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Nicole KarnView profile

Associate Professor

• Analytical Chemistry
• Chemistry Education
• Undergraduate Teaching Labs
• +4 more

Nicole Karn is an Associate Professor of Teaching and Analytical Lab Supervisor in the Department of Chemistry and Biochemistry at The Ohio State University. Her roles include undergraduate teaching and laboratory supervision, particularly in analytical chemistry. She holds a B.S. from Muskingum University (2004), M.S. and Ph.D. from The Ohio State University (2008, 2011). Before returning to OSU in 2015, she served as an Assistant Professor at Ohio Northern University. Her research focuses on two primary areas: 1) spectroscopic analysis of pyrazolopyrimidine proton transfer mechanisms in varying solvents/pH conditions, and 2) charge transfer dynamics in cross-conjugated organic compounds for molecular electronics and solar energy applications. She employs UV-Vis spectroscopy, time-resolved techniques, and donor-bridge-acceptor molecular models in these investigations. Dr. Karn's educational contributions emphasize improving STEM pedagogy through systemic curriculum reforms, student perception analysis, and innovative lab instruction methods like reciprocal peer teaching. Her work addresses challenges in distance learning, AI integration in education, and equitable teaching practices. Her publications span both analytical chemistry research (e.g., benzo[a]fluorenone photophysics) and education-focused studies (e.g., ChatGPT vs. student performance comparisons). She has extensively studied DNA photocleavage mechanisms using transition metal complexes and pioneered cross-conjugated molecule characterization techniques.

• Interactive Data Visualization
• Intelligence Augmentation
• Machine Learning Interpretability
• +3 more

Arvind Satyanarayan is an Associate Professor of Computer Science at MIT, leading the Visualization Group within MIT CSAIL. His research focuses on intelligence augmentation through interactive data visualization, exploring how computational tools can amplify human cognition and creativity while respecting user agency. He holds a PhD from Stanford University, advised by Jeffrey Heer. Key research themes include visualization design tools, machine learning interpretability, accessible visualization for visually impaired users, and behavioral principles for information systems. His work has been recognized with an NSF CAREER Award and a 2024 Alfred P. Sloan Fellowship, alongside best paper awards at CHI and IEEE VIS. Education: PhD in Computer Science from Stanford University (Interactive Data Lab) Notable Achievements: Developed widely-used systems like Altair and Vega-Lite, impactful in data science communities Advising: Mentors over 20 graduate and undergraduate researchers, with notable alumni advancing to faculty roles at Brown CS and Utah CS Recent projects include tactile visualization systems (Tactile Vega-Lite), semi-formal programming frameworks (Pluto), and cultural interpretability of AI models. His lab emphasizes collaboration with diverse stakeholders to ensure inclusive design practices.

Jiapan GuoView profile

Assistant Professor

• Machine Learning
• Pattern Recognition
• Medical Image Analysis
• +4 more

Jiapan Guo is a Tenure Track Assistant Professor at the University of Groningen, affiliated with the Faculty of Science and Engineering's Intelligent Systems — Bernoulli Institute. His expertise lies in machine learning, pattern recognition, and medical image analysis. He holds dual roles as a Researcher in the Machine Learning Lab - Data Science Center in Health (DASH) and the Department of Radiation Oncology at the UMCG. His research focuses on applying deep learning to medical imaging challenges, particularly in oncology outcomes prediction and agricultural automation. Key areas of research include developing advanced algorithms for PET/CT image analysis, tumor segmentation, and real-time object detection in complex environments. He collaborates across disciplines to bridge computer science and healthcare, contributing to clinical decision support systems. Publications highlight innovations in multi-modal imaging, radiomics, and lightweight YOLO models optimized for agricultural applications.

Liang MaoView profile

Associate Professor

• Medical Geography
• Spatial Accessibility
• Geospatial Network Analysis
• +3 more

Liang Mao is an Associate Professor in the Department of Geography at the University of Florida, serving as Undergraduate Coordinator. His research focuses on geospatial science approaches to understand human-disease systems, including agent-based modeling, geospatial networks, and spatial accessibility disparities. He holds a PhD from SUNY Buffalo (2010), M.S. from Nanjing University (2005), and B.S. from Nanjing University (2002). Recent funded projects include Transportation Research Board studies on insect-borne diseases and UF Informatics Institute seed funds for rurality mapping. His work bridges GIScience with public health, addressing critical issues like healthcare access equity, disaster evacuation modeling, and disease transmission dynamics. Over 80 peer-reviewed articles showcase his expertise in spatial epidemiology and computational modeling. Advising spans 9 graduate students, including current PhD candidate Bailey Glover and notable alumni like Dr. Dawn Nekorchuck. His research integrates multi-modal transportation data and big spatial datasets, with applications in megacity pandemic control and urban resilience planning.

Michael WandView profile

Professor

• Computer Graphics
• Machine Learning
• Geometry Processing
• +5 more

Michael Wand is a Professor at the Institute of Computer Science , Johannes Gutenberg University Mainz , leading the Visual Computing Group . His research bridges Computer Graphics and Computer Vision , focusing on data-driven methods and structure-aware processing of geometric data. Research Themes Machine learning for geometric data analysis Symmetry and regularity detection in 3D shapes Generative models of visual data Big data in geometry processing Publication Trends : His recent work (2015-2016) explores integrating Markov Random Fields and Convolutional Neural Networks for image synthesis, generative adversarial networks for real-time texture synthesis, and algebraic models for parameterized shape editing. Earlier contributions (2007-2013) focus on point cloud processing , non-rigid shape reconstruction , and interactive editing of complex geometries. Academic Background : Doctoral degree (2004) in Computer Science from University of Tübingen Diploma (2000) in Computer Science from Universität-GH Paderborn

• Movement Disorders
• Parkinson's Disease
• Gait Analysis
• +3 more

Dr. Tuhin Virmani serves as Associate Professor in the Department of Neurology at the University of Arkansas for Medical Sciences (UAMS) College of Medicine. He holds key leadership positions including Vice Chair for Education, Chief of the Neurodegenerative Disorders Division, Director of the Huntington's Disease Society's Center of Excellence, Director of Mission MSA Center of Excellence, and Co-Director of CurePSP Center of Care. His academic training includes a BS in Physics/Biology from Brandeis University (1999), MD/PhD in Neuroscience from UT Southwestern Medical School (2007), Internal Medicine internship (2008), Neurology residency (2011), and Movement Disorders fellowship at Columbia University Medical Center (2013). His doctoral research investigated synaptic neurotransmission mechanisms, while his fellowship focused on freezing of gait in Parkinson's disease under Dr. Stanley Fahn. Dr. Virmani's research centers on Movement Disorders with specialized expertise in Parkinson's disease pathophysiology, particularly freezing of gait phenomena. His work integrates neuroscience, biomechanics, and digital health technologies to develop objective quantitative measures for clinical assessment. Current initiatives include establishing a dedicated gait laboratory and implementing telehealth solutions for rural Parkinson's patients through the Arkansas Imaging Enterprise System (ARIES). Analysis of his recent publications reveals strong interdisciplinary trends combining neurology with artificial intelligence and biomedical engineering. Key themes include machine learning applications for speech/gait analysis, virtual reality assessment tools, and gut-microbiota interactions in Parkinson's. His work emphasizes translational approaches to address healthcare disparities in rural populations through remote monitoring and digital biomarkers. His scientific recognition includes the UAMS Clinical Scientist Award (2016-2022). As principal investigator, Dr. Virmani directs multiple NIH, NSF, and Parkinson's Foundation-funded projects including gut-microbiota-metabolite cross-talk studies, machine learning for remote speech assessment, and virtual reality quantification of movement disorders. He collaborates with multidisciplinary teams spanning neurology, biomedical engineering, and data science to advance movement disorder diagnostics and therapeutics. He directs UAMS's Movement Disorders program and is developing advanced gait analysis capabilities to investigate freezing of gait mechanisms and test novel therapeutic interventions through national clinical trials.

• Data Mining
• Machine Learning
• Artificial Intelligence
• +6 more

Philip S. Yu is a distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago's College of Engineering. He received his PhD from Stanford University and has established himself as a leading researcher in data mining, machine learning, and artificial intelligence. His research spans multiple domains including graph neural networks, recommendation systems, fake news detection, and large language models. Professor Yu's research interests primarily focus on Data Mining , Machine Learning , Artificial Intelligence , Graph Neural Networks , Recommendation Systems , Fake News Detection , and Large Language Models . His work addresses fundamental challenges in knowledge discovery from complex data structures, with particular emphasis on developing robust algorithms for real-world applications. His recent publications demonstrate strong interest in the intersection of traditional data mining techniques with emerging large language model architectures. Analysis of Professor Yu's 2025 publication record reveals significant contributions across multiple high-impact venues including IEEE Transactions, ACM journals, and top conferences. His work shows a clear trend toward integrating traditional data mining approaches with large language models, particularly in areas like recommendation systems, fake news detection, and privacy-preserving technologies. Many publications take the form of comprehensive surveys, indicating his role as a thought leader synthesizing emerging trends in the field. While specific awards aren't listed in the current data, Professor Yu's extensive publication record in top-tier venues and his position as senior author on numerous collaborative projects demonstrate significant recognition within the research community. Professor Yu maintains active research collaborations across multiple institutions, as evidenced by his co-authorship on numerous papers with researchers from different universities. His work often involves interdisciplinary approaches, bridging computer science with applications in security, healthcare, and social media analysis.

• Quantitative Phase Microscopy
• Acoustic Microscopy
• Optical Imaging
• +5 more

Azeem Ahmad is a Researcher at the Department of Physics and Technology , UiT The Arctic University of Norway. His work focuses on advanced imaging techniques in ultrasound, microwaves, and optics , particularly in the areas of quantitative phase microscopy , acoustic microscopy , and photonic chip engineering . Key research areas: Quantitative phase imaging, acoustic wave modeling, biomedical diagnostics, interferometry, machine learning integration, and photonic device optimization Collaborative projects: Developments in label-free histology, super-resolution microscopy, and noise reduction algorithms Recent publications: 2025 studies on subsurface damage detection in ceramics and acoustic transducer modeling His work bridges optical engineering , biomedical applications , and computational imaging , with affiliations to the Ultrasound, Microwaves and Optics and Optical Nanoscopy research groups.

Danya RumoreView profile

Research Professor

• Environmental Law
• Collaborative Decision-Making
• Stakeholder Engagement
• +7 more

Danya Rumore is a Research Professor at the University of Utah's S.J. Quinney College of Law and Clinical Associate Professor in the Department of City and Metropolitan Planning . As Director of the Wallace Stegner Center Environmental Dispute Resolution (EDR) Program , she focuses on: Collaborative decision-making in environmental contexts Stakeholder engagement strategies Climate change adaptation frameworks Gateway community planning Conflict resolution training Research interests center on science-intensive environmental disputes, with particular emphasis on collaborative problem-solving through joint fact-finding, role-play simulations, and facilitated negotiations. Her work addresses: Transportation and housing challenges in natural amenity regions Air quality impacts on recreation Community resilience strategies Interactive governance models Conflict competence development Recent publications demonstrate trends in environmental dispute resolution, including: 2023: Analysis of rural gentrification dynamics 2022: GNAR Initiative's collaborative tools 2021: Gateway community planning challenges 2019: Air quality threshold research 2018: Innovative transportation planning pedagogy Scientific recognition: Recipient of the Rob Williams Award for Emerging Environment and Public Policy Leaders (2018) Held the MIT John S. Hennessy Fellowship (2014) Professional impact: Rumore's work spans environmental law, policy implementation, and community capacity building through: NSF-funded research on gateway communities National Park Service collaborations Co-design of climate adaptation simulations Training federal agencies in conflict resolution Founding the GNAR Initiative

Elizabeth YukoView profile

Adjunct Professor

• Reproductive and sexual health ethics
• Feminist bioethics
• Ethics of human enhancement
• +2 more

Dr. Elizabeth Yuko serves as an Adjunct Professor at Fordham University's Center for Ethics Education, where she acts as Program Administrator for the HIV and Drug Abuse Prevention Research Ethics Training Institute and founding senior editor of the Ethics & Society blog. An internationally recognized bioethicist with over a decade of experience in academia and media communications, she contributes to public discourse through both scholarly and general-audience platforms. Her educational foundation includes a BA in Political Science from The College of Wooster (2001), followed by advanced degrees from Dublin City University: an MA in International Security and Conflict Studies (2007), LLM in Migration Law (2009), and PhD in Bioethics (2012). Dr. Yuko's research centers on reproductive and sexual health ethics, feminist bioethics, human enhancement technologies, and research ethics frameworks. She uniquely bridges academic bioethics with popular culture analysis, examining how ethical dilemmas manifest in media, historical contexts, and contemporary social issues. Her work consistently addresses power dynamics in healthcare and the societal implications of medical technologies. Recent publications (2016-2022) reveal a strategic pivot toward urgent public health ethics, with significant contributions on pandemic impacts (long COVID homelessness, moral fatigue), vaccine equity, and reproductive technology ethics. She demonstrates exceptional skill in translating complex bioethical concepts for mainstream audiences through major publications like Rolling Stone and The Atlantic. Professionally, Dr. Yuko serves as an external ethics expert for the European Research Council and Advisory Board member for the Global Bioethics Initiative. Her leadership in the HIV and Drug Abuse Prevention Research Ethics Training Institute focuses on developing early-career scientists' capacity to conduct ethics research. Through the Ethics & Society blog, she maintains a vital public-facing platform for bioethical discourse. Based at Fordham's Dealy Hall (441 E. Fordham Road, Bronx), she integrates her communications expertise with academic rigor to address evolving ethical challenges in medicine and society.

Masahiko ItohView profile

Professor

• Information visualization
• User interface
• Data visualization
• +5 more

Dr. Masahiko Itoh serves as Professor at Hokkaido Information University's Department of Information Media, where he leads research in information visualization and user interface design. His academic journey spans prestigious institutions including Hokkaido University (where he earned his PhD in Information Science), University of Tokyo, and National Institute of Information and Communications Technology. Dr. Itoh's research focuses on information visualization , user interface design , and 3D collaborative systems , with particular expertise in spatiotemporal data visualization, VR/AR technologies, and big data analytics. His work bridges theoretical foundations with practical applications across domains including news analysis, financial data, and cultural heritage preservation. His publication record demonstrates consistent contributions to visualization conferences and journals, with recent work emphasizing collaborative VR environments, spatiotemporal network analysis, and techniques for visualizing probabilistic search results. Dr. Itoh's research has evolved from foundational work in 3D information collaboration to cutting-edge applications in immersive analytics and multi-modal data exploration. Scientific Awards: Outstanding Paper Award of DEIM 2011 Outstanding Award of the 72nd National Convention of IPSJ (June 2010) Dr. Itoh maintains active research collaborations through multiple grants from Japan Society for the Promotion of Science and industry partners like Teikoku Databank Ltd. He advises graduate students through his visualization seminar, with the first cohort graduating in 2021. His professional service includes extensive conference organization and reviewing activities for IEEE Pacific Visualization and other major venues in the visualization community. Dr. Itoh leads the Itoh Visualization Laboratory, which focuses on developing innovative visualization techniques for complex data analysis, with particular emphasis on collaborative approaches that enhance understanding of stochastic structural changes in data.

Maria Moreno EscobarView profile

Researcher

• Classical Archaeology
• Landscape Archaeology
• Geographic Information Systems (GIS)
• +5 more

Maria Moreno Escobar is a Researcher at the Department of Archaeology and Ancient History within Lund University 's Joint Faculties of Humanities and Theology. Specializing in Classical archaeology , landscape archaeology , and GIS/spatial analysis , her work bridges historical research with computational methodologies. Education: PhD (Doctorado Internacional) - Pablo de Olavide University, Seville (2016) MSc in Spatial Technologies in Archaeology - University of Southampton (UK) BA in History (Prehistory, Ancient History, Archaeology) - University of Seville Her research examines Roman imperial systems , territorial transformations , and maritime connectivity through computational approaches. Key contributions include ARCA database development for southern Iberian archaeological sites and reassessments of Rome's port systems via GIS simulations. Current work on the Vetenskapsrådet -funded Beyond Ports project (2021-2025) explores Mediterranean movement networks. Scientific Achievements: Recipient of 2017 Extraordinary Doctorate Award (Premio Extraordinario de Doctorado) from Pablo de Olavide University Published 31 research outputs including Formal Modelling Approaches to Complexity Science in Roman Studies manifesto Teaching spans Classical archaeology , landscape archaeology , and computing applications across five European universities (Durham, Southampton, Seville, Marburg, Lund) since 2012.

Shangfei WangView profile

Professor

• Pattern Recognition
• Affective Computing
• Probabilistic Graphical Models
• +3 more

Shangfei Wang is a full Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). His research focuses on pattern recognition, affective computing, and probabilistic graphical models, with significant contributions to facial expression analysis, emotion recognition, and multimodal human-robot interaction. He leads the Key Laboratory of Computing and Communication Software of Anhui Province and has received multiple international competition awards including placements in the OMG Empathy Prediction Challenge and Detecting Depression with AI Sub-Challenge. PhD in Computer Science, USTC (2002) MSc in Electronic Science and Technology, USTC (1999) BSc in Electronic Engineering, Anhui University (1996) His work combines domain knowledge with advanced machine learning techniques, including adversarial learning, dual learning, and multimodal deep regression Bayesian networks. Research themes include: Emotion-aware medical consultation systems Thermal image-based facial recognition Privileged information learning for emotion detection Spontaneous vs. posed expression differentiation EEG and physiological signal integration AI applications in mental health support Key projects include multiple National Nature Science Foundation of China grants and international collaborations with French institutions. He serves as Associate Editor for IEEE Trans. on Affective Computing and ACM Trans. on Multimedia Computing, and organized several international conference tracks.

• Online Communication
• Computational Linguistics
• Social Psychology
• +3 more

Dr. Tess van der Zanden serves as Assistant Professor in the Department of Languages, Literature and Communication within Utrecht University's Faculty of Humanities. Her research focuses on language use in digital contexts, particularly examining impression formation through online dating profiles. She employs multimethod approaches combining textual analysis, experimental studies, and computational linguistics to investigate how linguistic features affect social perceptions. Her primary research interests include online communication dynamics , language error impacts in digital interactions, and creativity in profile construction . Van der Zanden's work reveals how metaphor usage, concrete self-disclosure, and grammatical accuracy significantly influence perceived intelligence, humor, and romantic attractiveness. Her studies demonstrate that original yet appropriate profile texts generate higher dating interest compared to conventional or error-ridden content. Analysis of her publication record shows consistent focus on Multimodal impression formation (text+image interactions) Relationship goal signaling through language Cross-cultural error perception in dating contexts Quantitative analysis of rejection patterns Computational modeling of profile effectiveness Her research has generated significant international media attention including coverage in The Guardian , The Times , and De Standaard , with particular focus on how grammatical errors harm dating prospects and how original phrasing increases attractiveness. Van der Zanden teaches courses including Communication, Media and Interaction, and coordinates BA/MA theses in Communication Studies.

James PeoplesView profile

Professor

• Transportation Economics
• Labor Economics
• Transportation Costs
• +3 more

James Peoples is a Professor in the Department of Economics at the University of Wisconsin-Milwaukee, within the College of Letters & Science. His research focuses on transportation economics, labor economics, regulatory reform, and industry analysis. PhD, University of California at Berkeley MA, University of California at Berkeley BA, California State University, Dominguez Hills His work examines technical efficiency in airlines, labor market dynamics in transportation sectors, and economic impacts of regulatory changes. Recent studies include analyzing COVID-19 effects on airline productivity and racial disparities in unemployment benefit access. Publications span topics like modal analysis in air cargo, driver turnover economics, and biofuel supply chain optimization, reflecting interdisciplinary applications of economic theory to real-world transportation and labor challenges.