Dr Harrison Stubbs is a Clinical Lecturer in Respiratory Medicine at the University of Glasgow , affiliated with the Clinical Research Gartnavel. His research focuses on pulmonary hypertension (PH), lung cancer imaging, and cardiopulmonary physiology. Research Interests Development of digital health tools for PH patient inclusion Physiological mechanisms in precapillary PH Advanced imaging techniques for lung cancer diagnosis Risk stratification models in chronic thromboembolic PH NT-proBNP biomarker applications in PH diagnostics Publications Overview His recent publications (2021-2025) demonstrate expertise in PH pathophysiology, digital health implementation, and lung cancer imaging advancements. Key themes include hemodynamic interactions, remote diagnostic tools, and precision medicine applications. Contact Email: Harrison.Stubbs@glasgow.ac.uk ORCID: 0000-0003-1786-342X
Dr. Orwa Aboud is an Assistant Professor in the Department of Neurology and Neurological Surgery at the University of California, Davis, specializing in Neuro-oncology. He treats patients with primary brain tumors including Glioblastoma, Oligodendroglioma, and Low grade gliomas, and co-established UC Davis's multidisciplinary adult brain tumor program focused on novel clinical trials and optimal treatment delivery. Dr. Aboud's educational background includes: M.D. from the University of Damascus, Damascus, Syria (2007) Ph.D. from the University of Arkansas for Medical Sciences, Little Rock, AR (2013) Neurology Internship at the University of Arkansas for Medical Sciences (2013-2014) Neurology Residency at the University of Arkansas for Medical Sciences (2014-2017) Neuro Oncology Clinical Fellowship at Johns Hopkins Hospital (2019-2020) Neuro Oncology Clinical Research Fellowship at the National Institutes of Health (2017-2019) His research centers on designing clinical trials for glioblastoma and brain tumors, with specific focus on oral anticoagulation safety for brain tumor patients with blood clots. He emphasizes evidence-based medicine and teaching critical appraisal skills to equip future healthcare providers. His collaborative work extends to national colleagues studying new brain tumor therapies. Publications from 2012-2020 reveal concentrated expertise in neuro-oncology and brain tumor clinical trials, including NCI-CONNECT contributions for rare CNS tumors and body image studies in brain tumor populations. Earlier work spans neurodegenerative diseases (Alzheimer's, Parkinson's), reflecting broad neurological research scope. Scientific recognition includes: Resident of the Year Award, University of Arkansas for Medical Sciences (2017) Jess R. Nickols Award, Department of Veterans Affairs (2017) AAN 2016 Travel Award (2016) Best Scientific Poster Award, University of Arkansas (2015) NINDS 2013 Meeting Travel Award (2013) As an educator, Dr. Aboud prioritizes fostering critical thinking and evidence-based practice among students. His multidisciplinary brain tumor program integrates clinical care, research trials, and community outreach initiatives to advance brain tumor treatment accessibility. He maintains active involvement with the UC Davis Comprehensive Cancer Center and national networks like NCI-CONNECT.
David S. Wack is an Associate Professor at the University at Buffalo's Jacobs School of Medicine & Biomedical Sciences, Department of Radiology. His research focuses on medical image analysis and neuroimaging, particularly in auditory processing and parameter estimation from PET, MRI, and CT images. He has held academic positions since 1992, progressing from instructor roles to his current rank. His work includes NIH-funded projects on imaging technologies and collaborations with institutions like Canon Medical Systems and UBNS. Education: PhD in Communicative Disorders and Sciences (SUNY Buffalo, 2010) MA in Applied Mathematics (SUNY Buffalo, 1992) BA in Applied Mathematics and Music Performance (SUNY Buffalo, 1989) Research Interests: Development of parametric maps from medical images Machine learning applications in neuroimaging Neuroimaging of auditory and language processing Dynamic image noise reduction and segmentation algorithms Neural markers of top-down compensation in speech processing Recent Projects: NIH-funded self-collimating SPECT breast tomography system 4D flow MRI for intracranial atherosclerotic disease assessment Machine learning for neuroimage classification Grants & Service: Member of Jacobs School faculty council and UB Faculty Student Association board Editorial roles at Frontiers in Neuroscience (Brain Imaging Methods, Decision Neuroscience) Labs & Teams: Buffalo Neuroimaging Analysis Center Collaborations with UBNS, Canon Medical, and VA Western New York Health Care System
Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)
Jan Pfister is a researcher at the Chair of Data Science (Informatik X) , affiliated with the Faculty of Mathematics and Computer Science at the University of Würzburg . Holding an M.Sc. in Computer Science from 2021, he contributes to the DMIR group while maintaining the BibSonomy platform. His work bridges Natural Language Processing and Deep Learning , with a focus on structured sentiment analysis and German language modeling. His research explores the integration of Large Language Models with Pointer Networks to extract fine-grained sentiment information. He has developed ModernGBERT , a German-only encoder model, and LLäMmlein , advancing NLP capabilities for the language. Pfister's recent publications highlight trends in encoder-decoder architectures, model-agnostic hallucination detection, and multi-label classification frameworks that refine LLM outputs. He has taught courses including Information Retrieval (SS '22), Text Mining (WS '23), and Selected Topics in Machine Learning (SS'21, WS '21, WS'24). His collaborative projects span medical informatics, human-AI interaction, and social media analysis, often involving interdisciplinary teams and real-world data.
Mitchel S. Berger is a Professor of Neurological Surgery at the University of California, San Francisco (UCSF) School of Medicine , serving as Director of the Brain Tumor Center and co-Director of the Adult Brain Tumor Surgery Program . His clinical expertise spans brain and spinal cord tumors, tumor-related epilepsy, and advanced techniques like Gamma Knife radiosurgery and brain mapping. With a focus on glioblastoma and neurosurgical oncology, Berger's work integrates molecular profiling , awake craniotomy , and AI-driven diagnostics . Harvard College (1975) - Bachelor's Degree University of Miami Leonard M. Miller School of Medicine (1979) - Medical Degree His research interests emphasize improving surgical outcomes through precision neurosurgery , metabolic imaging , and targeted therapies . Recent publications highlight applications of 5-aminolevulinic acid , CRISPRoff epigenetic editing , and deep learning algorithms for glioma classification. Trends in his work include multicenter collaboration , standardized tissue sampling , and AI integration in intraoperative decision-making. Berger's leadership roles include UCSF Residency Training and participation in NIH-funded grants such as P01CA118816 and P50CA097257. He contributes to neurosurgical education and clinical trial design , with affiliations to the American Association of Neurological Surgeons and American Board of Neurological Surgery .
Benedetta Catanzariti is a British Academy Postdoctoral Fellow at the University of Edinburgh's School of Social and Political Science, with dual affiliation as a PostDoctoral Affiliate at the Centre for Technomoral Futures within the Edinburgh Futures Institute. She actively contributes to the AI Ethics & Society network, focusing on the social, historical, and political dimensions of data-driven technologies through qualitative STS (Science and Technology Studies) methodologies. Her work critically examines machine learning data practices, classification systems in algorithmic decision-making, and engineering cultures across industry, research, and educational contexts. Education: PhD in Science, Technology and Innovation Studies, University of Edinburgh (2023) MScRes in Science and Technology Studies, University of Edinburgh (2019) Master in Philosophy, University of Turin (2016) Her research investigates how data objectivity claims emerge within specific cultural imaginaries, with current emphasis on translating medical uncertainty into diagnostic AI outputs. Recent projects analyze facial expression recognition in healthcare, generative AI threats to parliamentary democracy, and ethical integration in computer science curricula. She develops reflexive tools to address algorithmic harm while documenting global labor practices in AI development and anti-surveillance resistance tactics. Article trends reveal escalating focus on AI's societal crises: 2025 works dissect objectivity construction in data annotation and AI governance metaphors, while 2024 outputs target democratic vulnerabilities (Chamberfakes), CS curriculum politics, and translational ethics teaching. Medical AI and emotion recognition studies (2020-2023) establish foundations for current work on medical imaging uncertainty. All publications consistently apply STS lenses to expose hidden power structures in data systems. Scientific Awards: SPS Outstanding Dissertation Award (2023) for 'Seeing affect: knowledge infrastructures in facial expression recognition systems' AsSIST-UK Andrew Webster PhD Prize (2024) She supervises Oksana Dorofeeva (visiting PhD, Aarhus University) and four CDT project students (Jacqueline Rowe, Amanda Horzyka, Osman Batur Ince, Cyndie Demeocq), previously guiding Sandra Wheeler's MSc in Data Science for Health and Social Care. Funded by a British Academy Postdoctoral Fellowship (2023-2026) for 'Technology in Translation: Investigating Organizational Contexts of AI Development', she also secured DCMS Policy Fellowship support under AHRC's BRAID programme. Current teaching includes Data and AI Ethics as Practice (2025) and Data Ethics in Health and Social Care (2024). Operates within the Centre for Technomoral Futures and AI Ethics & Society network, collaborating with Scottish Centre for Crime & Justice Research on parliamentary democracy threats. Organizes key events like the 2024 'AI as the Broken Machine' conference and 2022 'Ethics of Care and Community in AI Practice' workshop, while developing conceptual tools for medical AI practitioners through her active British Academy project.
Arief A. Suriawinata, MD is a Professor of Pathology and Laboratory Medicine at the Geisel School of Medicine, Dartmouth College . He serves as Interim Chair of his department and is a Diplomate of the American Board of Pathology in Anatomic and Clinical Pathology, as well as Clinical Informatics. His primary research focuses on Gastrointestinal Pathology Liver Pathology Pancreatic Cancer Clinical Informatics . Education: MD, Universitas Indonesia Faculty of Medicine (1995) Anatomic and Clinical Pathology Residency, Mount Sinai Medical Center (1996-2000) Oncologic Pathology Fellowship, Memorial Sloan-Kettering Cancer Center (2000-2001) Gastrointestinal Oncologic Pathology Fellowship, Memorial Sloan-Kettering Cancer Center (2001-2002) His work combines digital pathology and machine learning for cancer detection and prognosis, including innovations in whole-slide imaging analysis and automated urine cytology . Recent publications focus on AI-driven survival prediction models histologic classification of pancreatic tumors deep learning for colorectal cancer detection . He is affiliated with the Dartmouth Cancer Center and maintains the website www.liverpathology.net .
Mildred M. Ramirez, M.D. , is a Professor in the Department of Obstetrics & Gynecology at Baylor College of Medicine , Houston, TX. She specializes in Maternal-Fetal Medicine and has contributed to high-impact studies in obstetric interventions, maternal outcomes, and neonatal resuscitation. Education: MD from University of Puerto Rico School of Medicine (1987); Residency (1991), Fellowship (1993), and Clinical Fellowship (1995) in maternal-fetal medicine. Research Interests focus on: Maternal-Fetal Medicine : Shoulder dystocia interventions, labor induction, cesarean delivery trends, and group B streptococcus protocols. High-Risk Pregnancies : Obesity-related surgical techniques, prenatal diagnostics for cervical shortening, and fetal death management. Clinical Guidelines : Co-authored 2010 American Heart Association neonatal resuscitation standards. Key Publications (2010-2013) span: Obstetric emergencies (shoulder dystocia, pulmonary embolism diagnosis) Minimally invasive surgical techniques (laparoscopic procedures in late-term pregnancy) Clinical trials on labor augmentation (oxytocin regimens) Largest-scale studies on cesarean delivery practices (2010) with multi-institutional collaborations
Josien Pluim is a Full Professor of Medical Image Analysis at Eindhoven University of Technology (TU/e), where she leads the Medical Image Analysis group and serves as vice-dean of the Department of Biomedical Engineering. She also holds a part-time professorship at the University Medical Center Utrecht. Her research is centered at the intersection of artificial intelligence and clinical medicine, with strong affiliations to EAISI (Eindhoven Artificial Intelligence Systems Institute) and the EAISI Health initiative. Her academic background includes a Master's in Computer Science from the University of Groningen (1996), specializing in Scientific Computing and Imaging, followed by a PhD (2001) from the Image Sciences Institute at UMC Utrecht on multimodality image registration using mutual information. She advanced from assistant to associate professor at UMC Utrecht before joining TU/e as a Full Professor in 2014, with a concurrent part-time appointment at UMC Utrecht since 2015. Pluim’s research interests span medical image analysis, including image registration, segmentation, detection, and deep learning, with clinical applications in neurology and oncology. She investigates both methodological development and real-world clinical translation. Recent work emphasizes generative AI for synthetic data, robustness in deep learning models, and super-resolution techniques for brain MRI. Her publications reveal a strong trend toward addressing data scarcity, generalization, and evaluation in medical AI, particularly through simulation and diffusion models. She has co-authored over 250 peer-reviewed papers and is recognized with prestigious fellowships: Fellow of the MICCAI Society IEEE Fellow Pluim has served in leadership roles across the academic community, including Associate Editor for journals such as IEEE Transactions on Medical Imaging , IEEE TBME , and Medical Image Analysis . She has chaired major conferences like WBIR 2006 and MICCAI 2010, and served on the Executive Board of the MICCAI Society. She actively supervises research and educational projects, including team challenges and capstone courses in medical image analysis. Her group is involved in significant collaborative research, such as the EU-funded openGTN project, which supports PhD training in generative models for medical imaging. She also contributes to scientific advisory boards, including the Hanarth Fonds.
Bowei Xi is an Associate Professor at the Department of Statistics at Purdue University. He holds a Ph.D. in Statistics from the University of Michigan (2004) and has made significant contributions to adversarial machine learning, cybersecurity, and metabolomics. Education: Ph.D. in Statistics, University of Michigan (2004) His research focuses on: Adversarial Machine Learning – Developing defenses against attacks on AI systems Cybersecurity – Integrating statistical methods with network security Metabolomics – Applying statistical analysis to biological data Big Data – Creating scalable analytical frameworks Differential Privacy – Balancing data utility with privacy preservation Recent publications highlight: Advancements in causal inference under privacy constraints Novel cyber deception strategies for battlefield IoT Applications of graph neural networks with dropout techniques Statistical defense mechanisms against adversarial examples Awards include: Faculty of 1000 Evaluation (2011) US Patents #7272707 and #7490234 for application server optimization He teaches STAT 514: Design of Experiments with a focus on hands-on learning and statistical software (SAS) integration. His office hours and contact details are publicly listed for student engagement.
Tor-Morten Grønli serves as Professor at the Department of Technology, School of Economics, Innovation and Technology, Kristiania University College (Norway). He is also a Visiting Research Scholar at Copenhagen Business School's Department of Information Technology Management and an affiliate of the Center of Business Data Analytics (cbsDBA). Education PhD in Computer Science from Brunel University, London (2011) Master of Technology (with distinction) from Brunel University, London (2007) Research Focus Grønli leads research in context-aware systems, mobile/pervasive computing, and Internet of Things (IoT). He founded/directs the Mobile Technology Lab at Kristiania and has co-authored 70+ publications. Core expertise includes: IoT architecture and applications Machine learning for transport systems Mobile computing frameworks Blockchain-security integration Edge-cloud computing paradigms Publication Trends Recent works (2023-2025) demonstrate strong focus on converging IoT, blockchain, and AI technologies, particularly for intelligent transport and healthcare systems. Dominant themes include federated learning implementations, privacy-preserving architectures, and sustainable edge computing solutions, with increasing emphasis on real-world applications in medical diagnostics and public infrastructure. Professional Activities Founder/Director of Mobile Technology Lab General Chair: Norwegian Conference on ICT Co-organizer: International Conference on Mobile Web Editorial Board: International Journal of Pervasive Computing, Journal of Online Information Review, Computers & Electrical Engineering TPC Member for IEEE BigData, Percom, HICSS, COMPSAC Guest Editor for special issues in Future Generation Computer Systems
Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .
Miguel Sales Dias is affiliated with ISCTE - Instituto Universitário de Lisboa, specifically the Iscte Business School. His research spans multiple domains, including Machine Learning , Immersive Technologies , and Urban Mobility . His work focuses on: Developing AI-driven systems for energy consumption optimization in public buildings Creating co-design interfaces for housing customization Exploring mHealth applications for cardiac diagnostics Advancing virtual reality for education and visitor experiences Recent publications demonstrate expertise in smart city solutions , bioinformatics , and human-computer interaction . While specific awards or educational background weren't explicitly mentioned, his extensive publication record indicates significant contributions to interdisciplinary research.
Chris Hartley, M.D. is a Physician Scientist at Mayo Clinic's Department of Laboratory Medicine and Pathology, specializing in gastrointestinal and liver pathology, cytopathology, and pancreatobiliary cytology. He completed his MD at Wake Forest University School of Medicine (2012) and fellowships at Washington University School of Medicine (2018) and University of Wisconsin Hospital (2017). 2018 Fellow - Liver and Gastrointestinal Pathology 2017 Fellow - Cytopathology 2016 Resident - Anatomic & Clinical Pathology Hartley's research integrates artificial intelligence with cytopathology and histopathology , focusing on improving diagnostic accuracy for pancreatic ductal adenocarcinoma, colonic graft-versus-host disease, and hepatocellular carcinoma. His work explores AI-driven tools for bile duct brushing evaluation and spatially resolved iron quantification in liver samples. Recent publications highlight his leadership in digital pathology and AI applications across gastrointestinal, hepatic, and pulmonary diseases. Key trends include: AI for histologic differentiation in cancers Cytomorphologic studies of rare neoplasms Molecular marker analysis in transplant-related pathologies Scientific recognitions include: 2019 'Good Catch' Patient Safety Award 2017 Young Investigator Award - Cancer Cytopathology 2015 USCAP Abstract Award Runner-up As a member of the Rodger C. Haggitt Gastrointestinal Pathology Society and American Society for Clinical Pathology, Hartley contributes to precision medicine initiatives and multidisciplinary cancer care teams.
