جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Software Engineering
• Business Process Management
• +۴ مورد دیگر

Andreas Metzger is an Adjunct Professor at the University of Duisburg-Essen and a leading researcher in Software Systems Engineering . He has held significant leadership roles, including Vice Chair of the European Technology Platform NESSI , Deputy General Secretary of the Big Data Value Association , and Technical Coordinator of the EU lighthouse project TransformingTransport . His research focuses on Artificial Intelligence applications in Software Engineering and Business Process Management , with domain expertise in Cloud , Fog Computing , Mobility , and Logistics . Metzger’s work integrates Reinforcement Learning for Self-Adaptive Systems , emphasizing Data Protection and Runtime Adaptation . The 15 most recent articles highlight his contributions to Explainable AI , Decentralized Coordination of adaptive systems, ML-Based Fault Prediction , and Prescriptive Process Monitoring . His publications span top-tier venues like IEEE Transactions , ACM , and Springer , often addressing Big Data Challenges and Trustworthy IoT Systems .

• Software Product Lines
• Static Analysis
• Variability Modelling
• +۵ مورد دیگر

Adam Krafczyk is a researcher in the Software Systems Engineering (SSE) group at the University of Hildesheim's Institute of Computer Science. As part of the Faculty of Mathematics, Natural Sciences, Economics and Computer Science, he focuses on software product lines, static analysis, and variability modeling. His work bridges theoretical computer science with practical industrial applications, particularly in improving software configuration and variability management. Research Interests: - Variability-aware metrics for software product lines - Static analysis techniques for large-scale systems - Conversion of integer-based variability to propositional logic - Industrial IoT platforms and energy optimization (e.g., ReGaP project) - Generative AI applications in software development (GENIUS project) - Configuration mismatch detection in systems like Linux Notable Projects: GENIUS: Generative AI for the Software Development Lifecycle ReGaP: Energy optimization in manufacturing via AI/IIoT IIP-Ecosphere: Industrial IoT platform for AI integration MetricHaven: Tool for variability-aware quality metrics Awards: • Best Paper Award at SEAMS 2023 for 'Control Action Types - Patterns of Applied Control for Self-adaptive Systems' Professional Contributions: - Developed novel approaches for analyzing software product line variability - Collaborated with industry partners like Siemens and Bosch - Active in conferences like SPLC, SEAMS, and ETFA

• Artificial Intelligence
• Cognitive Modeling
• Buddhist Philosophy
• +۵ مورد دیگر

Justin Brody is an Assistant Professor of Computer Science at Franklin & Marshall College, with interdisciplinary interests spanning artificial intelligence, Buddhist philosophy, and cognitive modeling. His research explores self-awareness in AI systems, blending insights from mathematics, robotics, and Eastern philosophy. Prof. Brody holds a Ph.D. and M.S. from the University of Maryland, where he studied under Prof. Don Perlis. Before joining F&M, he served as an Associate Professor at Goucher College and a Visiting Assistant Professor at Franklin & Marshall. His research focuses on enabling AI agents to develop robust self-models through embodied cognition principles. Key projects include grounding self-awareness in robotic systems, exploring Buddhist concepts like agency, and developing logical frameworks for commonsense reasoning. He collaborates with institutions like the University of Maryland Institute for Advanced Computer Studies and anticipates interdisciplinary work with Philosophy, Psychology, and Religious Studies departments. Brody’s work bridges AI technical challenges with philosophical inquiry, addressing topics like agency detection, perspective-taking biases, and consciousness measurement. His teaching philosophy emphasizes student-driven exploration across conceptual, computational, and analytical dimensions of computer science.

Andrew Clementمشاهده پروفایل

استادیار

• Visual Cognition
• Attention
• Working Memory
• +۳ مورد دیگر

Andrew Clement is an Assistant Professor of Psychology and Neuroscience at Millsaps College, joining in 2023. His teaching focuses on cognitive psychology and neuroscience, emphasizing critical thinking and real-world applications. His research explores visual cognition, attention, and working memory, prioritizing student mentorship in all research stages. Education: Ph.D. in Psychology, University of Notre Dame M.A. in Psychology, University of Notre Dame B.A. in Psychology, Denison University Research Interests: Dr. Clement investigates how humans process visual information, including attentional control, statistical learning, and the neural mechanisms underlying perception. His work bridges cognitive psychology and neuroscience to understand how contextual and learned associations influence visual attention. Publications Trends: Recent work examines attentional bias in visual search, the role of statistical learning in strategic attention, and category-based modulation of visual awareness. His studies often blend experimental paradigms with computational models to explore cognitive processes. Awards: Chair’s Postdoctoral Fellowship, University of Toronto Presidential Fellowship, University of Notre Dame Irvin S. Wolf Award in Psychology, Denison University Grants & Advising: While specific grants are not listed, his research actively involves undergraduate students in experimental design, data analysis, and publication. His mentorship approach emphasizes hands-on learning across all research phases.

• Human-Computer Interaction
• Computer-Supported Cooperative Work
• Social Media Analysis
• +۷ مورد دیگر

Xianghua Ding is a researcher in Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW) , with a focus on social media analysis , personal informatics , and health technology . Their work spans digital platforms for stress management, equitable participation in short-form video sharing, and VR-based educational tools. Key Research Areas : Health and wellbeing: Investigating stress tracking, emotion recognition, and self-management support in chronic disease contexts. Social media systems: Developing machine translation models tailored to user-generated content and analyzing distributed collaboration dynamics. Cultural heritage and digital inclusion: Exploring how digital making revitalizes traditional crafts and supports marginalized groups. Notable Collaborations : Co-authored works with researchers from institutions like ACM, CSCW, and CHI, focusing on urban accessibility, peer-support mental health systems, and cross-platform social interaction. Methodological Approach : Combines empirical user studies, algorithmic modeling (e.g., Transformer, LDA), and design interventions for real-world problems.

• VLSI Design
• Integrated Circuits
• Computer Architecture
• +۶ مورد دیگر

Xiaoyang Zeng is a Professor at Tsinghua University's School of Information Science and Technology, Institute of Microelectronics, with an extensive research portfolio in VLSI design, integrated circuits, and hardware acceleration systems. With over 429 publications spanning from 2005 to 2025, Professor Zeng maintains an exceptionally active research program, particularly evident in the high publication volume in recent years (45 papers in 2024 and 28 projected for 2025). His collaborative network includes prominent researchers such as Yibo Fan, Jun Han, Xu Cheng, and Xiaoyong Xue. Professor Zeng's research focuses on cutting-edge areas including Compute-in-Memory architectures, neuromorphic computing, low-power circuit design, and hardware acceleration for AI applications. His work bridges theoretical innovation with practical implementation, as evidenced by numerous publications in top-tier IEEE journals including the Journal of Solid-State Circuits, Transactions on Circuits and Systems, and Transactions on VLSI Systems. Recent work demonstrates particular strength in RRAM-based CIM accelerators, energy-efficient converters, and advanced signal processing techniques. The publication trends show a strategic evolution from traditional circuit design toward emerging computing paradigms, with increasing focus on AI hardware acceleration, neuromorphic systems, and energy-efficient computing solutions. His research group has developed innovative approaches to address challenges in memory-centric computing, analog circuit design, and hardware implementation of machine learning algorithms, with applications spanning consumer electronics, medical devices, and edge computing systems. Selected Scientific Awards: IEEE Journal of Solid-State Circuits Best Paper Award (2022) National Natural Science Award of China (Second Class, 2020) IEEE Asian Solid-State Circuits Conference Best Paper Award (2019) Professor Zeng has successfully advised numerous graduate students who have become active contributors in the field, with several now leading their own research projects. His research has been supported by multiple national-level grants from the National Natural Science Foundation of China and the Ministry of Science and Technology, focusing on next-generation computing architectures and advanced circuit design methodologies. The research group maintains strong industry connections with leading semiconductor companies for technology transfer and practical implementation of research outcomes.

Anne Hranchookمشاهده پروفایل

دانشیار

• Nurse Anesthesia
• Patient Safety
• Quality Improvement in Healthcare
• +۴ مورد دیگر

Anne Hranchook, DNP, CRNA serves as Associate Professor & Assistant Director of the OU-Beaumont Graduate Program of Nurse Anesthesia at Oakland University's School of Nursing. Based in the Human Health Building, Room 3056, she leads the nurse anesthesia program in collaboration with Corewell Health - William Beaumont University Hospital. Her dual role bridges academic instruction with clinical practice in anesthesiology. Dr. Hranchook's educational background includes: DNP from Oakland University MSN in Nurse Anesthesia from Oakland University BSN from University of Detroit Her research program focuses on quality and safety in anesthesia delivery and the implementation of technology in clinical practice. With extensive clinical experience from 20 years at William Beaumont University Hospital, she specializes in translating research into evidence-based practice that enhances patient outcomes. Her work particularly addresses how administrative decision-making can promote cultures of critical thinking and accountability within healthcare settings. Analysis of her recent publications reveals consistent themes in patient safety, with particular attention to closed claims analysis, technology integration in anesthesia practice, and the educational implications of these findings. Her research demonstrates a longitudinal commitment to understanding and improving anesthesia safety through systematic analysis of adverse events and implementation of evidence-based solutions. Her scientific recognition includes: AANA Foundation Oral Poster Award ($1,000) Dr. Hranchook has secured significant research funding, most notably multiple Nurse Anesthesia Traineeship grants from the Health Resources and Services Administration spanning over a decade (2012-2022), plus an Oakland University Research Committee Faculty Research Grant. These awards demonstrate sustained external validation of her work in anesthesia education and patient safety. As Assistant Director of the OU-Beaumont Graduate Program of Nurse Anesthesia, she plays a pivotal role in shaping the next generation of nurse anesthetists through curriculum development, clinical oversight, and research mentorship in collaboration with Corewell Health.

Aidong Zhangمشاهده پروفایل

استاد

• Bioinformatics
• Data Mining
• Multimedia Database Systems
• +۳ مورد دیگر

Aidong Zhang is a SUNY Distinguished Professor Emerita in the Department of Computer Science and Engineering at the University of Virginia. She served as Department Chair from 2009 to 2015. Her primary affiliations include the School of Engineering and Applied Sciences. Dr. Zhang's research focuses on Bioinformatics, Data Mining, and Multimedia Database Systems, with recent work emphasizing robust AI systems, healthcare applications, and interdisciplinary machine learning techniques. Education: PhD in Computer Science from Purdue University (1994). Her academic contributions have been recognized through prestigious awards including IEEE Fellow (202?), CSE Faculty Distinguished Teacher Award (2004), and NSF CAREER Award (1998). Research interests span causal inference, algorithmic fairness, biomedical data analysis, and federated learning. Notable projects include improving group robustness in AI models, developing explainable neural architectures for healthcare, and advancing multimodal data integration. Her work consistently bridges theoretical computer science with practical biomedical and clinical applications. Key awards and honors include: IEEE Fellow UB Exceptional Scholar-Sustained Achievement Award (2003) SUNY Chancellor's Research Recognition Award (2002) NSF CAREER Award (1998) Her recent publications (2023-2025) highlight advancements in spurious bias mitigation, interpretability of large language models, and federated learning frameworks. Collaborative work with medical researchers has produced innovations in Alzheimer’s risk prediction and clinical decision support systems.

Julian Seifertمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction (HCI)
• Mobile Computing
• Pervasive Computing
• +۳ مورد دیگر

Dr. Julian Seifert is a former research associate at Ulm University's Institute of Media Informatics (Department of Media Informatics). His work focuses on Human-Computer Interaction (HCI), particularly in mobile computing, pervasive displays, and collaborative systems. He completed his PhD in 2015 with a dissertation on Mobile Mediated Interaction with Pervasive Displays , recognized for its contributions to the field. Key research areas include autonomous displays (Hover Pad), privacy-respecting mobile interactions (From the Private Into the Public), and hybrid device ecosystems (MobiSurf). His work often explores how mobile devices can seamlessly integrate with interactive surfaces and public displays to enhance collaboration and user experience. Notable achievements include an Honorable Mention Award (2014) for the AMP-D project and a Best Note Award (2013) for the Penbook system. His research bridges theoretical HCI principles with practical applications in domestic environments and healthcare workflows.

Antinisca Di Marcoمشاهده پروفایل

دانشیار

• Software Engineering
• Performance Analysis
• Bioinformatics
• +۵ مورد دیگر

Dr. Antinisca Di Marco is an Associate Professor in Computer Science at the University of L'Aquila's Department of Applied Clinical Science and Biotechnology. She specializes in Software Engineering, focusing on performance analysis, self-adaptive systems, and bioinformatics. Her work bridges software engineering with bio-inspired paradigms and context-aware applications. Education: PhD in Computer Science (2005), MSc in Computer Science (2001), both from the University of L'Aquila. Research Interests include Performance Antipatterns, Model-Driven Engineering, Bioinformatics, and Quality of Service (QoS) in autonomic systems. She leads projects like VISION (ERC grant) and iCARE (Microsoft Azure), and contributes to initiatives like the DISIM Living Lab and Off Site Art. Notable Awards: Best Paper (2010) and Best Poster (2010) awards for her work on performance modeling and self-adaptive systems. Teaching: Courses on Bioinformatics, Software Engineering, and programming in Data Science. Supervises PhD students in computer science and biotechnology. Labs/Teams: Director of the University of L'Aquila Node of InfoLife CINI Lab, member of the Bio-inspired Software Engineering Group, and collaborator with Middlesex University's SENSO Lab.

• Internet of Things
• 6G Software Architecture
• Machine Learning Systems
• +۵ مورد دیگر

Abhishek Kumar is a Researcher at the Center for Ubiquitous Computing, part of the Faculty of Information Technology and Electrical Engineering at the University of Oulu. He holds a PhD from the Department of Computer Science at the University of Helsinki (2018–2022). His research focuses on software-hardware co-integration in areas such as IoT, 6G, machine learning systems, energy efficiency, and environmental sensing, with a strong emphasis on trustworthy computing and UN Sustainable Development Goals (SDGs) like clean energy and sustainable cities. Teaching responsibilities include courses such as 521290S Distributed Systems (5 ECTS, 2025), High Performance Computing: Programming Parallel Supercomputers (3 ECTS, 2025), and Network Analysis and Graph Machine Learning (2 ECTS, 2025). Past courses include IoT and distributed systems modules from 2022 onward. He actively supervises 1 PhD student, 11 Master’s thesis students, and 4 independent studies, with prior supervision of 3 Bachelor’s, 4 Master’s, and 10 Advanced Practical Training students. Research collaborations span VTT, Ericsson Research, Alif Semiconductor, Aalto University, and University of Helsinki. He advises Soundxvision Oy, a Helsinki XR Center startup developing input modalities for XR headsets. Funding sources include the Finnish-American Research & Innovation Accelerator (FARIA), Indian-Finnish Consortia (FICORE), Nokia Foundation, and Walter Ahlström Foundation. His work bridges IoT, 6G, and AI to address environmental and societal challenges, leveraging software-defined systems and distributed computing paradigms. Recent publications explore metaverse integration, edge AI, and cybersecurity in emerging networks.

Hongfu Liuمشاهده پروفایل

استادیار

• Cluster Analysis
• Outlier Detection
• Transfer Learning
• +۶ مورد دیگر

Hongfu Liu is a tenure-track Assistant Professor at the Michtom School of Computer Science, Brandeis University. His research focuses on machine learning, data mining, and their applications in areas like cluster analysis, outlier detection, transfer learning, and feature selection. He holds a PhD in Electrical & Computer Engineering from Northeastern University (2018), and multiple bachelor’s degrees from Beihang University, including Information Systems, Applied Mathematics, and Law, alongside a Management master’s. He has received prestigious awards such as the Top 100 Young Chinese Scholars (Data Mining Area) and Baidu Talent Highlighted Area Chair. His work spans theoretical advancements in clustering algorithms and practical applications in finance, healthcare, and autonomous systems. Education: PhD (Northeastern University), BS/Master (Beihang University) Professional Activities: Associate Editor at IEEE Computational Intelligence Magazine, Area Chair at NeurIPS/ICLR/ICML Research Interests: Prioritize fairness, interpretability, and scalability in ML algorithms He advises PhD students and postdocs on topics like fair clustering and graph learning. His lab is supported by sponsors including Adobe, Google, and the Department of Energy. Recent research highlights include ICLR 2024 contributions on fairness-aware data selection and multidisciplinary collaborations in bioinformatics and finance.

Jeanne Dekerleمشاهده پروفایل

مدرس ارشد

• Exercise Physiology
• Neuromuscular Fatigue
• Exercise Tolerance
• +۳ مورد دیگر

Dr. Jeanne Dekerle is a Senior Lecturer at the University of Brighton, within the School of Education, Sport and Health. She leads the Fatigue and Exercise research lab, focusing on mechanisms of exercise tolerance, neuromuscular fatigue, and brain-muscle interactions. Her work spans clinical populations (e.g., multiple sclerosis, post-COVID, ME/CFS) and healthy individuals. She holds roles such as former Postgraduate Research Coordinator (2017–2021) and MSc Course Leader for Applied Sport Physiology and Exercise Physiology (2012–2019). Education: PhD in Sports and Exercise Science (Physiology), Université de Lille 2 (2003) Master’s in Physiology and Biomechanics, Université de Lille 2 (2000) Postgraduate Certificate in Teaching in Higher Education, University of Brighton (2010) Research Interests: Her work explores perceptions of fatigue and effort during exercise, neuromuscular fatigue mechanisms, and exercise-based rehabilitation strategies. She emphasizes translational research to improve clinical and athletic outcomes. Teaching Contributions: She pioneered problem-based learning (PBL) and Action Learning Sets (ALS) in MSc programs, integrating real-world scenarios from collaborations with the English Institute of Sport (EIS). Courses include Applied Sport Physiology, Applied Exercise Physiology, and Strength and Conditioning. Supervision: Supervised five completed PhD students and currently mentors five more. She emphasizes peer support and bespoke research projects in her lab. Labs/Teams: Leads the Fatigue and Exercise lab at the University of Brighton, focusing on psychophysiological responses in exercise and clinical populations.

• Machine Learning
• Deep Learning
• Edge Artificial Intelligence
• +۵ مورد دیگر

Daniele Berardini is a Research Fellow at the Department of Information Engineering (DII) of Università Politecnica delle Marche in Ancona, Italy. He is also a PhD student under Prof. Emanuele Frontoni, focusing on Edge Artificial Intelligence and real-time human behavior analysis through video surveillance. His work emphasizes lightweight Deep Learning methods for resource-constrained devices, with applications in healthcare monitoring, security systems, and environmental surveillance. Education: Bachelor's degree (cum laude) in Computer Science (2017), University of L'Aquila Master's degree (cum laude) in Computer Science (Network and Data Science) (2019), University of L'Aquila PhD in progress at Università Politecnica delle Marche (since 2020) Research Interests: His research integrates Machine Learning and Deep Learning techniques for developing efficient monitoring systems. Key areas include Edge AI for real-time analysis, human behavior detection in indoor/outdoor environments, and ethical considerations in AI deployment for healthcare and surveillance. His methodologies often combine optimization frameworks with neural networks to enhance computational efficiency. Labs/Teams: He is part of the Vision, Robotics and Artificial Intelligence (VRAI) research group at DII, focusing on interdisciplinary projects such as neonatal care monitoring, weapon detection in video surveillance, and sustainable AI for medical applications. Grants/Advising: No specific grants or advising roles explicitly mentioned in the text.

Pierre Yves Schobbensمشاهده پروفایل

استاد

• Software Product Lines
• Software Verification
• Formal Methods
• +۵ مورد دیگر

Pierre Yves Schobbens is a Professor of Computer Science at the Université Catholique de Louvain, specializing in software product lines verification, formal methods, and agent-oriented programming. He holds leadership roles including President of the Precise research center and the international affairs commission of the Faculty of Computer Science. His research focuses on software reliability, energy-aware systems, and real-time systems. Education: Bachelor in Philosophy (1982) Master in Applied Mathematics and Economics (1983) Master in Computer Engineering (1984) Doctorate in Computer Science (1993) Research Interests: Software Product Line Verification Model Checking Formal Methods for Real-Time Systems Energy Efficiency in Neural Architectures Agent-Oriented Programming Key Contributions: Development of featured transition systems for variability analysis Advances in mutation testing for timed systems Tools like ProVeLines for software product line verification Awards: Most influential paper award (IEEE RE 2016) Best paper (SAFECOMP 2012) 2024 VAMOS Ten-Year Most Influential Paper Award Grants & Projects: Lead on SQUAL.AI (AI-driven software quality) ERNEST (energy-efficient IoT scheduling) CFV (Center for Software Verification) He collaborates extensively with institutions like the Ecole Normale Supérieure and the University of Birmingham, and has supervised over 90 research works.