Efrain Noa Yarasca | Data Science and Analytics | Excellence in Research Posted on 17/08/202417/08/2024 by sciencefatherLeave a Comment on Efrain Noa Yarasca | Data Science and Analytics | Excellence in Research Efrain Noa Yarasca | Data Science and Analytics | Excellence in Research Research assistant at Texas A&M Agrilife , United States Dr. Efrain Noa-Yarasca stands out as an exemplary candidate for the Excellence in Research Award, with a distinguished career in Water Resources, Geographic Information Science (GIS), and Machine Learning. With over a decade of experience, Dr. Noa-Yarasca has demonstrated exceptional expertise in geospatial and statistical analysis, flow and water quality modeling, and the development of Decision Support Systems (DSS). His contributions to the field are marked by his innovative use of advanced GIS tools and machine learning techniques, which have significantly advanced our understanding and management of water resources. Publication Profile Google scholar Education Dr. Noa-Yarasca earned his Ph.D. in Civil Engineering with a focus on Water Resources from Oregon State University (OSU) in 2021. His doctoral research involved hydrological and water quality modeling and the development of the WRESTORE decision support system. He also holds a Master’s degree in Civil Engineering from OSU, where his work included modeling bird habitats and wetlands. His undergraduate studies in Civil Engineering were completed at the National University of San Cristobal de Huamanga (UNSCH) in Peru. Professional Development Dr. Noa-Yarasca is currently a Water Resources, GIS, and Remote Sensing Specialist. His professional experience includes serving as a Research Assistant at Oregon State University, where he led projects involving hydrological modeling and the development of web-based decision support tools. He has also held teaching positions as a Graduate Teaching Assistant at OSU and as an Assistant Professor at the National University of Engineering in Peru. Research Focus Dr. Noa-Yarasca’s research is centered on hydrological and water quality modeling, GIS applications, and machine learning. His work includes developing models to simulate wetland plans and assess their impact on water quality and flood management. His research has contributed to innovative solutions in environmental management, including the development of the WRESTORE DSS and the Watershed Hydrologic Information System (WHIS). His publications cover topics such as biomass forecasting, water balance modeling, and the integration of remote sensing with hydrological models. Skills Dr. Efrain Noa-Yarasca possesses a comprehensive skill set that underscores his expertise and versatility in research and practical applications. His advanced proficiency in Geographic Information Systems (GIS) is evidenced by his adept use of tools such as ArcGIS, ArcGIS Pro, and ENVI, coupled with GIS programming in Python and model building techniques. Dr. Noa-Yarasca excels in remote sensing and geospatial data analysis, utilizing Landsat and MODIS data for various environmental assessments. His programming skills extend to Python, Fortran, Java, Matlab, and Octave, reflecting his capability in software development and data analysis. Award and Honors National Oceanic and Atmospheric Administration (NOAA) Award: Dr. Noa-Yarasca received this award for his outstanding research contributions during his doctoral program, recognizing his work in hydrological and water quality modeling. Grand Prize in the Ecological Visualization Contest: His project on sea level rise-induced migration, developed under the guidance of Dr. Bo Zhao, won the Grand Prize in the 2017 contest, sponsored by the National Science Foundation (NSF). Research Excellence Award for Machine Learning Models: Dr. Noa-Yarasca’s work on machine learning models for predicting shade-affected stream temperatures was acknowledged and approved for publication in the Journal of Hydrologic Engineering, marking a significant achievement in applying advanced computational techniques to environmental research. Conclusion Dr. Efrain Noa-Yarasca’s impressive track record in research, education, and practical applications makes him a highly deserving candidate for the Excellence in Research Award. His contributions to water resources management, his expertise in GIS and machine learning, and his innovative development of decision support systems reflect a commitment to advancing the field and addressing critical environmental challenges. His research not only enhances scientific understanding but also provides valuable tools for decision-making and conservation. Publications “An Improved Model of Shade-Affected Stream Temperature in Soil & Water Assessment Tool” E Noa-Yarasca, M Babbar-Sebens, C Jordan Hydrology and Earth System Sciences Discussions, 2022 🏞️📊 “A Machine Learning Model of Riparian Vegetation Attenuated Stream Temperatures” E Noa-Yarasca, M Babbar-Sebens, C Jordan An Abstract of the Dissertation, 2021 🌿🤖 “Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study” E Noa-Yarasca, JM Osorio Leyton, JP Angerer Agronomy 14 (2), 349, 2024 🌾📈 “Biomass Time Series Forecasting Using Deep Learning Techniques. Is the Sophisticated Model Superior?” E Noa-Yarasca, J Osorio, J Angerer ASA, CSSA, SSSA International Annual Meeting, 2023 🌱🔍 “Use of Remote Sensing and Machine Learning to Identify and Quantify Invasive Plant Encroachment in West Texas Rangelands” S Sarkar, E Noa-Yarasca, J Osorio, XB Wu, JM Mata, JP Wied ASA-CSSA-SSSA International Annual Meeting, 2023 🌵📡 “Review of Statistical Water Temperature Models for a Peruvian Andean River” E Noa-Yarasca, DC Ayuque, HAG Ccora, IAA Bizarro, A Arancibia Journal of Environmental Science and Engineering, 155-164, 2022 🌊📖 “Comparison of Expectimax and Monte Carlo Algorithms in Solving the Online 2048 Game” E Noa-Yarasca Pesquimat 21 (1), 1-10, 2018 🎮🔢 “Water Balance of Huacachina Oasis, Peru” E Noa-Yarasca, GN Miranda South Sustainability 5 (2), e101, 2024 💧🏜️ “Machine Learning Models for Prediction of Shade-Affected Stream Temperatures” E Noa-Yarasca, M Babbar-Sebens, CE Jordan Journal of Hydrologic Engineering, 2024 🌡️💡 “Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks” E Noa-Yarasca, JM Osorio Leyton, JP Angerer Machine Learning and Knowledge Extraction 6 (3), 1633-1652, 2024 📉🔍
Dr. Efrain Noa-Yarasca’s impressive track record in research, education, and practical applications makes him a highly deserving candidate for the Excellence in Research Award. His contributions to water resources management, his expertise in GIS and machine learning, and his innovative development of decision support systems reflect a commitment to advancing the field and addressing critical environmental challenges. His research not only enhances scientific understanding but also provides valuable tools for decision-making and conservation.
MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award Posted on 10/07/202410/07/2024 by sciencefatherLeave a Comment on MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award MohammadSadeq Mottaqi | Machine Learning | Best Researcher Award Dr. MohammadSadeq Mottaqi, City University of New York, United States. Dr. MohammadSadeq Mottaqi is a Ph.D. candidate in Biochemistry at the City University of New York, with a background in Biotechnology from the University of Tehran. Proficient in Python and R, Mohammad excels in bioinformatics tools like PyMol and Gromacs. His research spans machine learning-based drug action prediction, molecular dynamics simulations, and protein-ligand interactions. He has published on topics such as SARS-CoV-2 infection responses and cyanobacterial genera identification. Mohammad’s technical prowess extends to data analysis, visualization, and high-performance computing, reflecting his commitment to advancing biotechnological and computational research. Publication Profile Scopus orcid Education Ph.D. in Biochemistry City University of New York, New York, NY Aug 2022 – Present Bachelor of Science in Biotechnology University of Tehran, Tehran, Iran Sep 2017 – March 2022 Experience Graduate Assistant City University of New York, New York, NY Aug 2022 – Present Assisting in bioinformatics and computational biochemistry research. Research Experience Conducted molecular dynamics simulations of mutated HER2 proteins. Investigated protein-ligand interactions using Python and PyMOL. Developed machine learning models for predicting drug actions. Professional Development Mr.Mohammad Sadeq Mottaqi is currently immersed in his Ph.D. journey in Biochemistry at the City University of New York, focusing on computational biochemistry. His research expertise includes pioneering machine learning approaches for predicting drug actions and analyzing transcriptomics data to advance personalized medicine. Mohammad’s impactful contributions extend to molecular dynamics simulations of protein interactions and investigating the molecular mechanisms of SARS-CoV-2 mutations. With a foundation in biotechnology from the University of Tehran, he blends technical skills in Python, R, and bioinformatics tools like Gromacs and PyMol. Mohammad is dedicated to pushing the boundaries of bioinformatics, bridging theoretical insights with practical applications in healthcare and environmental management. Research Focus Mr. MohammadSadeq Mottaqi is a biochemist and computational scientist specializing in bioinformatics and machine learning applications in drug discovery and molecular biology. His research includes predictive modeling for drug actions, molecular dynamics simulations, and protein-ligand interactions. Mohammad has contributed significantly to understanding SARS-CoV-2 through machine learning approaches and automated identification of toxigenic cyanobacterial genera for water quality control. His work, cited extensively, showcases his expertise in integrating computational tools and biotechnological research, making impactful advancements in these fields. Publications Automated identification of toxigenic cyanobacterial genera for water quality control purposes, Journal of Environmental Management, 2024 Contribution of machine learning approaches in response to SARS-CoV-2 infection, Informatics in Medicine Unlocked, 2021 Mutations in SARS-CoV-2; Consequences in structure, function, and pathogenicity of the virus, Microbial Pathogenesis, 2021