Bekir Taner San | Multicriteria Decision Analysis | Best Researcher Award

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Prof. Dr. Bekir Taner San | Multicriteria Decision Analysis | Best Researcher Award

Prof. Dr. Bekir Taner San, Akdeniz University, Turkey

Prof. Dr. Bekir Taner San is a distinguished academic in geological engineering with extensive expertise in remote sensing, image processing, and GIS applications. Currently a Professor at Akdeniz University, he holds a doctorate in geological engineering from Middle East Technical University. He has contributed significantly to various geotechnical and geological research areas, with a strong focus on advanced technologies in engineering. πŸŒπŸ“ŠπŸ’‘

Publication Profile

Scopus

Orcid

Education

Prof. San’s academic journey began at Middle East Technical University, where he completed his undergraduate degree in Geological Engineering (1993-1998), followed by postgraduate (1999-2002) and doctoral studies (2002-2008) in Natural and Applied Sciences. His doctoral thesis focused on hyperspectral image processing and its applications in lithological and mineralogical mapping. πŸŽ“πŸ“–πŸ“

Experience

Prof. San has had a long-standing career at Akdeniz University, serving as a Professor in the Faculty of Engineering since 2020. He has held several administrative roles, including Rector’s Advisor and BAP Scientific Commissioner. Additionally, he has served as the Head of the Department of Geological Engineering and has developed and taught various courses in GIS, remote sensing, and image processing. πŸ«πŸ“šπŸ”§

Awards and Honors

Prof. San has been recognized for his impactful research and academic leadership. He has received numerous awards for his contributions to geotechnical engineering and remote sensing. His commitment to academic excellence is evident in his ongoing research and leadership roles in scientific committees. πŸ†πŸ”¬πŸŽ–οΈ

Research Focus

Prof. San’s research focuses on geotechnical engineering, remote sensing, image processing, GIS, and multi-criteria decision-making analysis. He has conducted extensive work in lithological mapping, earthquake-induced changes, and environmental monitoring using advanced image processing techniques. His research is pivotal in applying cutting-edge technologies to geosciences. πŸ›°οΈπŸ“πŸŒŽ

Publication Top Notes

  • A two‐level approach to geospatial identification of optimal pitaya cultivation sites using multi‐criteria decision analysis | Journal of the Science of Food and Agriculture | 2025 | DOI: 10.1002/jsfa.14297 πŸ“‘

  • Evaluation of the effect of spatial and temporal resolutions for digital change detection: case of forest fire | Natural Hazards | 2023 | DOI: 10.1007/s11069-023-06199-0 πŸ”₯

  • Geochemistry of Red Soils in the Kas District of Antalya (TΓΌrkiye) Using Multivariate Statistical Approaches and GIS | Minerals | 2023 | DOI: 10.3390/min13060772 πŸ§‘β€πŸ”¬

  • Data Integration for Lithological Mapping Using Machine Learning Algorithms | Earth Science Informatics | 2022 | DOI: 10.1007/s12145-022-00826-3 πŸ€–

  • Origins of diverse present displacements in a paleo-landslide area (Isiklar, Trabzon, northeast Turkey) | Environmental Earth Sciences | 2022 | DOI: 10.1007/s12665-022-10372-2 🌍

  • Akdeniz University’s Process of Becoming a Research-Oriented University: Institutional Structuring, Goals, Activities and Outcomes | Yuksekogretim Dergisi | 2021 | DOI: 10.2399/yod.20.808759 πŸŽ“

  • Assessment of Academic Performance at Akdeniz University | NETWORKING COMMUNICATION AND DATA KNOWLEDGE ENGINEERING | 2020 | DOI: 10.1007/978-3-030-36178-5_87 πŸ“Š

  • YeraltΔ± Suyu KaynaklarΔ±nΔ±n Uzaktan AlgΔ±lama ve CBS Teknikleri Kullanarak Modellenmesine YΓΆnelik bir Yaklaşım: KΔ±rkgΓΆz HavzasΔ± (Antalya) | Geomatik | 2020 | DOI: 10.29128/geomatik.649221 πŸ’§

  • Landslide susceptibility mapping using an automatic sampling algorithm based on two level random sampling | Computers & Geosciences | 2019 | DOI: 10.1016/j.cageo.2019.104329 πŸŒ„

  • Geographical information systems (GIS) and Multi-Criteria Decision Analysis (MCDA) integration for sustainable landfill site selection considering dynamic data source | Bulletin of Engineering Geology and the Environment | 2019 | DOI: 10.1007/s10064-017-1135-z πŸ—‘οΈ

Reza sheikh | Decision Sciences | Best Researcher Award

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Assoc. Prof. Dr. Reza sheikh | Decision Sciences | Best Researcher Award

Assoc. Prof. Dr. Reza sheikh, shahrooduniversity of technology, Iran

Assoc. Prof. Dr. Reza Sheikh is a distinguished academic in Production and Operations Management at the Shahrood University of Technology, Iran. With over two decades of teaching and administrative experience, he has served in key leadership roles, including Vice President and Dean of Faculty. He is a prolific author and researcher with expertise in axiomatic design, decision modeling, and quality systems. His work has significantly contributed to the advancement of industrial engineering education and research in Iran. Dr. Sheikh is recognized for his dedication to academic excellence, innovative research, and institutional development in higher education. πŸ“˜πŸ§ 

Publication Profile

Scopus

Google Scholar

πŸ“š Education

Dr. Reza Sheikh earned his Ph.D. in Industrial Engineering from Tehran University in 2006, focusing on lean production systems using axiomatic design. He holds a Master’s degree in Industrial Engineering from Tarbiat Modares University (1998), where he explored fuzzy logic applications in network analysis. His academic journey began with a Bachelor’s degree in Industrial Engineering from Shahid Beheshti University, Tehran (1996). Throughout his education, Dr. Sheikh developed a solid foundation in decision-making techniques, systems analysis, and optimization, which have shaped his research and teaching philosophy. His academic background supports his multifaceted contributions to industrial management. πŸŽ“πŸ“ˆπŸ› οΈ

πŸ’Ό Experience

Since 1999, Dr. Sheikh has been a faculty member at Shahrood University of Technology, contributing as a professor, dean, and vice president. He played pivotal roles in various administrative positions including Director of Monitoring & Evaluation, Vice President of Science & Technology Park, and Incubator Centers Manager. His leadership extended to managing academic productivity and overseeing finance and administration at the university. His roles across research, education management, and university governance have shaped institutional policies and promoted academic excellence. His service reflects a deep commitment to strategic planning, quality assurance, and innovation in higher education. πŸ›οΈπŸ“ŠπŸ§‘β€πŸ«

πŸ… Awards and Honors

Dr. Reza Sheikh has been consistently recognized for his contributions to education and research. He received the Distinguished Professor of the Year in Research Award (2015) and was honored multiple times (2010, 2011, 2013, 2015, and 2019) for his excellence in teaching at the Faculty of Industrial Engineering and Management, Shahrood University of Technology. These honors underscore his commitment to advancing knowledge and fostering innovation in the fields of production and operations management. His dedication has significantly impacted faculty development, student learning, and the university’s academic reputation. πŸ₯‡πŸ“œπŸ†

πŸ”¬ Research Focus

Dr. Sheikh’s research spans Production and Operations Management, Multi-Criteria and Multi-Objective Decision Making (MCDM & MODM), Axiomatic Design, and Statistical Analysis. He specializes in developing mathematical models for lean production scheduling, integrating decision-making tools like TRIZ, rough set theory, and fuzzy logic. His studies also explore service quality, ethics in academia, institutional meritocracy, and faculty performance systems. His work contributes both theoretical and applied insights, addressing organizational efficiency and quality improvement. His scholarly output includes numerous journal articles, research projects, and books that influence academic and industrial practices alike. πŸ“Šβš™οΈπŸ“š

Publication Top Notes

πŸ“˜ Base-criterion on Multi-Criteria Decision-Making Method and Its Applications – πŸ”’ Cited by: 134 – πŸ“… 2020
πŸ“˜ The Impact of Digital Marketing Strategies on Customer’s Buying Behavior in Online Shopping Using the Rough Set Theory – πŸ”’ 80 – πŸ“… 2022
πŸ“˜ Grey SERVQUAL Method to Measure Consumers’ Attitudes Towards Green Products – πŸ”’ 72 – πŸ“… 2018
πŸ“˜ A Novel Approach for Group Decision Making Based on the Best–Worst Method (G-BWM) – πŸ”’ 69 – πŸ“… 2021
πŸ“˜ Evaluation and Selecting the Contractor in Bidding with Incomplete Information Using MCGDM Method – πŸ”’ 49 – πŸ“… 2019
πŸ“˜ Base Criterion Method (BCM) – πŸ”’ 43 – πŸ“… 2022
πŸ“˜ Ranking Financial Institutions Based on Trust in Online Banking Using ARAS and ANP Method – πŸ”’ 43 – πŸ“… 2013
πŸ“˜ Assessing the Agility of Hospitals in Disaster Management Using Fuzzy Flowsort – πŸ”’ 39 – πŸ“… 2021
πŸ“˜ Assessing Hospital Preparedness for Disasters Using Rough Set Theory (COVID-19) – πŸ”’ 36 – πŸ“… 2022
πŸ“˜ Self-Assessment of Parallel Network Systems with Intuitionistic Fuzzy Data – πŸ”’ 31 – πŸ“… 2019
πŸ“˜ Extension of Base-Criterion Method Based on Fuzzy Set Theory – πŸ”’ 27 – πŸ“… 2020
πŸ“˜ Project Portfolio Selection with Interactions under Uncertainty (Hesitant Fuzzy Set) – πŸ”’ 26 – πŸ“… 2018
πŸ“˜ Analysis and Classification of Companies on Tehran Stock Exchange with Incomplete Information – πŸ”’ 18 – πŸ“… 2021
πŸ“˜ Proximity/Remoteness Measurement for Customer Classification – πŸ”’ 17 – πŸ“… 2022
πŸ“˜ Extension of Best–Worst Method Based on Spherical Fuzzy Sets – πŸ”’ 16 – πŸ“… 2024
πŸ“˜ Product Portfolio Optimisation Using Teaching–Learning-Based Optimisation Algorithm – πŸ”’ 16 – πŸ“… 2016

Adam Kapelner | Statistics | Best Faculty Award

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Prof. Adam Kapelner | Statistics | Best Faculty Award

Prof. Adam Kapelner, Queens College CUNY, United States

πŸ“Š Prof. Adam Kapelner is an Associate Professor of Mathematics at Queens College, CUNY, where he also directs the Undergraduate Data Science and Statistics Program. He earned his Ph.D. in Statistics from the Wharton School, University of Pennsylvania (2014). His research focuses on experimental design, randomization, machine learning, and statistical software. He has been a visiting scholar at The Technion, Israel. Recognized for excellence in teaching and research, he received the President’s Award for Teaching (2023) and an NSF Graduate Fellowship. He actively publishes and speaks at international conferences. πŸ†πŸ“ˆπŸŽ“

Publication Profile

Google Schlolar

Academic Background

Prof. Adam Kapelner holds a Ph.D. in Statistics (2014) from the Wharton School, University of Pennsylvania, where he was advised by Abba Krieger and Edward George. He also earned an A.M. in Statistics (2012) from Wharton under the guidance of Dean Foster. His academic journey began at Stanford University, where he completed a B.S. in Mathematical & Computational Science (2006), with minors in Physics & Economics. πŸ“ŠπŸ”¬ His strong foundation in statistics, mathematics, and computational science has significantly contributed to his expertise in data analysis and statistical modeling. πŸ“ˆπŸ“š

Academic EmploymentΒ 

Prof. Adam Kapelner is an Associate Professor of Mathematics at Queens College (since August 2021) and has been the Director of the Undergraduate Data Science and Statistics Program since 2019. Previously, he served as an Assistant Professor of Mathematics (2014–2021). πŸ“ŠπŸ“š In addition to his role at Queens College, he has been a Visiting Scholar at The Technion – Israel Institute of Technology since 2018, contributing to the Faculty of Industrial Engineering & Management. πŸ«πŸ”¬ His expertise in statistics, data science, and mathematical modeling continues to shape the next generation of scholars. πŸŽ―πŸ“ˆ

Research Interest

Prof. Adam Kapelner’s research spans experimental design, randomization, and statistical software development. πŸŽ²πŸ“Š He explores data science and machine learning, applying advanced statistical methods to real-world problems. πŸ€–πŸ“ˆ His work includes crowdsourced social science experiments, leveraging public participation for innovative research. 🌍🧠 Additionally, he focuses on biomedical applications, using statistical modeling to enhance healthcare analytics. πŸ₯🧬 Prof. Kapelner is also passionate about educational technology, integrating data-driven approaches to improve learning experiences. πŸŽ“πŸ’‘ His interdisciplinary expertise contributes significantly to advancing statistical methodologies and their applications across multiple domains. πŸš€πŸ“‰

Honors & AwardsΒ 

Prof. Adam Kapelner has received numerous accolades for his teaching, research, and academic contributions. πŸŽ“πŸ“Š In March 2023, he was honored with the President’s Award for Excellence in Teaching. πŸ‘¨β€πŸ«πŸ… His research in economic behavior earned him a Highly Cited Research Certificate (2017). πŸ“ˆπŸ“œ He was a National Science Foundation Graduate Research Fellow (2010-2013) and received the J. Parker Bursk Memorial Award for Excellence in Research (2013). πŸ…πŸ”¬ His dedication to teaching was recognized with the Donald S. Murray Award (2012), and he was an Intel Science Talent Search Semifinalist early in his career. πŸš€πŸŽ–οΈ

Teaching ExperienceΒ 

Prof. Adam Kapelner has extensive teaching experience in statistics, probability, and data science. πŸŽ“πŸ“Š At Queens College, CUNY, he teaches courses such as Computational Statistics for Data Science, Probability Theory, Statistical Theory, and Machine Learning Fundamentals. πŸ“ˆπŸ€– Since 2014, he has also instructed Bayesian Modeling, Statistical Inference, and Advanced Probability. πŸ“ŠπŸ“š Previously, at The Wharton School, University of Pennsylvania, he taught Predictive Analytics and Probability & Statistics while also serving as a teaching assistant for multiple statistics courses, including Linear Regression and MBA-level Statistics. πŸŽ“πŸ“‰ His expertise has shaped many aspiring statisticians and data scientists. πŸš€πŸ“–

Industry Experience

Prof. Adam Kapelner has a diverse industry background in data science, software engineering, and consulting. πŸ“ŠπŸ’» Since 2014, he has provided private consulting in prediction modeling, data mining, and statistical testing for tech, real estate, and finance clients. πŸ’πŸ“ˆ He worked as a Data Scientist at Coatue, optimizing algorithmic trading. πŸ€–πŸ“‰ As Founder & CTO of DictionarySquared, he developed a web app for vocabulary learning, securing federal grant funding. πŸš€πŸ“š He was also Eventbrite’s first engineer, helping design its platform. πŸ’‘ At Stanford University, he developed image-processing software for biomedical research using machine learning. πŸ”¬πŸ“Š

Research Focus

Dr. Adam Kapelner specializes in statistical learning, Bayesian additive regression trees (BART), and data-driven decision-making. His work spans machine learning, causal inference, and predictive modeling 🎯. Notable contributions include BART-based predictive analytics, individual conditional expectation plots, and efficient experimental designs πŸ“ˆ. His interdisciplinary research extends to social media-based well-being predictions, crowdsourcing motivation, and personalized medicine πŸ’‘. He has also explored biostatistics, oncology-related immune analysis, and ketogenic therapies for cancer 🧬. His impactful research blends theoretical innovation with practical applications, advancing both statistics and computational methods πŸ”.

Publication Top Notes

1️⃣ Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation – Journal of Computational and Graphical Statistics, 2015, Cited by: 1718 πŸ“ŠπŸ“ˆ

2️⃣ Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets – Journal of Economic Behavior & Organization, 2013, Cited by: 584 πŸ’‘πŸ‘₯

3️⃣ bartMachine: Machine Learning with Bayesian Additive Regression Trees – Journal of Statistical Software, 2016, Cited by: 451 πŸ€–πŸ“‰

4️⃣ Predicting individual well-being through the language of social media – Biocomputing 2016 Proceedings, 2016, Cited by: 244 πŸ“±πŸ§ 

5️⃣ Variable selection for BART: an application to gene regulation – Journal of Statistical Software, 2014, Cited by: 205 πŸ§¬πŸ“Š

6️⃣ Preventing Satisficing in Online Surveys – Proceedings of CrowdConf, 2010, Cited by: 143 πŸ“πŸ“‘

7️⃣ Prediction with missing data via Bayesian additive regression trees – Canadian Journal of Statistics, 2015, Cited by: 105 πŸ“‰πŸ“ˆ

8️⃣ Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients – Journal of Translational Medicine, 2013, Cited by: 60 πŸ§ͺπŸŽ—

9️⃣ Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes – PLOS ONE, 2010, Cited by: 60 πŸ”¬πŸ¦ 

πŸ”Ÿ Nearly random designs with greatly improved balance – Biometrika, 2019, Cited by: 46 πŸ“ŠπŸ“

1️⃣1️⃣ Matching on-the-fly: Sequential allocation with higher power and efficiency – Biometrics, 2014, Cited by: 40 🏹🎯