جست‌وجوی استاد و پژوهشگر

Jianhua Ruanمشاهده پروفایل

استاد

• Bioinformatics
• Computational Systems Biology
• Machine Learning for Life Sciences
• +۷ مورد دیگر

Dr. Jianhua Ruan is a Professor in the Department of Computer Science at The University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Washington University in St. Louis and has been a faculty member at UTSA since 2007. His research focuses on bioinformatics, computational systems biology, and machine learning applied to life sciences. Specific areas include modeling cis-regulatory networks, analyzing biological networks via graph algorithms, and biomarker discovery using omics data. Dr. Ruan collaborates extensively with biologists at UTSA and the UT Health Science Center on genomic, transcriptomic, proteomic, and epigenomic studies. Key research interests include: Machine learning for cis-regulatory network modeling Graph algorithms for biological network analysis Integrated omics data analysis for disease prediction Epigenetic regulation and cancer biology His work has been supported by NIH, NSF, and the San Antonio Life Sciences Institute. Notable achievements include a Best Performer award in the DREAM Reverse Engineering Challenges (2008) and the UTSA Faculty Research Award (2007). Dr. Ruan organizes the UTSA Computer Science Graduate Research Seminar, fostering interdisciplinary collaboration. He has developed software tools like TSCAN for single-cell RNA-seq analysis and contributes to computational methods for biomarker discovery and network-based classification.

• Compilers
• Parallel Computing
• Automatic Differentiation
• +۳ مورد دیگر

Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.

Erik Mannensمشاهده پروفایل

استاد

• Semantic Intelligence
• Big Data Analytics
• Semantic Web
• +۴ مورد دیگر

Erik Mannens is a Professor at Ghent University and Research Valorisation Director at imec, specializing in Semantic Technologies and Artificial Intelligence since 2005. He leads the Data Science team at IDLab, managing over 50 researchers focused on advancing semantic data science and its applications. Education: PhD in Computer Science Engineering (2011), Master’s in Computer Science (1995), and Master’s in Electro-Mechanical Engineering (1992) The team explores the fusion of semantics and AI, aiming to: (1) analyze the Web’s societal impact, (2) study Web-enabled lifestyle changes, and (3) develop future Web innovations. Their work emphasizes Open Source , Open Access , and Open Knowledge principles. His research spans Semantic Intelligence , Big Data Analytics , and Linked Data applications, with notable publications on RDF mappings, IoT platforms, and distributed data querying. Key projects include MapVOWL, RMLEditor, and the MASSIF platform. Current affiliations include Ghent University’s IDLab , where he contributes to cutting-edge research in data science and semantic technologies.

Christoph Kesslerمشاهده پروفایل

استاد

• Parallel Programming
• Compiler Technology
• Optimization Algorithms
• +۳ مورد دیگر

Christoph Kessler is a Professor and Head of the Software and Systems (SAS) division at the Department of Computer and Information Science (IDA), Linköping University, Sweden. He leads the Programming Environment Laboratory’s research group focusing on compiler technology, parallel computing, and heterogeneous systems. His work includes the development of tools like OPTIMIST, PARAMAT, and SkePU, and he has contributed over 100 publications in journals and conferences. He holds a PhD from the University of Saarbrücken and a Habilitation from the University of Trier. Research interests span parallel programming, compiler optimization, and energy-efficient scheduling for heterogeneous systems. He has secured a 30M SEK grant from SSF for the ASTECC project, advancing adaptive software for edge-cloud computing. Notable contributions include frameworks for GPU-based systems and methodologies for optimizing resource allocation on many-core architectures. His team’s work emphasizes practical applications in high-performance computing, including tools for course management (StASy) and energy-aware scheduling algorithms. The SAS division, under his leadership, focuses on software engineering and computer systems research with strong industry collaboration.

Yudi Pawitanمشاهده پروفایل

استاد

• Bioinformatics
• Statistical Genetics
• Genomics
• +۲ مورد دیگر

Yudi Pawitan is Professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet, where he leads the research group on Statistical and Bioinformatics Analyses of High-Throughput Molecular Data. His work focuses on developing statistical methods for genomic studies including SNP/RNA arrays and next-generation sequencing. Education includes BSc in Statistics (Bogor Agriculture Institute, 1982), MSc in Statistics (UC Davis, 1984), and PhD in Statistics (UC Davis, 1987). Research interests span bioinformatics, cancer genomics, and statistical genetics with emphasis on high-dimensional data analysis, genetic correlations, and neural cell biology. His group addresses fundamental questions in genomic data interpretation and neurodegenerative processes. Publications demonstrate strong focus on genetic epidemiology, single-cell analytics, and statistical methodologies. Recent works explore machine learning applications in longitudinal data visualization, neural cell characterization, and cancer biomarker discovery. Awards: Not documented in provided texts. Supervises doctoral candidates including Linda Lindström and Ralf Kuja-Halkola. Manages the Live Imaging Facility at St. Vincent's Centre for Applied Medical Research. Research funded by Swedish Research Council and Swedish Cancer Society grants. Leads interdisciplinary collaborations through the Statistical and Bioinformatics research group, integrating computational biology with experimental neuroscience.

• Materials Science
• Mechanical Engineering
• Computational Mechanics
• +۳ مورد دیگر

Antoine Sanner is a Researcher affiliated with the Institute of Construction Materials (Institute of Building Materials) at ETH Zurich, part of the College of Architecture, Civil and Environmental Engineering. His work focuses on computational mechanics of building materials, surface topography analysis, and adhesion science. He co-developed the dtool and dserver systems to enhance FAIR data practices in research. Research interests include the structural behavior of cement-based materials, mechanisms of material adhesion under varying surface conditions, and multiscale analysis of surface roughness effects. He has also contributed to engineering tools for characterizing surface topography across scales, emphasizing the creation and use of 'digital surface twins' for material studies. His publications consistently explore topics at the intersection of materials science and mechanical engineering, with recent work advancing understanding of surface adhesion dynamics and cement curing processes. No scientific awards are explicitly mentioned in the provided texts. As part of the Professorship for Computational Mechanics of Building Materials, Antoine contributes to research infrastructure and computational methods development. He has no listed advisees or grants, and his role appears to be full-time with no indication of part-time status. He collaborates on projects involving advanced materials characterization, particularly in polycrystalline diamond coatings and bearing component analysis, leveraging both experimental and computational methodologies.

• Computer Architecture
• Embedded Systems
• High-Performance Computing
• +۹ مورد دیگر

Zapater Sancho Marina is an Associate Professor at the ReDS Institute (Institute of Reconfigurable and Embedded Digital Systems) within the School of Engineering and Management Vaud (HEIG-VD), part of the University of Applied Sciences and Arts Western Switzerland (HES-SO). She holds dual master's degrees in Electronic and Telecommunication Engineering from Universitat Politècnica de Catalunya (2010) and a PhD in Computer Science from Universidad Politécnica de Madrid (2015). Her career includes postdoctoral work at EPFL (2016-2020) and assistant professorship at Universidad Complutense de Madrid (2015-2016). Education BSc & MSc in Electronic Engineering (UPC 2010) PhD in Computer Science (UPM 2015) Research Focus spans cross-layer optimization of heterogeneous architectures for performance and energy efficiency, with emphasis on: Embedded systems (IoT/edge computing) High-performance compute architectures Analog in-memory computing for AI Thermal/power management in 3D chips Cloud-edge AI workload orchestration Publication Trends show expertise in RISC-V simulation frameworks, analog computing tiles for CNNs, virtual memory redesign, and AI-driven cloud performance prediction. Her recent work explores thermal-aware 3D chip management, hybrid-cache reliability optimization, and open-source teaching platforms for radio theory. Awards include a Spanish government PhD fellowship. She has led 4 European H2020 projects since 2016 and currently serves as PI for 4 industrial collaborations (Facebook/Intel/Huawei), Innosuisse projects, and HES-SO initiatives. Labs & Teams include the ReDS Institute, EPFL's Embedded Systems Laboratory, and collaborations with Yale/Edinburgh. She co-developed the ALPINE simulation framework and SO3 operating system modifications for Midgard project validation.

Paul Liangمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Artificial Intelligence
• Human-Computer Interaction
• +۳ مورد دیگر

Paul Liang is Assistant Professor at the Massachusetts Institute of Technology with joint appointments in the Media Lab and the Department of Electrical Engineering and Computer Science, where he directs the Multisensory Intelligence research group. His work spans foundational research in multisensory AI technologies and their applications in enhancing human experiences and real-world human-AI interaction. Liang's research focuses on three interconnected thrusts: developing theoretical foundations for multisensory machine learning systems; designing interactive AI technologies to augment human capabilities; and addressing societal concerns in real-world human-AI interaction. His work integrates principles from machine learning, natural language processing, and human-computer interaction to create novel multimodal architectures. Professor Liang's research group actively publishes on multimodal representation learning, evaluation frameworks for foundation models, and methods for quantifying multimodal interactions. Recent work includes developing the Holistic Evaluation of Multimodal Models (HEMM) framework and the MultiBench benchmarking suite.

• Software Engineering
• Industry 4.0/IIoT Platforms
• Model-Based Software Development
• +۷ مورد دیگر

Dr. Holger Eichelberger is part of the Academic Staff in the Software Systems Engineering (SSE) department at the University of Hildesheim's Institute of Computer Science. He is affiliated with Faculty 4: Mathematics, Natural Sciences, Economics and Computer Science. His roles include membership in the Managing Committee of the Institute of Computer Science and the Committee for Student Scholarships. He has extensive experience in model-based software development, Industry 4.0 platforms, and performance engineering. Research Interests: Software Engineering for adaptive systems, Asset Administration Shells (AAS), IIoT platforms, MLOps, container orchestration, and open-source tools like EASy-Producer and SPASS-meter. His work focuses on bridging research and industrial needs, particularly in smart manufacturing and edge computing. Publications highlight contributions to IIoT platform analysis, AI integration in Industry 4.0, and performance benchmarking of communication protocols. He has organized conferences like ICPE and SSP and reviewed for top journals such as IEEE Transactions on Software Engineering. Key projects include the IIP-Ecosphere platform and contributions to standards like AAS. Collaborations involve institutions like the University of the West Indies and industry partners through funded projects like BMBF AI-Lab HAISEM. His research emphasizes reproducibility, interoperability, and scalable solutions for industrial challenges.

DAVID LOPEZ VILARIÑOمشاهده پروفایل

استاد

• Computer Architecture
• Computer Vision
• FPGA Acceleration
• +۳ مورد دیگر

David Lopez Vilariño is a **Professor** at the **University of Santiago de Compostela**, affiliated with the **Department of Electronics and Computing** within the **Faculty of Physics**. He earned his PhD in 2001 with a thesis titled *"Active contours at the pixel level: design and implementation on cellular network architectures,"* advised by Dr. Diego Cabello Ferrer. His research focuses on **Computer Architecture**, **FPGA Acceleration**, **LiDAR Data Analysis**, and **Embedded Systems**, with notable contributions to LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He is part of the **ARQCOMP (Computer Architecture)** and **Artificial Vision** research groups. His work spans topics such as high-performance computing, parallel processing, and hardware optimization for vision-capable systems. Key projects include developing FPGA-based solutions for real-time video surveillance, retinal vessel analysis, and autonomous navigation systems. Publications emphasize **LiDAR data processing**, including algorithms for road detection, power line characterization, and 3D point cloud analysis. He also pioneered tools like the *Open Lidar Visualizer and Analyser* for 3D stereoscopic visualization. His expertise bridges hardware design and software development, particularly in leveraging FPGAs for embedded vision systems. No scientific awards or grants are explicitly listed, but his prolific publication record highlights sustained innovation in computer vision and geospatial technologies. His research team collaborates on projects involving manycore systems, GPU acceleration, and reconfigurable computing architectures.

• Complex Event Processing
• Healthcare Informatics
• Semantic Computing
• +۵ مورد دیگر

Fabio Persia is a Professor at the University of Naples Federico II with an extensive publication record spanning 15 years (2009-2025), demonstrating continuous academic engagement. His research portfolio spans multiple institutions through collaborations with over 70 co-authors, most notably Daniela D'Auria (43 publications), Mouzhi Ge (17), and Giovanni Pilato (14). Dr. Persia's research interests focus on the intersection of semantic computing, event processing, and practical applications. His work evolved from foundational contributions to multimedia recommender systems (2013) to developing the ISEQL interval-based surveillance event query language (2016), and most recently to healthcare AI applications. He has pioneered complex event processing frameworks for video surveillance, created multi-agent systems for epilepsy detection (PredictMed-epilepsy), and explored social sensing for personalized routing during the pandemic. His recent work increasingly integrates large language models with healthcare monitoring systems, reflecting current AI trends. Analysis of his publication trends since 2020 reveals a strong healthcare focus (65% of recent work), particularly in patient monitoring architectures, clinical decision support, and medical AI integration. His publications demonstrate consistent methodological rigor across domains, often combining semantic computing with real-time event processing to address practical challenges in healthcare and social computing contexts. Dr. Persia has served as guest editor for six special issues in the International Journal of Semantic Computing (2023-2025) covering Robotic Computing, Transdisciplinary AI, and Multimedia Computing, confirming his leadership in these research communities. His editorial roles complement his extensive publication record, which includes 25 journal articles and 76 conference papers. His collaborative research includes significant projects with Stefania Costantini on patient monitoring systems, with Mouzhi Ge on multimedia recommenders, and with Sven Helmer on interval joins and event detection. While specific grant information isn't detailed in publications, his sustained output suggests successful funding from multiple sources. His current research trajectory indicates growing emphasis on LLM integration in medical contexts and context-aware systems for public health applications.

• Cyber Security
• Digital Forensics
• Biometric Authentication
• +۷ مورد دیگر

Fudong Li is a Professor and Principal Academic in Cyber Security at Bournemouth University, leading the Cyber Security pathway for multiple undergraduate programs and serving as Programme Leader for BSc Forensic Computing and Security. His expertise spans over 15 years in teaching and research, focusing on digital forensics, biometric authentication, ethical hacking, and network security. He holds certifications such as EC-Council’s CHFI, CEH, and Cisco’s CCNA. Fudong has supervised 12 completed PhD students and authored over 60 publications in journals and conferences. His research interests include wearable computing for authentication, blockchain security, privacy-preserving technologies, and cloud storage verification. Notable work includes studies on Ethereum’s consensus mechanism transition and smartwatch-based continuous authentication systems. He has led the FORESIGHT grant (2019) for advanced cyber-security simulation platforms and serves as an external examiner for multiple UK universities. Fudong’s contributions align with UN SDGs for Quality Education and Gender Equality through cybersecurity education initiatives and inclusive technology design. His advisory roles include overseeing 13+ PhD candidates and collaborating on grants addressing cybersecurity challenges in aviation, power grids, and naval systems. He also leads external engagement activities, including visiting lectureships and international examiner roles.

Merry-Lynn McDonaldمشاهده پروفایل

دانشیار

• Genetic Epidemiology
• COPD
• Cachexia
• +۵ مورد دیگر

Dr. Merry-Lynn McDonald is an Associate Professor in the Department of Medicine's Division of Pulmonary, Allergy, and Critical Care Medicine at the University of Alabama at Birmingham (UAB). She is also Director of Integrative 'Omics in the same division. Her academic roles include appointments across multiple UAB schools, including the School of Public Health and Academic Joint Departments in Genetics. Dr. McDonald holds dual faculty status in the School of Medicine and School of Public Health, reflecting her interdisciplinary focus. Education: PhD in Molecular Biophysics from Baylor College of Medicine, MS in Epidemiology from University of Saskatchewan, and BS in Biochemistry from University of Waterloo. Postdoctoral training at Brigham and Women's Hospital in Network Medicine. Research Focus: Her work centers on genetic epidemiology of complex traits, particularly COPD cachexia, osteoarthritis, and smoking-related comorbidities. She leads the McDonald Laboratory of Integrative 'Omics, leveraging omics data (genomics, proteomics, metabolomics) to uncover disease mechanisms. Key projects include the Million Veteran Program (MVP), Trans-Omics for Precision Medicine (TOPMed), and Cachexia in COPD Etiology of Low-Lean Muscle (CCELL). Grants & Funding: Active grants from NIH (K99/R00, NHLBI) and the Parker B. Francis Foundation. Her research explores network medicine approaches to COPD, osteoarthritis prevalence in veterans, and biomarker development for lung disease comorbidities. Labs/Teams: Directs the Integrative 'Omics Lab, collaborating with multidisciplinary teams across UAB's Lung Health Center, CAMBAC, and the Nutrition Obesity Research Center (NORC). The lab actively recruits postdoctoral fellows, bioinformaticians, and students with expertise in computational biology and statistical genetics.

Stephen Ramseyمشاهده پروفایل

دانشیار

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +۶ مورد دیگر

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

Chengming Zhangمشاهده پروفایل

استادیار

• Efficient machine learning systems
• AI algorithm-hardware Co-design
• Effective efficiency algorithms
• +۵ مورد دیگر

Chengming Zhang is a tenure-track assistant professor in the Computer Science Department at the University of Houston. He recently completed his Ph.D. in Computer Engineering from Indiana University in May 2024, where he was a member of the HiPDAC group working on building efficient and scalable deep learning systems under the advisement of Prof. Dingwen Tao. He has extensive industry research experience with multiple projects at Microsoft Research, Meta Reality Labs, and Argonne National Laboratory. His educational background includes: Ph.D. in Computer Engineering, Indiana University (May 2024) Dr. Zhang's research focuses on creating efficient machine learning systems that can operate effectively across diverse hardware platforms. His work bridges the gap between theoretical algorithms and practical implementation with particular emphasis on efficient machine learning systems for training and inference on parallel, distributed, and heterogeneous hardware; AI algorithm-hardware co-design, particularly for GPU architectures; effective efficiency algorithms including model compression, data efficiency, and parameter-efficient tuning; and large-scale deep learning applications such as Large Language Models, Agents, and Image/Video Generation systems. His publication record demonstrates a consistent focus on optimizing deep learning systems through hardware-aware approaches. The majority of his work centers around making deep learning more efficient through techniques like model compression, hardware-algorithm co-design, and memory optimization. His research spans both theoretical algorithm development and practical system implementation, with publications in top-tier conferences including the International Conference on Supercomputing, PPoPP, and AAAI. A notable trend in his work is the emphasis on practical efficiency - not just theoretical improvements but solutions that deliver real-world performance gains on actual hardware. Dr. Zhang is actively building his research group at the University of Houston and has secured significant computational resources for his lab, including multiple high-performance computing servers worth a total of $130K (two 8-Ada6000 servers and one dual-4090 servers).