جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Fetal Monitoring
• Signal Processing
• +۳ مورد دیگر

Judith OEH van Laar is an Assistant Professor in the Signal Processing Systems group at Eindhoven University of Technology (TU/e) and a gynecologist at Máxima Medical Center. Her research focuses on technological advancements for maternal and fetal health, including fetal monitoring systems and congenital defect detection. She holds a Master's in Medicine from Radboud University (2004) and a PhD from TU/e (2012) on fetal autonomic responses during pregnancy. She completed gynecology specialization at Máxima Medical Center (2014). Research interests include fetal electrocardiography, heart rate variability, and cardiotocogram analysis. She leads projects like HASTA (Healthy Ageing Starts with a HealThy stArt) and contributes to MEDEIA (MEDical Engineering Innovations and Applications). Recent work involves AI-driven fetal health assessment and maternal physiological monitoring. Key collaborations span biomedical engineering and clinical medicine.

Vivienne Szeمشاهده پروفایل

استاد

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +۷ مورد دیگر

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

• Networking
• Cybersecurity
• Machine Learning

Dr. Ahmad Azab is a Lecturer at the University of Sydney’s School of Computer Science, specializing in Networking, Cybersecurity, and Machine Learning. He holds a PhD and industrial certifications including CISSP, CCSP, and CCNA. His teaching spans networking, cybersecurity, ethical hacking, and machine learning applications. Research interests focus on network traffic classification, IoT integration in cognitive radio networks, malware analysis, and cybersecurity countermeasures. His work bridges academic research with industrial applications, emphasizing practical solutions for emerging threats. Publications highlight innovations in botnet detection, malware classification, and cybersecurity frameworks. Supervision of bachelor and master’s projects underscores his commitment to mentoring future technologists. Professional affiliations include IEEE and ISC².

Bright Vargheseمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Bright Varghese, Ph.D., is an Assistant Professor of Computer Science at Maryville University. With 17 years of experience including 16 years in teaching, his expertise spans programming, web development, Android app development, and project management. He holds a B.E. from Manonmaniam Sundaranar University, M.E. from Vinayaka Missions University, and Ph.D. from Karunya Institute of Technology and Sciences. His research focuses on software re-modularization using hybrid heuristic approaches, deep learning for sign language recognition, and optimization algorithms. He has deployed learning management systems like Open edX LMS for web platforms and Moodle LMS for mobile applications. His academic contributions include advancing software engineering practices and integrating machine learning into educational technology solutions. Though no formal awards are listed, his work highlights innovation in both theoretical and applied computer science domains. No grants or specific lab affiliations are detailed in the provided information.

Wenqi Shiمشاهده پروفایل

استادیار

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +۳ مورد دیگر

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +۲ مورد دیگر

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .

Yalong Yangمشاهده پروفایل

استادیار

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +۳ مورد دیگر

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

Chi-Chun Leeمشاهده پروفایل

استاد

• Speech and Language Processing
• Affective Computing
• Health Analytics
• +۳ مورد دیگر

Chi-Chun Lee (Jeremy) is a Professor and Associate Chair in the Department of Electrical Engineering at National Tsing Hua University (NTHU), Taiwan. He also serves as Director of the NVIDIA-NTHU Joint Innovation Center and leads the Behavioral Informatics & Interaction Computation (BIIC) Lab. His academic journey includes a B.S. (magna cum laude) and Ph.D. in Electrical Engineering from the University of Southern California (USC), USA (2007 and 2012), followed by roles as a data scientist at id:a lab and technical consultant for companies like E.Sun Bank and Allianz Taiwan. Research focuses on speech processing, affective computing, health analytics, and behavior signal processing. He is an IEEE Senior Member and holds editorial roles in top journals such as IEEE Transactions on Affective Computing and Multimedia. Key contributions include leading teams to international competitions (e.g., 1st place in INTERSPEECH 2009 Emotion Challenge) and developing AI frameworks for clinical applications like respiratory sound classification and tumor image synthesis. Recipient of prestigious awards including the NTHU-Novatek Distinguished Talent Chair (2024), National Science and Technology Council Outstanding Research Award (2023), and multiple best paper awards. His work bridges academia and industry, with collaborations extending to NVIDIA and startups like AHEAD Medicine. Research has been featured in major media outlets including Scientific American and Discovery.

• Liver Diseases
• Artificial Intelligence in Medicine
• Epidemiology
• +۵ مورد دیگر

Basile Njei, MD, PhD, MPH is an Assistant Professor in the Department of Medicine (Digestive Diseases) at Yale School of Medicine, serving as Co-Director of the International Medicine Program and Associate Director of the Clinical and Translational Core at the Yale Liver Center. He holds adjunct roles at Memorial Health System (Ohio University) and is a Fulbright Visiting Professor in Global Health. Education: MD, University of Yaoundé I (2008) MPH, University of Edinburgh (2009) Residency in Internal Medicine, University of Connecticut (2014) Fellowship in Gastroenterology, Yale University (2017) PhD in Investigative Medicine/Data Science, Yale University (2022) MBA in AI, University of Cumbria (2025) Research Interests: Dr. Njei focuses on advancing liver disease management through AI-driven diagnostics, epidemiology, and global health initiatives. His work emphasizes noninvasive fibrosis prediction, MASLD pathophysiology, and addressing healthcare disparities in liver diseases across populations. He has pioneered AI applications in cholangioscopy and digital health solutions for hepatology. Publications: His 2025 work includes groundbreaking studies on cholangiocarcinoma diagnostics, MASLD cardiovascular outcomes, and global digestive disease disparities. His 2024 publications explore GLP-1 agonist efficacy, hepatotoxicity scoring systems, and the burden of liver disease in MENA regions. Awards: AASLD Young Investigator Award VA DEI Research Supplement Award Fulbright Visiting Professorship Howard Levine-MD Science Award Grants & Mentorship: Leads NIH-funded projects on AI in hepatology and mentors global health initiatives in Africa. Collaborates with health ministries to strengthen medical education systems. Labs/Teams: Active in the Yale Liver Center and International Medicine Program, developing AI tools for liver diagnostics and global health equity initiatives.

Lisa Feldman Barrettمشاهده پروفایل

استاد

• Affective Neuroscience
• Psychology of Emotion
• Social and Personality Psychology
• +۴ مورد دیگر

Lisa Feldman Barrett is a University Distinguished Professor of Psychology at Northeastern University with an appointment at Massachusetts General Hospital (MGH). She leads the Interdisciplinary Affective Science Laboratory (IASLab), studying the brain and body mechanisms underlying emotion, motivation, and cognition. Her work integrates psychology, neuroscience, anthropology, and computational methods to challenge traditional emotion theories. She serves as Chief Science Officer for MGH’s Center for Law, Brain and Behavior and actively engages in public science communication through books, lectures, and media. **Research Focus**: Her lab develops systems-level models of affective neuroscience, emphasizing predictive processing, interoception, and cultural influences on emotion. She critiques universal facial expression theories and advocates for a constructionist model of emotion as contextually constructed experiences. **Awards & Honors**: Recipient of the NIH Pioneer Award, APS Mentor Award for Lifetime Achievement, and APA’s Distinguished Scientific Contribution Award. Elected Fellow of the American Academy of Arts and Sciences and Royal Society of Canada. Former President of the Association for Psychological Science (APS). **Key Contributions**: Pioneered the theory of constructed emotion, emphasized the role of interoception in emotion, and demonstrated variability in facial expressions across cultures. Her work bridges basic science and clinical applications, including mental health and legal contexts. **Lab & Teams**: IASLab collaborates across disciplines, with sites at Northeastern and MGH. Current projects explore neural mechanisms of emotion, allostasis, and the impact of aging on memory and resilience.

• Computational Neuroscience
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Rui Ponte Costa is an Associate Professor at the University of Oxford's Department of Physiology, Anatomy & Genetics (DPAG). He leads the Neural & Machine Learning group, focusing on computational models of learning in the brain by integrating AI principles. His research emphasizes cortical circuits, neuromodulation, and subcortical regions to understand credit assignment mechanisms. He holds a PhD in Computational Neuroscience and Machine Learning from the University of Edinburgh and has held postdoctoral positions at the University of Oxford, University of Bern, and McGill University. His group's work bridges theoretical and experimental neuroscience, collaborating with institutions like MILA and Google DeepMind. Education: Bachelor's in Computer Science, University of Coimbra (Portugal) PhD in Computational Neuroscience, University of Edinburgh (UK) Research Interests: Neural mechanisms of learning and plasticity Machine learning inspired by biological systems Cerebro-cerebellar interactions Neuromodulatory systems' role in reinforcement learning Collaborators include Christopher Summerfield, Timothy Behrens, and Yarin Gal. His lab's work has been published in Nature Communications , Cell Reports , and top machine learning venues. He co-organizes the Oxford NeuroTheory Forum and has pioneered models explaining cortical dynamics in task acquisition and consolidation. Advising and Grants: No specific grants listed, but active in collaborative research networks. Advising focuses on PhD students in computational neuroscience and AI. Labs/Teams: Neural & Machine Learning Group at DPAG, part of Oxford's broader neuroscience and AI ecosystem.

• Neuroscience
• Neurocardiology
• Gut-Brain Axis
• +۵ مورد دیگر

Rui Chang is an Associate Professor at Yale University School of Medicine , jointly appointed in the Department of Neuroscience and Department of Cellular and Molecular Physiology . His research focuses on organ-to-brain circuits and neurocardiology with specific interest in Parkinson's disease . He completed his B.S. in Biological Sciences at Tsinghua University (2005) Ph.D. in Neuroscience at University of Southern California (2011) Postdoctoral training in the lab of Stephen Liberles at Harvard Medical School (2017) Research Highlights: The Chang lab employs single-cell gene expression profiling , virus-based anatomical mapping , and optogenetics to investigate vagal interoceptive systems and their roles in cardiovascular regulation and gut-brain axis dysfunction in neurodegenerative diseases. Their work has revealed a multidimensional coding architecture for visceral sensory signals. Notable Scientific Awards: McKnight Neurobiology of Brain Disorders Award (2021) NIH Director’s New Innovator Award (2019) Kavli Faculty Innovative Research Award (2019) NIH K01 Mentored Research Scientist Award (2017) Keystone Symposia Future of Science Scholarship (2016) Collaborative Network: Chang collaborates with experts in neurodegeneration (David A. Hafler, MD), biostatistics (Hongyu Zhao, PhD), and neural imaging (Le Zhang, PhD). His work intersects with the Stephen & Denise Adams Center for Parkinson’s Disease Research and Kavli Institute for Neuroscience .

Peter van der Heijdenمشاهده پروفایل

استاد

• Categorical Data Analysis
• Data Science
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.

Anna-Maria Meisterمشاهده پروفایل

استاد

• History of Modern Architecture
• History of Science and Technology
• Material Histories
• +۵ مورد دیگر

Anna-Maria Meister is a Professor at the Karlsruhe Institute of Technology (KIT) and Lise Meitner Group Leader at the Kunsthistorisches Institut in Florenz (KHI), a Max Planck Institute. Her work bridges histories of architecture and technology, focusing on the political, social, and aesthetic consequences of design processes and standardized systems. Born from PhD at Princeton, Master's at Columbia, and Diploma at TUM Co-director of saai Archive for Architecture and Engineering Executive Board Member of 'Das große kleine Haus' housing cooperative Research Focus: Investigates 'coded objects' as intersections of design, bureaucracy, and technology, including: Proto-algorithmic thinking in pre-digital architecture Metadata's role in archival knowledge systems Material consequences of standardization regimes Entangled temporalities in design processes Her article trends (2020–2025) reveal cross-disciplinary analysis of: Material epistemologies in 20th-century Germany Algorithmic traces in modernist pedagogies Archival fragility as design method Postwar normative design systems Scientific Awards & Roles: Lise Meitner Fellowship (2022–present) Max Planck Institute for the History of Science Fellow (2020s) Co-curator of 'Radical Pedagogies' research project Advising & Collaborations: Co-director of saai Archive (KIT) with international impact DFG-funded Impact Research Network Collaboration with Center for Critical Studies in Architecture (CCSA)

JIANG Lingxiaoمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Professor Lingxiao Jiang is a full-time faculty member at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU) , where he serves as Director of the Centre for Research on Intelligent Software Engineering (RISE) . His research and teaching focus on software engineering , program analysis , code search & reuse , and deep learning of code to enhance software quality, development productivity, and security. PhD, University of California, Davis (2009) Professor Jiang's research explores context-aware software deep learning , code clone detection , and security & privacy through tools like DECKARD (code clone detection), EqMiner (functionally equivalent code), and SmartEmbed (smart contract security). His work leverages distributed computing , symbolic execution , and neural program models for large-scale code analysis. Recent publications highlight trends in deep learning-based code analysis , smart contract vulnerabilities , and automated program transformation . Key themes include code clone detection , semantic patches , and cross-language API mappings . Scientific Awards ACM SIGSOFT Impact Paper Award (2018) for scalable code clone detection As advisor, Professor Jiang has mentored 14 graduate students, including Lucia (Ph.D. 2014) , Shaowei Wang (Ph.D. 2015) , and current advisees in intelligent software engineering. He leads multiple grants on code mining , security of digital platforms , and AI systems governance . RISE Lab at SMU develops tools like MANDO (smart contract analysis), iTiger (issue title generation), and TreeCaps (code processing with capsule networks). The lab actively recruits researchers in software engineering and AI.