جستجوی استاد راهنمای دانشگاهی

Arti Singh is an Assistant Professor in the Department of Agronomy at Iowa State University. Her research focuses on plant breeding, soybean diseases, genomics, and phenomics, with a strong emphasis on integrating artificial intelligence and high-throughput technologies into agricultural systems. She leads projects involving AI-driven disease identification, precision agriculture, and crop improvement strategies. Her expertise includes developing machine learning models for real-time weed and insect classification (e.g., WeedNet and InsectNet), deploying drones and ground robots for crop phenotyping, and leveraging genomic data to map traits like flowering time and disease resistance in legumes. Singh collaborates on initiatives like the AIIRA Institute for Resilient Agriculture and the BioTrove biodiversity dataset. Singh’s work spans plant stress phenotyping, digital twin technologies for plant sciences, and multi-sensor phenotyping for early disease detection. Her research bridges computational methods with traditional agronomy, aiming to enhance crop resilience and sustainability in the face of environmental challenges. Her recent projects include optimizing robotic navigation for precision agriculture, improving soybean yield estimation via video analysis, and dissecting genetic architectures of traits in mungbean and soybean using GWAS and genomic tools. She actively contributes to conferences and publishes in high-impact journals, advancing both foundational and applied aspects of agricultural science.

• Plant breeding
• Soybean diseases
• Genomics
• +۳ مورد دیگر

Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member at the Scientific Computing and Imaging (SCI) Institute at the University of Utah. She holds a Ph.D. in Computer Science from Duke University and an undergraduate degree from the University of Bridgeport. Her research focuses on Topological Data Analysis (TDA), data visualization, computational topology, and machine learning, with applications in scientific data exploration and analysis. She has received prestigious awards including the NSF CAREER Award (2022) and the PECASE Award (2025). Her work spans projects funded by NSF, NIH, and DOE, including multiparameter TDA and topology-aware data compression. She advises numerous students and collaborates on interdisciplinary initiatives in astrophysics, climate science, and AI fairness. Education: Ph.D. in Computer Science, Duke University (2010) B.S. in Computer Science and Mathematics, University of Bridgeport (2003) Research Interests: Topological techniques for large-scale data analysis Integration of topological, geometric, and machine learning methods Applications in visualization, bioinformatics, and network analysis Key Projects: NSF-funded TDA research (DMS-2301361, OAC-2313124) DOE project on topology-preserving data compression Collaborations with NASA, Argonne National Lab, and Carnegie Institution of Washington Awards: Presidential Early Career Award for Scientists and Engineers (2025) NSF CAREER Award (2022) DOE Early Career Research Program (2020) Advising and Grants: Mentored over 30 students and postdocs Recipient of multiple NSF and DOE grants totaling millions

• Topological Data Analysis
• Data Visualization
• Computational Topology
• +۵ مورد دیگر

Professor Daniel Segrè is a faculty member at Boston University, holding the title of Professor of Biology, Bioinformatics, and Biomedical Engineering. His research focuses on systems biology, microbial ecology, and metabolic engineering, with an emphasis on understanding complex biological networks and their applications in bioenergy and biomedicine. Segrè leads the Segre Lab ( segrelab.bu.edu ), where theoretical and computational approaches are applied to study metabolism, microbial interactions, and synthetic biology. Segrè earned his PhD from the Weizmann Institute of Science, Israel. His work bridges fundamental science and applied engineering, addressing topics such as microbial community dynamics, metabolic pathway design, and environmental microbiome applications. Research Interests: Systems biology of metabolism, evolution of biochemical networks, microbial interactions, bioinformatics, and environmental microbiome engineering. His lab develops computational models (e.g., COMETS) to simulate microbial ecosystems and design synthetic microbial communities for climate change mitigation and bioenergy production. Teaching: Courses include BE 777 (Computational Genomics), BF 821 (Bioinformatics Seminar), and BF 571 (Dynamics and Evolution of Biological Networks). These courses reflect his expertise in integrating computational methods with biological systems analysis.

• Systems Biology
• Metabolic Engineering
• Microbial Ecology
• +۷ مورد دیگر

Karsten Borgwardt is a Professor and Director of the Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry. He holds a PhD in Computer Science (2007) from LMU Munich and has held academic positions at ETH Zürich, Universität Tübingen, and the Max Planck Institutes in Tübingen. His research focuses on machine learning applications in biology and medicine, including biomarker discovery, personalized medicine, and systems biology. Education: PhD in Computer Science (2007), LMU Munich M.Sc. in Biology (2003), University of Oxford Diplom (M.Sc. equivalent) in Computer Science (2004), LMU Munich Research Interests: Development of machine learning algorithms for large biomedical datasets Pattern recognition in genomic and clinical data Applications in sepsis biomarkers, antimicrobial resistance prediction, and personalized medicine Grants & Projects: Scientific Coordinator of Marie Curie Networks (2013-2022) Swiss National Science Foundation Starting Grant (2014) Personalized Swiss Sepsis Study (CHF 5.3M, 2018) Awards: Krupp Award (2013), Golden Owl Teaching Award (2017), multiple 'Top 40 under 40' recognitions (2014-2016). Labs: Leads the Machine Learning and Systems Biology Department at MPI, collaborating with 22+ labs in sepsis research and international networks.

• Machine Learning
• Systems Biology
• Bioinformatics
• +۳ مورد دیگر

Dr. Ian Stavness is a Professor and Department Head in the Department of Computer Science at the University of Saskatchewan. His research focuses on interdisciplinary applications of computer science, including deep learning in agriculture, biomedical computation, and 3D display technologies. He leads the Biological Imaging & Graphics (BIGLAB) laboratory, which develops tools for plant phenotyping and musculoskeletal modeling. Education: Ph.D. in Computer Engineering, University of British Columbia, 2010 M.A.Sc. in Computer Engineering, University of British Columbia, 2006 B.Sc. in Computer Science & B.Eng. in Electrical Engineering, University of Saskatchewan, 2004 Research Interests: Deep Learning for Plant Phenomics & Agriculture 3D Displays and VR/AR Technologies Musculoskeletal Biomechanical Simulation (OpenSim, ArtiSynth) Computer Vision and Image Analysis Awards: ACM CHI 2019 Honourable Mention ACM VRST 2018 Polyphony Digital Award Key Projects: Deep Plant Phenomics Platform Parametric Human Project (Digital Human Modeling) P2IRC (Plant Phenotyping & Imaging Research Center)

• Biomedical Computation & Visualization
• Computer Graphics
• 3D Displays
• +۶ مورد دیگر

Jennifer L. Clarke is a Professor in the Department of Statistics at the University of Nebraska–Lincoln and Director of the Quantitative Life Science Initiative. She holds leadership roles in enabling big data integration across the University of Nebraska system through collaborative research programs. Her affiliations include the Institute of Agriculture and Natural Resources (IANR) and the College of Agriculture and Natural Resources. Dr. Clarke's research focuses on statistical methodology for high-dimensional data, computational biology, bioinformatics, and bacterial genomics. Her work bridges statistical innovation with applications in oncology, microbiome analysis, and agricultural phenomics. Key areas include predictive modeling, machine learning, and genomic/metagenomic data integration. Her recent publications span cancer biomarker discovery, plant phenotyping methodologies, and microbial community analysis, reflecting her interdisciplinary approach. Articles emphasize translational applications like therapeutic target identification and precision agriculture. Dr. Clarke leads initiatives fostering collaboration between statisticians and domain scientists, including the Quantitative Life Science Initiative and contributions to the Agricultural Genome-to-Phenome Initiative (AG2PI). Her work advances data-driven solutions for healthcare and food security challenges. Notable projects include developing statistical tools for microbiome studies, analyzing root architecture via 3D imaging, and investigating cranberry-derived compounds' cancer-inhibitory mechanisms. Her methodological contributions include hybrid clustering techniques and predictive model validation frameworks.

• Statistical Methodology
• Bioinformatics/Computational Biology
• Data Mining/Machine Learning
• +۳ مورد دیگر

Distinguished Professor Peter Ralph is a faculty member at the University of Technology Sydney (UTS), holding the position of Professor of Marine Biology within the Faculty of Science and serving as the Executive Director of the Climate Change Cluster (C3). He is also the founder of the NSW Deep Green Biotech Hub and an influential member of global initiatives such as UNESCO’s Blue Carbon Scientific Working Group and the Czech Academy of Sciences’ Global Change Research Centre. Leadership Roles: Director of the Climate Change Cluster, Deputy-Chair of Sydney Institute of Marine Sciences. Research Collaborations: Partnerships with CSIRO, industry, NGOs, and international institutions. Key Contributions: Over 280 publications and $15M+ in research funding. His research focuses on algae-based solutions to climate change and sustainability, including carbon capture, bioplastic production, waste-water remediation, and circular bio-economy models. He explores advanced manufacturing through Industry 4.0 integration and zero-waste bio-refinery approaches, while also advancing algal phenomics using automated high-throughput screening systems. Recent articles highlight innovations in AI-driven biorefinery optimization, microalgal bioprospecting, and mutagenesis techniques for rare earth element extraction. His work bridges environmental science with engineering, addressing challenges in algae cultivation scalability and commercialization. Scientific Awards: 2012 and 2018 UTS Vice-Chancellor’s Research Excellence Awards. Peter has secured major grants, including the Fermentalg PhD Project on Microalgae Screening, PNG Natural Seafood’s Macroalgae Product Development, and the Winifred Trust Foundation’s Aquaculture initiative. His research teams collaborate across disciplines to transform algae into sustainable resources for food, energy, and biomanufacturing. He leads UTS’s Climate Change Cluster (C3), a hub for interdisciplinary climate research, and actively promotes algae-building technologies and carbon storage innovations through biomasonry products.

• Algae Biotechnology
• Marine Biology
• Seagrass Ecology
• +۹ مورد دیگر

Ioannis Athanasiadis is a Full Professor and Chair of Artificial Intelligence at Wageningen University & Research (The Netherlands). He leads the Artificial Intelligence (AIN) group, focusing on advancing AI methods for global challenges in agriculture, ecology, and sustainability. Previously, he was faculty at the Dalle Molle Institute for Artificial Intelligence (IDSIA, Switzerland) and the Democritus University of Thrace (Greece). He holds a PhD (2005, cum laude) in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research integrates machine learning, knowledge engineering, and environmental modeling to address food security, climate adaptation, and ecosystem services. He leads initiatives like AgML (AgMIP's machine learning benchmarking effort) and coordinates European grants such as LTER-LIFE and CYBELE . Prof. Athanasiadis has supervised over 40 PhD/postdoc researchers and serves as Editor of Environmental Modelling and Software . He collaborates internationally on projects involving AI for crop modeling, digital twins, and sustainable agriculture. His team develops frameworks like Crop2ML and PyCrop2ML to enhance interoperability between process-based models and machine learning systems.

• Artificial Intelligence
• Machine Learning
• Agricultural Modeling
• +۷ مورد دیگر

Jonas Anderegg is a Lecturer at the Department of Environmental Systems Science, ETH Zurich. He holds a PhD and MSc from ETH Zurich in Agricultural Sciences. His research focuses on crop disease phenotyping, precision agriculture, and host-pathogen interactions. Current projects include automated image analysis for disease detection and quantification of Zymoseptoria tritici under field conditions. He has held postdoc positions in Plant Pathology (Institute of Integrative Biology) and Crop Science (Institute of Agricultural Sciences) at ETH Zurich. His work involves developing scalable imaging and deep learning methods for field phenotyping, with applications in quantitative resistance assessment and crop disease management. Education: PhD, Group of Crop Science, ETH Zurich (2016-2020) MSc in Agricultural Sciences, ETH Zurich (2015) Research interests emphasize high-throughput phenotyping, automated disease detection, and integrating imaging technologies into agricultural practices. Key contributions include datasets like SYMPATHIQUE and FIP 1.0, which provide foundational resources for crop disease research. Grants and advising: While no formal student advisees are listed, his postdoc roles and collaborative projects likely involve mentoring junior researchers. His work aligns with ETH Zurich's focus on sustainable agriculture and technological innovation in plant sciences. Labs and teams: Active member of the Crop Disease Phenotyping group, leveraging UAVs, thermal imaging, and deep learning for field-scale monitoring of crop health and senescence dynamics.

• Plant Pathology
• Precision Agriculture
• High Throughput Phenotyping
• +۳ مورد دیگر

Dr. Mahendra Bhandari is an Assistant Professor at Texas A&M AgriLife Research and Extension Center in Corpus Christi, affiliated with the Texas A&M College of Agriculture and Life Sciences. He holds a B.S. in Agriculture from Tribhuvan University (2011), an M.S. in Plant, Soil and Environmental Science from West Texas A&M University (2016), and a Ph.D. in Agronomy from Texas A&M University (2020). Affiliations: Texas A&M AgriLife Research, Texas A&M College of Agriculture and Life Sciences Roles: Lead researcher in Digital Agriculture, UAS-based phenotyping, and precision agriculture His research focuses on integrating remote sensing (UAS, satellite, ground sensors), big data analytics, and machine learning to improve crop management and breeding. Key areas include high-throughput phenotyping for cotton, corn, and sorghum; UAS data integration for crop yield prediction; and digital twin frameworks for in-season management. Collaborators include Dr. Juan Landivar-Bowles and Dr. Jinha Jung. Publications emphasize UAS applications in crop monitoring, yield estimation, and disease detection. His work bridges agronomic principles with emerging technologies to enhance agricultural resilience. Key Projects: UAS-based HTP system development Satellite-UAV data fusion for precision irrigation Mechanistic models for cotton yield forecasting Labs/Teams: Leads the Digital Agriculture research team at Texas A&M AgriLife, focusing on UAS innovation and AI-driven agricultural solutions.

• Digital Agriculture
• Remote Sensing
• Precision Agriculture
• +۳ مورد دیگر

Matteo Bolner is a Post-Doctoral Research Fellow at the University of Bologna's Department of Agricultural and Food Sciences, specializing in livestock genomics and metabolomics. He holds a PhD in Agricultural and Food Sciences (defended March 2025) and an International Master in Bioinformatics from the University of Bologna. His research integrates genomic and metabolomic data to improve livestock sustainability, particularly in pig production systems. Key focuses include identifying metabolic pathways influencing production traits, analyzing pig viromes for disease outbreaks, and leveraging big data for One Health applications. His educational background includes a Biological Sciences degree (2018) and a bioinformatics master's (2021). He interned at CINECA's SCAI department, focusing on HPC software containerization. Current affiliations include membership in the Animal and Food Genomics group, where he explores genomic solutions for breed conservation and sustainable production. Research trends in his articles emphasize multi-omics integration to understand pig metabolism, stress responses, and breed-specific adaptations. He also applies genomics to authenticate food products and enhance conservation strategies for endangered livestock breeds like the Mora Romagnola pig. His work bridges animal science, computational biology, and agricultural sustainability. Notable contributions include developing genomic tools for honey bee population analysis and creating a catalog of mitochondrial insertions in pig genomes. Future directions involve advancing metabolomics-based precision livestock farming and applying big data analytics to livestock One Health challenges.

• Livestock Systems
• Animal Breeding
• Genetics
• +۳ مورد دیگر

Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.

• Probabilistic Machine Learning
• Bayesian Optimization
• Multi-Agent Systems
• +۴ مورد دیگر

Detmer Sipkema is an Associate Professor at Wageningen University & Research, affiliated with the VLAG WIMEK faculty and the Department of Molecular Ecology. His research focuses on microbial ecology, marine biology, and biotechnology, particularly in aquaculture systems and sponge symbionts. He leads projects on metabolic modeling of marine bacteria, ecological impacts of seaweed farming, and probiotics development for aquaculture. Key research interests include microbial diversity, anaerobic digestion in aquaculture waste, and applications of artificial intelligence in microbial studies. He has supervised multiple PhD candidates exploring topics like microbial symbiosis, gut microbiota dynamics, and sustainable aquaculture practices. His work integrates multi-omics approaches, environmental microbiology, and bioprospecting for novel bioactive compounds. Sipkema has published widely on sponge microbiomes, anaerobic processes, and aquaculture sustainability. His datasets include bacterial community analyses from sponges and microalgae, contributing to understanding symbiotic relationships and biotechnological applications.

• Microbiology
• Marine Biology
• Environmental Science
• +۳ مورد دیگر

Dr. Mohsen Yoosefzadeh Najafabadi is an Assistant Professor in the Department of Plant Agriculture at the University of Guelph, Ontario Agricultural College. He holds a PhD in Plant Breeding from the University of Guelph (2022), following M.Sc. and B.Sc. degrees from the University of Tehran. His research focuses on dry bean breeding, computational biology, and integrating omics technologies to enhance crop resilience and productivity. Key areas include developing stress-tolerant dry bean varieties, leveraging remote sensing for trait prediction, and optimizing genomic selection methods. He leads the Dry Bean Breeding & Computational Biology Program and has contributed to over 30 peer-reviewed publications since 2017. Education : PhD, Plant Breeding, University of Guelph (2022) M.Sc., University of Tehran B.Sc., University of Tehran Research interests emphasize computational tools development (e.g., AllInOne preprocessing framework), omics-based selection strategies, and non-Mendelian heredity mechanisms. His lab combines machine learning with field phenotyping to address agricultural challenges such as disease resistance and climate adaptation. Collaborative projects include soybean cold stress analysis and cannabinoid profile prediction in cannabis. Publications span genomic approaches to crop improvement, remote sensing applications, and transcriptomic studies. He teaches courses in plant breeding methodologies and actively engages in technology transfer initiatives. Lab activities include developing high-yielding dry bean cultivars resistant to biotic/abiotic stresses and advancing data-driven pipelines for crop breeding. Future work aims to synergize AI with multi-omics data to enhance crop resilience in diverse environments.

• Dry Bean Breeding
• Computational Biology
• Omics-Based Selection
• +۵ مورد دیگر

Dr. Michael Alfaro is a Professor in the Department of Ecology and Evolutionary Biology at UCLA, where he leads the UCLA Alfaro Lab. His research focuses on understanding evolutionary dynamics of organismal diversification, particularly in marine fishes. He employs interdisciplinary approaches combining evolutionary morphology, molecular phylogenetics, and theoretical evolution to explore patterns and mechanisms of morphological and functional diversity. Education: Ph.D. in Evolutionary Biology, University of Chicago (2000) M.A. in Biology, Humboldt State University (1995) B.A. in Dramatic Arts, University of California, Davis (1989) Research interests emphasize the interplay between morphological diversity and ecological/functional dynamics. Notable themes include the drivers of diversification in marine lineages, the role of physiological traits in lineage success, and the integration of computational tools for large-scale phenotypic analysis. Alfaro has contributed to advancements in phylogenetic methodology and genomic resources for studying fish evolution. His work bridges macroevolutionary theory with empirical data, often leveraging crowdsourced phenotypic data and bioinformatics tools like Sashimi and Charisma for high-throughput analysis. The lab actively engages in collaborative projects addressing biodiversity patterns and evolutionary mechanisms across marine and terrestrial systems. Grants and advising activities are central to his academic contributions, though specific details are not detailed in the provided text. The UCLA Alfaro Lab collaborates internationally, emphasizing both basic research and applied conservation contexts.

• Evolutionary Biology
• Marine Fishes
• Phylogenetics
• +۳ مورد دیگر