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. 📘🧠

Publication Profile

Scopus

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📚 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. 🎓📈🛠️

💼 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. 🏛️📊🧑‍🏫

🏅 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. 🥇📜🏆

🔬 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. 📊⚙️📚

Publication Top Notes

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

📊 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. 🏆📈🎓

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. 📊🔬 His strong foundation in statistics, mathematics, and computational science has significantly contributed to his expertise in data analysis and statistical modeling. 📈📚

Academic Employment 

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). 📊📚 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. 🏫🔬 His expertise in statistics, data science, and mathematical modeling continues to shape the next generation of scholars. 🎯📈

Research Interest

Prof. Adam Kapelner’s research spans experimental design, randomization, and statistical software development. 🎲📊 He explores data science and machine learning, applying advanced statistical methods to real-world problems. 🤖📈 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. 🏥🧬 Prof. Kapelner is also passionate about educational technology, integrating data-driven approaches to improve learning experiences. 🎓💡 His interdisciplinary expertise contributes significantly to advancing statistical methodologies and their applications across multiple domains. 🚀📉

Honors & Awards 

Prof. Adam Kapelner has received numerous accolades for his teaching, research, and academic contributions. 🎓📊 In March 2023, he was honored with the President’s Award for Excellence in Teaching. 👨‍🏫🏅 His research in economic behavior earned him a Highly Cited Research Certificate (2017). 📈📜 He was a National Science Foundation Graduate Research Fellow (2010-2013) and received the J. Parker Bursk Memorial Award for Excellence in Research (2013). 🏅🔬 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. 🚀🎖️

Teaching Experience 

Prof. Adam Kapelner has extensive teaching experience in statistics, probability, and data science. 🎓📊 At Queens College, CUNY, he teaches courses such as Computational Statistics for Data Science, Probability Theory, Statistical Theory, and Machine Learning Fundamentals. 📈🤖 Since 2014, he has also instructed Bayesian Modeling, Statistical Inference, and Advanced Probability. 📊📚 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. 🎓📉 His expertise has shaped many aspiring statisticians and data scientists. 🚀📖

Industry Experience

Prof. Adam Kapelner has a diverse industry background in data science, software engineering, and consulting. 📊💻 Since 2014, he has provided private consulting in prediction modeling, data mining, and statistical testing for tech, real estate, and finance clients. 🏢📈 He worked as a Data Scientist at Coatue, optimizing algorithmic trading. 🤖📉 As Founder & CTO of DictionarySquared, he developed a web app for vocabulary learning, securing federal grant funding. 🚀📚 He was also Eventbrite’s first engineer, helping design its platform. 💡 At Stanford University, he developed image-processing software for biomedical research using machine learning. 🔬📊

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 📈. His interdisciplinary research extends to social media-based well-being predictions, crowdsourcing motivation, and personalized medicine 💡. 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 🔍.

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 📊📈

2️⃣ Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets – Journal of Economic Behavior & Organization, 2013, Cited by: 584 💡👥

3️⃣ bartMachine: Machine Learning with Bayesian Additive Regression Trees – Journal of Statistical Software, 2016, Cited by: 451 🤖📉

4️⃣ Predicting individual well-being through the language of social media – Biocomputing 2016 Proceedings, 2016, Cited by: 244 📱🧠

5️⃣ Variable selection for BART: an application to gene regulation – Journal of Statistical Software, 2014, Cited by: 205 🧬📊

6️⃣ Preventing Satisficing in Online Surveys – Proceedings of CrowdConf, 2010, Cited by: 143 📝📑

7️⃣ Prediction with missing data via Bayesian additive regression trees – Canadian Journal of Statistics, 2015, Cited by: 105 📉📈

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 🧪🎗

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 🔬🦠

🔟 Nearly random designs with greatly improved balance – Biometrika, 2019, Cited by: 46 📊📏

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

Decision Sciences

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Introduction of Decision Sciences

 

Decision Sciences research serves as a compass for informed decision-making across various industries and domains. This multidisciplinary field combines elements of mathematics, economics, psychology, and management to tackle complex problems, optimize choices, and enhance the quality of decisions. Decision Sciences researchers employ data analysis, modeling, and behavioral insights to help organizations and individuals make more effective and strategic decisions.

Operations Research:

Operations research, often called OR, involves the application of mathematical and analytical methods to optimize decision-making in areas like logistics, supply chain management, and resource allocation. Researchers use mathematical modeling and algorithms to find the best solutions to complex problems.

Risk Management and Decision Analysis:

This subfield focuses on assessing and mitigating risks in decision-making. Researchers employ probability theory, statistics, and decision trees to evaluate uncertain outcomes and make decisions under uncertainty.

Behavioral Decision Making:

Behavioral decision-making research delves into the psychology of decision-making. Researchers investigate cognitive biases, heuristics, and how individuals and groups make decisions, providing insights into improving the quality of choices.

Business Analytics and Big Data:

With the advent of big data, researchers in this subtopic explore data-driven decision-making. They use advanced analytics, data mining, and machine learning to extract valuable insights from vast datasets, aiding in strategic planning and forecasting.

Healthcare Decision Sciences:

This subfield applies decision science techniques to healthcare settings. Researchers work on optimizing healthcare delivery, resource allocation, and patient care decisions to improve the quality, efficiency, and effectiveness of healthcare systems.

 

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