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

๐Ÿ“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 Presidentโ€™s Award for Teaching (2023) and an NSF Graduate Fellowship. He actively publishes and speaks at international conferences. ๐Ÿ†๐Ÿ“ˆ๐ŸŽ“

Publication Profile

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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. ๐Ÿ“Š๐Ÿ”ฌ His strong foundation in statistics, mathematics, and computational science has significantly contributed to his expertise in data analysis and statistical modeling. ๐Ÿ“ˆ๐Ÿ“š

Academic Employmentย 

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 (2014โ€“2021). ๐Ÿ“Š๐Ÿ“š In addition to his role at Queens College, he has been a Visiting Scholar at The Technion โ€“ Israel Institute of Technology since 2018, contributing to the Faculty of Industrial Engineering & Management. ๐Ÿซ๐Ÿ”ฌ His expertise in statistics, data science, and mathematical modeling continues to shape the next generation of scholars. ๐ŸŽฏ๐Ÿ“ˆ

Research Interest

Prof. Adam Kapelner’s research spans experimental design, randomization, and statistical software development. ๐ŸŽฒ๐Ÿ“Š He explores data science and machine learning, applying advanced statistical methods to real-world problems. ๐Ÿค–๐Ÿ“ˆ His work includes crowdsourced social science experiments, leveraging public participation for innovative research. ๐ŸŒ๐Ÿง  Additionally, he focuses on biomedical applications, using statistical modeling to enhance healthcare analytics. ๐Ÿฅ๐Ÿงฌ Prof. Kapelner is also passionate about educational technology, integrating data-driven approaches to improve learning experiences. ๐ŸŽ“๐Ÿ’ก His interdisciplinary expertise contributes significantly to advancing statistical methodologies and their applications across multiple domains. ๐Ÿš€๐Ÿ“‰

Honors & Awardsย 

Prof. Adam Kapelner has received numerous accolades for his teaching, research, and academic contributions. ๐ŸŽ“๐Ÿ“Š In March 2023, he was honored with the Presidentโ€™s Award for Excellence in Teaching. ๐Ÿ‘จโ€๐Ÿซ๐Ÿ… His research in economic behavior earned him a Highly Cited Research Certificate (2017). ๐Ÿ“ˆ๐Ÿ“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). ๐Ÿ…๐Ÿ”ฌ 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. ๐Ÿš€๐ŸŽ–๏ธ

Teaching Experienceย 

Prof. Adam Kapelner has extensive teaching experience in statistics, probability, and data science. ๐ŸŽ“๐Ÿ“Š At Queens College, CUNY, he teaches courses such as Computational Statistics for Data Science, Probability Theory, Statistical Theory, and Machine Learning Fundamentals. ๐Ÿ“ˆ๐Ÿค– Since 2014, he has also instructed Bayesian Modeling, Statistical Inference, and Advanced Probability. ๐Ÿ“Š๐Ÿ“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. ๐ŸŽ“๐Ÿ“‰ His expertise has shaped many aspiring statisticians and data scientists. ๐Ÿš€๐Ÿ“–

Industry Experience

Prof. Adam Kapelner has a diverse industry background in data science, software engineering, and consulting. ๐Ÿ“Š๐Ÿ’ป Since 2014, he has provided private consulting in prediction modeling, data mining, and statistical testing for tech, real estate, and finance clients. ๐Ÿข๐Ÿ“ˆ He worked as a Data Scientist at Coatue, optimizing algorithmic trading. ๐Ÿค–๐Ÿ“‰ As Founder & CTO of DictionarySquared, he developed a web app for vocabulary learning, securing federal grant funding. ๐Ÿš€๐Ÿ“š He was also Eventbriteโ€™s first engineer, helping design its platform. ๐Ÿ’ก At Stanford University, he developed image-processing software for biomedical research using machine learning. ๐Ÿ”ฌ๐Ÿ“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 ๐ŸŽฏ. Notable contributions include BART-based predictive analytics, individual conditional expectation plots, and efficient experimental designs ๐Ÿ“ˆ. His interdisciplinary research extends to social media-based well-being predictions, crowdsourcing motivation, and personalized medicine ๐Ÿ’ก. He has also explored biostatistics, oncology-related immune analysis, and ketogenic therapies for cancer ๐Ÿงฌ. His impactful research blends theoretical innovation with practical applications, advancing both statistics and computational methods ๐Ÿ”.

Publication Top Notes

1๏ธโƒฃ Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation โ€“ Journal of Computational and Graphical Statistics, 2015, Cited by: 1718 ๐Ÿ“Š๐Ÿ“ˆ

2๏ธโƒฃ Breaking Monotony with Meaning: Motivation in Crowdsourcing Markets โ€“ Journal of Economic Behavior & Organization, 2013, Cited by: 584 ๐Ÿ’ก๐Ÿ‘ฅ

3๏ธโƒฃ bartMachine: Machine Learning with Bayesian Additive Regression Trees โ€“ Journal of Statistical Software, 2016, Cited by: 451 ๐Ÿค–๐Ÿ“‰

4๏ธโƒฃ Predicting individual well-being through the language of social media โ€“ Biocomputing 2016 Proceedings, 2016, Cited by: 244 ๐Ÿ“ฑ๐Ÿง 

5๏ธโƒฃ Variable selection for BART: an application to gene regulation โ€“ Journal of Statistical Software, 2014, Cited by: 205 ๐Ÿงฌ๐Ÿ“Š

6๏ธโƒฃ Preventing Satisficing in Online Surveys โ€“ Proceedings of CrowdConf, 2010, Cited by: 143 ๐Ÿ“๐Ÿ“‘

7๏ธโƒฃ Prediction with missing data via Bayesian additive regression trees โ€“ Canadian Journal of Statistics, 2015, Cited by: 105 ๐Ÿ“‰๐Ÿ“ˆ

8๏ธโƒฃ Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients โ€“ Journal of Translational Medicine, 2013, Cited by: 60 ๐Ÿงช๐ŸŽ—

9๏ธโƒฃ Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes โ€“ PLOS ONE, 2010, Cited by: 60 ๐Ÿ”ฌ๐Ÿฆ 

๐Ÿ”Ÿ Nearly random designs with greatly improved balance โ€“ Biometrika, 2019, Cited by: 46 ๐Ÿ“Š๐Ÿ“

1๏ธโƒฃ1๏ธโƒฃ Matching on-the-fly: Sequential allocation with higher power and efficiency โ€“ Biometrics, 2014, Cited by: 40 ๐Ÿน๐ŸŽฏ

Waqas Munir | Statistics | Best Researcher Award

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Waqas Munir | Statistics | Best Researcher Award

Mr Waqas Munir, Quaid-i-Azam University, Islamabad , Pakistan

Based on Mr. Waqas Munir’s academic and professional profile, he stands out as a suitable candidate for the Best Researcher Award. Hereโ€™s a breakdown of his qualifications and achievements, structured in a title-paragraph format:

Publication profile

scopus

Educational Background

Mr. Waqas Munir holds a Master of Philosophy in Statistics from Quaid-i-Azam University, completed between 2014 and 2017. His thesis, titled โ€œNew Cumulative Sum Control Charts for Monitoring Process Mean and Process Dispersion,โ€ showcases his ability to contribute significantly to statistical methodologies. Additionally, he completed his Master of Science in Statistics at the same institution from 2011 to 2013, providing him with a strong foundational knowledge in various statistical concepts.

Life Philosophy

Mr. Munir embodies a profound enthusiasm for research and continuous learning. He recently completed his postgraduate studies and is now pursuing a Ph.D. program in Statistics. His strong foundation in statistical methodologies and programming fuels his ambition to uncover insights within the realm of Statistics and elevate his educational attainment.

Teaching Experience

His teaching experience is notable, having served at Fast University, Islamabad since Fall 2019, and at Quaid-i-Azam University, Islamabad since Spring 2023. This experience not only demonstrates his commitment to education but also highlights his role in shaping the next generation of statisticians.

Research Interests

Mr. Munir’s research interests encompass a range of statistical fields, including Statistics Process Control, Machine Learning, and Applied Statistics. His focus on practical applications of statistics positions him as a forward-thinking researcher in the field.

Publications

Mr. Munir has made substantial contributions to academic literature, with several publications in reputable journals. Notable articles include:

  • โ€œNew cumulative sum charts for monitoring process variabilityโ€ (2017) – This publication explores innovative approaches to process control, demonstrating Mr. Munir’s expertise in cumulative sum (CUSUM) charts.
  • โ€œImproved CUSUM charts for monitoring process meanโ€ (2018) – Co-authored with Haq A, this work enhances existing methodologies in process monitoring, reflecting his ability to improve statistical tools.
  • โ€œNew CUSUM and Shwhart-CUSUM charts for monitoring the process meanโ€ – This research further establishes his focus on improving statistical methodologies, contributing to advancements in quality control.

Publication Top Notes

New CUSUM and EWMA charts with simple post signal diagnostics for two-parameter exponential distribution

New CUSUM and Shewhart-CUSUM charts for monitoring the process mean

Improved CUSUM charts for monitoring process mean

New cumulative sum control charts for monitoring process variability

Conclusion

In summary, Mr. Waqas Munir’s academic qualifications, teaching experience, research interests, and impactful publications position him as a strong candidate for the Best Researcher Award. His commitment to advancing the field of Statistics through rigorous research and education exemplifies the qualities sought in an award recipient.

 

Neelesh Shankar Upadhye | Probability and Statistics Award | Best Researcher Award

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Prof Dr. Neelesh Shankar Upadhye | Probability and Statistics Award | Best Researcher Award

Prof Dr. Neelesh Shankar Upadhye, Indian Institite Of Technology Madras, India

๐ŸŽ“ Prof. Dr. Neelesh Shankar Upadhye, renowned mathematician, earned his Ph.D. in Mathematics from IIT Bombay in 2009, with a remarkable 9.67/10. With a rich academic background including an M.Sc. from IIT Bombay and a B.Sc. from Wilson College, Mumbai, he has excelled in teaching and research. Currently a Professor at IIT Madras, he has significantly contributed to various courses, enhancing student learning experiences. With expertise in areas like Applied Statistics, he’s garnered praise for his innovative teaching methodologies. Upadhye’s dedication to education and quantitative research reflects his commitment to academic excellence. ๐Ÿ“š

 

Publication Profile

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Academic Achievements:

Dr. Neelesh Shankar Upadhye boasts an impressive academic journey, culminating in a Ph.D. in Mathematics from IIT Bombay in 2009, where he also earned his M.Sc. and B.Sc. degrees. His doctoral thesis, supervised by Professor P. Vellaisamy, focused on “Compound Negative Binomial Approximations to Sums of Random Variables.” ๐Ÿ“š

๐Ÿ† Professional Experience

With over a decade of teaching experience, Upadhye has held prestigious positions at IIT Madras, currently serving as a Professor. His expertise extends beyond academia, having worked as a Quantitative Researcher at Dolat Investments Ltd. ๐Ÿ›๏ธ

๐Ÿ… Awards and Recognitions

Upadhye’s scholarly contributions have been acknowledged with numerous awards, including Senior and Junior Research Fellowships from CSIR and IIT Bombay. His qualifications and dedication underscore his status as a distinguished academician and researcher in the field of mathematics. ๐ŸŒŸ

 

Research Focus

Dr. Neelesh Shankar Upadhye’s research primarily centers around probabilistic methods and statistical approximations, with a particular emphasis on compound distributions and their applications in diverse contexts. His work delves into advanced topics such as Stein operators, discrete approximations, and tail behavior analysis of random variables. Upadhye’s contributions extend to developing novel techniques for estimating parameters in time series models and exploring the tail behavior of functions of random variables. Through his publications, he consistently explores and advances the boundaries of statistical theory and its practical applications, fostering a deeper understanding of complex probabilistic phenomena. ๐Ÿ“Š

Publication Top Notes