Statistics Faculty

Innovative approaches propel STAT 250 to new heights at George Mason �鶹��

Teresa Donnellan — Mon, 31 Mar 2025 14:40:51 +0000

Innovative approaches propel STAT 250 to new heights at George Mason �鶹�� Teresa Donnellan Mon, 03/31/2025 - 10:40

In This Story

Many university students taking a general education statistics course might be tempted to shirk attendance. But George Mason �鶹��’s STAT 250 Introductory Statistics I boasts an impressive attendance rate of 80% across its sections. With between eight and ten sections per semester, the course is offered in-person and online both synchronously and asynchronously. It serves as one of the most popular quantitative reasoning courses in Mason Core, the university's general education program, with enrollments of nearly one thousand students per semester.

The course’s success can be attributed in part to Associate Professor Kenneth Strazzeri, the course coordinator. Strazzeri tweaks the curriculum each semester to optimize student engagement.

Kenneth Strazzeri, Photo by Office of �鶹�� Branding

“I want students to succeed. That's been my number one goal since taking this type of role at Mason,” said Strazzeri. “When exposed to statistics again, I want them to think back and say, ‘Oh, I remember that. I took stat, and I had a good handle on these basic concepts.’”

Strazzeri’s rich background in statistics education makes him ideal for his role. He holds a PhD in mathematics education and an MS in statistics from the �鶹�� of Virginia.

In STAT 250, Strazzeri focuses on increasing student engagement through various motivational strategies. Attendance bonuses and small incentives for completing course evaluations were introduced to encourage students to actively participate. Another key improvement was the restructuring of assignments. Previously, students were required to complete three major assignments, but these have now been split into six smaller assignments, a change that allows for more frequent feedback and helps students stay on track throughout the semester.

One course innovation that excites Strazzeri has been the introduction of simulation-based methods to teach statistical inference, a process used to draw conclusions about a population based on a sample of data taken from that population.

"We are teaching inference at an informal level first to then get to probability and modeling," Strazzeri explained.

This approach helps students grasp complex concepts related to statistical inference such as the interpretation of a confidence interval by using simulation methods such as bootstrapping—a technique used to estimate the variation of a sample statistic using only the original sample selected—at an informal level before delving into more formal methods.

Looking ahead, Strazzeri is excited about the future of STAT 250, particularly with the transition to the Canvas learning management system. He is working with Instructional Designer Abigail Smith in George Mason’s Stearns Center for Teaching and Learning to make the course even more interactive and user-friendly.

"The future of STAT 250 is bright because of the new design in Canvas," Strazzeri said. Their plans include embedding questions in instructional videos and using those questions live during in-person classes to enhance student interaction and understanding.

Overall, the efforts led by Strazzeri have significantly improved the learning experience in STAT 250. By incorporating modern teaching methods, restructuring assignments, and increasing student engagement, the Department of Statistics is ensuring students not only succeed in the course but also develop a strong foundation in statistical concepts.

Topics

Department of Statistics

New scalable computing technique will make analyzing Big Data easier

Teresa Donnellan — Tue, 17 Sep 2024 20:23:22 +0000

New scalable computing technique will make analyzing Big Data easier Teresa Donnellan Tue, 09/17/2024 - 16:23

In This Story

Lily Wang

With the advancement of data collection techniques, there has been an exponential increase in the availability and complexity of datasets, particularly spatiotemporal data; finding the computing power to analyze such Big Data, however, has remained a challenge for many researchers in various fields. Through a collaborative research project funded by the National Science Foundation, George Mason �鶹�� statistics professor Lily Wang hopes to change that.

Professor Lily Wang, Department of Statistics, College of Engineering and Computing. Photo by Creative Services

Wang and the Chair of the Department of Statistics at The George Washington �鶹��, Huixia Judy Wang, are developing a form of scalable, distributed computing that could lessen the power demand on any single computer by distributing the analysis across a network of computers.

“In the past, we knew there were insights hidden in the data, but due to computing limitations, we couldn’t access them,” said Lily Wang. “Now, with scalable quantile learning techniques, we can gain a deeper understanding of the entire data distribution and extract insights into variability, outliers, and tail behavior, which are critical for more informed decision-making.”

Spatial and temporal data are increasingly being used in such research areas as climate study and health care, among others, noted Lily Wang.

“This data richness presents a lot of opportunities for getting deep insights into dynamic patterns over time and space; but it also brings many, many challenges,” said Wang. Large datasets often exhibit heterogeneous and dynamic patterns, requiring new approaches to capture meaningful relationships.

This project uses two large datasets: the National Environmental Public Health Tracking Network database from the Centers for Disease Control and Prevention and the outdoor air quality data repository from the Environmental Protection Agency.

“Both datasets have been challenging to analyze in the past due to their size and complexity,” explained Wang. “But through scalable and distributed learning techniques, we’re now able to handle large-scale heterogeneous data across the entire United States.”

One of the project's major innovations is the use of distributed computing to divide the data into smaller, manageable regions. Each region is analyzed separately, and the results are efficiently aggregated to form a comprehensive understanding of the entire dataset.

“You can think of it like dividing the U.S. into small regions, analyzing each one separately, and then combining the results to create a comprehensive national analysis,” Wang said. “This method allows us to analyze millions of data points simultaneously without the need for supercomputers.”

Beyond its goals for technical advancements, the project also emphasizes training the next generation of data scientists. Graduate students at George Mason and The George Washington will gain hands-on experience working with real-world data, helping to develop new computational methods.

The project began on September 1, 2024, and is expected to last three years. It has already garnered attention, including recognition from the office of Congressman Gerry Connolly (D-VA).

The potential applications of this research are far-reaching, from improving air quality predictions to understanding public health trends and beyond. Wang explained, "This work empowers researchers and policymakers to leverage vast amounts of data to address rising societal issues more effectively.”

Topics

Department of Statistics

Statistics Faculty

Computational statistics

Big Data

big data analytics

Research Interests: Nonstationary Time Series Analysis; Spectral Analysis; Nonparametric Statistics; Big Data; Bayesian Data Analysis; Applications in Medicine

Research

Statistics professor aims to use fMRI data to identify, predict Alzheimer’s

Teresa Donnellan — Fri, 21 Jun 2024 14:58:51 +0000

Statistics professor aims to use fMRI data to identify, predict Alzheimer’s Teresa Donnellan Fri, 06/21/2024 - 10:58

In the quest to combat Alzheimer's disease, George Mason �鶹�� professor Abolfazl Safikhani is working with neuroscientists and imaging experts at the �鶹�� of Washington's Alzheimer's Disease Research Center (ADRC) to pioneer groundbreaking advancements in biomarker identification.

Abolfazl Safikhani. Photo by Creative Services

"We're trying to understand and come up with the biomarkers for Alzheimer’s disease using imaging data,” said Safikhani, who is an assistant professor in the Department of Statistics. “[We will] try to distinguish and classify between people who have Alzheimer’s disease or will be developing it in the near future compared to healthy individuals."

At the core of the ADRC project lies functional magnetic resonance imaging (fMRI) data, which researchers are using to identify biomarkers for Alzheimer's disease. Unlike conventional imaging techniques, fMRI provides researchers with a comprehensive view of brain activity, enabling them to discern subtle connectome-based changes associated with the disease.

Collecting and analyzing fMRI data comes with challenges, as imaging available for research is limited. Nevertheless, the researchers remain undeterred in their quest to unlock the secrets of Alzheimer's disease. They have already made early progress.

"We were able to get some really good preliminary results, from which a [National Institutes of Health] proposal has been submitted and is currently under review," said Safikhani. He added, "If we could come up with a combination of statistical and machine learning techniques to detect Alzheimer’s progression, it would be one of the most interesting projects I have ever worked on.”

By combining expertise from diverse disciplines and leveraging the power of interdisciplinary collaboration, this project promises to make an important step toward early detection and intervention strategies for Alzheimer's disease.

In This Story

Abolfazl Safikhani

Delve into Statistics at George Mason

Topics

Department of Statistics

College of Engineering and Computing

Strategic Direction

Professor applies statistics and AI to land use modeling and real estate pricing

Teresa Donnellan — Wed, 29 May 2024 16:18:12 +0000

Professor applies statistics and AI to land use modeling and real estate pricing Teresa Donnellan Wed, 05/29/2024 - 12:18

In This Story

Abolfazl Safikhani

George Mason �鶹�� statistics professor Abolfazl Safikhani recently applied his cutting-edge, interdisciplinary research to analyzing land use dynamics and property pricing shifts over time, work that underscores the transformative potential of data-driven insights, especially in urban planning and real estate.

Safikhani earned bachelor’s and master’s degrees in mathematics before earning a doctorate in statistics.

“I decided to do a PhD in statistics because throughout the master’s I had become more and more interested in connecting real world problems to data. And I'm very happy that I made that decision,” he said.

Abolfazl Safikhani

Along with a former colleague at the �鶹�� of Florida in the urban planning department, Safikhani applied machine learning techniques to a dataset comprising millions of land parcels in Florida. The two endeavored to decipher the intricate dynamics of land use transformations over time and predict future developments with unprecedented accuracy. Their predictions surpassed 98% accuracy.

But the team didn't stop with successful predictions. They recognized the importance of understanding the underlying mechanisms driving these predictions. With the addition of a new collaborator, Tianshu Feng in George Mason’s Systems Engineering and Operations Research Department, the researchers aim to present their land use analysis software as explainable artificial intelligence (XAI). By elucidating the black box of machine learning algorithms, Safikhani hopes local government decision-makers and urban planners can confidently leverage the software to optimize resource allocation effectively.

Another of Safikhani’s projects considers land use and value specifically concerning the price of residential real estate. Safikhani’s own experience buying real estate in Fairfax County, Virginia, in 2022, inspired this project. When he asked his real estate agent to estimate a fair price of a certain house, the agent came back with an estimate based on the price of three comparable local properties that had recently sold. Ever a “quant guy,” Safikhani said, he thought there could be a better way: applying the idea of transfer learning.

“The big idea of transfer learning is, within your big data set, try to find areas that have similar dynamics to your area of interest. And then use that similarity to improve your prediction,” Safikhani explained. “So, imagine that there is a little neighborhood somewhere in DC or somewhere in Maryland or somewhere in California that has dynamics very similar to the specific neighborhood where you want to buy a house in Northern Virginia. Once you account for some changes, let's say, regulations and things that are different, then the remaining dynamics are their similarities.”

He continued, “If you only use your neighborhood, you can have three data points. If you use another, similar neighborhood, it's going to be 20. If you use neighborhoods from other places over the 50 states of the U.S., you may end up getting a thousand data points.”

Safikhani is working with a colleague from the �鶹�� of California – Los Angeles to bring in funding to develop this pricing software. Their preliminary results show the benefit of their proposed model versus current pricing systems.

Safikhani's research is poised to revolutionize sectors like urban planning and real estate. In fact, his research has attracted the attention of startups keen to translate his findings into real estate–disrupting tools.

“It seems there's actually a growing interest in having such AI tools that would understand land use development and then really match it with pricing,” he said. “And sooner or later, this [technology] is going to come out. Platforms like Zillow are doing a good job, but there's much more that can be done.”

Topics

Applied Statistics

Department of Statistics

Statistics Faculty

Computational statistics

Big Data

big data analytics

real estate entrepreneurship

Artificial Intelligence

Research

Abolfazl Safikhani

Nawwaf Al Ghamdi — Wed, 03 Aug 2022 20:42:12 +0000

Abolfazl Safikhani Nawwaf Al Ghamdi Wed, 08/03/2022 - 16:42

Assistant Professor, Department of Statistics

Contact Information

Campus: Fairfax
Building: Nguyen Engineering Building
Room 1723
Mail Stop: 4A7
Email: Abolfazl Safikhani

Personal Websites

Personal Website

In the News

Statistics professor aims to use fMRI data to identify, predict Alzheimer’s
June 21, 2024
Professor applies statistics and AI to land use modeling and real estate pricing
May 29, 2024

Biography

Abolfazl Safikhani is currently an assistant professor in the department of statistics at George Mason �鶹��. He received his PhD from the department of statistics and probability, at Michigan State �鶹��. Prior to Mason, he held positions at Columbia �鶹�� and the �鶹�� of Florida. His main research interests include network modeling, high-dimensional statistics, spatiotemporal models, unsupervised learning (change point detection, clustering), random forest, and applications in urban planning, neuroscience, and smart cities.

Degrees

PhD, Statistics, Michigan State �鶹��

Nicholas Rios

Nawwaf Al Ghamdi — Mon, 18 Jul 2022 18:56:31 +0000

Nicholas Rios Nawwaf Al Ghamdi Mon, 07/18/2022 - 14:56

Assistant Professor, Department of Statistics

Contact Information

Phone: (973) 356-7366
Campus: Fairfax
Building: Nguyen Engineering Building
Room 1715
Mail Stop: 4A7
Email: Nicholas Rios

Personal Websites

Personal Website

In the News

Biography

Nicholas Rios is interested in a wide variety of research areas in the field of Statistics. His primary research focuses on experimental design in the presence of real-world constraints. He is also interested in functional data analysis, computational statistics, Gaussian process modeling, and models for compositional data analysis, with applications to chemical engineering and pharmaceutical industries.

Nicholas earned his PhD in Statistics at Penn State �鶹�� in 2022. His dissertation was focused on designing optimal mixture experiments. In these experiments, multiple reagents and chemicals are mixed to produce a response. The dissertation addressed the practical issue of finding optimal experiments when the order of addition of the components was important. He developed novel algorithms for finding relatively cheap and efficient experiments that allow researchers to estimate the optimal mixture and order settings.

Nicholas is currently teaching applied statistics (STAT 554) for the department. He is very passionate about teaching students to think like statisticians. He challenges students to apply statistical thinking and methodology to analyze real-world data.

Degrees

PhD, Statistics, The Pennsylvania State �鶹��
MS, Statistics, Montclair State �鶹��
BS, Statistics, �鶹�� of Delaware

Research Interests

Functional data analysis
Computational statistic
Gaussian process modeling
Models for compositional data analysis

John Stufken

Nawwaf Al Ghamdi — Mon, 18 Jul 2022 17:25:58 +0000

John Stufken Nawwaf Al Ghamdi Mon, 07/18/2022 - 13:25

Professor, Department of Statistics

Contact Information

Campus: Fairfax
Building: Nguyen Engineering Building
Room 1710
Mail Stop: 4A7
Email: John Stufken

Personal Websites

Personal Website

In the News

Biography

John Stufken’s research interests are in the design and analysis of experiments and in extracting information from data, small or large. His interests have also increasingly moved in the direction of data science. He has continuously published in top professional journals with grant support from the National Science Foundation. He enjoys teaching statistics and data analytics, both for students from statistics and students from other disciplines. He has been recognized for both research and teaching excellence.

Stufken’s research interests are broad and range from applications to theory. He is best known for his work on orthogonal arrays (including the book “Orthogonal Arrays: Theory and Applications” with A.S. Hedayat and N.J.A. Sloane), crossover designs, optimal design of experiments, and sub-data selection methods. He was also co-editor of the book “Handbook of Design and Analysis of Experiments”, with A.M. Dean, M.D. Morris, and D. Bingham.

Before joining George Mason �鶹��, Stufken served, among others, as Director of Informatics and Analytics and Bank of America Excellence Professor at UNC Greensboro (2019-2022), Charles Wexler Endowed Professor of Statistics at Arizona State �鶹�� (2014-2019), Head of the Department of Statistics at the �鶹�� of Georgia (2003-2014), and Program Director for Statistics at the National Science Foundation (2000-2003). He is an Elected Fellow of both the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and served in 2011 as the Rothschild Distinguished Visiting Fellow at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK.

Degrees

PhD, Mathematics, �鶹�� of Illinois at Chicago
Doctoraal, Mathematics, Universiteit Nijmegen
Kandidaats, Mathematics, Universiteit Nijmegen

Mason Statistics Collaboration Core partners with Inova

Tama Moni — Wed, 11 May 2022 20:27:14 +0000

Mason Statistics Collaboration Core partners with Inova Tama Moni Wed, 05/11/2022 - 16:27

The George Mason �鶹�� College of Engineering and Computing statistics department and its Statistics Collaboration Core is working with Inova Health Care Services as part of a partnership called “iTHRIV.” Inova is Northern Virginia’s leading nonprofit healthcare provider, with 20,000+ team members providing more than two million patient visits each year at five hospitals. This partnership is funded by NIH’s National Center for Advancing Translational Sciences.

“The collaboration with Inova Health is one of the examples of Mason Statistics actively collaborating in and/or taking leadership positions on large projects aimed at impacting science and society,” says Jiayang Sun, chair of the Mason Department of Statistics. Ben Sieyon Lee, a faculty member in the department, adds, “By connecting experts from both Mason and Inova, we are able to explore exciting research opportunities at the intersection of statistics and health care research. Many would have been otherwise overlooked.”

The Collaboration Core works with external organizations to provide support in the areas of statistical expertise; grant, manuscript writing, and conference abstract writings; survey design; research in statistics and data science; and interdisciplinary collaboration. The Collaboration Core website guides outside groups through the project proposal process.

The Core allows Mason faculty to be engaged with research in new ways and helps new, tenure-track faculty get exposed to grant work and external collaborators. In addition, it allows the university to establish itself as a leader in data science. “�鶹�� graduate students also play an active role in these collaborations,” says Lee. “They gain valuable experience in applying their statistical knowledge to real-world challenges.”

Topics

Inova

Statistics Faculty

Healthcare

National Institutes of Health

Mason to host major international statistics conference

Tama Moni — Wed, 11 May 2022 20:25:17 +0000

Mason to host major international statistics conference Tama Moni Wed, 05/11/2022 - 16:25

George Mason �鶹�� will host the 22nd Meeting of New Researchers in Statistics and Probability sponsored by the Institute of Mathematical Statistics, August 3-6. Pramita Bagchi, assistant professor in the College of Engineering and Computing’s Department of Statistics is a co-organizer of the event. The conference promotes interaction and networking among early career researchers in statistics and probability, with attendance limited to those who have received a PhD in or after 2015 or expects to receive a PhD by the end of 2022, and by invitation only. Bagchi says, “It is an amazing opportunity to organize this event and help grow the future leaders of statistics. We’re honored to host here at Mason, showcasing our status as a global leader in this area.” The international conference is the only one of its kind for new researchers.

Topics

Statistics Faculty

Inchi Hu

Martha Bushong — Wed, 08 Sep 2021 17:41:38 +0000

Inchi Hu Martha Bushong Wed, 09/08/2021 - 13:41

Professor, Department of Statistics

Contact Information

Building: Nguyen Engineering Building, Fairfax
Room 1854
Phone: 202-309-5651
Email: Inchi Hu

Biography

Inchi Hu is a professor of statistics at the College of Engineering and Computing. He received his PhD in statistics from Stanford �鶹��. Prior to joining Mason, he was on the faculty of the Hong Kong �鶹�� of Science and Technology (HKUST), the �鶹�� of Pennsylvania, and the �鶹�� of Maryland. His research interests include sequential analysis, stochastic control, high-dimensional data analysis, and causal inference.

Among his colleagues and students, he is known for his broad scientific interests, and his passion and excitement to see statistics expanding its role in data science. His research papers appeared in top journals of Statistics (Annals of Statistics, Biometrika, Journal of American Statistical Association), Probability (Annuals of Probability), Engineering (IEEE Trans on Automatic Control, IEEE Trans on Information Theory), Operations Management (Operations Research, Mathematics of Operations Research), and Life Science (Bioinformatics, Nucleic Acids Research). Selected honors and awards include Fellow, Institute of Mathematical Statistics, Franklin Prize for Teaching Excellence (2006, 2016), and Best Ten Lecturers, elected by HKUST Student Association (2002, 2004, 2005). He is the Associate Editor of Statistica Sinica.

At Mason, he will be teaching STAT 535 Analysis of Experimental Data as well as assisting with graduate admissions, data science-economics initiatives, and helping with research task force and initiatives.

Statistics Faculty

Innovative approaches propel STAT 250 to new heights at George Mason �鶹����

In This Story

Topics

New scalable computing technique will make analyzing Big Data easier

In This Story

Topics

Statistics professor aims to use fMRI data to identify, predict Alzheimer’s

In This Story

Delve into Statistics at George Mason

Related News

Topics

Professor applies statistics and AI to land use modeling and real estate pricing

In This Story

Topics

Abolfazl Safikhani

Contact Information

Personal Websites

In the News

Biography

Degrees

Nicholas Rios

Contact Information

Personal Websites

In the News

Biography

Degrees

Research Interests

John Stufken

Contact Information

Personal Websites

In the News

Biography

Degrees

Mason Statistics Collaboration Core partners with Inova

Topics

Mason to host major international statistics conference

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Inchi Hu

Contact Information

Biography

Innovative approaches propel STAT 250 to new heights at George Mason �鶹��