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Joshua M. ScaccoView profile

Associate Professor

• Political Communication
• Media Content and Effects
• Quantitative Research Methods
• +2 more

Joshua M. Scacco is an Associate Professor in the Department of Communication and founding Director of the Center for Sustainable Democracy at the University of South Florida, College of Arts & Sciences. His work bridges political communication, media effects, and quantitative research methods, with a focus on democratic governance in the digital age. Education: PhD, University of Texas at Austin Dr. Scacco’s research explores how communication technologies can strengthen democratic institutions, addressing topics like presidential digital rhetoric, health misinformation, election integrity, and media accountability. He employs computational methods and experimental designs to analyze media ecosystems. Recent publications examine: Health communication challenges during the pandemic Political misinformation on social platforms Democratic backsliding and press responsibility Interactive media effects on political behavior Dr. Scacco partners with the U.S. Department of State, journalism nonprofits, and local governments. He is affiliated with the Center for Media Engagement at the University of Texas at Austin.

Hernan G. ReyView profile

Assistant Professor

• Anterior Temporal Lobectomy
• Drug Resistant Epilepsy
• Electroencephalography
• +6 more

Hernan G. Rey, PhD, is an Assistant Professor in the Department of Neurosurgery at the Medical College of Wisconsin (MCW) and the Marquette-MCW Joint Department of Biomedical Engineering. He previously held an Assistant Professor position at Baylor College of Medicine until July 2022. His research focuses on understanding human episodic memory, improving epilepsy diagnosis and treatment, and developing tools for electrophysiological data analysis. Rey's lab records single-neuron activity and intracranial EEG from epilepsy patients to investigate brain mechanisms underlying memory and neurophysiological processes. Education: PhD in Engineering, University of Buenos Aires (2009) Postdoctoral Fellowship in Biomedical Informatics, University of Leicester (2012–2015) Bachelor's in Electronics Engineering, University of Buenos Aires (2002) Research Interests: Dr. Rey explores anterior temporal lobectomy, drug-resistant epilepsy, electrophysiology, hippocampal function, machine learning applications in neuroscience, and signal processing. His work bridges clinical neurosurgery, biomedical engineering, and cognitive neuroscience to advance both fundamental understanding and clinical interventions. Publications: His recent work highlights studies on parietal cortex function in action monitoring, single-neuron responses in memory encoding, and neurophysiological correlates of depression. These reflect a focus on translational neuroscience and interdisciplinary collaboration. Awards: EPSRC Rising Star Award (2014) Labs/Teams: The ReyLab drives innovation in electrophysiological data acquisition and analysis, emphasizing clinical application for epilepsy and memory disorders.

Lesia SemenovaView profile

Assistant Professor

• Machine Learning
• Responsible and Trustworthy AI
• Interpretability
• +4 more

Lesia Semenova is an incoming Assistant Professor in the Department of Computer Science at Rutgers University, set to join in Fall 2025. She currently serves as a postdoctoral researcher at Microsoft Research, NYC. Her research focuses on machine learning, responsible AI, interpretability, and healthcare applications. She holds a PhD from Duke University's Computer Science Department and prior degrees from Taras Shevchenko National University of Kyiv. Education Background: Ph.D., Computer Science, Duke University M.S. and B.S., Applied Mathematics, Taras Shevchenko National University of Kyiv Research Interests: Machine Learning Interpretability Responsible AI Frameworks Healthcare Applications of AI Reinforcement Learning Human-Centered Design Selected Awards and Recognition: 2024 Rising Stars in Computational and Data Sciences PhD Dissertation Award from Duke University Advising and Professional Activities: Semenova advises students such as Harry Chen and Dennis Tang. She has organized workshops at NeurIPS and presented at conferences like the International Joint Conference CFE-CMStatistics. Her work bridges theoretical machine learning with practical healthcare and policy applications. Professional Engagement: She is affiliated with the Artificial Intelligence and Machine Learning research groups at Rutgers, and has collaborated with institutions like the Johns Hopkins Applied Physics Lab.

Sandrine DudoitView profile

Professor

• Statistical Methodology
• Computational Biology
• Genomics
• +5 more

Sandrine Dudoit is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She earned her PhD in Statistics from UC Berkeley in 1999 and joined the faculty in 2001. Her research focuses on statistical methodology and computing with applications to genomics, biomedical research, and precision health. She co-founded the Bioconductor Project , an open-source software initiative for biological data analysis, and leads interdisciplinary projects in single-cell transcriptomics and computational biology. Education: PhD in Statistics (UC Berkeley, 1999), M.Sc. in Mathematics (Carleton University, Canada). Research interests include high-dimensional statistical learning, single-cell RNA-Seq analysis, stem cell differentiation in the olfactory system, and statistical computing. She collaborates with biologists like John Ngai to study neuroepithelial regeneration using cutting-edge sequencing technologies. Recent work emphasizes trajectory inference, biomarker discovery, and methodological advances in handling high-dimensional genomic data. Her lab develops tools for normalization, clustering, and differential expression analysis in large-scale biological datasets. She teaches courses on statistical genomics and serves as a leader in UC Berkeley’s Division of Computing, Data Science, and Society (CDSS). Advising: Supervises PhD students in statistical methodology, computational biology, and bioinformatics. Grants: Active in securing funding for interdisciplinary research projects in genomics and data science. Labs/Teams: Core member of the Center for Computational Biology (CCB) and contributes to the Bioconductor community.

• Natural and Artificial Lighting
• Solar Energy (Passive and Active)
• Building Technology
• +3 more

Jean-Louis Scartezzini is an Honorary Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Solar Energy and Building Physics Laboratory (LESO-PB). His research focuses on natural/artificial lighting, solar energy systems, and building technology, with a strong emphasis on energy efficiency and sustainability. Director of LESO-PB since 1994 Founded and led several institutes, including the Institute for Infrastructure, Resources, and Environment (2002–2009) Doctorat in Physics from EPFL (1986) Extensive international collaborations, including visiting roles at NUS (2009) and LBNL/UCLA (1988) Research interests include: - Daylighting and lighting control systems - Passive/active solar technologies - Urban microclimate and energy systems - Stochastic simulation and predictive control Recent work addresses climate change impacts on energy systems, urban sustainability, and machine learning applications in energy optimization. Key publications span lighting health impacts, renewable integration, and microclimate modeling Awards include the European Solar Prize (2001/2002) and Walsh-Weston Bronze Medal (1998) Mentored over 20 PhD students, many leading in academia and industry (e.g., Marilyne Andersen at EPFL, Flavio Foradini at E4Tech).

• Digital Media Studies
• Rhetoric
• Professional and Technical Communication
• +4 more

John Jones is an Associate Professor at The Ohio State University specializing in Digital Media Studies, with a focus on rhetoric, professional/technical communication, and digital literacies. His academic journey includes a PhD in English from the University of Texas – Austin and an MA from the University of Tennessee – Chattanooga. Previously, he served as an associate professor at West Virginia University and a visiting assistant professor at the University of Texas at Dallas. Research explores intersections of writing, computational processes, and digital culture Expertise spans digital media studies, visual rhetoric, and networked communication His work investigates the impact of wearable technologies, algorithmic systems, and network structures on knowledge creation and communication practices. Current projects examine computational ethics in digital writing and the role of digital networks in shaping rhetorical strategies. Analysis of John Jones’s publications reveals consistent engagement with: Digital media and computational rhetoric Networked communication frameworks Wearable technology implications Medical usability challenges Visual data representation Collective intelligence dynamics

Raquel IniestaView profile

Professor

• Machine Learning for Precision Medicine
• Ethics of AI
• Topological Data Analysis
• +3 more

Raquel Iniesta is a Reader in Statistical Learning for Precision Medicine at King's College London, leading the Fair Modelling and TDA lab within the Department of Biostatistics & Health Informatics. Her expertise spans mathematics, statistics, and machine learning applied to precision medicine, with a focus on ethical AI integration in healthcare. She teaches advanced machine learning and statistical modeling courses and actively engages in scientific communication through workshops and media outreach. Her research emphasizes developing transparent AI models for personalized medicine, particularly in depression, hypertension, and neurodegenerative diseases. Notable contributions include studies on fasciculation analysis in ALS and ethical frameworks for healthcare AI. Publications highlight interdisciplinary approaches, combining machine learning with clinical and genetic data to improve predictive models. Dr. Iniesta leads educational initiatives, including the Machine Learning module and Introduction to Statistics programs, and contributes to public engagement by designing digital content and managing social media for research dissemination.

• Machine Learning
• Artificial Intelligence
• Virtual Reality
• +3 more

Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.

Rachel HarrisonView profile

Professor

• Software Engineering
• Machine Learning
• Requirements Engineering
• +3 more

Professor Rachel Harrison is a Professor in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. Her research focuses on software metrics, machine learning, and requirements engineering with emphasis on empirical and automated software engineering solutions. She has over 160 publications and extensive industry collaborations with organizations like IBM and Philips Research Labs. Her work has been recognized through roles as Editor-in-Chief of the Software Quality Journal and leadership in conferences such as ICSE and ESEM. She leads the Dependable System Engineering Centre (DSERC) and is part of the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Applied Software Engineering and Data Analytics (ASEDA) Group. Her research projects include AI applications for big data analysis (AIMi), automated review classification (ReClass), and software quality improvement (SEQUIN). Professor Harrison has served on over 50 international program committees and initiated workshops like RAISE and AIRE. Her teaching includes advanced computer science modules and leadership in courses like Essential Maths for University Study and Advanced Software Development . Her work bridges academic research and practical applications, particularly in healthcare technology (e.g., diabetes management systems) and mobile application usability. She advocates for rigorous software quality practices and has contributed to frameworks for requirements validation and risk assessment in software projects.

• Medical Imaging
• Biomedical Engineering
• Magnetic Resonance Imaging (MRI)
• +3 more

Yonghyun Ha is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His research focuses on advancing magnetic resonance imaging (MRI) technologies, particularly in low-field MRI systems, RF pulse design, and imaging hardware innovation. He collaborates with experts like Duy Phan, Haifan Lin, and Nikhil Malvankar, contributing to projects such as RF pulse distortion compensation and novel RF coil development. Research Interests: His work spans low-field MRI systems , RF engineering , imaging algorithm optimization , and hardware design . He explores applications like point-of-care imaging and cost-effective MRI solutions. Articles Trends: Recent publications address gradient-free imaging, field-cycling magnets, and deep learning for data compression. His work bridges engineering and clinical needs, emphasizing practical MRI advancements. Advising & Grants: No formal advisees are listed, but his collaborations suggest involvement in interdisciplinary research teams. No specific grants are mentioned, but his projects imply funding through institutional or NIH channels. Labs/Teams: Active in Yale’s Radiology & Biomedical Imaging department, contributing to MRI technology development and translational research initiatives.

• Computer Science
• Data Visualization
• Scientific Visualization
• +3 more

Robert S. Laramee is a Professor at the University of Nottingham (previously at Swansea University), specializing in visualization research. His work focuses on data visualization, scientific visualization, and computational fluid dynamics. He has authored over 170 publications in top journals like IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and IEEE Computer Graphics and Applications. Research Interests: His research spans information visualization, flow visualization, visual literacy, and educational aspects of visualization. He emphasizes practical applications in fields like healthcare, digital humanities, and computational science. Recent Trends: Recent work includes studies on treemap literacy, educational frameworks for visualization, and interactive systems for clinical data. He has also contributed to visualization resources and surveys, aiming to bridge academic and industry needs. Grants & Collaborations: Collaborations include projects on visualization for smart cities, protein-lipid interactions, and quantum chromodynamics data analysis. No specific grant details are provided in the text. Labs & Teams: Affiliated with visualization research groups at Nottingham and Swansea, though specific lab names are not mentioned.

Georg LangsView profile

Professor

• Machine Learning
• Medical Imaging
• Precision Medicine
• +3 more

Georg Langs is a Full Professor of Machine Learning in Medical Imaging at the Medical University of Vienna and Founding Director of the Computational Imaging Research Lab (CIR). He leads a 25-member interdisciplinary team focusing on machine learning methodologies for medical image analysis. Key roles include Director of the Joint Initiative on AI in Medical Imaging (European Institute of Biomedical Imaging Research) and Scientific Lead of the Respiratory Disease Phenotype Observatory (ZODIAC, UNO/IAEA). He is affiliated with MIT’s CSAIL and serves on advisory boards for global AI initiatives. Education: PhD in Computer Science, Graz University of Technology (2007) M.Sc. in Mathematics, Vienna University of Technology (2003) Research Interests: Machine learning-driven precision imaging, neuroimaging, clinical data phenotyping, and cross-species brain connectivity analysis. His work bridges imaging biomarkers with biological mechanisms and large-scale clinical data integration. Grants & Funding: Over €6M in competitive grants as Principal Investigator in the last two years. Projects include ARTEMIS (fatty liver disease digital twins) and AI-POD (personalized risk scores via imaging). Awards: 2022 IS3R Emerging Leaders Club 2022 National Academy of Medicine Emerging Leader Programme 2018 Advisor, AI Mission Austria 2030 Lab & Teams: CIR Lab focuses on AI-driven medical imaging solutions. Co-founded contextflow GmbH , a MedUni spin-off developing AI software for imaging analysis.

• Biomechanics
• Gerontology
• Chronic Obstructive Pulmonary Disease (COPD)
• +5 more

Dr. Jenna Yentes is an Associate Professor in the Department of Kinesiology and Sport Management at Texas A&M University, affiliated with the College of Education and Human Development. Her research focuses on functional resiliency in aging populations, biomechanics of chronic obstructive pulmonary disease (COPD), and nonlinear analysis of human movement. She leads studies on gait stability, respiratory-gait coupling, and exoskeleton-assisted walking. Notable contributions include quantifying locomotor reserve and exploring firefighter performance in protective gear. Education: Ph.D. in Biomechanics (University of Nebraska, 2013), M.S. in Kinesiology (California State University Fullerton, 2006), B.A. in Kinesiology (University of Northern Colorado, 2000). Research Interests: Reserve capacity in older adults' mobility and cognition Biomechanical adaptations in COPD patients Methodological rigor in nonlinear data analysis (e.g., entropy metrics) Firefighter physical performance under protective gear Recent Articles Trends: Focus on entropy-based gait analysis, COPD biomechanics, dual-task interference in aging, and exoskeleton effects on interlimb coordination. Methodological rigor in parameter selection for nonlinear algorithms is a recurring theme. Awards: 2023 Faculty Climate Award (Texas A&M), 2019 Promising Scientist Award (International Society of Posture and Gait Research), and 2019 Chancellor's Commission on the Status of Women Award (University of Nebraska). Advising & Grants: Mentors graduate students (noted in publications) and collaborates with TEEX Fire Academy and multiple Texas A&M research centers including the Huffines Institute for Sports Medicine and the Center for Population Health and Aging. Research supported by institutional and federal grants. Labs/Teams: Active in Texas A&M's Human Movement and Aging Lab, collaborating with interdisciplinary teams in sports medicine, rehabilitation engineering, and pulmonary research.

Steve BellView profile

Professor

• Auditory Neuroscience
• Biomedical Engineering
• Hearing Aid Technology
• +3 more

Professor Steve Bell is a faculty member at the University of Southampton, specializing in auditory neuroscience and biomedical engineering. He leads the EPSRC-funded project 'Personalized fitting and evaluation of hearing aids with EEG responses' and is a registered Clinical Scientist. His research focuses on measuring brain responses to sound for hearing and balance system evaluation, optimizing hearing aid technologies, and improving diagnostic methods for infants and elderly patients. He manages the Hearing and Balance Centre Clinic and holds roles in professional organizations like the International Evoked Response Audiometry Study Group. Education & Roles: Professor at University of Southampton Lead researcher on EPSRC project (2015-2025) Council member, International Evoked Response Audiometry Study Group Research Interests: Steve's work centers on evoked responses in hearing and balance systems, evaluating hearing aids/cochlear implants, and developing objective neurophysiological assessment techniques. His lab explores speech processing, signal analysis, and clinical applications of EEG-based methods. Awards: Vice-Chancellor's Teaching Award (201X) Advising & Grants: Accepting PhD students in audiology and biomedical engineering. Principal investigator on EPSRC grant £X million. Collaborates with Prof. David Simpson and Dr. Ben Lineton in the Signal Processing Audio and Hearing Group. Labs & Teams: Part of the Signal Processing Audio and Hearing Group, focusing on technological innovation in auditory signal processing and audiological diagnostics.

• Biomedical Informatics
• Data Science
• Machine Learning
• +7 more

Lucila Ohno-Machado, MD, PhD, MBA, is the Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science at Yale University. She serves as Deputy Dean for Biomedical Informatics and Chair of the Department of Biomedical Informatics and Data Science at the Yale School of Medicine. Her leadership roles include overseeing informatics infrastructure for Yale’s academic health system and fostering interdisciplinary collaboration across departments such as Medicine and the Halicioğlu Data Science Institute (previously at UCSD). Ohno-Machado holds an MD from the University of São Paulo (Brazil), an MBA from Fundação Getúlio Vargas (Brazil), and a PhD in Medical Information Sciences and Computer Science from Stanford University. She has held faculty positions at Harvard Medical School, MIT’s Health Sciences and Technology Division, and the UCSD Health Department of Biomedical Informatics, where she pioneered federated learning and privacy-preserving AI methodologies. Her research focuses on predictive analytics, federated learning, quantum computing in healthcare, and blockchain applications to enhance data security. She emphasizes addressing algorithmic bias and promoting health equity through data-driven solutions. Recent work includes developing frameworks for medical device safety evaluation and guiding principles to mitigate disparities in algorithmic healthcare applications. Key achievements include the Inaugural Helen M. Ranney Award (2024), election to the National Academy of Medicine (2024), and the William W. Stead Award (2019). She has led NIH-funded informatics centers and contributed to the first large-scale clinical data-sharing initiative across five UC medical systems. Her grants span AHRQ, PCORI, NSF, and blockchain-related initiatives through the IT/NIST Challenge Award. Ohno-Machado advises on translational research strategies and mentors teams in YBIC (Yale Biomedical Informatics & Computing). Her lab collaborates globally, leveraging federated models and AI to advance personalized medicine while prioritizing patient privacy. She also chairs the OHER Awards for Yale Research Excellence, promoting interdisciplinary health equity research.