Assoc. Prof. Dr. Sebnem Bora | Image Processing Awards | Best Researcher Award

Assoc. Prof. Dr. Sebnem Bora | Image Processing Awards | Best Researcher Award 

Assoc. Prof. Dr. Sebnem Bora, Ege University, Turkey

Dr. Sebnem Bora is an Associate Professor at Ege University, Faculty of Engineering, Computer Engineering Department in Izmir, Turkey. He earned his Ph.D. in Computer Engineering from Ege University in 2006, following M.Eng. degrees from Stevens Institute of Technology, USA, and Dokuz Eylül University, Turkey, and a B.Sc. in Electrical Engineering from Dokuz Eylül University. Since joining Ege University as an Assistant Professor in 2006, he has advanced to Associate Professor in 2019, specializing in computer software and embedded systems.

Professional Profile:

SCOPUS

Summary of Suitability for Best Researcher Award: 

Dr. Şebnem Bora has a solid academic foundation, with degrees in electrical engineering, electronics, communication, and computer software engineering. Her international education experience, including a Master’s at Stevens Institute of Technology (USA), highlights her global academic exposure.

🎓 Education:

  • 📜 Ph.D. (1999–2006): Computer Engineering, Ege University, Graduate School of Natural and Applied Science, Turkey 🖥️
  • 📜 M.Eng. (1995–1997): Electrical Engineering and Computer Science, Stevens Institute of Technology, USA 🇺🇸
  • 📜 M.Eng. (1993–1995): Electronics and Communication, Dokuz Eylül University, Turkey 📶
  • 📜 B.Sc. (1989–1993): Electrical Engineering, Dokuz Eylül University, Turkey ⚡

🎓 Academic Career:

  • 📚 Associate Professor (2019–Present): Ege University, Computer Engineering Department 🏫
  • 📚 Assistant Professor (2006–2019): Ege University, Computer Engineering Department 👩‍🏫

🖥️ Courses Taught:

  • Microcontroller-Based System Design 🤖
  • Electrical Circuits 🔋
  • Microprocessors 🧮
  • Embedded and Real-Time Systems ⏱️
  • Digital Electronics 💡
  • Complex Adaptive Systems 🌐
  • Agent-Based Modeling 🤝

📖 Thesis Supervision Highlights:

  • 🌱 Deep Learning for Detecting Nutrient Deficiencies in Plants (2023) 🌾
  • 🫒 Agent-Based Modeling for Olive Fruit Fly Control in Turkey (2021) 🦟
  • 🖧 Heterogeneous Load Balancing Algorithm for Hadoop (2019) 📊
  • ⚙️ Self-Adaptive Systems Global Behavior Modeling via Agent-Based Systems (2017) 🚀
  • 🖼️ Real-Time Implementation of Digital Image Enhancement Algorithms in Embedded Systems (2012) 🖼️

🔬 Research Interests:

  • Agent-Based Modeling 🤝
  • Embedded Systems 🤖
  • Digital Electronics 💡
  • Complex Adaptive Systems 🌐

Publication top Notes:

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning

Exploiting Image Processing and Artificial Intelligence Techniques for the Determination of Antimicrobial Susceptibility

Computational Fluid Dynamics and Numeric Analysis of Aortic Wall Shear Stress Alterations Induced by Fatty Streaks

External Interventions: Bluffing and Adaptive Learning in Civil Wars

Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil

Behavioural Representation of the Aorta by Utilizing Windkessel and Agent-Based Modelling

Company Security Assesment with Agent Based Simulation

Dr. Stefan Baar | Image Analysis Award | Best Researcher Award

Dr. Stefan Baar | Image Analysis Award | Best Researcher Award 

Dr. Stefan Baar, Muroran Institute of Technology, Japan

Dr. Stefan Baar, born on January 21, 1987, in Germany, is a distinguished researcher specializing in machine learning and image processing applications in agriculture and cell biology. He is currently a researcher at the Muroran Institute of Technology in Japan, where he focuses on detecting and classifying cell features and movements and estimating plant phenotyping using innovative machine learning techniques. His work is conducted at the Computational Intelligence Laboratory under the direction of Prof. Dr. Shinya Watanabe. Dr. Baar’s academic journey began with a Bachelor of Science in Physics from Friedrich-Schiller-Universität Jena, followed by a Master of Science in Physics from the same institution. He earned his Ph.D. in Physics from the Muroran Institute of Technology, with a thesis on scanning tunneling studies of the pseudo gap in high-temperature superconductors under the supervision of Prof. Dr. Naoki Momono.

Professional Profile:

ORCID

Summary of Suitability:

Stefan Baar’s extensive background in both machine learning and astrophysics, combined with his advanced technical skills and significant research contributions, position him as an exceptional candidate for the Best Researcher Award. His work on novel machine learning techniques for cell and plant phenotyping and his previous research in astrophysics demonstrate his versatility and impact in diverse scientific fields. His impressive publication record and innovative research methodologies further underscore his qualifications for this award.

Education

2013 – 2016
Ph.D. in Physics
Muroran Institute of Technology, Japan

  • Thesis: Scanning Tunneling Studies of the Pseudo Gap in High Temperature Superconductors
  • Supervisor: Prof. Dr. rer. nat. Naoki Momono
  • Institute: Material Science Unit

2011 – 2013
Master of Science in Physics
Friedrich-Schiller-Universität Jena, Germany

  • Thesis: The Westerbork Synthesis Radio Telescope (WSRT): Legacy Survey: Radio Relics in Galaxy Clusters
  • Grade: 1.4
  • Supervisor: Dr. rer. nat. Matthias Hoeft
  • Institute: Thüringer Landessternwarte Tautenburg (TLS)

2008 – 2011
Bachelor of Science in Physics
Friedrich-Schiller-Universität Jena, Germany

  • Thesis: The setup of the Small Radio Telescope (SRT) Jena
  • Grade: 1.1
  • Supervisor: PD Dr. rer. nat. habil. Katharina Schreyer (Assistant Professor)
  • Institute: Astrophysikalisches Institut und Universitäts-Sternwarte (AIU)
  • Laboratory Experience: Gamma spectroscopy, Superconductivity, Lasers, Fourier interferometry, Scanning tunneling microscopes, Optical and radio telescopes

2004 – 2007
High School
Pestalozzi Gymnasium Meerane, Germany

2003 – 2004
High School
Ellsworth Community High School, USA

Work Experience

2020 – Present
Researcher in Machine Learning and Image Processing for Applications in Agriculture and Cell Biology
Muroran Institute of Technology, Japan

  • Research on detection and classification of cell features and movements using novel machine learning techniques
  • Estimating plant phenotyping with advanced machine learning methods
  • Institute Director: Prof. Dr. Shinya Watanabe
  • Institute: Computational Intelligence Laboratory

2016 – 2020
Researcher (Tenure Track) in Astrophysics and Cosmology
University of Hyogo, Japan

  • Research on detection and classification of diffuse shock emission in large-scale radio surveys using novel machine learning techniques
  • Automation of robotic telescopes
  • Education: Lectured on astrophysics for university students, high school students, and the general public
  • Institute Director: Prof. Dr. Yoichi Itoh
  • Institute: Center for Astronomy, Nishi-Harima Astronomical Observatory

Publication top Notes:

Fiduciary-Free Frame Alignment for Robust Time-Lapse Drift Correction Estimation in Multi-Sample Cell Microscopy