جست‌وجوی استاد و پژوهشگر

Christopher Kananمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Deep Learning
• Continual Learning
• +۵ مورد دیگر

Christopher Kanan is a tenured Associate Professor of Computer Science at the University of Rochester, leading the AI Initiative within the Hajim School of Engineering & Applied Sciences. He holds secondary appointments in Brain and Cognitive Sciences, the Goergen Institute for Data Science and AI (GIDS-AI), and the Center for Visual Science. His research focuses on deep learning systems for artificial general intelligence (AGI), including continual learning, medical computer vision, and visual question answering. Previously, he was an Associate Professor at RIT’s Carlson Center for Imaging Science and a leader at Paige.AI, contributing to the FDA-cleared Paige Prostate system. Kanan earned his PhD from UC San Diego, completed postdoctoral work at Caltech, and worked at NASA JPL. Education: PhD in Computer Science, UC San Diego MS in Computer Science, University of Southern California Bachelor’s in Philosophy and Computer Science, Oklahoma State University Research Interests: Kanan’s work spans foundational AI capabilities like continual learning, medical imaging (pathology and radiology), multi-modal reasoning, and cognitive science-inspired models. His lab develops bias-robust AI systems and applies deep learning to healthcare and fusion research. Articles Trends: His recent work emphasizes out-of-distribution generalization, foundation models in pathology, and stability in continual learning. Key themes include AI applications in healthcare, model robustness, and neuroscience-inspired algorithms. Awards: NSF CAREER Award Senior Member, AAAI and IEEE DoE and NSF grants totaling $5M+ DARPA/ARL awards Advising & Grants: Mentored over 10 PhD students, including Robik Shrestha and Usman Mahmood. Secured grants for AI in nuclear fusion and medical imaging. Led RIT’s Center for Human-aware AI (CHAI) as Associate Director. Labs & Teams: Heads the University of Rochester AI Initiative, collaborates with Paige.AI, and leads teams advancing AI in pathology and robotics. His lab’s KLab (klab.cis.rit.edu) focuses on vision and learning systems.

Gonzalo Guillén Gosálbezمشاهده پروفایل

استاد

• Process Systems Engineering
• Sustainable Chemistry
• Environmental Impact Assessment
• +۷ مورد دیگر

Prof. Dr. Gonzalo Guillén Gosálbez is a Full Professor at the Department of Chemistry and Applied Biosciences , ETH Zürich. He holds a PhD in Process Systems Engineering (UPC, 2005) and has held academic positions at Imperial College London (Reader), University of Manchester (Senior Lecturer), and Universitat Rovira i Virgili (Assistant/Associate Professor). His research focuses on Sustainable Chemical Processes , integrating life cycle assessment, optimization techniques, and planetary boundary analysis to evaluate and design low-carbon technologies. Current position: Full Professor, ETH Zürich (2019–present) Prior roles: Imperial College London (2016–2019), University of Manchester (2014–2016), URV Spain (2008–2014) Education: PhD (UPC, 2005), MEng/BEng (University of Murcia) His research explores CO2 valorization , green methanol synthesis , circular marine fuels , and planetary boundary compliance in energy and chemical systems. Recent work emphasizes machine learning for process modeling, single-atom catalysis , and decentralized ammonia production . Scientific contributions include 15+ peer-reviewed articles (2023–2025) in journals like Nature Chemical Engineering , ACS Sustainable Chemistry & Engineering , and Energy & Environmental Science . Key themes: Optimization of hybrid fossil/renewable carbon systems Environmental impacts of energy transitions Catalyst design for sustainable chemistry Life cycle assessment of emerging technologies Awarded UPC Top Doctoral Student Award and Top National Student Award , he combines process systems engineering with sustainability metrics to address global challenges in chemical and energy systems.

Henry Mossمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +۳ مورد دیگر

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

Wengong Jinمشاهده پروفایل

استادیار

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +۵ مورد دیگر

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

Connor Coleyمشاهده پروفایل

استادیار

• Machine Learning for Chemistry
• Computer-Aided Molecular Design
• Synthesis Planning
• +۵ مورد دیگر

Connor Coley is the Henri Slezynger (1957) Career Development Assistant Professor at the Massachusetts Institute of Technology (MIT) School of Engineering. His research bridges chemistry and machine learning, focusing on autonomous molecular discovery, predictive chemistry, and laboratory automation. Education: Ph.D., MIT (2019) M.S.CEP., MIT (2016) B.S., Caltech (2014) Research Interests: Dr. Coley’s work centers on domain-informed machine learning for chemistry, computer-aided molecular design, and autonomous laboratories. Key themes include predictive modeling of chemical reactivity, optimization of synthesis pathways, and integration of AI with experimental data for drug discovery and materials science. Publications: His recent articles highlight advancements in AI-driven reaction prediction, molecular representation learning, and laboratory automation. Trends include applications of Bayesian optimization, contrastive learning, and diffusion models to chemical discovery. Scientific Awards: Camille Dreyfus Teacher-Scholar Award (2025) James W. Swan Outstanding Faculty (2025) Schmidt Futures AI2050 Early Career Fellow (2022) NSF CAREER Award (2021) Forbes 30 Under 30: Healthcare (2019) Software & Tools: He leads the open-source ASKCOS software suite for synthesis planning, adopted by 35,000+ chemists and deployed at 15+ pharmaceutical companies. His team also develops tools for metabolomics and molecular representation learning.

Jan Madsenمشاهده پروفایل

استاد

• Embedded Systems
• Cyber-Physical Systems
• Microfluidic Biochips
• +۵ مورد دیگر

Jan Madsen is a Professor at DTU Compute, Technical University of Denmark, and Head of the Embedded Systems Engineering section. His research focuses on system-level modeling and design of embedded computing systems, particularly cyber-physical systems, microfluidic biochips, and synthetic biology applications. Develops design automation tools and methodologies for embedded systems Supervises numerous PhD students and leads major research projects Research Interests Key areas include: Embedded systems-on-a-chip Cyber-Physical Systems (Internet-of-Things) Microfluidic Lab-on-Chip devices Synthetic biology with molecular computing Design, modeling, and optimization of complex systems Scientific Awards DATE Fellow (2019) IEEE CEDA Outstanding Recognition (2019) DTU Scientific Advise Award (2013) Best Paper Awards at MECO (2013) and CASES (2009) Jorck’s Foundation Research Award (1995) Publications His 14+ journal papers and 115+ conference papers demonstrate expertise in: SystemC-based modeling frameworks Energy-aware sensor networks Self-healing eDNA architectures Microfluidic biochip synthesis RTOS modeling and MPSoC exploration

Ambuj K. Singhمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Network Science
• +۷ مورد دیگر

Ambuj K. Singh is a Distinguished Professor of Computer Science at the University of California, Santa Barbara (UCSB), with a part-time appointment in the Biomolecular Science and Engineering Program. He holds a PhD from the University of Texas at Austin (1989), an MS from Iowa State University (1984), and a BTech from the Indian Institute of Technology, Kharagpur (1982). His campus affiliations include the Center for Bio-Image Informatics, Information Network Academic Research Center, and IGERT on Network Science. PhD, University of Texas at Austin, 1989 M.S., Iowa State University, 1984 B.Tech., Indian Institute of Technology, Kharagpur, 1982 Research interests span network science, machine learning, and bioinformatics, with a focus on graph-based methodologies. His work addresses: Data-centric modeling of dynamic networks Representation learning and explainability in graph neural networks Network analysis in social systems and biological networks Applications in drug discovery and brain sciences Geometry-preserving distance metrics for data integrity Recent publications highlight advancements in counterfactual explanations, GNN benchmarking, molecular graph pretraining, and self-attention for event detection. Scientific contributions include: Founding Acelot, Inc., an in silico drug discovery company Editorial roles at IEEE Transactions on Knowledge & Data Engineering and BMC Journal of Clinical Bioinformatics NSF-IGERT (2013-2018), ARL-funded Information Networks Academic Research Center (2009-2014), and US Army MURI grants Advising has involved mentoring over 50 graduate/postdoctoral students, including 30+ PhD candidates. He leads a multidisciplinary research group at UCSB and collaborates with off-campus entities like Acelot, Inc.

Knut Reinertمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Genomic Sequence Analysis
• Proteomics
• +۷ مورد دیگر

Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.

• Energy Systems Optimization
• Conceptual Process Design
• Life Cycle Assessment
• +۸ مورد دیگر

Prof. Dr. André Bardow is a Full Professor in Energy and Process Systems Engineering at ETH Zurich , leading research at the intersection of thermodynamics, machine learning, and sustainable energy systems. Previously, he held professorships at RWTH Aachen University (2010-2020) and TU Delft (2007-2010). He also served as part-time director at Forschungszentrum Jülich (2017-2022) and visiting professor at UC Santa Barbara (2015/16). His work focuses on energy systems optimization , computer-aided molecular design , and CO2 capture & utilization . PhD from RWTH Aachen University Current ETH Zurich affiliation Former roles at RWTH Aachen, TU Delft, Jülich Research Center His research integrates machine learning with thermodynamic modeling to optimize processes like crystallization and electrochemical cooling . Recent publications demonstrate advancements in solvent design, CO2 transport LCA, and ORC working fluid optimization. He chairs the VDI Technical Committee for Thermodynamics (2016-2024) and has received multiple awards including the Covestro Science Award and Arnold-Eucken-Award . Current projects address carbon circular economies , electrified chemical production , and AI-driven process optimization . His lab at ETH Zurich develops cutting-edge technologies like ML-CAMPD frameworks for sustainable separation processes and photoacid-based CO2 capture systems. Funding from the H2020 Systemic Expansion of Circular Ecosystems (grant 101036854) supports these initiatives. 2024 Clarivate Highly Cited Researcher 2022 Inaugural Lecture: "To sustainability and beyond: A computer-animated story on energy & chemicals" Recipient of multiple teaching and research excellence awards

Thomas Papeمشاهده پروفایل

استاد

• Systematics
• Taxonomy
• Phylogeny
• +۷ مورد دیگر

Thomas Pape is a Professor at The Natural History Museum of the University of Copenhagen, specializing in the systematics, taxonomy, phylogeny, biogeography and evolution of Diptera (two-winged insects), with special emphasis on Calyptratae families including Sarcophagidae, Oestridae, and Calliphoridae. He has been a professor since 2025 after serving as an Associate Professor from 2004-2024. PhD from Copenhagen University (1990) Docent at Stockholm University (1998) Associate Professor at Danish Bilharziasis Laboratory (1990-1992) Research Entomologist at Naturhistoriska Riksmuseet, Stockholm (1994-2004) His research spans taxonomy, systematics, phylogeny, biogeography and evolution of Diptera with special emphasis on calyptrate families. He collaborates on Systema Dipterorum, conducts comparative larval morphology studies, and investigates the phylogeny of Calyptrata. His work includes Diptera nomenclature and large-scale biodiversity inventories. Analysis of his recent publications reveals a strong trend toward molecular phylogenetics (using RAD-seq and anchored phylogenomics), forensic entomology applications, and biodiversity informatics. His research spans entomology, systematics, evolutionary biology, and increasingly intersects with bioinformatics and conservation science, with significant contributions to understanding flesh flies, bot flies, and blow flies. President of the International Council of Zoological Nomenclature (since 2016) Chair of the Council for the International Congresses of Dipterology (2010-2018) Member of editorial boards for Entomotropica, Studia dipterologica, Stuttgarter Beiträge zur Naturkunde, and Tijdschrift voor Entomologie Dr. Pape teaches courses in Entomology (structure, phylogeny and biology of terrestrial arthropods), Field Biology II - Zoology, and Ecology and Evolution of East Africa (a 2-week field course in Tanzania with Nikolaj Scharff). He has served on scientific panels for SYNTHESYS and as chair of the scientific advisory committee for Museum Koenig in Bonn (2006-2009). His research group employs both morphological and molecular approaches to address evolutionary questions in Diptera, with particular expertise in calyptrate families. The laboratory maintains active international collaborations across multiple continents, as evidenced by his extensive publication record and research network.

Lourdes Pena-Castilloمشاهده پروفایل

استاد

• Bioinformatics
• Genomics
• Machine Learning
• +۵ مورد دیگر

Dr. Lourdes Pena-Castillo is a Professor jointly appointed in the Departments of Computer Science and Biology at Memorial University of Newfoundland's Faculty of Science. Her research focuses on applying machine learning and bioinformatics to study bacterial gene regulation, with emphasis on transcriptomics, gene expression pathways, and microbiology. She leads the Bioinformatics Lab at MUN, developing computational tools like Promotech for promoter prediction and sRNARFTarget for sRNA target identification. Education: BSc in Information Systems Engineering, ITESM-Mexico MSc in Computer Science, University of Alberta PhD in Computer Science (Doktoringenieurin), Otto-von-Guericke Universität Magdeburg Postdoc in Bioinformatics, University of Toronto Research Interests: Bioinformatics, Genomics, Machine Learning, Artificial Intelligence, Transcriptomics, Gene Regulation, Microbiology Her work integrates computational methods with biological data to address challenges in molecular biology, including analyzing bacterial sRNA functions, promoter recognition, and disease diagnostics using machine learning. She has advised numerous graduate students, including PhD candidates Purvikalyan Pallegar and Bonita McCuaig, and MSc students like Ruben Chevez-Guardado and Kratika Naskulwar. Her lab focuses on translational research with applications in both basic science and clinical contexts. Publications span computational methods for bacterial gene regulation, bioinformatics tool development, and interdisciplinary projects in VR and healthcare informatics. Her research has contributed to understanding symbiotic relationships in marine organisms, inflammatory bowel disease diagnostics, and clavulanic acid production in Streptomyces. Grants & Collaborations: Works with interdisciplinary teams across computer science and biology, supported by grants enabling projects in bacterial genomics and computational tool development. Labs & Teams: Leads the Bioinformatics Lab at MUN, fostering collaborations with researchers in microbiology, computer science, and healthcare.

• Microbiology
• Infectious Diseases
• Cell Biology
• +۷ مورد دیگر

Manuel R. Amieva is a Professor at Stanford University School of Medicine , holding joint appointments in Pediatrics - Infectious Diseases and Microbiology & Immunology . He is also a member of the Maternal & Child Health Research Institute (MCHRI) . His clinical practice at Stanford Medicine Children's Health focuses on pediatric infectious diseases. Education: Medical Education: Stanford University School of Medicine (1997) Fellowship: Stanford University Pediatric Infectious Disease Fellowship (2004) Internship & Residency: Stanford Health Care at Lucile Packard Children's Hospital (1998-1999) Dr. Amieva's research investigates host-pathogen interactions at epithelial barriers, with specific expertise in Helicobacter pylori , Listeria monocytogenes , Salmonella enterica , and Staphylococcus aureus . His lab develops innovative organoid culture systems with controlled polarity to study microbial colonization and oncogenic mechanisms. Key discoveries include: H. pylori's manipulation of epithelial junctions via the CagA protein Listeria's exploitation of cell extrusion sites for invasion Staphylococcus toxin interactions with adherens junctions Gastric stem cell activation by pathogens Recent publication trends show continued leadership in infectious disease mechanisms (2020-2025), with a focus on: Pathogen-specific epithelial breach strategies Organoid modeling of viral/bacterial interactions Redox-dependent host factor regulation Single-cell spatial transcriptomic analyses Multi-institutional educational frameworks His scientific collaborations span disciplines including: Gastric cancer genomics initiatives COVID-19 lung infection models Stem cell-microbe interactions Medical education reform projects Dr. Amieva maintains active clinical research while mentoring students in both the Microbiology & Immunology and Pediatrics programs. His lab at Stanford employs advanced 3D confocal microscopy and organ-on-a-chip technologies to visualize epithelial colonization dynamics.

• Bioinformatics
• Genomics
• Transcriptomics
• +۶ مورد دیگر

Owen R. White is a Professor in the Department of Epidemiology & Public Health at the University of Maryland School of Medicine, serving as Associate Director of the Institute for Genome Sciences and Associate Director of Research Collaboration & Development. He leads a team of 25 scientists and engineers developing genomic annotation pipelines and data analysis tools for state-of-the-art research in microbiome and multi-omic studies. His academic background includes: BS in Biotechnology from the University of Massachusetts (1985) PhD in Molecular Biology from New Mexico State University (1992) Postdoctoral Fellowship in Genome Informatics at the Institute for Genomic Research (TIGR) (1994) Dr. White's research spans bioinformatics, genomics, transcriptomics, and metagenomics with emphasis on data management, metadata standards, ontologies, and cloud systems. His work has been foundational for large-scale initiatives like the Human Microbiome Project (HMP) and Integrative Human Microbiome Project (iHMP), generating over 50,000 datasets totaling 10 terabytes of multi-omic data. Analysis of his recent publications reveals a strong trend toward neuroscience multi-omics (BRAIN Initiative), cloud-based data infrastructure, and ethical data sharing frameworks. His work consistently bridges microbiome research with emerging fields like single-cell analysis and Alzheimer's disease biomarker discovery through integrated data platforms. Notable awards include: Benjamin Franklin Award for Open Access in the Life Sciences (2015) Kumho Science International Award in Plant Molecular Biology and Biotechnology (2001) As Principal Investigator for major NIH-funded centers, he has secured sustained support for the HMP Data Analysis and Coordination Center and iHMP Data Coordination Center. His team's work combines fee-for-service models with collaborative research funding to maintain cutting-edge genomic analysis capabilities. The Institute for Genome Sciences houses his computational team responsible for developing production annotation pipelines, database systems, and visualization tools that serve researchers across the University of Maryland School of Medicine and national consortia.

• Cardiology
• Electrophysiology
• Cardiac Arrhythmias
• +۴ مورد دیگر

Professor Carlo Pappone is a Full Professor of Cardiology at Vita-Salute San Raffaele University (since 2019) and Director of the Arrhythmology Department at IRCCS Policlinico San Donato Hospital (since 2015). He has held previous academic/clinical leadership roles at IRCCS San Raffaele Hospital (2000-2010), Villa Maria Cecilia Hospital (2010-2015), and University of Naples Federico II (1990-2000). With 212 publications in top journals like NEJM, JAMA, and Circulation, he has made significant contributions to cardiac arrhythmia research. Current Positions Vita-Salute San Raffaele University (2019-present): Full Professor of Cardiology IRCCS Policlinico San Donato Hospital (2015-present): Director of Arrhythmology Department Previous Roles University of Naples Federico II (1990-2000) University of Michigan Ann Harbor (1990-2000) IRCCS San Raffaele Hospital (2000-2010) Villa Maria Cecilia Hospital (2010-2015) Research Focus: Specializing in cardiovascular diseases, his work spans atrial fibrillation ablation techniques, Brugada syndrome pathogenesis, heart failure device therapy, and ion channel disorders. His H-index of 53 and 18,407 citations reflect his substantial academic impact. Notable Scientific Contributions Author of 44 patents Principal Investigator in 14 clinical trials (clinicaltrials.gov) Developed circumferential pulmonary vein ablation technique Innovator in biventricular pacing systems for heart failure Pioneered research on non-excitatory current for cardiac contractility Scientific Recognition Awarded as Elite Reviewer of JACC (2005) Editorial Board Member of 6 leading journals Reviewer for NEJM, JAMA, Lancet, and Nature Medicine Education Medical Doctorate: University of Naples Federico II

Harri Lähdesmäkiمشاهده پروفایل

دانشیار

• Probabilistic Machine Learning
• Deep Generative Models
• Artificial Intelligence
• +۴ مورد دیگر

Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.