جست‌وجوی استاد و پژوهشگر

• Autonomous vehicles
• Drones
• Embedded systems
• +۱۲ مورد دیگر

Marcello Chiaberge is an Associate Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin. He serves as Coordinator of the Interdepartmental Center for Service Robotics (PIC4SeR) and oversees quality management for the Master's Degree in Mechatronic Engineering. With over 110 publications and 9 international patents, his work bridges academic research and industrial applications in robotics and electronics. Research Interests span a wide array of fields including Autonomous vehicles Drones Embedded systems Precision agriculture Search and rescue robotics Simultaneous localization and mapping (SLAM) Smart cities infrastructure His projects often integrate machine learning with hardware acceleration, focusing on real-time solutions for robotics applications. Recent Publications highlight his contributions to: Deep reinforcement learning for robot navigation Hybrid MPPT algorithms for solar energy optimization UAV-based remote sensing in agriculture Transformer architectures for action recognition Hardware-accelerated computer vision systems Research Leadership is evident in his coordination of PIC4SeR and principal investigator roles in projects like: HERITALISE (2025-2028) - Cultural heritage digitization EMPATHY (2024-2026) - Empathetic mobility platforms NG-UWB (2019-2023) - Next-generation localization systems MArcEL (2019-2022) - Electric agricultural machinery His teaching includes courses on electronic fundamentals and mechatronic systems, all delivered in English at the Master's level. He has served as doctoral consortium member since 2002 and currently supervises multiple PhD candidates in electronics and mechatronics fields.

• Bioinformatics
• Plant Stress Biology
• Gene Expression Analysis
• +۵ مورد دیگر

Angelica Lindlöf is an Associate Professor in Bioinformatics at the University of Skövde's School of Bioscience, where she conducts research at the intersection of computational methods and biological systems. Her work spans plant stress biology and cancer genomics, demonstrating a strong interdisciplinary approach to complex biological questions. Dr. Lindlöf's research interests focus on bioinformatics applications in plant stress responses and cancer biology. Her work in plant biology examines molecular mechanisms of drought and salinity tolerance in crops like wheat, barley, and rice, with particular attention to melatonin biosynthesis pathways. In cancer research, she investigates genetic factors in neuroblastoma, a childhood cancer affecting the nervous system. Her methodological expertise includes gene expression analysis, systems biology, and computational approaches to understanding complex biological networks. Analysis of her recent publications reveals a consistent pattern of interdisciplinary research bridging computational methods with biological questions. Her work shows particular strength in identifying regulatory networks and key genes involved in stress responses in plants, as well as genetic factors influencing cancer progression. The integration of bioinformatics tools with experimental biology is a hallmark of her research approach. Dr. Lindlöf actively mentors students through her role as a course coordinator and thesis project supervisor. She is involved in several research projects, including the ongoing project 'Identifying precision medicine treatments in myelodysplastic syndrome' (January 2025-March 2027) and the recently completed project 'Genes as targets for cancer treatment' (January 2015-July 2021). She teaches a range of bioinformatics courses including 'Bioinformatics analysis with Python,' 'Analysis of NGS-data,' and 'Bioinformatics – research process,' emphasizing practical computational skills for biological data analysis. Her teaching reflects her research expertise, providing students with hands-on experience in applying computational methods to biological problems.

Kun-Mao Chaoمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Computational Molecular Biology
• +۳ مورد دیگر

Kun-Mao Chao is a Full-Time Professor at the Department of Computer Science and Information Engineering, National Taiwan University, where he has held multiple roles since 2002, including Chair Professor and Academician of Academia Sinica. He also serves in the Graduate Institute of Biomedical Electronics and Bioinformatics (2006–present) and holds adjunct appointments at various institutions. Education: PhD in Computer Science, Pennsylvania State University (1993) MS in Computer Engineering, National Chiao Tung University (1987) BS in Computer Engineering, National Chiao Tung University (1985) Research Expertise spans Algorithm Design (applied algorithms, approximation algorithms), Bioinformatics , and Computational Molecular Biology . His work focuses on problems like sequence alignment, spanning tree optimization, and DNA copy number variation analysis, often with interdisciplinary applications in medicine and epidemiology. Recent Publications (2012–2022) include studies on mathematical modeling for influenza vaccination policies , nanocarrier drug delivery systems , and XML database query algorithms , reflecting his diverse interests in both computer science and biomedical applications . Professional Activities: He has served as Editor for Information Processing Letters , PC Co-Chair for IEEE BIBE 2011 and ISAAC 2012, and on advisory committees for numerous conferences (APBC, BIBE, GIW, etc.). Laboratory: Leads the Algorithms and Computational Biology Laboratory at National Taiwan University, mentoring students in cutting-edge research areas.

Jessica Kevillمشاهده پروفایل

پژوهشگر

• Environmental Virology
• Wastewater Pollution
• SARS-CoV-2 Pandemic
• +۳ مورد دیگر

Jessica Kevill is a Postdoctoral Research Officer in Environmental Virology at Bangor University's School of Environmental & Natural Sciences. She leads the BlueAdapt project investigating sewage-borne viral risks in coastal environments under climate change. Her expertise includes molecular methods for viral detection in wastewater and environmental samples. Dr. Kevill holds a PhD in Biological Sciences from the University of Salford (2018), focusing on honeybee virus resistance. She developed SARS-CoV-2 detection techniques during the pandemic and currently explores climate impacts on viral viability in marine systems. Education: PhD in Biological Sciences, University of Salford, Manchester (2018) Research on Deformed Wing Virus resistance in honeybees Research Interests: Environmental Virology with focus on wastewater surveillance Climate change effects on pathogen survival in coastal zones Emerging molecular technologies for viral analysis Pandemic preparedness through environmental monitoring Her work bridges public health and environmental science, addressing critical gaps in understanding sewage-derived viruses' ecological and human health impacts. Grants & Projects: BlueAdapt Project : Investigating climate-related pathogen risks (lead researcher) Citizen Science Project : Assessing Conwy River pathogen risks (2024-2025 PI) Labs/Teams: Public and Environmental Health Surveillance Lab at Bangor University Collaborations with environmental microbiology teams

Yee Man Margaret Ngمشاهده پروفایل

استادیار

• Social Media
• Information Diffusion
• Computational Methods
• +۴ مورد دیگر

Yee Man Margaret Ng is an Assistant Professor in the Department of Journalism at The University of Illinois Urbana–Champaign, with a faculty affiliate appointment in the Department of Computer Science. She holds a Ph.D. from the University of Texas at Austin and an MA from the Missouri School of Journalism. Her research focuses on technology adoption, social media dynamics, and information diffusion using computational methods and large-scale data. Key affiliations include the Siebel School of Computing and Data Science, the Institute of Communications Research, and the National Center for Supercomputing Applications (NCSA). She previously served as an Infodemics Researcher with the UN Global Pulse initiative. Ng’s work analyzes social media platforms (e.g., Twitter, Mastodon, YouTube) to study user behavior, content dissemination, and algorithmic impacts. Recent studies include transitions between social platforms, vaccine communication on YouTube, and regional web usage patterns. Her interdisciplinary approach bridges journalism, computer science, and data science. Her publications highlight trends in digital media ecosystems, including platform migration, misinformation dynamics, and the role of algorithms in shaping public discourse. She collaborates widely, with projects addressing food technology communication, pandemic-related content, and computational social science education. Labs/Teams: Active in the Center for Social & Behavioral Science, National Center for Supercomputing Applications (NCSA), and the Siebel School’s Digital Agriculture initiatives. Her research often involves cross-disciplinary teams leveraging big data and computational tools.

• genomics
• digital health
• health humanities
• +۴ مورد دیگر

Ine Van Hoyweghen is a full professor at the Faculty of Social Sciences, KU Leuven , and head of the Life Sciences & Society Lab . She serves as a member of multiple institutes including the Leuven Centre for Health Humanities , DigiSoc , and iSi Health , with a focus on the ethical, legal, and social implications of digital and genomic medicine. Research Focus: Genomics, digital health, health humanities, responsible innovation Leadership: Life Sciences & Society Lab; involved in EU health data policy initiatives Awards: Francqui Prize in Human Sciences (2025) Her recent work explores in silico medicine , next-generation sequencing implementation , and genetic discrimination policy , emphasizing cross-disciplinary collaboration and public values in algorithmic healthcare. Publications address European Health Data Space challenges and genomic diagnostic ethics . Notable scientific awards include the 2025 Francqui Prize, recognizing her contributions to human sciences. She actively engages in EU policy debates through projects like REIMAGINE ADM and NEUROCOV , with a focus on stakeholder engagement and trust-building in digital medicine.

• Human Genomics
• Prostate Cancer Research
• Cancer Disparities
• +۴ مورد دیگر

Professor Vanessa Hayes is the Petre Chair of Prostate Cancer Research at the University of Sydney's School of Medical Sciences. She leads the Ancestry & Health Genomics Laboratory at the Charles Perkins Centre, focusing on genomic studies to address cancer disparities, particularly in prostate cancer among African populations. Her research integrates genomic and clinical science to develop precision medicine approaches. Education and affiliations include roles as an Extraordinary Professor at the University of Pretoria and Honorary Professor at the University of Limpopo. She directs the HEROIC Prostate Cancer Precision Health Africa1K and TARGET Africa projects, aiming to close health equity gaps through genomic research. Research interests span human genomics, cancer disparities, and evolutionary genetics. Recent projects include mapping early human evolution via genomic diversity and identifying genetic drivers of aggressive prostate cancer in Africa. Over 150 publications highlight her work on genomic rearrangements, ancestral health links, and clinical applications. Scientific achievements include election as a Fellow of the Australian Academy of Health and Medical Sciences (2024) and recognition as an Eureka Prize finalist (2019). Grants exceed $15M, supporting studies on mutational signatures, genomic diversity, and precision health initiatives. She advises Kangping ZHOU and collaborates globally through networks like the South African Prostate Cancer Study (SAPCS). Her lab's vision combines ancestral genomics with clinical science to improve cancer outcomes worldwide.

• Structural Health Monitoring (SHM)
• Nondestructive Evaluation (NDE)
• Smart Sensing Technologies
• +۷ مورد دیگر

Professor Alex Ching-Tai Ng is a faculty member at the University of Adelaide, serving as a Professor in the School of Architecture and Civil Engineering within the Faculty of Sciences, Engineering and Technology. He joined the university in 2011 as a Lecturer and was promoted to Professor, holding administrative roles such as Associate Dean of Graduate Studies and Director of Graduate Studies (2018–2022). Education: B.Eng. (First-class Honours, City University of Hong Kong), M.Phil. (City University of Hong Kong), Ph.D. (University of Queensland) His research interests span Structural Health Monitoring (SHM), Nondestructive Evaluation (NDE), smart sensing technologies, and vibration testing. He focuses on developing advanced SHM/NDE techniques for structural integrity assessment, with applications in engineering reliability and decarbonization of construction. His publications highlight innovations in guided wave-based damage detection, nonlinear ultrasonic methods for composite materials, and machine learning integration in structural diagnostics. Key trends include FRP-strengthened structures, graphene composites, and real-time hybrid simulation for wind response analysis. Scientific awards include: The Nishino Prize (EASEC, 2016) Australian Research Council Discovery Early Career Researcher Award (2013) He actively supervises Masters and PhD candidates and has served as an assessor for international grants (ARC, RGC, ERC, SNSF, NCN, CONICYT). Professional leadership includes Co-Editor-in-Chief of Engineering Structures and roles in technical committees like ISHMII and ANSHM.

Dmitry Bagaevمشاهده پروفایل

پژوهشگر

• Bayesian Inference
• Probabilistic Programming
• Reactive Message Passing
• +۲ مورد دیگر

Dmitry Bagaev is a Researcher at Eindhoven University of Technology's Signal Processing Systems department, specializing in Bayesian inference and probabilistic programming. His work develops computational tools like RxInfer.jl and ReactiveMP.jl for scalable probabilistic modeling. Research spans Bayesian inference frameworks, reactive programming architectures, and applications in computational immunology. Recent publications focus on probabilistic programming languages (GraphPPL.jl), multi-agent trajectory planning, and SARS-CoV-2 T-cell receptor databases. Articles demonstrate consistent innovation at the intersection of computational statistics, biological data analysis, and real-time systems engineering. Awards and advising roles are not documented in available records. Research occurs within the BIASLab group, focusing on signal processing systems and probabilistic AI.

Honglak Leeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Representation Learning
• +۵ مورد دیگر

Honglak Lee is a Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and Executive Vice President and Chief Scientist at LG AI Research. He holds a Ph.D. from Stanford University (2010) under Andrew Ng. His research focuses on deep learning, representation learning, and their applications in computer vision, robotics, and healthcare. Lee has served on the editorial boards of IEEE TPAMI and as an area chair for ICML, NIPS, and CVPR. Education: Ph.D. in Computer Science, Stanford University (2010); M.S. in Computer Science and Applied Physics (2006); B.S. in Physics and Computer Science, Seoul National University (2003). Research Interests: Machine learning, deep learning, representation learning, multimodal learning, reinforcement learning, and applications in healthcare and robotics. His work emphasizes scalable unsupervised feature learning, invariant feature extraction, and deep generative models. Key Contributions: Pioneered convolutional deep belief networks (ICML 2009), multimodal autoencoders (ICML 2011), and weakly supervised learning techniques. His work on disentangled representations (ICML 2014) and reinforcement learning algorithms (NIPS 2015) has been highly influential. Awards: Sloan Research Fellowship (2016), NSF CAREER Award (2015), AI's 10 to Watch (2013), and multiple best paper awards. Students and Advising: Advised over 50 students, including PhD graduates Xinchen Yan (Uber), Ruben Villegas (Adobe), and Junhyuk Oh (DeepMind). Current students include Lajanugen Logeswaran, Kibok Lee, and Sungryull Sohn.

• Data Mining
• Text Mining
• Health Informatics
• +۵ مورد دیگر

Raymond T. Ng is a Professor of Computer Science at the University of British Columbia (UBC), where he also serves as the Director of the Data Science Institute. He holds a part-time role as the Chief Informatics Officer at the PROOF Centre of Excellence for the Prevention of Organ Failures. Ng's research spans data mining, health informatics, and bioinformatics, with a focus on biomarker discovery for organ failures and computational methods for analyzing medical data. He earned his PhD in Computer Science from the University of Maryland, College Park, and has been a faculty member at UBC since 1992. Education: BSc (Hons.) in Computer Science, University of British Columbia (1986) M.Math in Computer Science, University of Waterloo (1988) PhD in Computer Science, University of Maryland, College Park (1992) Research Interests: Ng's work integrates data science with healthcare, including genomic data analysis, biomarker development for COPD and heart failure, and natural language processing for medical text. His projects address challenges in precision medicine, such as non-invasive diagnostics for organ rejection in heart transplants and predicting pulmonary exacerbations in cystic fibrosis. He leads initiatives like the MERIDIAN project to advance ocean acoustic data infrastructure. Awards: Genome BC Award Killam Research Prize Fellow of the Royal Society of Canada Bio-IT Best Practices Award CASCON Best Exhibit Award Grants & Advising: Ng has secured significant funding for projects like PROOF Centre biomarker research and the MERIDIAN data infrastructure. His advisory work focuses on computational biology and data-driven healthcare solutions. He supervises students in machine learning, text mining, and health informatics. Labs & Teams: Ng oversees the Data Systems and Mining Laboratory and collaborates with interdisciplinary teams at the PROOF Centre and the Natural Language Processing Lab. His work bridges academia and industry, driving translational research in precision health and environmental data science.

• Biostatistics
• Genomic Data Analysis
• Statistical Methodologies
• +۷ مورد دیگر

Dr. Zhide Fang is a Full Professor of Biostatistics at the LSU School of Public Health and holds a conjoint appointment in the Department of Genetics, School of Medicine at LSU. He is also a statistician at the Louisiana Clinical & Translational Science Center and a member of the Southeast Biomedical Informatics Consortium. His work focuses on developing statistical methodologies for genomic data analysis, experimental design optimization, and big data algorithms. Education: PhD in Statistics, University of Alberta (1999) MS in Statistics, Huazhong Normal University (1989) BS in Mathematics, Huazhong Normal University (1986) Research Interests: Dr. Fang specializes in genomic data analysis (including microarrays, NGS, and metagenomics), statistical modeling for public health and biomedical research, optimal experimental design theory, and algorithms for big data. His collaborative work spans oncology, ophthalmology, and pediatric medicine. Publications: Recent work includes studies on ethnic disparities in diabetes management, AI-driven surgical metrics, prostate cancer genetics, and optimal design methodologies. His articles reflect interdisciplinary collaboration in biostatistics, computational biology, and clinical research. Labs/Teams: Active in translational science through the Louisiana Clinical & Translational Science Center and the Southeast Biomedical Informatics Consortium. Engaged in collaborative projects at LSU Health's School of Medicine and School of Public Health.

NICOLAS VILA BLANCOمشاهده پروفایل

استادیار

• Artificial Intelligence
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Nicolás Vila Blanco is an Assistant Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Higher Technical School of Engineering. His research focuses on applying artificial intelligence techniques to medical and dental imaging, including age and sex prediction from dental radiographs, oral microbiome analysis, and automated medical diagnostics. He holds a PhD in Computer Science from the same university, awarded in 2022 for his thesis on deep learning methodologies for dental age assessment. Education: Doctorate in Computer Science (2022) from University of Santiago de Compostela, specializing in deep learning applications for medical imaging. Research Interests: Nicolás' work bridges computer vision and healthcare, emphasizing explainable AI systems for forensic anthropology (e.g., age estimation from dental X-rays), clinical diagnostics (e.g., plaque analysis software), and microbiome characterization. He leads development of tools like DenTiUS Deep Plaque for plaque quantification and IDALib for image data augmentation. His recent publications highlight innovations in fetal ultrasound analysis, heart segmentation algorithms, and primer evaluation for microbiome sequencing. Nicolás collaborates with the GSI Group (Intelligent Systems) and Artificial Vision research groups, advancing interdisciplinary projects in healthcare technology. Advising and Grants: Currently supervising research in AI-driven medical solutions and actively pursuing grants for oral microbiota studies. His DenTiUS platform has been validated through randomized clinical trials. Labs/Teams: Core member of the Intelligent Systems Group (GSI) and collaborator with the Artificial Vision group, driving advancements in automated medical image analysis systems.

Alexandros Stamatakisمشاهده پروفایل

استاد

• Algorithms
• Parallel Computing
• Parallel Architectures
• +۳ مورد دیگر

Prof. Alexandros Stamatakis is a Professor of High Performance Computing at Karlsruhe Institute of Technology (KIT) and Group Leader of the 'Scientific Computing' group at the Heidelberg Institute for Theoretical Studies (HITS). He holds an adjunct professorship at the University of Arizona. His research focuses on algorithms, parallel computing architectures, and evolutionary bioinformatics. Stamatakis earned his PhD in Computer Science from TU Munich (2004) and has held roles including DFG Emmy-Noether Junior Research Group Leader (2008-2010). He is a member of multiple scientific advisory boards, including Elixir Greece and the Computational Biology Institute in Montpellier. His teaching awards (2015, 2016) highlight his contributions to bioinformatics education. His work spans computational methods for phylogenetic analysis, energy-efficient computing, and genomic data processing, with notable tools like RAxML-NG and EPA-ng. Research interests include parallel computing optimizations, phylogenetic inference algorithms, and applications in evolutionary biology. His recent work addresses challenges in large-scale genomic data analysis, including error detection in DNA barcoding systems and accelerating phylogenetic computations through algorithmic innovations. He leads collaborative projects on avian genome complexity and ancient DNA analysis, contributing to both computational methods and biological discoveries. Stamatakis collaborates internationally, with affiliations spanning institutions in Europe and the U.S. His lab at HITS emphasizes interdisciplinary approaches, merging computer science with biological problems. Key contributions include developing carbon-aware energy management frameworks and predictive models for phylogenetic inference stability.

Ivan D'Orsoمشاهده پروفایل

استاد

• HIV Research
• Transcriptional Regulation
• Molecular Biology
• +۳ مورد دیگر

Dr. Ivan D'Orso is a Professor at the University of Texas Southwestern Medical Center, specializing in molecular mechanisms of transcriptional regulation in HIV-1 latency, immune cell fate, and cancer. He earned his B.S. and M.S. from the National University of Mar del Plata and his Ph.D. in Biochemistry from the National University of Buenos Aires, followed by postdoctoral training at UCSF with Dr. Alan Frankel. His research integrates genetics, protein biochemistry, and virology to elucidate HIV transcriptional control and oncogenic processes. Supported by NIH grants (NIAID, NCI), The Welch Foundation, and others, his work bridges fundamental biology with therapeutic applications for HIV, cancer, and immune disorders. Research interests include HIV latency mechanisms, transcription elongation regulation, and the interplay between epigenetics and oncogenesis. Notable achievements include defining roles of KAP1/TRIM28, PPM1G, and CDK9 in transcription control, as well as developing tools like the CIPHER bioinformatics workflow. His lab employs CRISPR-Cas9 genome editing and systems biology approaches to dissect viral and cellular networks. Key awards include the NIH Pathway to Independence Award (2009) and fellowships from amfAR and the Human Frontier Science Program. Over 35 publications span high-impact journals like Nature Communications, Cell Reports, and Molecular Cell, with recent focus on reactivating latent HIV reservoirs and understanding ARF-mediated tumor suppression. Grants and collaborations reflect interdisciplinary strengths, with funding supporting studies on HIV transcriptional programs, cancer epigenetics, and cellular signaling pathways. Ongoing projects aim to translate molecular insights into novel antiviral and anticancer therapies.