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

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, ��AV 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 University, 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, ��AV professor Abolfazl Safikhani is working with neuroscientists and imaging experts at the University 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

��AV 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 University 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 University 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

Joint postdoc program develops from partnership between Statistics Department, Inova Health

Teresa Donnellan — Thu, 28 Sep 2023 18:42:25 +0000

Joint postdoc program develops from partnership between Statistics Department, Inova Health Teresa Donnellan Thu, 09/28/2023 - 14:42

In This Story

Jiayang Sun

A partnership between ��AV’s Statistics Department and Inova Health Care Services has entered its fifth year and continues to grow. The partnership has motivated a new initiative: a postdoc program for Inova researchers interested in developing skills in statistics and data science, run jointly by George Mason’s statistics and global and community health departments. Mohamad Bahij Moumneh, MD, and Jason F. Goldberg, MD, are the program’s standing inaugural members.

“As a fresh Lebanese medical graduate interested in expanding his acquired skills in the field of research, especially cardiology, and academia, the joint program provided me with this golden opportunity,” said Moumneh. He added, “By maximizing the resources of each institute, enhancements in the overall picture and quality of research and healthcare education occur, and allow both institutions to keep, if not exceed, pace with novel technologies and healthcare advancements and institutions.”

The partnership between Mason Statistics and Inova Health was supported by a contract between the university and the healthcare system via a parent award from NIH to the iTHRIV partnership. It supported the experts in George Mason’s Statistics Collaboration Core (SCC) to work with colleagues from Inova Health Care Services on research projects involving statistics or data science. To initialize each new project, INOVA researchers visit statinova.gmu.edu.

“The Statistics Collaboration Core (SCC) is a collaboration platform that supports and facilitates statistical collaborations with internal and external investigators or seekers for evidenced-based research, or statistical, biostatistical, or data science support to decision-making,” explained Jiayang Sun of Mason’s statistics department. Sun is a Principal Investigator on the new grant establishing the postdoc program, alongside co-PI Carolyn Drews-Botsch, the chair of the department of global and community health in Mason’s School of Public Health.

“The partnership between Inova and Mason will help with our research efforts aimed at prolonging life after heart transplantation,” said Goldberg. He noted that, for his case, SCC has provided advanced analysis of mircoRNA clinical rejection scores and has used machine learning to evaluate risk modeling of adverse outcomes within the first year of heart transplantation.

“These collaborations will continue to produce meaningful cardiovascular research,” he said.

Of the five-year partnership between Mason Statistics and Inova Health, Sun noted the relationship has been fruitful, producing additional grants, publications in the health and medical sector, and new grant proposals. She added that it serves as a welcome opportunity for both tenure track and term faculty to perform original research and collaborate with colleagues dealing with real-world problems. Research projects from the partnership have covered such important topics as opioid use, sepsis, heart failure, and—before there were reliable tests—the spread of COVID-19 among children in northern Virginia.

Sun, Moumneh, and Goldberg are likewise optimistic about the new joint postdoc program.

“Researchers can collaborate on various projects that eventually might lead to medical breakthroughs. One could say that the joint program fosters an environment of innovation and growth. The joint program also allows the addressing of pressing healthcare dilemmas, an event that might encourage students to think of possible solutions and managements,” said Moumneh.

Topics

Statistics

Inova

Department of Statistics

College of Public Health

College of Engineering and Computing

School of Computing

Community Partners

Students

Department of Statistics leaps ahead in rankings

Martha Bushong — Wed, 30 Mar 2022 16:18:25 +0000

Department of Statistics leaps ahead in rankings Martha Bushong Wed, 03/30/2022 - 12:18

Strategic thinking and increased research led to a major leap forward in the College of Engineering and Computing’s Department of Statistics ranking. Since U.S. News and World Report last measured, the statistics department rose from 82 to 54 in the rankings.

When Jiayang Sun arrived as the new chair in 2019, she came with a plan to hire more faculty and increase research. Since 2020 the department has set new records in research expenditures, hiring seven new faculty members in the fall of 2020-fall 2021 and welcoming other faculty including those on sabbatical leave. Sun says, “The new faculty brought with them outstanding strengths in statistics and data science.”

“We are very pleased and excited by the significant jump in the rankings by our Department of Statistics and congratulate the entire department and its leadership for their success,” says Ken Ball, dean of the College of Engineering and Computing. “Over the past several years, the College of Engineering and Computing has made a considerable investment in our statistics programs, including over $1.5 million for new faculty start-up funds and graduate research assistants.”

The department also strengthened its four-year-old partnership with Inova Health which has provided tenure-track and term faculty opportunities for collaborative research. Assistant Professor David Kepplinger, a new hire, is involved with the partnership. He says, “The research culture at Mason and the statistics department is built around the idea of collaboration. Everyone is open to sharing ideas and joining forces. There are many opportunities around campus and in the D.C. area to work with experts from diverse fields and translate our statistical research into practice— leveraging our expertise in methodology, theory, and computation—to open the doors for impactful scientific discoveries.”

“We have outstanding faculty who are national leaders in their areas of expertise. With proper support and expected growth, knowing the facts about other peers, we hope for another leap in the future,’ says Sun.

Topics

Department of Statistics

graduate program rankings

Mason Department of Statistics welcomes new faculty

Rena Malai — Tue, 14 Dec 2021 16:32:42 +0000

Mason Department of Statistics welcomes new faculty Rena Malai Tue, 12/14/2021 - 11:32

In This Story

The ��AV Department of Statistics welcomed eight new faculty members in 2020 – 2021.

“The research portfolios of these new faculty members are excellent, representing wide-ranging important fields in modern statistics and data science,” says Jiayang Sun, department chair.

The new faculty members, listed alphabetically, are as follows: Jonathan Auerbach, Isuru Dassanayake, Inchi Hu, David Kepplinger, Ben Seiyon Lee, Mary Meyer, Kenneth Pasiah, and Lily Wang.

According to Kepplinger, the Department of Statistics, which is a part of the School of Computing within Mason’s College of Engineering and Computing, is poised for growth.

“The research culture at Mason and the statistics department, in particular, is built around the idea of collaboration,” he says. “Everyone is open to sharing ideas and joining forces.”

A recent article in AMSTAT News describes the background of each of the eight new hires, who are internationally recognized experts in the areas of health care, economics, technology, clinical trials, and public policy. 

Topics

Department of Statistics

health care

economics

Science & Technology

Statistics professor takes on big data challenges with multi-faceted research

Martha Bushong — Fri, 19 Nov 2021 19:19:36 +0000

Statistics professor takes on big data challenges with multi-faceted research Martha Bushong Fri, 11/19/2021 - 14:19

In This Story

Lily Wang

��AV’s outstanding location, available opportunities, and growing reputation combined to produce a winning formula that attracted statistics professor Lily Wang to the College of Engineering and Computing Department of Statistics in fall 2021.

“Collaboration is the key for my profession, and the Washington, D.C. area has so many government agencies and top technology companies, and it creates fantastic opportunities. Mason is growing so fast and on an impressive trajectory,” says Wang.

Wang’s primary areas of research are broad and diverse. They include non- and semi-parametric modeling and inference, statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, survey sampling, and data reduction methods. Working at the interface of statistics, mathematics, and computer science, she is also interested in general issues related to data science and big data analytics. Her methods have a wide application in engineering, neuroimaging, epidemiology, environmental studies, economics, and biomedical science.

For example, she has been heavily involved with the Centers for Disease Control and spatiotemporal data related to the COVID-19 pandemic. The team’s research findings created a dashboard with multiple apps embedded. The dashboard provides a real-time seven-day forecast and a long-term forecast of COVID-19 infection and death count at the county and state level, and the corresponding risk analysis. “We are honored to be one of the teams that the CDC is relying on to better understand and forecast COVID-19 in the United States,” says Wang.

Another area of great interest to Wang is using functional data to learn how to apply statistics to help with early disease diagnosis and disease prognosis prediction. Currently, most existing studies focus on one-dimensional (1D) function. For example, 1D children’s growth charts are commonly used to screen children’s growth. “Modern technologies produce large volumes of multi-modality imaging data that might be used as biomarkers for diseases,” Wang said. So, Wang’s research team uses 2D and higher dimensional medical imaging data and other clinical and genetic data for Alzheimer’s disease research.

The wealth of data presents new opportunities to innovate in science and technology; however, it also requires a parallel effort in statistical method development that enables researchers to make a rigorous inference. She says, “If you think about a high-resolution image, you can have a million pixels for just the one image, and beyond the image, you also have the patient medical and genetic information, so-called ‘big data squared.’ Analysis of these big data can easily go beyond the capability of the traditional methods. Our state-of-art statistical models and powerful learning tools can help to delineate associations among these data.”

Wang is also teaching a split undergraduate/graduate course in applied regression analysis. She really cares about her students, and she always offers various modalities of her classes. She says, “I always try my best to accommodate students’ special needs, especially under the pandemic.”

Whether she is teaching students or working with research collaborators, Wang’s excitement about being a new faculty member at Mason comes through. “Statistics is a thriving and fast-developing discipline in the data science era. Our department at Mason is home to renowned researchers in statistics, biostatistics, and data science/analytics with a bright future. I am so happy to be part of it,” she says.

Topics

Department of Statistics

COVID-19; Research

CEC faculty research

Faculty member evaluates efficacy of 2020 U.S. Census

Anonymous — Tue, 26 Oct 2021 15:53:32 +0000

Faculty member evaluates efficacy of 2020 U.S. Census Anonymous (not verified) Tue, 10/26/2021 - 11:53

In This Story

Jonathan L. Auerbach

Today, we count on data and data-based systems in our daily lives. The United States census is the backbone of most of these data sets that we rely on, so it is important to ensure its accuracy.

Jonathan Auerbach joined the Department of Statistics at Mason in August, 2021. Photo provided

The American Statistical Association (ASA) Census Quality Indicators Taskforce selected assistant professor of statistics Jonathan Auerbach to independently review the 2020 census as part of a three-person research team. The team released their findings in September.

In 2020, the pandemic, accusations of political interference, and many other factors created doubt that the census wouldn’t be accurate, says Auerbach.

Traditionally, the ASA and the statistics community spend the ten years between censuses looking over the data and offering improvements for the next census. However, because of the efficacy questions of the 2020 census, Auerbach’s research team investigated it in half a year.

“The ASA has historically been involved in advocating for the best census since 1840, right before the Civil War, which was a very formative census, and it became highly politicized. And partially because of that, the ASA as we know it today was born,” says Auerbach.

Auerbach was one of three researchers on the independent review team. He and his fellow researchers, including Paul Biemer and Joseph Salvo, had access to all census data to see how it was collected and what errors could have been introduced.

“Our major finding is what we didn’t find. We didn’t find evidence of malfeasance or politicking or any direct evidence of wrongdoing. However, we did find that largely because of the pandemic, the ways people were counted were of higher risk than previous censuses,” says Auerbach.

For example, in the 2010 census, if someone hadn’t reported their household information, the U.S. Census Bureau would send a worker to follow up. In 2020, because of COVID-19, that was not always a safe option. Instead, the Census Bureau relied more heavily on administrative records, like tax documents. “These practices could lead to more errors than the methods used in the 2010 census,” he says.

Reporting college students was another area they noticed might lead to some potential errors. “Typically, colleges will report the number of students in dorms to the census bureau, but since many students left their dorms halfway through the semester, it was unclear whether students were counted properly.”

Auerbach says that he and the research team found no evidence suggesting the 2020 Census is unreliable despite riskier counting methods. Another group at the National Academies of Sciences, Engineering, and Medicine, which includes other ASA members, will continue to help the Census Bureau improve the census and process the data, but Auerbach’s work is finished for now.

“Our census is a historical document. We really don’t have too many of them in our lifetime, so it was very rewarding to be a part of it,” he says.

Topics

Department of Statistics

Statistics

CEC faculty research

Statistics alum finds rewarding career opportunities

Martha Bushong — Tue, 19 Oct 2021 20:14:21 +0000

Statistics alum finds rewarding career opportunities Martha Bushong Tue, 10/19/2021 - 16:14

In This Story

Kenneth Strazzeri

Tae Song, MS STAT '21. Courtesy photo

For students who are good at math and want a rewarding, interesting profession, statistics offers a great deal of certainty.

“Statistics is about trying to find certainty in uncertain situations,” says Tae Song (MS Statistics, ’21). Song now works as a statistician for the Washington National Tax Office at Grant Thornton, a public accounting and consulting firm. He says, “Many of our clients want us to give them answers, but that’s not really what we do as statisticians. We’re more about interpreting data and saying, ‘Here’s what the data suggests, but you have to make the decision.’”

Several years after finishing his BS in accounting and finance and working as a data analyst in a Washington, D.C. law firm, Tae Song realized that if he wanted to be a statistician, he needed more education. He looked at a variety of different schools and found the best fit at Mason.

“I was an international student at the time, so I wasn't expecting any kind of financial assistance, but the department’s generous offer really surprised me, and I was very, very thankful,” he says. Beyond the financial support, the atmosphere of teamwork was also a big benefit.

As a teaching assistant in the department, Song appreciated the interaction with students and sharing his excitement about the field with them.

“Before the COVID lockdown, I really enjoyed engaging with the students helping them out, especially right before exams,” he says. “They would kind of swarm that room, and we would have four or five TAs helping 20-30 students. I miss that environment, it was very cooperative with a lot of energy. It was rewarding and respectful.”

“Tae was one of the best graduate teaching assistants in my nearly 10 years here at Mason,” says Kenneth Strazzeri, associate professor in the department. “He has the patience and ability to explain difficult concepts in a way that people can understand and use.” Song now uses this same skill set with clients at Grant Thornton.

Song says statistics is a practical and versatile field that he became interested in while pursuing an undergraduate degree at New York University Stern School of Business. As part of a student internship, Song and some other students worked for a local advocacy group, Scenic Hudson. One of their professors, who was also a consultant, had engaged in pro bono work for the group, and he asked some students to do some research.

“It was a great opportunity to do some good work,” says Song. “Scenic Hudson’s mission is to keep the Hudson River and its shorelines as beautiful and pristine as possible. They were concerned about railway tank cars that traveled along the riverside and potential spills. They wanted models about what would happen if there was a spill or leak. We didn’t actually build the models, but the research really opened my eyes to what is possible with statistics.”

Song believes that his degree in statistics has set him up for a great career. “With a degree in statistics, you are extremely marketable. It can be super academic if you want it to be, or super commercial. It’s used in government, finance, healthcare, economics, so many places.”

Topics

Department of Statistics

Graduate Education

career-ready graduates

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 University. Prior to joining Mason, he was on the faculty of the Hong Kong University of Science and Technology (HKUST), the University of Pennsylvania, and the University 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.

Department of Statistics

New scalable computing technique will make analyzing Big Data easier

In This Story

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

In This Story

Delve into Statistics at George Mason

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

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Joint postdoc program develops from partnership between Statistics Department, Inova Health

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Department of Statistics leaps ahead in rankings

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Mason Department of Statistics welcomes new faculty

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Statistics professor takes on big data challenges with multi-faceted research

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Faculty member evaluates efficacy of 2020 U.S. Census

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Statistics alum finds rewarding career opportunities

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Inchi Hu

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Biography