🏅

Tai’s work has been widely recognized in both the academic and industrial sectors. his contributions to sonar and radar technology have been instrumental in advancing both marine environmental sciences and automotive safety. his transition to academia as an interim professor at the dortmund university of applied Information Engineering sciences is a testament to his recognition as a leading expert in his field. throughout his career, he has worked alongside esteemed professors such as prof. dieter kraus and prof. abdelhak zoubir, further validating his standing in the scientific community.

Impact and influence 🌍

Tai fei’s impact spans multiple industries and disciplines. his early work on synthetic aperture sonar has contributed to advancements in underwater target detection, Information Engineering with implications for both marine research and defense technologies. in the automotive industry, his expertise in radar systems has played a role in enhancing vehicle safety through the development of cutting-edge radar technologies. now, as an interim professor, he is poised to influence the next generation of researchers and engineers, shaping the future of computer vision and robotics.

Legacy and future contributions 🔮

Tai’s legacy is defined by his interdisciplinary expertise and his ability to transition between academia and industry. his work in signal processing, sonar, and radar technology will continue to influence both marine and automotive fields. as he takes on a more prominent academic role, his future contributions are likely to expand into the realms of computer vision and robotics, further bridging the gap between theoretical research and practical applications. his commitment to innovation ensures that his impact will be long-lasting and far-reaching.

Notable Publications

  1. Title: Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
    Authors: Y. Sun, T. Fei, X. Li, A. Warnecke, E. Warsitz, N. Pohl
    Journal: IEEE Sensors Journal, 1-11, 2020
  2. Title: Contributions to Automatic Target Recognition Systems for Underwater Mine Classification
    Authors: T. Fei, D. Kraus, A.M. Zoubir
    Journal: IEEE Transactions on Geoscience and Remote Sensing 53 (1), 505-518, 2014
  3. Title: Gesture Classification with Handcrafted Micro-Doppler Features using a FMCW Radar
    Authors: Y. Sun, T. Fei, F. Schliep, N. Pohl
    Journal: 2018 IEEE MTT-S International Conference on Microwaves for Intelligent Systems, 2018
  4. Title: Automatic Radar-based Gesture Detection and Classification via a Region-based Deep Convolutional Neural Network
    Authors: Y. Sun, T. Fei, S. Gao, N. Pohl
    Journal: ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2019
  5. Title: A High-Resolution Framework for Range-Doppler Frequency Estimation in Automotive Radar Systems
    Authors: Y. Sun, T. Fei, N. Pohl
    Journal: IEEE Sensors Journal 19 (23), 11346-11358, 2019

Dr. Sue Han Lee – Computational Botany – Women Researcher Award

Dr. Sue Han Lee - Computational Botany - Women Researcher Award

Swinburne University of Technology Sarawak Campus - Malaysia

Author Profile

GOOGLE SCHOLAR 

Early academic pursuits 🎓

Lee Sue Han's academic journey began with a Bachelor of Engineering (Honours) in Electronics Engineering from Multimedia University, Malaysia. Her passion for technology and innovation propelled her to pursue a Master of Engineering in Electrical and Electronics at Shinshu University, Japan. Under the guidance of Professor Kiyoshi Tanaka, she focused on computer vision and machine learning, laying a solid foundation for her future research. Her quest for knowledge culminated in a Doctor of Philosophy in Computer Science & IT at University Malaya, where she further specialized in computer vision and deep learning under the mentorship of Professor Ir. Dr. Chan Chee Seng.

Professional endeavors 🏢

Since February 2020, Lee has served as a Lecturer at Swinburne University of Technology, Sarawak Campus, where she has taken on significant responsibilities, including Head of the Department of Information and Communication Technologies. Her role as an Interdisciplinary AI Research Driver emphasizes her commitment to advancing AI applications across various fields. Prior to this, she gained valuable experience as a Postdoctoral Fellow at INRAE in Montpellier, France, specializing in computational botany, which showcases her diverse expertise and adaptability in research.

Contributions and research focus 🔍

Lee's research is primarily centered on computer vision and deep learning, with a strong emphasis on machine learning applications. She has made significant strides in developing algorithms and techniques that enhance the understanding and analysis of visual data. Her contributions to computational botany highlight the Computational Botany intersection of technology and environmental science, where she explores innovative solutions for plant research through advanced computational methods. Her interdisciplinary approach not only enriches her field but also opens new avenues for research and application.

Accolades and recognition 🏅

Lee Sue Han's contributions to the field have been recognized through her active involvement in professional organizations. As a member of IEEE Young Professionals since 2019, she has engaged with a global community of engineers and technologists. Recently, she was designated a Graduate Technologist by the Malaysian Board Computational Botany of Technologists (MBOT) in 2023, acknowledging her expertise and commitment to the engineering profession. Her achievements reflect her dedication to excellence and her impactful role in the academic and professional landscape.

Impact and influence 🌍

Lee's work significantly influences the fields of computer vision, machine learning, and computational botany. Her research not only advances theoretical knowledge but also has practical implications for various industries, including agriculture and environmental science. By bridging the gap between technology and real-world Computational Botany applications, she contributes to solving pressing global challenges. Her mentorship and leadership at Swinburne University inspire the next generation of engineers and researchers to explore innovative solutions.

Legacy and future contributions 🔮

As Lee Sue Han continues her academic and research endeavors, her legacy is defined by her commitment to interdisciplinary collaboration and technological advancement. Her future contributions are expected to push the boundaries of knowledge in computer vision and AI, particularly in addressing environmental issues and enhancing agricultural practices. Lee's vision for the future is one where technology plays a pivotal role in creating sustainable solutions, and her ongoing work promises to have a lasting impact on both academia and industry.

Notable Publications 

How deep learning extracts and learns leaf features for plant classification 2017

Deep-plant: Plant identification with convolutional neural networks 2015

New perspectives on plant disease characterization based on deep learning  2020

Multi-organ plant classification based on convolutional and recurrent neural networks  2018

Attention-based recurrent neural network for plant disease classification 2020