جستجوی استاد راهنمای دانشگاهی

José Alberto Benítez Andradesمشاهده پروفایل

دانشیار

• Biomedical Analytics
• Machine Learning
• Social Network Analysis
• +۴ مورد دیگر

José Alberto Benítez Andrades is an Associate Professor at the University of León's School of Industrial, Computer and Aerospace Engineering, affiliated with the Department of Electrical, Systems and Automation Engineering. He holds leadership roles as Deputy Director of the School and coordinates multiple academic programs including the 3rd year of Computer Engineering and Erasmus mobility initiatives. His research focuses on: Biomedical analytics using semantic technologies and machine learning Social network analysis in health contexts AI applications for clinical diagnostics and predictive modeling Robotics and sensor data processing Recent publications demonstrate strong interdisciplinary work combining computer science with healthcare, particularly in: Medical NLP for psychiatric and neurological disorders Robust machine learning models for surgical outcome prediction Multi-sensor fusion for autonomous systems Public health informatics through systematic reviews Awards include Outstanding Cum Laude for his 2017 doctoral thesis on semantic technologies in health-focused social network analysis. He leads the ALBA research group and previously contributed to SALBIS.

• Machine Learning
• Biomedical Research
• Data Management
• +۴ مورد دیگر

Patrick Ruch is a Full Professor at HES-SO's Geneva School of Management and Head of Research. His work focuses on machine learning applications in biomedical research, data management systems, and cybersecurity. He leads projects like the DOME Registry for machine learning validation and the ELIXIR RDM Community for FAIR data practices. His research spans interdisciplinary areas including genomic variant curation (Variomes), health informatics (SIBiLS search engines), and AI ethics. He collaborates with institutions like the Swiss Institute of Bioinformatics (SIB) and has contributed to global initiatives like BiCIKL for biodiversity data integration. Ruch's publications emphasize improving reproducibility in machine learning, addressing biases in scientific publishing, and developing tools for clinical decision support. His work bridges computational methods with practical applications in medicine and life sciences.

• Medical Informatics
• Semantic Web
• Ontology Engineering
• +۶ مورد دیگر

Calbimonte Jean-Paul is an Associate Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Department of Information Systems Management. His research focuses on Medical Informatics, Semantic Web technologies, and eHealth applications, with a strong emphasis on integrating machine learning and knowledge engineering into healthcare systems. Research Highlights: Developed systems like SemPryv for decentralized health data management and EREBOTS for personalized patient support. Explores agent-based architectures for clinical decision support and chatbots in healthcare and tourism. Active in projects addressing Alzheimer's diagnosis, cancer survivorship care, and ethical AI in nutrition coaching. Key Projects: SMARTEDGE (2023-2025) : Low-code tools for edge intelligence in automotive, city, factory, and health domains. PERSIST : AI-driven care plans for cancer survivors using machine learning. MedRED : Health data platform for clinical trials. Grants & Collaborations: Swiss National Science Foundation (SEFRI), Innosuisse, and EU H2020 funding. Industrial partners include Dell, Siemens, NVIDIA, and Pryv SA. Labs & Teams: Collaborates with research groups in wind energy informatics, multi-agent systems, and blockchain-based healthcare solutions.

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

Zerara Mohammed is a Researcher and Project Unit Manager at HES-SO Geneva, specializing in Applied Mathematics and Data Science. Their work focuses on machine learning applications in spectroscopy, nanotechnology, and biomedical imaging. Key projects include combining deep learning with Raman spectroscopy for bacterial detection in infant formula milk and developing hyperspectral image classification methods funded by an international organization. Education: Master's in Applied Mathematics - Data Science from Cergy Paris Université (in collaboration with PSL/Paris Dauphine University). Research Interests: Machine learning algorithms for spectral analysis, miniaturized spectrometer design (Nature Communications 2024), computational chemistry (e.g., spin-crossover complexes), and applications in material science. Notable contributions include a 2023 IEEE review on miniaturized spectrometers and foundational work on logP prediction models in the 2000s. Grants & Projects: Ongoing collaborations include a 2022-2023 project on Enterobacter sakazakii detection and hyperspectral anomaly detection research. Previously led studies on chemical pressure effects in metal complexes (Chimia 2002) and Rhea biochemical reaction database updates (Nucleic Acids Res. 2015). Labs/Teams: Primary researcher in HES-SO teams for computational spectroscopy and nanotechnology projects. Collaborates internationally on biomedical sensor development and material science applications.

• Epigenetics
• Hematology
• Leukemia Research
• +۵ مورد دیگر

Andreas Lennartsson is a Principal Researcher (Docent) at the Department of Medicine, Huddinge, Karolinska Institutet. His research focuses on epigenetic mechanisms underlying acute myeloid leukemia (AML) and normal blood development. Dr. Lennartsson holds a Master's in Biotechnology from Lund University and a PhD in Experimental Hematology, followed by postdoctoral training in Japan and Sweden. His group investigates epigenetic perturbations in AML, leveraging genome-wide technologies, patient-derived models, and collaborations with clinical researchers. Research interests include metabolic-epigenetic crosstalk in blood cells, drug development for AML, and the role of epigenetic regulators in leukemia. Notable tools developed include the EpiFactors database. Key funding sources include grants from Barncancerfonden, Cancerfonden, and the Swedish Cancer Society. His lab employs cutting-edge methods to analyze transcriptomics and epigenomics in rare cell populations like leukemic stem cells. Recent work highlights vulnerabilities in AML caused by IDH mutations and explores therapeutic strategies targeting epigenetic pathways. Current training opportunities include a PhD position through the MSCA Doctoral Network NUCLEAR.

• FAIR Data Principles
• Computational Workflows
• Reproducibility
• +۷ مورد دیگر

Stian Soiland-Reyes is a Senior Lecturer at the Department of Computer Science, University of Manchester, where he co-leads the eScience Lab research group. His work focuses on FAIR data principles, computational workflows, and research reproducibility, with active participation in European projects including EuroScienceGateway and TREvolution. He holds a PhD from the University of Amsterdam and MSc/BSc degrees from the Norwegian University of Science and Technology. Education: PhD in FAIR Research Objects and Computational Workflows – University of Amsterdam (2025) MSc in Computer Science – Norwegian University of Science and Technology (2006) BSc in Computer Science – Norwegian University of Science and Technology (2003) His research explores FAIR data sharing, metadata standards, and reproducibility in computational science. Key interests include: Developing lightweight approaches for packaging research outputs (RO-Crate) Enabling reproducible workflow execution and provenance tracking Implementing FAIR principles in high-performance computing environments Building interoperable systems for health data analytics His publications demonstrate a consistent focus on enhancing research infrastructure through standardized metadata, workflow reproducibility, and FAIR data objects across domains including bioinformatics and health informatics. Awards: Best Reviewer (ISWC 2017) F1000Research Poster Award (2017) He currently supervises three PhD students working on computational reproducibility and FAIR data infrastructures. As principal investigator or co-investigator, he leads multiple funded projects including EuroScienceGateway (EU FP7), TREvolution (DARE UK), and CLIMATE-ADAPT4EOSC (Horizon Europe). He co-leads the eScience Lab research group, developing tools and practices for data-driven computational research. The group focuses on 'Translational Computer Science' - creating production-ready solutions for digital research infrastructures, particularly in biomedical and biodiversity domains.

• Model-Driven Engineering
• Software Engineering
• Model Transformation
• +۳ مورد دیگر

Gertrude Kappel is a Full Professor (O.Univ.Prof.in) in the Business Informatics Research Area at TU Wien, Austria. Her research focuses on model-driven engineering, software systems modeling, and industrial applications of formal methods. She has contributed extensively to advancing UML/fUML standards, model transformation techniques, and interdisciplinary approaches to software engineering. Education and Academic Background: While specific educational details are not explicitly listed, her academic role at TU Wien indicates a PhD in Computer Science or related field. She has been actively involved in shaping curricula for model-based software engineering and adapting educational methods for remote learning environments. Research Interests: Her work bridges theoretical modeling frameworks with practical industrial challenges. Key areas include: Model-driven software development lifecycle Integration of IoT and CPS systems Formal verification of software models Temporal and time-series data modeling Interdisciplinary modeling methodologies Collaborations and Impact: Over 120+ publications since 2000 demonstrate her leadership in advancing model-based approaches across academia and industry. Recent work addresses Industry 4.0 challenges and digital transformation in production systems. Advising and Grants: While specific grant details are not provided, her extensive publication record and involvement in thesis supervision (evidenced by contributed theses) indicate active research leadership. She has guided student research on topics like cloud modeling languages, sensor data analysis, and model-driven testing frameworks. Labs and Teams: Core contributor to TU Wien's Network Lab and the Model-Driven Engineering research group. Active in interdisciplinary initiatives like the CPS/IoT Ecosystem platform and industrial process improvement projects.

Chen Wangمشاهده پروفایل

استادیار

• Random Matrix Theory
• Time Series Analysis
• High-dimensional Data Analysis

Dr. Chen Wang serves as an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Hong Kong, with visiting appointments at the University of Cambridge Faculty of Economics during June-August 2024, July-August 2022, and July-December 2019. His academic position and active research output confirm his status as a current faculty member engaged in interdisciplinary statistical research. His core research focuses on: Random Matrix Theory for high-dimensional covariance estimation Time Series Analysis in complex stochastic systems High-dimensional Data Analysis methodologies These statistical frameworks provide foundational tools for modern data-intensive scientific domains. Analysis of his 2022-2025 publications reveals a strategic expansion into biomedical AI applications, particularly in single-cell genomics and spatial biology. His work demonstrates consistent innovation in developing AI agents for biological experimentation (e.g., PerTurboAgent for Perturb-seq, SpatialAgent) and advancing molecular design through diffusion models. This trajectory shows a deliberate integration of his statistical expertise with cutting-edge computational biology challenges. No scientific awards or honors were documented in the provided materials. The available information contains no details regarding graduate student supervision, research grant funding, or laboratory affiliations. His visiting positions at Cambridge suggest collaborative international research activities, but specific advising relationships or grant mechanisms remain unreported in the source text.

• Holocaust Education
• Jewish Studies
• 20th Century Literature
• +۳ مورد دیگر

Joan Peterson is a Professor in the Department of Teacher Education at Saint Mary's College of California's Kalmanovitz School of Education. Her work focuses on intersections between Holocaust education, literary analysis, and ethical pedagogy. She has been actively involved in academic conferences such as the Annual Scholars' Conference on the Holocaust and the Churches since 2015. Her research explores topics including anti-Judaism in digital contexts, post-Holocaust literature, and moral dimensions of teaching. Notable contributions include analyses of works by W.G. Sebald, Heinrich Böll, and Italian-Jewish authors. She has received awards like the O. DeSales Perez Award and Bilha Sperling Holocaust Education Award for her scholarly contributions. Publications span 1994–2018, emphasizing Holocaust pedagogy, literary criticism, and ethical inquiry Active in community initiatives like the Alameda County Holocaust Remembrance Committee Developed influential teaching guidelines for Holocaust education curricula Her work bridges historical memory studies with contemporary educational practice, addressing both scholarly and public audiences through multidisciplinary approaches.

• Video coding
• Video analysis
• Video adaptation
• +۳ مورد دیگر

Fernando Manuel Bernardo Pereira is a Full Professor at the Telecommunications Institute of Instituto Superior Técnico (IST), Lisbon. His research focuses on video coding, multimedia representation standards, and advanced multimedia systems. He actively contributes to academic and industry collaborations on standards like JPEG Pleno and security frameworks for media annotation. Key Affiliations : Instituto Superior Técnico, Telecommunications Institute Roles : Full Professor, Researcher in Multimedia Communication His scientific interests span video analysis, adaptation techniques, and multimedia services. Recent work emphasizes deep learning applications in point cloud coding, quality metrics for 3D data, and explainable AI in facial recognition systems. He collaborates internationally on projects involving light field technology and trustworthy media systems. Publications reflect a focus on cutting-edge areas like neural network-based image coding, compression artifact analysis, and capsule networks for expression recognition. While no awards are explicitly listed, his prolific publication record indicates significant contributions to the field. No advising students or grant details are provided in the text. His research teams likely engage in collaborative efforts given his work on standards and multi-institutional responses to industry calls for evidence.

Yaoyao Liuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Continual Learning
• +۵ مورد دیگر

Yaoyao Liu is an Assistant Professor at the University of Illinois Urbana-Champaign, holding joint appointments in the School of Information Sciences and Coordinated Science Laboratory. He is affiliated with the Siebel School of Computing and Data Science, National Center for Supercomputing Applications (NCSA), and Illinois Informatics. His research focuses on computer vision and machine learning, particularly in areas like continual learning, few-shot learning, and 3D geometry modeling. He completed his PhD at the Max Planck Institute for Informatics and a BS at Tianjin University. Research Interests: Continual learning and class-incremental learning Generative models and diffusion models 3D geometry and medical imaging applications Awards: ECVA PhD Award (2024). Professional Activities: Area Chair for CVPR, NeurIPS, ICLR, and other top conferences. Served as Senior Program Committee member for AAAI and IJCAI. Labs/Teams: Active in interdisciplinary projects at NCSA and Illinois Informatics, collaborating on medical imaging and 3D vision challenges.

Sachin Kattiمشاهده پروفایل

استاد مدعو

• Wireless Communication
• Machine Learning
• Sensor Networks
• +۵ مورد دیگر

Sachin Katti is an Adjunct Professor in the Department of Electrical Engineering at Stanford University. His research focuses on wireless communication systems, machine learning integration in sensors and networks, and edge computing. He explores full-duplex radios, decentralized cellular networks, and innovative applications of RF and IoT technologies. Katti has contributed to projects like Backscatter communication, PIM video coding, and ML-EXray for edge model visibility. His work bridges theoretical advancements with practical implementations in areas like network optimization, sensor transparency, and autonomous systems. Education: Not explicitly stated in the provided text. Research Interests: Sachin Katti’s work spans wireless communication protocols, machine learning for edge devices, and decentralized network architectures. He investigates how to improve efficiency, reliability, and security in systems ranging from full-duplex radios to IoT-enabled sensor networks. His projects often address real-world challenges like energy efficiency, trust-free connectivity, and intelligent data processing. Recent efforts include trust-free cellular networks (d-Cellular), ML-driven video compression (PIM), and federated learning for autonomous driving. Articles Trends: His recent publications emphasize blending machine learning with communication systems (e.g., ML sensors, co-design for cloud robotics) and advancing RF technologies (backscatter networks, ISM band utilization). There’s also a focus on edge computing challenges, such as visibility into deployed ML models and network offloading policies for robotics. Awards: No scientific awards explicitly mentioned in the provided text. Advising & Grants: No student advisees or grant details explicitly listed in the text. His contributions are primarily through research publications and systems development. Labs/Teams: Involved in collaborative projects like Backscatter Communication, Full Duplex Radios, and SoftRAN (Software-Defined Radio Access Networks). His work often leverages Stanford’s infrastructure for testing dense WiFi networks (e.g., BeHop testbed) and RF localization systems (SpotFi).

• Artificial Intelligence
• Data Mining
• Natural Language Processing
• +۵ مورد دیگر

Ee-Peng Lim is a Professor at the School of Information Systems, Singapore Management University. His research spans artificial intelligence, data mining, natural language processing, and computer vision, with applications in healthcare, education, finance, and food computing. He leads projects developing AI systems for behavioral counseling, educational analytics, and multimodal food recognition. Research Interests: Dr. Lim's work focuses on conversational AI for mental health interventions, educational data mining for student performance prediction, food computing for nutrition analysis, and multimodal learning frameworks. His recent projects leverage large language models for complex reasoning tasks and develop robust computer vision systems for real-world applications. Publication Trends: Recent articles (2024-2025) show strong emphasis on multimodal AI systems, large language model applications in behavioral science and healthcare, educational analytics, and advanced food computing techniques. His work increasingly integrates cognitive science principles with deep learning architectures. Leadership: Dr. Lim collaborates extensively with international researchers and has co-organized academic workshops including the International Workshop on Talent and Management Computing (TMC) at KDD.

Jiming Jiangمشاهده پروفایل

استاد

• Plant Genetics
• Epigenetics
• Environmental Stress Response
• +۲ مورد دیگر

Jiming Jiang is a Professor at Michigan State University (MSU), holding the distinguished title of MSU Foundation Professor in the Department of Horticulture within the College of Agriculture and Natural Resources. He leads the Jiang Lab, which focuses on plant molecular biology and genomics research. Research Focus Dr. Jiang's research investigates: Plant gene expression mechanisms under environmental stresses (heat, cold, drought) Epigenetic regulation of centromere function and chromosome dynamics Genomics and epigenomics of potato and other crops for agricultural improvement His lab employs cutting-edge technologies in genetics, epigenetics, and functional genomics to address fundamental questions in plant biology with applications in crop breeding. Publication Trends Recent articles (2022-2025) demonstrate strong emphasis on: Chromosome biology and genome evolution in polyploid crops Epigenetic mechanisms of stress response and gene regulation Development of genomic resources for diverse plant species Application of functional genomics in potato, rice, legumes, and other crops Laboratory The Jiang Lab maintains active research programs in plant epigenomics and stress biology, with detailed information available at jianglab.plantbiology.msu.edu .

Valentina Peonaمشاهده پروفایل

پژوهشگر

• Comparative Genomics
• Speciation
• Transposable Elements
• +۱۷ مورد دیگر

Valentina Peona is a Visiting Researcher at the Department of Organismal Biology, Systematic Biology, Uppsala University, Sweden. She is actively engaged in research on comparative genomics, evolutionary biology, and genome dynamics, with a focus on transposable elements, sex chromosomes, and avian genomics, particularly in birds of paradise and other non-model organisms. Her research interests span a wide range of topics including: Comparative and population genomics Transposable elements and repeat annotation Sex chromosome evolution and neo-sex chromosomes Genome assembly using long-read technologies (PacBio, Hi-C) Structural variation and genome architecture Adaptive radiation and speciation mechanisms The analysis of her recent publications reveals a strong trend in utilizing cutting-edge genomic technologies to explore evolutionary processes in non-traditional model systems. Her work frequently involves multi-platform sequencing, manual curation of repetitive elements, and comparative analyses across diverse taxa such as birds, insects, and fungi. She has made significant contributions to understanding how transposable elements influence genome evolution, speciation, and sex chromosome dynamics. Valentina Peona has contributed to high-impact journals including Nature , Nature Communications , Molecular Ecology , and Mobile DNA . Her collaborative research spans international teams, reflecting a strong network in evolutionary genomics. She has not been explicitly listed as advising students, nor are any grants or scientific awards mentioned in the provided text. However, her role as a postdoctoral or visiting researcher suggests active participation in research projects and likely supervision of junior researchers. Her doctoral thesis, "The exploration and evolution of the avian genomic dark matter" (2021), highlights her deep expertise in avian genome complexity. Valentina Peona is associated with the Evolutionary Biology Centre (EBC) at Uppsala University, where she conducts research in a collaborative environment focused on systematics and organismal biology.