Sophia Christin Weißgerber is a Researcher in the Department of General Psychology at the University of Kassel, where she has worked since 2018. Her academic career spans institutions such as New York University Abu Dhabi and the Central Institute for Mental Health. She is currently on care and family leave until May 2026. PhD in Psychology (2019), University of Kassel M.Sc. in Social Psychology (2011), Free University of Amsterdam BA in Sociology/Psychology (2009), University of Mannheim Research Interests: Weißgerber focuses on cognitive psychology, metacognition, and educational psychology, particularly the impact of perceptual disfluency on learning, retrieval practice effectiveness, and social thermoregulation theory. Her work addresses reproducibility challenges in psychological research. Publications Trends: Recent articles emphasize replication studies, metacognitive beliefs, and cross-cultural analyses. Key collaborations include the Human Penguin Project, exploring climate and social integration effects on physiology. Projects: She is involved in the ZFF-PROJEKT investigating moderators of the "Seductive Details" effect using eye-tracking, alongside Prof. Dr. Ralf Rummer.
Nataša Erceg is an Associate Professor of Physics Education at the Faculty of Physics, University of Rijeka, Croatia. She has been with the institution since 2009, progressing from Assistant to her current position as Associate Professor since 2022. Her academic career includes significant administrative roles such as Pro-dean of the Faculty (2022-2023) and Deputy Head of the Department (2018-2022). She earned her PhD in Methodological Sciences in Physics from the University of Sarajevo (2011-2013), following a Master's degree in Natural Sciences Education from the University of Split (2003-2006), and her initial teacher education at the University of Rijeka (1992-1998). Before joining academia, Dr. Erceg taught physics and mathematics at primary and secondary schools in Rijeka from 1999 to 2009, providing her with practical classroom experience that informs her educational research. Dr. Erceg's research focuses on physics education, particularly on conceptual understanding in physics, development of concept inventories, and physics teacher training. Her work examines how students understand microscopic models of electrical and thermal conductivity, gravitational acceleration measurements, and wave optics. She investigates the causes of physics teacher shortages in Croatia and develops pedagogical approaches to improve physics instruction at various educational levels. Her research has resulted in numerous publications in high-impact journals including Physical Review Physics Education Research, European Journal of Physics, and Education Sciences. Her work often involves collaboration with international researchers and addresses both theoretical and practical aspects of physics education. 2019: Award for teaching excellence at the University of Rijeka 2009: Award from the City of Rijeka for exceptional work with a student who won 3rd place in the National Physics Competition She actively mentors students, having guided numerous master's and diploma theses on topics related to physics education methodology. Her administrative service includes committee memberships focused on curriculum development, quality assurance, and academic recognition processes at both institutional and national levels. She organizes professional development workshops for physics teachers across multiple Croatian counties and leads popular science initiatives such as the 'Saturday Morning Physics' program for high school students.
Ilaria Lucrezia Amerise is an Associate Professor in the Department of Economics, Statistics and Finance 'Giovanni Anania' (DESF) at the University of Calabria (UNICAL). Her research focuses on multivariate analysis, time series, nonparametric statistics, and statistical methods for complex/high-dimensional data including functional and spatial data. Editor-in-Chief of JP Journal of Biostatistics (ANVUR Area 13) Editorial Board Member of International Journal of Statistics and Systems (ANVUR Area 13) Recent research involves: Statistical preprocessing of crowdsourced data for Nigerian food prices Quantile regression with heteroskedasticity and non-crossing constraints Electricity demand forecasting via Reg-SARMA models Exchange rate prediction using simultaneous prediction intervals Time series outlier detection and smoothing techniques She contributes to academic governance through the Laboratorio Statistico Informatico (Statistical Informatics Lab) within DESF. Teaching includes undergraduate and graduate courses in Statistics, with materials available in both Italian and English.
Dr. Carl Fernandes serves as Clinical General Practice Associate Professor in Primary Care & Public Health at Brighton and Sussex Medical School, University of Sussex. A Brighton native, he holds MBBS, BSc, MRCGP (2013), MSc (with distinction in Clinical Education), MAcadMEd, and FHEA qualifications. His role encompasses teaching general practice across all undergraduate years with specialized focus on Years 4-5 GP curriculum. His educational background includes medical training at The Royal Free and University College Medical School, followed by GP specialization through University College London Hospitals. He returned to Brighton to teach after clinical practice, developing expertise in simulated consultations and headache management pedagogy. Research centers on undergraduate medical education in general practice , with dual streams: contemporary pedagogy (consultation skills, clinical uncertainty management, technology-enhanced learning) and historical medical research (Brighton's 19th-century medical practitioners). His work demonstrates consistent publication output in medical education journals with increasing focus on practical frameworks like 'The Brighton Guide' for consultation dynamics. Recent publications (2023-2025) reveal three dominant trends: 1) NHS sustainability through general practice ('holding patient care' concept), 2) Innovative teaching methods ('show don't tell' demonstrations), and 3) Historical analysis of compassion concepts ('tenderness' in patient care). This triangulation of past, present, and future practice defines his scholarly approach. Distinction in MSc Clinical Education Fellow of the Higher Education Academy As Personal Clinical Tutor and academic supervisor for F2 doctors in Years 4-5, he guides students through GP placements and simulated consultations. His supervision emphasizes 'supervised independence' - gradual autonomy within safe boundaries. Current projects expanding primary care exposure include 'out-of-area' placement programs and student self-arranged placement initiatives, addressing critical workforce pipeline challenges. He collaborates extensively within a diverse academic GP team, focusing on curriculum development for headache management and consultation skills. His historical research on Brighton's medical heritage provides unique context for modern practice, particularly regarding physician-patient relationship evolution.
Daniel Cores Costa is an Associate Professor of Computer Science and Artificial Intelligence at the University of Santiago de Compostela (USC) and a researcher at the Research Center on Intelligent Technologies (CiTIUS). He completed his PhD in Research in Technologies in November 2022 with a dissertation on 'Spatio-temporal convolutional neural networks for video object detection' under the supervision of Victor Manuel Brea Sanchez. Dr. Cores Costa's research focuses on computer vision with specialized expertise in several interconnected areas: Video object detection leveraging spatio-temporal information Small object detection challenges Advancing few-shot learning techniques for object detection with limited training data Developing open-vocabulary and category-free detection approaches Practical applications in medical imaging (particularly COVID-19 diagnosis) and UAV technology His recent publications (2023-2025) demonstrate a strong research trajectory with multiple first-author papers in top venues including ICCV, BMVC, and CAIP, as well as high-impact journals. Dr. Cores Costa's work consistently addresses fundamental challenges in object detection while developing practical solutions for real-world applications across various domains. Key contributions include: Development of the Downsampling GAN for small object data augmentation Innovative approaches to few-shot object detection through pseudo-label mining (xPAND) Pioneering work on category-free detection using open-vocabulary models Creation of TVBench, a new temporal benchmark for video-language models Advancements in spatiotemporal tubelet feature aggregation for small object detection Dr. Cores Costa actively collaborates with researchers both within USC and internationally, as evidenced by his diverse co-authorship patterns. His research bridges theoretical computer vision with practical implementations, making significant contributions to both academic knowledge and real-world applications in healthcare, autonomous systems, and visual recognition technologies.
