Prof. Kristen Meiburger, Politecnico di Torino, Italy
Dr. Kristen M. Meiburger is an Associate Professor of Biomedical Engineering at the Department of Electronics and Telecommunications, Politecnico di Torino, Italy, a position she has held since February 2025. She earned her Master’s Degree in Biomedical Engineering from Politecnico di Torino in 2010, followed by a Ph.D. in Biomedical Engineering in 2014 from the Scuola Interpolitecnica di Dottorato at the same institution. Over the years, she has progressed through various academic roles, including Research Assistant, Postdoctoral Researcher, and Tenure-track Assistant Professor. Her research primarily focuses on ultrasound imaging, biomedical signal processing, and computational modeling in biomedical engineering. Dr. Meiburger has actively contributed to international research collaborations, including serving as a Visiting Ph.D. Student at the University of Texas at Austin and a Visiting Researcher at the University of Toronto. She is a member of the IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (UFFC) and the IEEE Engineering in Medicine and Biology Society (EMBS). Her contributions have been recognized with several prestigious awards, such as the Gruppo Nazionale di Bioingegneria (GNB) Master’s Thesis Award and the Politecnico di Torino Ph.D. Quality Award.
Professional Profile:
ORCID
Suitability of Dr. Kristen M. Meiburger for the Best Researcher Award
Dr. Kristen M. Meiburger is an accomplished biomedical engineer specializing in ultrasound imaging, biophotonics, and medical image processing. Her research contributions, international collaborations, and prestigious academic trajectory position her as a strong candidate for the Best Researcher Award. Below are key factors supporting her nomination.
🎓 Education & Work Experience
- 2010 – Master’s Degree in Biomedical Engineering, Politecnico di Torino 🇮🇹
- 2011 (Jan – Dec) – Research Assistant in Biomedical Engineering, Politecnico di Torino 🔬
- 2012 (Jan) – 2014 (Dec) – Ph.D. in Biomedical Engineering, Scuola Interpolitecnica di Dottorato, Politecnico di Torino 🎓
- 2013 (Oct) – 2014 (Apr) – Visiting Ph.D. Student, Ultrasound Imaging & Therapeutics Lab, University of Texas at Austin 🇺🇸
- 2015 (Jan – Mar) – Scholarship Holder in Biomedical Engineering, Politecnico di Torino 🎖️
- 2015 (Apr – May, Aug – Sep) – Visiting Researcher, Biomedical Simulation Lab, University of Toronto 🇨🇦
- 2016 (Mar) – 2018 (July) – Post-Doc Research Assistant in Biomedical Engineering, Politecnico di Torino 🔍
- 2018 (July) – 2021 (Dec) – Non-Tenure-Track Assistant Professor in Biomedical Engineering, Politecnico di Torino 👩🏫
- 2021 (Dec) – 2022 (Jan) – Research Assistant in Biomedical Engineering, Politecnico di Torino 🔬
- 2022 (Feb) – 2025 (Jan) – Tenure-Track Assistant Professor, Politecnico di Torino 📚
- 2025 (Feb – Present) – Associate Professor of Biomedical Engineering, Politecnico di Torino 🏛️
🏆 Achievements & Recognitions
- Published and presented research at multiple national and international conferences 🌍
- Contributed significantly to ultrasound imaging and biomedical research 📊
- Played an active role in international research collaborations with universities in the U.S. 🇺🇸 and Canada 🇨🇦
🎖 Awards & Honors
- 🏅 IEEE Memberships – Member of IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (UFFC) & IEEE Engineering in Medicine and Biology Society (EMBS)
- 🏆 Master’s Thesis Award – Awarded by Gruppo Nazionale di Bioingegneria (GNB)
- 🥇 Ph.D. Quality Award – Recognized by Politecnico di Torino for outstanding research work
Publication Top Notes:
CAROTIDNet: A Novel Carotid Symptomatic/Asymptomatic Plaque Detection System Using CNN-Based Tangent Optimization Algorithm in B-Mode Ultrasound Images
Softmax-Driven Active Shape Model for Segmenting Crowded Objects in Digital Pathology Images
Generative models for color normalization in digital pathology and dermatology: Advancing the learning paradigm
Segmentation and Multi-Timepoint Tracking of 3D Cancer Organoids from Optical Coherence Tomography Images Using Deep Neural Networks
Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study