Giuseppe Placidi | Medical Imaging | Best Research Article Award

Assoc. Prof. Dr. Giuseppe Placidi | Medical Imaging | Best Research Article Award

Professor | University of L’Aquila | Italy

Assoc. Prof. Dr. Giuseppe Placidi is an accomplished researcher whose work spans artificial intelligence, biomedical engineering, and human-computer interaction, with a strong emphasis on translational applications. His research demonstrates a rare combination of methodological innovation and practical impact, exemplified by his development of a lightweight convolutional neural network (CNN) for detecting COVID-19 from chest CT scans, which offers rapid and accurate diagnostic capabilities in clinical settings. In addition, he has contributed significantly to emotion recognition in human-robot interaction, advancing understanding of how AI systems can interpret and respond to human affective states. His work on EEG-based brain-computer interfaces driven by self-induced emotions highlights his expertise in integrating neurophysiological data with real-time computational algorithms, paving the way for more responsive and adaptive BCI systems. Beyond AI and neuroengineering, he has investigated neurocognitive function using semi-immersive virtual reality tasks combined with functional near-infrared spectroscopy, revealing insights into prefrontal cortex activation during complex motor tasks. Furthermore, his clinical research on gender differences in osteoporosis contributes to the understanding of disease mechanisms and patient-specific healthcare strategies. Published in high-impact journals such as Pattern Recognition Letters, Frontiers in Robotics and AI, and Computer Methods and Programs in Biomedicine, Dr. Placidi’s work is widely cited and recognized for its scientific rigor, interdisciplinary breadth, and societal relevance. His research consistently bridges cutting-edge computational methods with real-world applications, making him an exemplary candidate for recognition in research excellence.

Profile: Scopus | ORCID | Google Scholar

Featured Publications

Polsinelli, M., Cinque, L., & Placidi, G. (2020). A light CNN for detecting COVID-19 from CT scans of the chest. Pattern Recognition Letters, 140, 95–100.

Spezialetti, M., Placidi, G., & Rossi, S. (2020). Emotion recognition for human-robot interaction: Recent advances and future perspectives. Frontiers in Robotics and AI, 7, 532279.

Iacoviello, D., Petracca, A., Spezialetti, M., & Placidi, G. (2015). A real-time classification algorithm for EEG-based BCI driven by self-induced emotions. Computer Methods and Programs in Biomedicine, 122(3), 293–303.

Moro, S. B., Bisconti, S., Muthalib, M., Spezialetti, M., Cutini, S., Ferrari, M., … Placidi, G. (2014). A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: A functional near-infrared spectroscopy study. NeuroImage, 85, 451–460.

De Martinis, M., Sirufo, M. M., Polsinelli, M., Placidi, G., Di Silvestre, D., & Ginaldi, L. (2020). Gender differences in osteoporosis: A single-center observational study. The World Journal of Men’s Health, 39(4), 750.

Savannah Harpster | Multimodal Imaging | Best Researcher Award

Savannah Harpster | Multimodal Imaging | Best Researcher Award

Ms. Savannah Harpster, Boston University, United States

🎓 Savannah Harpster is a Visiting Researcher at Boston University, specializing in biomedical engineering with a focus on innovative solutions for women’s health. Her research experience spans targeted ultrasound imaging, nanoparticle synthesis, and biomaterials development. She holds a B.S. in Biomedical Engineering from the University of Miami and is completing her M.S. at Boston University under Dr. Joyce Y. Wong. Savannah has presented her findings at renowned conferences and published in top-tier journals. She is also passionate about community outreach, having participated in STEM education initiatives.

Professional Profile:

Orcid

Suitability Summary:

Savannah Harpster demonstrates significant potential as a candidate for a Best Researcher Award, especially in the field of biomedical engineering with an emphasis on women’s health. Her profile reflects a combination of rigorous academic training, innovative research contributions

Education

🎓 Boston University (M.S. in Biomedical Engineering, May 2024)
Thesis: Characterization of Fibrin-Targeted Microbubbles for Detection of Peritoneal Adhesions
Advisor: Dr. Joyce Y. Wong

🎓 University of Miami (B.S. in Biomedical Engineering, magna cum laude, May 2022)
Minor: Mathematics
Developed expertise in bioglass synthesis, diabetes research, and biomaterials for facial reconstruction.

Experience

🔬 Graduate Researcher (Boston University): Pioneered microbubble and SPION development for noninvasive imaging and targeted women’s health applications.

🔬 Undergraduate Research Assistant (University of Miami): Focused on diabetes-related molecular targets and cytotoxicity assays for biomaterials.

💼 Industry Roles: Service Advisor at Aritzia and Beauty Advisor at Sephora, showcasing strong interpersonal skills.

📚 Teaching: Grader for advanced engineering math and tutor in STEM subjects, including physics and chemistry, for diverse learners.

Awards and Honors

🏆 Magna Cum Laude (University of Miami): Recognized for academic excellence in Biomedical Engineering.
📜 Published Research: Authored groundbreaking work on targeted microbubble formulations.
🎤 Conference Speaker: Presented at the 98th New England Complex Fluids Meeting.
🌟 Certified Tutor: Level 2 Certified Tutor by the College Reading and Learning Association.

Research Focus

🔬 Targeted Imaging Agents: Specializes in the development of fibrin-targeted microbubbles for ultrasound imaging.
🧪 Nanoparticle Synthesis: Synthesizes SPIONs for targeted molecular imaging in women’s health.
🦠 Biomaterials: Innovates in sol-gel synthesis for bioglass, enabling facial reconstruction applications.
📊 Data Analysis: Utilizes SPSS for analyzing biomedical datasets, enhancing molecular targeting.

Publication Top Notes:

Methods for Rapid Characterization of Tunable Microbubble Formulations