Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.
Hunter N. Moseley is a Professor in the Department of Molecular and Cellular Biochemistry at the University of Kentucky. He maintains an active research laboratory focused on bioinformatics and computational biology, with a strong emphasis on metabolomics and systems biochemistry. His work bridges computational methodology development with biological applications, particularly in data integration and analysis. Education: B.A. in Chemistry, Computer Science, and Mathematics, Huntingdon College (1992) Ph.D. in Biochemistry, University of Alabama at Birmingham (1998) His research interests center on developing computational methods for analyzing biological and biophysical data, particularly through integration of omics-level datasets and leveraging public scientific databases. Key areas include metabolomics, systems biochemistry, structural biology, and data harmonization. His laboratory has produced numerous software tools for data analysis and database integration. The recent publications highlight a strong trend in developing computational frameworks for metabolite pathway prediction, database harmonization (particularly KEGG, Reactome, MetaCyc), and FAIR data principles. There is a consistent focus on methodological rigor, data quality, and software development for metabolomics data analysis, including tools for GPU utilization tracking, academic publication tracking, and metabolic database access. Dr. Moseley has mentored numerous students and collaborators who appear as co-authors on his publications. His lab has secured research support that enables collaborative work across disciplines, including environmental health, cancer research, and cardiovascular disease. He leads the Moseley Bioinformatics Lab, which develops and maintains several open-source software packages for the scientific community.
Dr. Caroline H. Shiboski is a Professor in the Department of Stomatology at the University of California San Francisco (UCSF) School of Dentistry. She cares for adult and pediatric patients with oral mucosal diseases and other oral disorders, including inflammatory conditions (lichen planus, pemphigus, pemphigoid), infectious diseases (such as candidiasis and herpes virus infections), certain oral pain conditions (such as burning mouth syndrome), and the oral complications of graft-versus-host disease following stem cell transplants. She is also involved with the initial diagnosis of oral cancer and precancerous conditions, working closely with the head and neck surgery team for timely referral and treatment. Dr. Shiboski's educational background includes: Lycee Rabelais, Versailles Academy, France, BS, 1978, Biology, Math, Physics Universite R. Descartes, Paris V, France, DDS, 1984, General Dentistry University of California San Francisco, Certificate, 1991, Advanced General Dentistry University of California, Berkeley, MPH, 1992, Epidemiology University of California San Francisco, Certificate, 1993, Dental Public Health University of California San Francisco, Certificate, 1994, Dental Clinical Epidemiology University of California, Berkeley, PhD, 1997, Epidemiology University of California San Francisco, Certificate, 1998, Oral Medicine Dr. Shiboski's research primarily focuses on the oral manifestations of immune system dysfunction, oral cancer, and Sjögren's syndrome. Her work spans clinical investigations into oral mucosal diseases, epidemiological studies of oral conditions, and translational research connecting oral manifestations with systemic diseases. She has made significant contributions to understanding the oral complications of autoimmune disorders and has pioneered research on telemedicine applications in oral medicine, particularly during the COVID-19 pandemic. Analysis of Dr. Shiboski's recent publications (2021-2023) reveals a strong emphasis on Sjögren's syndrome research, with multiple studies examining genetic, epigenetic, and clinical aspects of this autoimmune disorder. Her work also demonstrates growing interest in telemedicine applications for oral disease management, reflecting adaptation to modern healthcare delivery challenges. Additionally, she continues to investigate oral cancer disparities and the intersection of oral health with systemic conditions. Dr. Shiboski has received numerous scientific awards throughout her career: Received High Honor for doctoral thesis titled "La prevention bucco-dentaire au Danemark" (1984) "Michele Bardet-Viatte Award" for best publication on Dental Health Promotion (1987) Dentist Scientist Award, Department of Stomatology, UCSF (2001) K23 award, Department of Stomatology, UCSF (2001) Diplomate of the American Academy of Oral Medicine (2001) Dean's Creativity fund Award (2003) Academic Senate New Investigator Award (2004) Teacher of the Year Award, Department of Stomatology, UCSF Dr. Shiboski has secured substantial research funding as Principal Investigator on multiple NIH grants, most notably serving as PI for the Sjögren's International Collaborative Clinical Alliance Next Generation Studies (SICCA-NextGen) from June 2020-May 2025 (NIH U01DE028891). Her extensive grant portfolio demonstrates consistent funding support for over 25 years, with projects spanning oral health outcomes in transplant recipients, HIV-positive women, and Sjögren's syndrome research. While specific students aren't listed in the provided information, her role as Teacher of the Year Award recipient and involvement in resident education through UCSF suggests active mentoring. Dr. Shiboski leads the Sjögren's International Collaborative Clinical Alliance (SICCA) research team, which has established a robust international network for studying Sjögren's syndrome. Her work with this consortium has generated significant insights into the genetic, clinical, and molecular aspects of this complex autoimmune disorder. The team has developed comprehensive clinical assessment protocols and biorepositories that have become valuable resources for the international research community studying autoimmune exocrinopathy.
EVGİN GÖÇERİ is an Associate Professor at Akdeniz University's Faculty of Engineering, Department of Biomedical Engineering. She has been serving in this position since 2018, having previously held the rank of Assistant Professor in the Department of Computer Engineering from 2015-2018. Her academic career is complemented by a postdoctoral fellowship at Ohio State University's College of Medicine in Biomedical Informatics (2015-2016). Dr. GÖÇERİ earned her Doctorate in Electrical and Electronics Engineering from Izmir Institute of Technology (2007-2013) and completed her undergraduate studies in Computer Engineering at Ege University (1997-2002). Her research primarily focuses on biomedical image processing, deep learning applications in medical diagnostics, and computer-aided diagnosis systems. Her scholarly work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging challenges, particularly in dermatology, oncology, and neuroimaging. Recent publications reveal extensive work on transformer architectures, GAN-based augmentation, and hybrid deep learning models for various medical image analysis tasks. Her research consistently addresses practical clinical challenges including polyp detection, skin cancer classification, brain tumor segmentation, and liver tumor analysis. Dr. GÖÇERİ has established herself as a productive researcher with 80 publications indexed in Web of Science, 30 in Scopus, and impressive citation metrics including an h-index of 683 in WoS and 2048 in Scopus. Her work spans journal articles, conference proceedings, and book chapters, demonstrating both depth and breadth in biomedical engineering research. Her technical expertise spans image processing algorithms, deep neural network architectures, and medical image analysis systems, with particular strengths in applying these technologies to real-world clinical problems. The trajectory of her publications indicates growing sophistication in model architectures, moving from traditional CNNs to more advanced hybrid approaches incorporating transformers and attention mechanisms.
Dr. Ana de Bettencourt-Dias serves as Chemistry Department Chair and the Susan Magee and Gary Clemons Professor of Chemistry at the University of Nevada, Reno within the College of Science. A full professor since 2013, she previously held administrative roles including Associate Vice President for Research (2015-2019) before returning to full-time research and teaching. Her academic journey spans institutions including Syracuse University and UC Davis, with foundational work in titanium CVD precursors and fullerene electrochemistry. Her educational background includes: Licenciatura (M.Sc. equivalent) in Technological Chemistry, University of Lisbon (1993) Dr. rer. nat. (Ph.D. equivalent) in Inorganic Chemistry, University of Cologne (1997) Research centers on designing luminescent lanthanide complexes for energy-efficient displays, photodynamic cancer therapy, and intracellular sensing. Her group pioneers organic ligands that enable efficient energy transfer to lanthanides, achieving multi-color emission for displays, singlet oxygen generation for tumor treatment, and viscosity/temperature reporting for disease diagnostics. This work bridges inorganic synthesis, photophysics, and biomedical applications through precise molecular engineering. Recent publications (2021-2025) reveal strong trends in environmental-responsive lanthanide probes, with emphasis on two-photon excitation for deep-tissue imaging, viscosity-sensing theranostics, and computational modeling of solution structures. Her work consistently advances ligand design strategies for optimizing energy transfer while expanding applications from OLEDs to cancer therapy. Major honors include: Royal Society of Chemistry Fellow (2024) UNR Outstanding Researcher Award (2023) AAAS Fellow (2022) ACS Fellow (2021) Technology Alliance of Central New York Science Award (2006) She has secured continuous funding from DOE, NSF, Petroleum Research Fund, USDA, and Brazilian agencies while mentoring graduate researchers. Her editorial leadership spans Inorganics (Associate Editor), Journal of Rare Earths (Managing Board), and Comments on Inorganic Chemistry . Conference organization highlights include chairing the 2014 Rare Earth Research Conference and leading the ACS Division of Inorganic Chemistry (2019-2022). The de Bettencourt-Dias research group operates advanced laboratories for synthesizing lanthanide complexes, characterizing photophysical properties, and testing biological applications, with current projects focusing on tumor-targeting photosensitizers and intracellular microenvironment probes.
Saurav Chopra, MBBS, serves as an Assistant Professor in the Department of Pathology and Laboratory Medicine at the University of Kansas Medical Center's School of Medicine. His professional journey began with an MBBS from the All India Institute of Medical Sciences in New Delhi, India, followed by residency training in anatomic and clinical pathology at the University of Iowa Carver College of Medicine, and a hematopathology fellowship at the University of Pittsburgh Medical Center. Dr. Chopra's research spans several critical areas in hematology and pathology, with particular focus on hematopathology diagnostics, myeloid neoplasms classification, and hematologic parameters in viral infections including SARS-CoV-2. His work demonstrates expertise in advanced diagnostic techniques such as flow cytometry and fluorescence in situ hybridization for monitoring hematologic malignancies. Analysis of his publication record reveals significant contributions to understanding myeloid neoplasms according to the International Consensus Classification and WHO 5th edition guidelines, hematologic alterations in viral respiratory infections, and therapeutic plasma exchange practices. His research bridges laboratory medicine with clinical applications, particularly in hematology and transfusion medicine. Kansas State Board of Healing Arts certification Dr. Chopra's scholarly work shows consistent contribution to pathology literature, with recent publications focusing on contemporary diagnostic challenges in hematopathology and the impact of viral pandemics on hematologic parameters. His collaborative approach is evident through numerous multi-institutional studies addressing critical questions in laboratory medicine and clinical pathology.
Claudia Martins Antunes is an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa. Her work focuses on data mining, pattern discovery, and knowledge integration across diverse domains including healthcare, education, and bioacoustics. Primary Affiliation: Instituto Superior Técnico, Universidade de Lisboa Academic Role: Associate Professor Research Interests: Claudia specializes in data mining methodologies that incorporate domain knowledge, with particular emphasis on temporal data analysis and structured pattern mining. Her research spans healthcare analytics, educational data modeling, and multi-dimensional pattern discovery. Temporal data mining Constraint-based pattern discovery Knowledge-driven data analysis Healthcare data repositories Publication Trends: Claudia's work demonstrates consistent innovation in pattern mining techniques applied to healthcare and educational contexts. Recent publications focus on blockchain data analysis, urban planning applications, and advanced feature engineering methods, while earlier works established foundations in student modeling and sequential pattern mining. Teaching Activities: She teaches courses in Programming for Data Science, Data Science fundamentals, and Computer Engineering, alongside supervision of integrative projects in Industrial Engineering and Management.
María José Fernández Guerrero is a researcher at the Universidad Pontificia de Salamanca , affiliated with the School of Psychology and part of the Clinical and Health Psychology: basic processes and intervention protocols research group. Her work focuses on borderline personality disorder, psychopathology, and psychoanalytic approaches to mental health. She earned a PhD in 2006 with a thesis on borderline personality disorder, supervised by Dr. Pablo Gallo Mezo. Her research interests span borderline personality disorder , complex PTSD , psychodynamic models , and health psychology . She has contributed to differential diagnosis frameworks and explored connections between somatic conditions and psychological states. Her publications analyze historical trends, diagnostic confusion, and therapeutic protocols for personality disorders. Recent publications highlight her focus on personality disorders (particularly borderline) and trauma-related psychopathology , with interdisciplinary keywords in clinical psychology , psychosomatics , and developmental psychopathology . She has also addressed aging-related mental health and hypochondriasis.
Ani Movsisyan is a postdoctoral researcher at the Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig Maximilian University of Munich (LMU), affiliated with the Pettenkofer School of Public Health. She holds a Master of Public Health (MPH) from the American University of Armenia, a Master of Science (MSc), and a DPhil (PhD) in Social Intervention from the University of Oxford. Her work focuses on adapting and evaluating complex population health interventions, integrating complexity perspectives into guideline development, and advancing the GRADE methodology for public health applications. Pettenkofer School of Public Health, LMU Munich GRADE Working Group WHO Collaborating Center for Evidence-based Public Health BITSS Catalyst Global Implementation Society Movsisyan's research emphasizes complex systems thinking and evidence-based practices in public health. She contributes to the World Health Organization (WHO) through guideline development for parenting interventions, postnatal care, and complex health measures. Her recent projects include the CEO-sys initiative on the COVID-19 evidence ecosystem and frameworks for adapting interventions to new contexts. She teaches qualitative research methods at the American University of Armenia and lectures on qualitative methods at LMU Munich’s Center for International Health. Movsisyan’s publications include systematic reviews and frameworks addressing pandemic responses, health equity, and methodological advancements in evidence synthesis.
Dr. Ulrike Luderer is a Professor in the Department of Environmental and Occupational Health at the Joe C. Wen School of Population & Public Health, University of California, Irvine (UCI). She holds secondary appointments in the Department of Medicine (School of Medicine) and Developmental & Cell Biology (Charle Dunlop School of Biological Sciences). As Director of the UCI Center for Occupational and Environmental Health, she leads initiatives to improve regional occupational and environmental health through academic-industry-government collaboration. MD and PhD in Neurobiology & Physiology from Northwestern University MPH from University of Washington B.S. in Biomedical Engineering and B.A. in French from Brown University Her research is anchored in reproductive toxicology , focusing on how environmental/occupational exposures (e.g., benzo[a]pyrene, PM2.5, space radiation) disrupt ovarian function and induce transgenerational effects. Key mechanisms under investigation include reactive oxygen species (ROS) and glutathione (GSH) pathways in ovarian aging and tumorigenesis. Current projects explore ROS-GSH-lipid interactions and space radiation-induced DNA damage . Her recent work has been recognized with the 2022 Paper of the Year in Particle and Fibre Toxicology and the 2017 Jean Spencer Felton Award . She has received sustained funding from the NIH R01ES020454 and NASA 80NSSC19K1620 , among others. Dr. Luderer serves on prestigious panels including the California Environmental Contaminant Biomonitoring Program Scientific Guidance Panel (Chair 2010-2015) and the National Toxicology Program Expert Panels . As a board-certified physician in occupational/environmental medicine and internal medicine, she integrates clinical and research expertise to address environmental health disparities and reproductive endocrinology . Her lab's transgenerational studies and space radiation models have redefined understanding of ovarian toxicology mechanisms.
Andreas Kleppe is an Associate Professor at the Department of Digital Signal Processing and Image Analysis, University of Oslo. His research focuses on integrating deep learning with medical imaging for cancer prognosis, particularly in gynecological and colorectal cancers. Research Interests Digital Signal Processing Medical Image Analysis Machine Learning Artificial Intelligence Deep Learning Medical Informatics Publication Trends Recent works emphasize deep learning for cancer outcome prediction, biomarker validation (e.g., DNA ploidy, L1CAM), and histopathological analysis. Collaborative studies span gynecological oncology, prostate cancer, and colorectal cancer, often leveraging automated staging markers. Labs/Groups He is affiliated with the Digital Signal Processing and Image Analysis (DSB) research group at UiO.
Sander Roet is a researcher at Utrecht University , affiliated with the Faculty of Science and the Structural Biochemistry department. His work spans computational chemistry, structural biology, and molecular dynamics, with a focus on advanced simulation techniques. Role: Infrastructure and Application Manager Email: sjsroet@uu.nl , s.j.s.roet@uu.nl Research Interests Sander Roet's research primarily involves computational methods to study complex biochemical systems. Key areas include: Path sampling techniques for rare events Molecular dynamics simulations Machine learning applications in reaction pathway identification Structural analysis of macromolecules using cryo-EM Development of simulation tools like PyRETIS The 15 most recent articles highlight his expertise in rare event simulations, replica exchange algorithms, and protein dynamics, particularly in cancer-related proteins like KRas. His work bridges theoretical chemistry and practical applications in biomedical research.
Teklu B. Legesse, MD serves as Associate Professor in the Department of Pathology at the University of Maryland School of Medicine, with clinical specialization in Genitourinary Pathology and Cytopathology. His dual fellowship training at Johns Hopkins Hospital (Genitourinary Pathology) and University of Maryland Medical Center (Cytopathology) establishes his expertise in diagnostic pathology across multiple organ systems. Dr. Legesse's educational background includes: MD from University of Gondar, Ethiopia Residency in Anatomic and Clinical Pathology, University of Maryland Medical Center Fellowship in Cytopathology, University of Maryland Medical Center Fellowship in Genitourinary Pathology, Johns Hopkins Hospital His research program centers on resolving diagnostic challenges in genitourinary malignancies and cytopathology, particularly focusing on molecular markers in urothelial carcinomas, thyroid cytology interpretation, and gynecologic tumor biomarkers. Current projects integrate digital image analysis to enhance diagnostic precision in fine-needle aspiration specimens and address pitfalls in immunohistochemical staining patterns. Analysis of his 2018-2019 publications reveals a concentrated research trajectory in genitourinary pathology (57% of output), with significant contributions to cytopathology methodology (29%) and exploratory work in neuro-oncology biomarkers (14%). Key thematic developments include standardization of NIFTP diagnostic criteria, molecular characterization of sarcomatoid renal tumors, and validation of quantitative nuclear analysis tools for thyroid cytology.
Olga B. Ioffe, MD is Professor of Pathology at the University of Maryland School of Medicine, where she also serves as Vice Chair for Education & Faculty Development and Director of the Pathology and Biorepository Shared Service at the University of Maryland Marlene and Stewart Greenebaum Cancer Center. She is an attending staff pathologist at University of Maryland Hospital, specializing in surgical pathology and cytopathology with particular expertise in breast and gynecologic pathology. Education and Training MD – Moscow State Medical Institute, Moscow, Russia Residency in Internal Medicine – Greater Baltimore Medical Center Residency in Anatomic and Clinical Pathology – University of Maryland School of Medicine Fellowship in Surgical Pathology (Breast & Gynecologic Pathology) – University of Maryland School of Medicine Research Interests Dr. Ioffe’s research centers on surgical pathology , breast pathology , and gynecologic pathology . She investigates breast cancer and premalignant breast disease , focusing on diagnostic accuracy and prognostic factors. Her work spans biomarker discovery, translational studies, and the integration of imaging modalities with pathologic assessment. She leads the institutional tissue bank and serves as central pathologist for major NIH and cooperative group studies. Publications Trend Her recent publications (2002-2016) demonstrate sustained productivity in breast and gynecologic pathology, with emphasis on prognostic biomarkers such as GP88/progranulin, risk-stratification tools like the Nottingham Prognostic Index, and large-scale epidemiologic studies validating long-term cancer risk following endometrial hyperplasia. Additional work addresses diagnostic reproducibility in endocervical glandular lesions and multimodal imaging-pathology correlation for breast cancer staging. Roles & Collaborations Director, Pathology and Biorepository Shared Service, UM Greenebaum Cancer Center Central reviewing pathologist for NIH Resource for Collection and Evaluation of Human Tissues and Cells Referee pathologist for Gynecologic Oncology Group, CALGB, and The Cancer Genome Atlas Biospecimen Core Resource Editor-in-Chief, American Journal of Surgical Pathology: Reviews and Reports Laboratory & Team Leadership As Director of the Pathology and Biorepository Shared Service, Dr. Ioffe oversees a comprehensive institutional tissue bank that supports translational research across multiple cancer types. Her team provides diagnostic confirmation and classification services for national cooperative groups and large-scale genomic initiatives.
Vahid Rezania is a Professor and Chair of the Department of Physical Sciences at MacEwan University since 2004. He holds a PhD in Theoretical Physics (Institute for Advanced Studies, Iran) and has previously served as a Postdoctoral Fellow and Research Associate at the University of Alberta (2000-2005). Education: PhD in Theoretical Physics (Institute for Advanced Studies, Iran, 2000) MSc in Physics (Shiraz University, Iran, 1995) BSc in Physics (Shiraz University, Iran, 1994) His research integrates biophysics , theoretical physics , and computational modeling to study complex biological systems. Current projects include simulating drug metabolism in the liver and analyzing electrostatic properties of microtubules for cancer therapy applications. Recent publications highlight his interdisciplinary approach: 2024 work on fibrotic liver modeling , 2021 studies of microtubule conductivity , and 2020-2019 collaborations on TTField therapy for glioblastoma. These span computational biology , nonlinear dynamics , and nanobiophysics . Scientific Awards: Strategic Research Grant (2015, 2020) Research Project Grants (2010, 2014, 2018) Dissemination Grants (2008, 2010, 2018) PhD Scholarship (Iranian Ministry of Culture, 1995-2000) First rank graduate (Shiraz University, 1994) Collaborations: University of Alberta University of Calgary University of California, Santa Barbara University of Wisconsin, Milwaukee He has supervised senior student research projects and developed physics courses from first-year to advanced levels. His work bridges mathematical modeling with experimental validation across biological and quantum systems.
