Department of Statistics

Innovative approaches propel STAT 250 to new heights at �鶹��Ƶ

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

Innovative approaches propel STAT 250 to new heights at �鶹��Ƶ 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 �鶹��Ƶ’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 �鶹��Ƶ 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 University Branding

“I want students to succeed. That's been my number one goal since taking this type of role at �鶹��Ƶ,” 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 University 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 �鶹��Ƶ’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.

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Department of Statistics

Cherry Blossom predictions are in! Will AI bloom or bust?

Nathan Kahl — Tue, 11 Mar 2025 21:05:10 +0000

Cherry Blossom predictions are in! Will AI bloom or bust? Nathan Kahl Tue, 03/11/2025 - 17:05

Students, researchers, and citizen scientists from around the world submitted predictions to �鶹��Ƶ’s annual International Cherry Blossom Prediction contest, with one new notable entrant….AI.

Jonathan Auerbach, an assistant professor in the Department of Statistics and the competition co-organizer, said, “Obviously we see AI as the calculator of the 21st century—students should be able to use this tool, but that raises the baseline, so students really have to up their game.”

The typical competitive entries use the concept of growing degree days, looking at heat accumulated by trees as measured by local temperatures. Although Auerbach said one trend he’s seeing is contestants increasingly modeling conditions in the fall preceding the bloom.

“We know that springtime temperatures are important, and as climates warm, blooms are happening earlier and earlier. But fall is also important because trees create (or form) buds in the fall," said Auerbach. "You can actually saw off a branch of a cherry tree and put it in your closet and as long as it's warm enough and in water, it'll actually bloom.”

The cherry trees at the tidal basin are expected to bloom at the end of March. Photo courtesy National Park Service.

Auerbach gets intrigued by what he called “zany” approaches, noting that people use unique data points for their predictions and that hotel prices have been used to predict bloom dates with some effectiveness. This year one entry used Google trends, and Auerbach noted there is some precedent for that—for some time Google searches for the flu predicted flu outbreaks, for example.

What most entrants foresee is that the Washington, D.C., cherry blossoms will reach peak bloom in late March. Even ChatGPT, which you could say is really branching out—is in agreement.

Check out the predictions

Topics

cherry blossoms

Statistics

Department of Statistics

Research

Chef’s KISS: Statistics PhD candidate creates new recipe for complex spatial data analysis

Teresa Donnellan — Thu, 06 Mar 2025 20:40:50 +0000

Chef’s KISS: Statistics PhD candidate creates new recipe for complex spatial data analysis Teresa Donnellan Thu, 03/06/2025 - 15:40

In This Story

Ben Seiyon Lee

Jin Hyung Lee, a PhD candidate in the Department of Statistics, in collaboration with his advisor, Assistant Professor Ben Seiyon Lee, has developed novel statistical methodology for analyzing large-scale complex data that can be up to 3,600 times faster than the current standard. He will receive the Korean International Statistical Society (KISS) 2024 Outstanding Student Paper Award for his work and present about it at the American Statistical Association’s Joint Statistical Meetings (JSM) 2025 in Nashville, Tennessee.

The primary application of Lee’s methodology is in the analysis of high-resolution satellite image data, which is inherently complex and high-dimensional. A major challenge lies in predicting values at unobserved locations within the satellite imagery. While traditional spatial modeling methods are computationally intensive and time-consuming, Lee's novel approach offers a solution that is significantly faster. This development has significant implications for various fields, such as atmospheric sciences, ecology, public health, and medical imaging.

Jin Hyung Lee, photo provided

Lee’s research focuses on the application of variational inference to massive spatial datasets using a unique machine learning algorithm. Variational inference is a method used to approximate complex probability distributions when true inference is not possible, making it a powerful method for modeling complex spatial datasets. The proposed approach significantly improves the computational efficiency of spatial data analysis, achieving speeds up to three orders of magnitude faster than existing methods while maintaining comparable accuracy.

Lee's method can be used to predict weather patterns in remote and rapidly changing environments like the Gulf Coast, southern California, and polar regions like Alaska or Antarctica. The ability to process high-dimensional data quickly and accurately is crucial for making timely and informed decisions in these regions, especially when they are at risk of geophysical hazards such as hurricanes, wildfires, or sea ice loss. Additionally, the methodology has potential applications in infectious disease modeling and public health data analysis, where rapid and accurate predictions are essential for effective intervention and policymaking.

Lee's research is part of his broader dissertation work, which focuses on developing and applying variational inference techniques to various statistical models. His goal is to simplify these methods, making them more accessible to non-experts and applicable to a wide range of data types. By doing so, Lee aims to bridge the gap between advanced statistical methodologies and practical applications, enabling more efficient and effective data analysis across different fields.

At the JSM 2025, Lee will have the opportunity to present his research to a large audience of statisticians from around the world. The conference, which attracts over 5,000 participants annually, provides a platform for sharing cutting-edge research, networking with peers, and exploring new developments in the field of statistics. Lee’s presentation will be part of a session hosted by KISS, where he and other award recipients will showcase their work and receive their awards.

Receiving the KISS Outstanding Student Paper Award is a significant milestone in Lee’s academic journey. It not only recognizes his contributions to the field but also provides him with valuable exposure and opportunities for future collaboration. As Lee continues to advance his research, his innovative methodologies and their applications are poised to make a lasting impact on the field of statistics and beyond.

Topics

Department of Statistics

Computational 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, �鶹��Ƶ 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 �鶹��Ƶ 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, �鶹��Ƶ 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 �鶹��Ƶ

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

�鶹��Ƶ 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 �鶹��Ƶ’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 �鶹��Ƶ’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 �鶹��Ƶ’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 �鶹��Ƶ 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 �鶹��Ƶ’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 �鶹��Ƶ’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 �鶹��Ƶ’s School of Public Health.

“The partnership between Inova and �鶹��Ƶ 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 �鶹��Ƶ 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 �鶹��Ƶ 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

�鶹��Ƶ Department of Statistics welcomes new faculty

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

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

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The �鶹��Ƶ 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 �鶹��Ƶ’s College of Engineering and Computing, is poised for growth.

“The research culture at �鶹��Ƶ 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

�鶹��Ƶ’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. �鶹��Ƶ 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 �鶹��Ƶ comes through. “Statistics is a thriving and fast-developing discipline in the data science era. Our department at �鶹��Ƶ 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.

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Department of Statistics

COVID-19; Research

CEC faculty research

Department of Statistics

Innovative approaches propel STAT 250 to new heights at �鶹��Ƶ

In This Story

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Cherry Blossom predictions are in! Will AI bloom or bust?

Check out the predictions

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Chef’s KISS: Statistics PhD candidate creates new recipe for complex spatial data analysis

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New scalable computing technique will make analyzing Big Data easier

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

In This Story

Delve into Statistics at George �鶹��Ƶ

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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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�鶹��Ƶ 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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