Find Academic Supervisors

Margherita Fantoli is a tenure track lecturer at the Faculty of Arts at KU Leuven, affiliated with the Cultural Studies Research Group. She is an active member of multiple research institutes including DigiSoc – KU Leuven Institute for Digital Society and LECTIO – KU Leuven Institute for the Study of the Transmission of Texts, Ideas and Images in Antiquity, the Middle Ages and the Renaissance. Her institutional involvement extends to the Faculty Council of Arts and the POC Digital Humanities. Dr. Fantoli's research spans Digital Humanities with a focus on historical linguistics, classical studies, and book history. Her work integrates computational methods with traditional humanities scholarship, particularly in analyzing ancient Greek and Latin texts, knowledge transmission networks, and multilingualism in historical contexts. She specializes in using digital tools to explore cultural transmission from antiquity through the Renaissance, with particular attention to network analysis of historical texts and academic communities. Her recent publications demonstrate a strong trend toward computational approaches to historical scholarship, particularly through the STUDIUM.AI project which maps knowledge networks around the premodern University of Leuven (1425-1797). Her work bridges classical philology with digital methods, examining topics such as canon formation in antiquity, multilingualism in early modern book catalogues, and the presence of classical texts in later periods. She frequently collaborates across disciplines, working with computer scientists, historians, and linguists. Dr. Fantoli serves as promoter or co-promoter on multiple significant research projects including CLARIAH-VL+ (toward the 'SSH Open Science Cloud' in Flanders), studies on the coherence of scientific language in ancient technical texts, and investigations into canon formation in Greco-Roman antiquity. Her teaching portfolio includes courses on data processing, linked data scholarship, and introduction to Digital Humanities, reflecting her commitment to training the next generation of digitally literate humanities scholars. She is actively involved with the Cultural Studies Research Group in Leuven, contributing to their mission of exploring cultural transmission across historical periods. Through her leadership in the STUDIUM.AI project and other digital initiatives, she helps build infrastructure for connecting historical knowledge networks and making premodern scholarship more accessible through digital means.

• Digital Humanities
• Historical Linguistics
• Classical Studies
• +7 more

Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.

• Computational Genomics
• Bioinformatics
• Pharmacogenomics
• +8 more

Eleni Stai is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), affiliated with the Division of Communication, Electronic and Information Engineering. She holds advanced degrees in Electrical Engineering, Mathematics, and Applied Mathematical Sciences from NTUA and the National and Kapodistrian University of Athens. Her academic credentials include: Diploma in Electrical and Computer Engineering, NTUA (2009) B.Sc. in Mathematics, National and Kapodistrian University of Athens (2013) M.Sc. in Applied Mathematical Sciences, NTUA (2014) Ph.D. in Electrical Engineering, NTUA (2015) Dr. Stai's research integrates advanced optimization techniques with communications networks and energy systems. She develops stochastic and deterministic optimization frameworks for network resource allocation, data analytics on complex topologies, and smart-grid control applications. Her work bridges theoretical foundations with practical implementations in energy-harvesting networks, network slicing, and reinforcement learning for distributed systems. Analysis of her recent publications reveals dominant research thrusts in AI-driven network management (particularly O-RAN and network slicing), energy-integrated communications, and optimization of energy communities. A significant portion of her work addresses the convergence of 5G/6G networking with power systems, emphasizing real-time control and sustainability. Her scientific contributions have been recognized through prestigious awards: Chorafas Foundation Best Ph.D. Thesis award Thomaidis Foundation Best M.Sc. Thesis award Best Paper Award at ICT 2016 Best Presenter Award at IEEE ENERGYCON 2022 Dr. Stai serves on technical program committees for major international conferences and has co-authored the book "Evolutionary Dynamics of Complex Communications Networks". She teaches undergraduate courses in Queuing Systems, Computer Networks, and Social Network Analysis, reflecting her expertise in network theory and applications. Her research trajectory demonstrates continuous evolution from fundamental network optimization to AI-enhanced solutions for next-generation communication-energy systems. Her work builds upon her postdoctoral experience at EPFL (2016-2020) and ETH Zurich (2020-2023), where she developed advanced frameworks for communications networks and energy systems.

• Optimization
• Communications Networks
• Data Analytics
• +4 more

Valerio Pascucci is a Professor at the University of Utah's School of Computing and a DOE Laboratory Fellow at Pacific Northwest National Laboratory. He directs the Center for Extreme Data Management Analysis and Visualization (CEDMAV) and previously led projects at Lawrence Livermore National Laboratory and University of Texas at Austin. PhD in Computer Science (Purdue University, 2000) MSc in Electrical Engineering (University 'La Sapienza', Rome, 1993) As a pioneer in Big Data Management , Scientific Visualization , and Computational Topology , his work connects topological methods with progressive algorithms to enable interactive exploration of petascale datasets. His research spans climate modeling , neuroscience , materials science , and precision agriculture , focusing on multi-resolution techniques and geometric compression . Recent publications show specialization in web-based visualization and AI-driven analytics for climate data, with emphasis on equity in data access and FAIR data principles . His ViSUS project enables real-time data streaming from supercomputers to desktops, while NAPA explores GPU-based architectures for streaming algorithms. Scientific Awards : Best Paper Award, IEEE Pacific Visualization 2011 Best Application Paper Award, IEEE VIS 2006 DOE Laboratory Fellow He advises numerous graduate students and leads collaborations across national laboratories , universities , and industry . Funded by NSF Grant #2127548 , he develops technologies for exascale computing and geospatial intelligence .

• Big Data Management
• Scientific Visualization
• Computational Topology
• +7 more

Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).

• Manufacturing Management
• Production Planning
• Production Control
• +14 more

Dr. Barry Cardiff is an Assistant Professor in the School of Electrical and Electronic Engineering at University College Dublin (UCD), where he has been a member of academic staff since September 2013. His career spans both industry and academia, with significant experience at Nokia Mobile Phone (UK) Ltd and Silicon & Software Systems (S3 group) before returning to complete his PhD at UCD. Education: B.Eng (1992), M.Eng.Sc. (1995), PhD (2011) from University College Dublin Professional Experience: Design Engineer at Nokia (1993-2001), Systems Architect at S3 group (2001-2007, 2011-2013) Current Position: Assistant Professor at UCD School of Electrical and Electronic Engineering Dr. Cardiff's research focuses on Digital Signal Processing applications in communication systems, with particular emphasis on theoretical analysis and practical implementation. His work bridges traditional communication theory with emerging biomedical applications, especially in wearable IoT sensors. He has made significant contributions to power/complexity reduction techniques in circuit design, specifically DSP algorithms for digitally assisted analog circuits. His research program addresses critical challenges in biomedical signal processing, sensor fusion, and efficient data transmission for healthcare applications. His recent publications demonstrate a strong trend toward biomedical applications of signal processing techniques, with a focus on ECG analysis, atrial fibrillation detection, and respiratory rate estimation using multimodal sensor fusion. The research shows a clear progression from traditional communication systems toward healthcare applications, with an emphasis on edge computing solutions that reduce power consumption in wearable devices. IEEE BioCas best paper award (2024) IEEE senior member since 2019 Active reviewer for multiple IEEE journals including Transactions on Biomedical Circuits and Systems, Circuits and Systems, and VLSI Systems Dr. Cardiff has supervised numerous research projects and has been instrumental in developing curriculum for digital communications, signal processing, and wireless systems. His teaching philosophy emphasizes open, friendly, and hands-on approaches that encourage independent thinking. He coordinates multiple modules including Communication Theory, Digital Electronics, DSP Technology, and Wireless Systems, demonstrating his commitment to both theoretical foundations and practical applications of electrical engineering principles. His research group works at the intersection of signal processing, machine learning, and biomedical engineering, developing innovative solutions for wearable healthcare monitoring. Current projects focus on event-driven processing architectures, decentralized classification systems, and signal quality-aware fusion techniques that enable robust performance in noisy real-world environments.

• Digital Signal Processing
• Communication Systems
• Biomedical Signal Processing
• +6 more

Andrea Continella is an Associate Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the University of Twente, where he contributes to the International Secure Systems Lab (iSecLab). His research focuses on systems security, particularly embedded firmware security, Android app security, malware detection, and program analysis techniques for vulnerability discovery. Ph.D. in Computer Science and Engineering, Politecnico di Milano (cum laude) Postdoctoral Researcher, Computer Science Department, UC Santa Barbara Visiting Researcher, School of Computer Science, University of Sydney Key research contributions include: Developing automated analysis techniques for embedded firmware (KARONTE, ShieldFS) Creating privacy leak detection mechanisms for mobile applications Designing ransomware defense systems using self-healing filesystems Advancing IoT security through misconfiguration detection (S3 buckets) and protocol analysis Pioneering semi-supervised methods for network traffic fingerprinting (FlowPrint) Scientific awards: Dutch Cyber Security Best Research Paper Award 2024 Runner-up USENIX Security Distinguished Reviewer Award 2024 Professional activities: Keynote speaker on firmware security (2024) Member of IPN Cyber Security Special Interest Group Oral presentations on automated vulnerability research (2023)

• Systems Security
• Malware Detection
• Privacy Leak Detection
• +7 more

George Vosselman is a Full Professor at the University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), specializing in Geo-Information Extraction with Sensor Systems. Educated with honours at Delft University of Technology (1986) and PhD in Photogrammetry from Rheinische Friedrich Wilhelms University of Bonn (1991), he has held academic roles at the University of Stuttgart, University of Washington, and Delft University of Technology (1993–2004). Since 2004, he has been a key figure at ITC, serving as department head (2012–2018, 2023–). Education: Delft University of Technology (BSc with honours, 1986), Rheinische Friedrich Wilhelms University of Bonn (PhD with honours, 1991) His research focuses on leveraging sensor technology advancements for large-scale geo-information production. Key expertise includes quality analysis of laser altimetry data, point cloud segmentation/classification, 3D building/road modeling, and model-driven imagery analysis. He has published over 220 papers and co-edited the textbook Airborne and Terrestrial Laser Scanning (2010). Recent work integrates deep learning with geospatial data, addressing semantic segmentation, visual question answering, and drone-based mapping. Recent publications (2025–2023) highlight trends in deep learning for remote sensing , including multimodal question answering benchmarks (HRVQA), vectorized building extraction (RoIPoly), latent diffusion for road modeling (LDPoly), and drone obstacle avoidance systems. His work bridges photogrammetry , computer vision , and robotic mapping , with applications in urban planning, disaster management, and informal settlement monitoring. Scientific Awards : Hansa Luftbild (1993), ISPRS Otto von Gruber (2000), Schwidefsky Medal (2012), Karl Kraus Medal (2012), ASPRS Fairchild Award (2015), ISPRS Fellow (2020) As an educator, Vosselman has taught photogrammetry, remote sensing, and laser scanning at Delft University of Technology and globally. He chaired the ITC Examination Board (2015–2023) and modernized geo-information education in Asia/Africa. His software for point cloud processing is commercialized in Europe, and he currently leads ISPRS working groups on point cloud methodologies. Labs/teams include the Earth Observation Science Chair Group at ITC, collaborating on UAV-based datasets (UAVid, UAVPal) and indoor laser scanning systems. Recent activities (2025) involve invited talks on pulse matching limitations in laser scanning and deep learning for point cloud classification.

• Photogrammetry
• Remote Sensing
• Laser Scanning
• +7 more

Jukka K Nurminen is a Professor of Computer Science at the University of Helsinki (since 2019) and a Research Professor at VTT. He leads the Empirical Software Engineering research group and supervises doctoral students in the Doctoral Programme in Computer Science. His career spans academia and industry, including roles as Adjunct Professor at Aalto University (part-time, 2016-2021) and Principal Scientist at VTT (2016-2019). His research focuses on efficient software systems , particularly energy-efficient software , mobile cloud computing , and data-intensive systems . Recent work addresses AI system testing , ethical decision-making in software , and quantum computing software . His publications highlight trends in quantum algorithms , machine learning for edge computing , and ethical AI . Best Paper Award (2023) Nurminen has supervised 6 PhD theses, 48 MSc theses, and 21 BSc theses. He has secured over 1 MEUR in research funding, including projects like FrameQ and EM4QS for quantum middleware. His teaching innovations include hackathons and summer schools, with excellence recognized in tenure-track evaluation (2018) and adjunct professorship (2015).

• Efficient Software Systems
• Energy-Efficient Software
• Mobile Peer-to-Peer
• +6 more

Bedrich Benes is a Professor and Associate Department Head in the Department of Computer Science at Purdue University. He holds a Ph.D. and M.S. in Computer Science from Czech Technical University in Prague (1998 and 1991, respectively). His research focuses on generative methods for geometry synthesis, procedural modeling, simulation of natural phenomena, and additive manufacturing. He has published over 200 research papers and secured grants from organizations like the NSF, NASA, and DOE. Editor-in-Chief of Elsevier's Graphical Models Senior Member of ACM and IEEE Fellow of Eurographics Association Research interests include graphics, visualization, geometric modeling, and computational biology. He leads projects on tree digital twins, urban forestry modeling, and immersive VR/XR education. Advised students include Bosheng Li and Xiaochen Zhou, who recently defended their Ph.D. theses. Notable contributions include neural ranking algorithms for forest reconstruction and tools like Tree-D Fusion for tree dataset generation. His work bridges computer graphics with environmental science and agriculture.

• Computer Graphics
• Geometric Modeling
• Computational Biology
• +5 more

Yung-Hsiang Lu is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on mobile/cloud computing, energy-efficient computing, and image/video processing. He holds a BSEE from National Taiwan University (1992), an MSEE (1996), and a PhD (2002) from Stanford University. Dr. Lu's academic background includes significant contributions to VLSI and circuit design, with primary emphasis on computer engineering. His work spans theoretical and applied domains, including optimizing neural networks for edge devices, securing deep learning models, and leveraging large language models for software development. Recent research trends in his articles emphasize energy efficiency in AI systems, interdisciplinary applications of transformers (e.g., music analysis), and challenges in model interoperability and security. His publications also highlight innovations in global camera networks and real-time visual data analysis. While no specific grants or awards are explicitly mentioned, his extensive list of publications reflects sustained academic engagement. His educational contributions include developing C programming resources and teaching large-scale image processing using global camera networks. Dr. Lu's professional address is at Purdue's Materials and Electrical Engineering Building in West Lafayette, Indiana, where he maintains an active research lab focused on embedded systems and low-power computing innovations.

• Mobile and cloud computing
• Energy-efficient computing
• Image and video processing
• +2 more

Anand Padmanabhan is a Research Associate Professor in the Department of Geography & Geographic Information Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the School of Earth, Society & Environment within the College of Liberal Arts & Sciences. He holds a Ph.D. in Computer Science from the University of Iowa, alongside an MS in Computer Science (University of Iowa) and a BE in Computer Engineering (University of Mumbai). His research focuses on advanced cyberinfrastructure, cyberGIS, geospatial data science, and high-performance computing. He leads the spatial algorithms and systems team at the CyberGIS Center for Advanced Digital and Spatial Studies, developing cyberGIS capabilities to leverage advanced computing for geospatial innovation. His work emphasizes scalable geocomputation, cloud-based frameworks, and reproducible research environments, with contributions to tools like CyberGIS-Compute and EasyScienceGateway. Recent publications highlight advancements in science gateway frameworks, middleware systems, and geospatial education platforms. He serves as Online MS Program Adviser and has secured NSF and EPA grants for interdisciplinary projects. His work spans transdisciplinary training programs, urban sensing analytics, and integration of social media with geospatial data.

• Advanced Cyberinfrastructure
• CyberGIS
• Geospatial Data Science
• +5 more

David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +4 more

Dr. Gavin McArdle is an Associate Professor at the University College Dublin (UCD) School of Computer Science, specializing in spatial data analysis and smart cities. He holds academic affiliations with the National Centre for Geocomputation (Maynooth University) and CeADAR (Data Analytics Centre). His research focuses on urban dynamics, geovisual analytics, smart transportation, and remote sensing applications. He has received a College of Science Teaching Excellence Award for his contributions to education. McArdle earned his BSc, PhD, and a Prof Dip in University Teaching & Learning from UCD. His work bridges academia and industry through collaborative grants, including those from Science Foundation Ireland and EU funding. Notable projects include the Dublin Dashboard (urban analytics platform) and DubSim (traffic simulation using digital footprints). His research outputs span over 147 publications, with recent work addressing Airbnb's impact on urban gentrification, sustainable mobility, and environmental monitoring via satellite data. He actively contributes to professional committees, including roles in the UCD Data Protection Impact Assessment Committee and international conferences like Web and Wireless GIS. McArdle coordinates courses such as Research Practicum and Computer Programming II, emphasizing practical research and technical skills. His interdisciplinary approach integrates machine learning, spatial statistics, and urban informatics to address real-world challenges in smart cities and environmental sustainability.

• Smart Cities
• Geovisual Analysis
• Urban Dynamics
• +5 more

Long Cheng is an Associate Professor at Clemson University's School of Computing, specializing in cybersecurity, IoT security, and machine learning for abuse detection. His work focuses on securing voice assistants, CPS/IoT systems, and network protocols. He holds dual PhDs from Virginia Tech (2018) and Beijing University of Posts and Telecommunications (2012), with postdoctoral research at Singapore University of Technology and Design (2015) and A*STAR (2012–2015). Research Interests: Online abuse detection, system/software security, network security, CPS/IoT privacy, mobile computing. Awards: NSF CAREER Award, Best Paper at AsiaCCS 2022, Distinguished Paper at ACSAC 2020. Grants: NSF grants (RAPID, SaTC-EDU), industry funding from Innova EV, USDOT, and Google. Service: Editorial boards of IEEE TDSC and ACM DTRAP; program chairs/co-chairs for SmartSP 2025, SecDev 2021–2023. Labs & Teams: AI-Cybersecurity Lab, leading research on abuse detection and CPS security. Collaborates with industry partners like Apple, Amazon, and Texas Tech University.

• Machine Learning Based Online Abuse Detection
• System and Software Security
• Network Security
• +3 more