جست‌وجوی استاد و پژوهشگر

Andrea Badenمشاهده پروفایل

دانشیار

• Primatology
• Primate Conservation Biology
• Landscape Genetics
• +۵ مورد دیگر

Dr. Andrea Baden is an Associate Professor in the Department of Anthropology at Hunter College, School of Arts and Sciences, and a Doctoral Faculty member at the CUNY Graduate Center. She directs the Primate Molecular Ecology Laboratory (PMEL) and contributes to the New York Consortium in Evolutionary Primatology (NYCEP). Her fieldwork in Madagascar focuses on the Ruffed Lemur Project, integrating ecology, genetics, and behavior. Hunter College, CUNY (2013–present) CUNY Graduate Center (Doctoral Faculty) New York Consortium in Evolutionary Primatology (Core Faculty) Her research explores how landscape features influence primate distribution, migration, and genetic diversity, linking these to social behavior evolution. Publications span journals like Nature Scientific Reports , Animal Behaviour , and American Journal of Primatology , with themes in landscape genetics, conservation, and behavioral endocrinology. Dr. Baden has received the Feliks Gross Endowment Award and a Fulbright Fellowship for her work. She teaches human evolution, primatology, and conservation biology, mentoring future scientists through fieldwork and lab research. PhD in Anthropology, Stony Brook University MA in Anthropology, Stony Brook University BA in Anthropology, University of Miami Postdoctoral Research, Yale University

Kirill Djebkoمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۴ مورد دیگر

Kirill Djebko is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science, Faculty of Mathematics and Computer Science at the University of Würzburg . His work spans AI-based optimization systems , autonomous spacecraft operations , and deep reinforcement learning applications. Doctorate completed in 2020 Active in research since 2015 Key projects: KINERGY (heating system optimization), VeriKI (AI verification for space), LeLaR (AI attitude control for nanosatellites) Research focuses on metaheuristics , genetic algorithms , regression algorithms , and deep reinforcement learning for practical applications. His work integrates digital twin modeling , decision support systems , and AI robustness in industrial and space contexts. Recent publications (2024-2016) analyze heating system optimization , spacecraft fault detection , and autonomous nanosatellite operations . These studies emphasize sim-to-real-world transfer , data-driven calibration , and AI quality assurance in complex environments. Current affiliations include the Center for Artificial Intelligence and Data Science (CAIDAS) . Contact details: Email - kirill.djebko@uni-wuerzburg.de | Phone +49 931 31-86405 | Room 03.010, Emil-Fischer-Straße 50, Würzburg.

Cresta W Jonesمشاهده پروفایل

دانشیار

• Substance Use Disorders in Pregnancy
• Maternal-Fetal Medicine
• Obstetrics
• +۳ مورد دیگر

Cresta W Jones is an Associate Professor at the University of Minnesota's School of Medicine, affiliated with the Department of Obstetrics, Gynecology and Women's Health. Her work focuses on maternal-fetal medicine, substance use disorders in pregnancy, and public health interventions. Active projects include Machine Learning-assisted Obstetric Ultrasound (MOBUS) Screening Algorithms (PI, 2024-2026) and the Healthy Brain and Child Development National Consortium (CoI, 2021-2026). Research spans opioid epidemics, neonatal abstinence syndrome, maternal health disparities, and machine learning in obstetric diagnostics. Recent articles address substance use treatment trends , alcohol screening protocols , and maternal mortality review committees .

Samuel McDermottمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Medical Imaging
• Biotechnology
• +۷ مورد دیگر

Samuel McDermott is an Associate Teaching Professor at the Department of Chemical Engineering and Biotechnology , University of Cambridge. He serves as the Sensor CDT Programme Manager , focusing on interdisciplinary research in healthcare, biotechnology, and open-source hardware. His research spans machine learning applications in medical imaging , laboratory automation , and web-of-things (WoT) integration for scientific equipment. Recent work emphasizes federated learning in healthcare, blood cell morphology classification, and low-cost diagnostic tools. Key article trends include: deep diffusion models for malaria detection , open-source microscopy platforms like OpenFlexure, and AI-driven clinical data generalization . His projects often combine 3D-printed hardware and IoT-enabled laboratory systems .

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

Zhang Yangمشاهده پروفایل

دانشیار

• Artificial Intelligence in Biomedical Imaging
• Deep Learning for Omics Data Analysis
• Pathogen Detection and Diagnostics
• +۷ مورد دیگر

Zhang Yang is an Associate Professor at the School of Medical Engineering, Harbin Institute of Technology (Shenzhen), with a joint appointment as Visiting Professor at the University of Tokyo starting in July 2024. He holds a PhD from the University of Cambridge's Department of Pathology and an M.Phil. from the University of Hong Kong's HKU-Pasteur Research Center. Previously, he served as an Assistant Professor at Harbin Institute of Technology (Shenzhen) from September 2015 to December 2020. His research integrates computational and experimental approaches to address challenges in pathogen and cancer research. On the computational side, his work focuses on developing AI-powered microscopic imaging systems, applying deep learning to analyze multi-omics data (including proteins, DNA, miRNAs, LncRNAs, and mRNAs), and utilizing deep learning in cheminformatics for drug discovery. On the experimental side, his laboratory combines imaging, high-throughput sequencing, mass spectrometry, and chemical biology to understand disease mechanisms at the molecular level. His publication record demonstrates significant impact, with over 50 SCI-indexed papers in high-impact journals including Nature Communications, Briefings in Bioinformatics, Bioinformatics, Analytical Chemistry, and Trends in Biotechnology. His work has been cited by prestigious journals such as Nature Reviews Methods Primers and Nature Communications, with three ESI highly cited papers. His research spans multiple interdisciplinary fields, combining artificial intelligence with biomedical applications to advance diagnostic and therapeutic approaches. World's Top 2% Scientists 2021 Fellow of the Royal Society of Biology Three ESI Highly Cited Papers Five authorized national invention patents As an academic leader, he serves as Associate Editor for BMC Biology and Frontiers in Microbiology, Academic Editor for PLOS Genetics, Editorial Board Member for Communications Biology, and Guest Editor for a Special Issue on AI in analytical chemistry in Trends in Analytical Chemistry. His laboratory actively collaborates with international institutions, with graduates pursuing further studies at Hong Kong Chinese University, Hong Kong University of Science and Technology, Hong Kong Polytechnic University, Macau University, and the University of New South Wales. He teaches Introduction to Modern Biology for undergraduates and Bioanalytical Chemistry for graduate students.

Min Hyung Ryuمشاهده پروفایل

استادیار

• Respiratory Medicine
• Environmental Health
• Genomics
• +۷ مورد دیگر

Dr. Min Hyung Ryu is an Assistant Professor at the University of British Columbia’s Faculty of Medicine, Division of Respiratory Medicine, and a Principal Investigator at the Centre for Lung Health and Vancouver Coastal Health Research Institute. His research combines systems biology, genomics, and data science to address environmental and genetic risk factors in chronic respiratory diseases. Moira and David Yeung Professorship in Respiratory Medicine (UBC) PhD in Experimental Medicine (UBC, 2021) Postdoctoral Training (Harvard Medical School, 2024) Dr. Ryu’s work focuses on transcriptomic and bioinformatic approaches to study air pollution effects, COPD subtypes, and personalized medicine. He employs machine learning and omics datasets to identify preventative strategies and biomarkers. His recent publications (2025–2024) emphasize transcriptomic profiling of airway responses to diesel exhaust/woodsmoke , sex-biased gene regulation , miRNA-immunity networks , and exposome-environment interactions in respiratory diseases. Scientific Award : Moira and David Yeung Professorship As a collaborating faculty member, Dr. Ryu engages in interdisciplinary research and undergraduate supervision. His lab works on data-driven COPD stratification and molecular epidemiology using large cohorts like COPDGene.

Mahsa Jessriمشاهده پروفایل

دانشیار

• Nutritional Epidemiology
• Population Health
• Big Data Analytics
• +۵ مورد دیگر

Mahsa Jessri is an Associate Professor in the Food, Nutrition and Health Program at the University of British Columbia's Faculty of Land and Food Systems. She holds the prestigious Canada Research Chair (Tier 2) in Nutritional Epidemiology for Population Health and is affiliated with the Centre for Health Services and Policy Research (CHSPR) and the Department of Health Services and Policy at the UBC School of Population and Public Health, Faculty of Medicine. Dr. Jessri also serves as an Affiliate Investigator of Evidence to Innovation (E2i) Theme at the BC Children's Hospital Research Institute. Her educational background includes a PhD (Vanier Scholar) in Nutritional Sciences from the University of Toronto, an M.Sc. in Nutrition and Metabolism from the University of Alberta, and a Banting Postdoctoral Fellowship from Statistics Canada. Dr. Jessri's research is uniquely positioned at the intersection of nutritional sciences, big data analytics, AI, and population and public health, with the overarching goal of providing evidence-based decision tools to inform guidelines and policies for reducing the burden of chronic diseases. Dr. Jessri's research program focuses on using big data and methodological advancements (including AI and machine learning) to identify dietary patterns and their interactions with health outcomes, population health impact assessment of food and nutrition policies, nutritional monitoring and population surveillance, development of robust methods for complex data analysis, and design of personalized nutrition risk assessment tools. Her Nutritional Epidemiology and Big Data Analytics Laboratory provides a stimulating environment for research in these areas. Her publication portfolio demonstrates a strong focus on dietary patterns, chronic disease prevention, and the application of advanced statistical methods to nutritional epidemiology. Her recent work has examined nutrient profiling models, cardiovascular disease risk, dietary pattern assessment tools, and the impact of dietary factors on health outcomes across diverse populations including Canadian adults, Inuit communities, and international cohorts. Among her notable honors are the Royal Society of Canada (RCS) Alice Wilson Award (2018), Emerging Leader in Nutrition Science from the American Society for Nutrition, and the Canadian Institute of Health Research (CIHR) Banting Postdoctoral Fellowship. She has also received the INMD/CNS Early Career Researcher Partnership Prize and the Discovery Award from the Banting Research Foundation. Dr. Jessri teaches FNH 398: Research Methods in Human Nutrition and has previously taught graduate-level courses at the University of Ottawa. Her academic career builds on decades of clinical and public health dietetics and nutrition policy experience working at academic and regulatory institutions. She has served as a past Core member of Health Canada's Nutrition Science Advisory Committee (NSAC) and is active in the American Society for Nutrition and American Academy of Nutrition and Dietetics Artificial Intelligence (AI) in Nutrition Research Taskforce.

Daniel Baumمشاهده پروفایل

استاد پژوهشی

• Scientific Visualization
• Computational Biology
• Image Analysis
• +۶ مورد دیگر

Daniel Baum is a Research Professor and Head of the Visual Data Analysis research group at the Zuse Institute Berlin (ZIB), which is affiliated with Freie Universität Berlin. His work spans across scientific visualization, computational biology, and image analysis, with a particular focus on developing methods for analyzing complex biological structures and neural circuits. He is actively involved in multiple interdisciplinary research projects including HFSP Chitons, Geometric Learning for Single-Cell RNA Velocity Modeling, and RobustCircuit. Dr. Baum's research interests center on visual and data-centric computing approaches to solve complex problems in biology and medicine. His work bridges the gap between computational methods and biological applications, with significant contributions to cryo-electron tomography analysis, neural circuit mapping, and geometric morphometrics. He develops innovative algorithms for 3D reconstruction, image segmentation, and visualization of biological structures, from molecular to organismal scales. His publication record demonstrates consistent contributions to visualization techniques applied to biological problems, with recent work focusing on neural circuit analysis in zebrafish and Drosophila, biomechanical studies of animal structures, and advanced methods for analyzing ancient artifacts. The research shows a clear trajectory toward increasingly sophisticated multimodal data integration and machine learning approaches. Dr. Baum leads a productive research group with several key collaborators who frequently appear as co-authors on his publications, indicating a strong mentoring relationship. His projects involve substantial funding from various sources supporting interdisciplinary collaborations across biology, computer science, and engineering. His laboratory at ZIB focuses on visual data analysis for complex biological systems, with particular strength in developing computational methods for neuroscience applications and biomaterial analysis. The group maintains strong collaborations with multiple institutions working on cutting-edge imaging technologies and biological model systems.

Silke Hamannمشاهده پروفایل

پژوهشگر

• Phonology
• Phonetics
• Language Acquisition
• +۸ مورد دیگر

Dr. Silke Hamann is a researcher at the University of Amsterdam, Faculty of Humanities, Department of Linguistics. Her work bridges phonology and phonetics, focusing on the emergence of phonological features, perceptual cues in segmental contrasts, and the interaction of phonology with orthography and language acquisition. Current projects: diachronic loanword adaptation, congenital amusia's impact on speech perception, Bantu languages (with Nancy Kula and Laura Downing), Catalan studies (with Francesc Torres-Tamarit) Research interests span synchronic/diachronic phonology, phonetic perception, computational modeling of phonological acquisition, and cross-linguistic analysis of retroflex consonants. Her publications analyze phenomena in Korean, Japanese, Bantu languages, German, Portuguese, and Slavic languages, with recurring themes of cue weighting, phonological constraints, and orthographic influence. Recent articles highlight her focus on creaky voice diagnostics, loanword phonotactics, congenital amusia, and Bantu intonation systems. Collaborations with Nancy Kula (Bantu) and Francesc Torres-Tamarit (Catalan) underscore her international research network.

Yuè Liمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Population Genetics
• +۴ مورد دیگر

Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.

• Computer Science
• Machine Learning
• Evolutionary Algorithms
• +۴ مورد دیگر

Raul Giraldez Rojo is a Professor at the University of Pablo de Olavide in Seville, Spain, working in the Department of Sports and Information Technology within the area of Computer Languages and Systems. He is affiliated with the DASE (Data Analytics Science & Engineering) research group and contributes to PhD programs in Engineering, Data Science, and Bioinformatics. His research interests focus on computer science with specialization in machine learning, evolutionary algorithms, and data analytics. Dr. Giraldez Rojo has made significant contributions to supervised learning techniques, clustering algorithms, and genetic programming applications. His work bridges theoretical computer science with practical applications in bioinformatics and data mining. His publication record demonstrates expertise in developing algorithms for data analysis, particularly in biclustering methods for expression data and supervised clustering techniques. His research shows a consistent focus on improving efficiency and effectiveness of computational methods for handling complex datasets. Dr. Giraldez Rojo collaborates extensively with researchers including Jesús Salvador Aguilar-Ruiz and José Cristobal Riquelme Santos, indicating strong research partnerships within the Spanish academic community specializing in computational intelligence.

• Data Mining
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Alzheimer's Disease
• Neuroscience
• Cognitive Neuroscience
• +۵ مورد دیگر

Jose Luis Cantero Lorente is a University Professor in the Department of Physiology, Anatomy and Cell Biology at Pablo de Olavide University, specializing in neuroscience with a primary focus on Alzheimer's disease and neurodegenerative disorders. His work bridges electrophysiology, neuroimaging, and biomarker discovery to understand cognitive decline in aging. Education: Doctorate from the University of Seville (1999) with thesis: "Electrophysiological characterization of alpha activity in three states of brain activation in human subjects: relaxed wakefulness, drowsiness and REM phase", supervised by Dr. Carlos María Gómez González. Research Interests: Dr. Cantero Lorente's research spans Alzheimer's Disease , Neuroscience , and Biomarkers , with emphasis on early detection mechanisms through salivary, blood, and CSF analysis. He investigates sleep-memory interactions , metabolic drivers of neurodegeneration (e.g., insulin resistance), and neuroinflammatory pathways using multimodal approaches including proteomics, lipidomics, and functional MRI. His pioneering work established salivary lactoferrin as a diagnostic tool for Alzheimer's. Publication Trends: His 2022-2025 publications reveal a strategic shift toward multimodal biomarker integration , combining amyloid-beta, tau, and metabolic markers for early Alzheimer's detection. Key themes include Parkinson's disease proteomics (e.g., candesartan neuroprotection), herpes virus-amyloid links in aging, and intracortical myelin alterations as precursors to cognitive decline. Recent work increasingly incorporates machine learning for database analysis and explores angiotensin-based neuroprotective strategies. Scientific Awards: No awards were specified in the source material. Advising and Grants: He supervises doctoral candidates in the Biotechnology, Biomedicine and Health Sciences program at Pablo de Olavide University, specifically guiding the "Early Detection of Alzheimer's Disease" track. His research is supported by Spain's PAIDI framework (Health Science and Techniques), with projects focusing on interdisciplinary audiological databases and neural network alterations in mild cognitive impairment. Labs and Teams: As leader of the LNF Functional Neuroscience research group, he directs studies on electrophysiological correlates of cognitive decline, utilizing EEG, MRI, and molecular techniques to map structural-functional brain changes in Alzheimer's progression. The group collaborates extensively on national initiatives for biomarker validation in neurodegenerative diseases.