Big Data

From punts to proof: George Mason adjunct helped solve the NFL鈥檚鈥�4th-down problem鈥�

Nathan Kahl — Fri, 13 Dec 2024 14:13:37 +0000

From punts to proof: George Mason adjunct helped solve the NFL鈥檚鈥�4th-down problem鈥� Nathan Kahl Fri, 12/13/2024 - 09:13

4th Down Bot

The New York Times'聽Fourth Down Bot is based on Burke's models

Read Burke's reflection

George Mason 麻豆国产 alumnus Brian Burke鈥檚 career path, from fighter pilot to ESPN data analyst, demonstrates that whether it鈥檚 a 4th-and-inches decision or a career pivot into analytics, calculated risks can pay off.

Brian Burke. Photo by Evan Cantwell/Office of 麻豆国产 Branding

In 2009, the NFL鈥檚 two best teams鈥攖he New England Patriots and Indianapolis Colts鈥攆aced off in a pivotal showdown. Late in the game, acclaimed football genius and Patriots coach Bill Belichick attempted to get a first down on a fourth-down play deep in Pats鈥� territory. Converting would win the game for the Patriots, but a failure would give the ball to the Colts in prime field position. When the Colts stopped Tom Brady鈥檚 completion inches short of the first-down marker, Peyton Manning took the field and easily won the game for the Colts.

At the time, many considered the decision a huge blunder, given the conservative, risk-averse culture among NFL coaches, who usually choose the 鈥渟afe鈥� option of punting the ball on fourth down. But today鈥檚 coaches are more likely to be aggressive in these situations, supported by an emerging focus on analytics and a column written at the time by Burke, now a Systems Engineering and Operations Research (SEOR) adjunct faculty member.

He turned a hobby of analyzing sports data into something of a big deal, founding Advanced Football Analytics in 2006, where he published his findings, in addition to consulting with NFL teams; the New York Times would sometimes print his writing and analysis. A column on Belichick鈥檚 decision turned him into an overnight stats celebrity.

鈥淚 did the math and estimated that the decision to go for it increased the Patriots鈥� chance to win the game by 9 percentage points. I wrote up a short article for the Times saying it was the right thing to do, and didn't realize it was going to be controversial,鈥� said Burke, who completed a master's degree in operations research at George Mason in 2015. 鈥淭he Times cross posted to my website and when I woke up the next morning my phone was blowing up.鈥�

The data shows that NFL coaches should be more aggressive on the 4th down than they frequently are. Photo by Wikimedia

In 2015 ESPN recruited him to be a sports data scientist, which he does from his home in Reston, Virginia. 鈥淚鈥檝e got a charmed existence, for sure,鈥� he laughed.

His career started far from campus or a football field; Burke fulfilled a childhood dream when after high school he became an F-18 Naval fighter pilot. He earned a bachelor鈥檚 in aerospace engineering from the U.S. Naval Academy and after leaving the service worked for a defense contractor.

But he couldn鈥檛 help marrying his love of sports with an analytical mind. 鈥淚 was talking with a coworker one day of this notion that 鈥榙efense wins championships鈥� in football; people say it, but is it true?鈥� Burke said. 鈥淵ou could download of data from ESPN.com in the mid 2000鈥檚 and I had a regression analysis software and decided to put in the numbers, build some models, and see what came out.鈥�

This love of data and desire to launch his second career led him to wander into the SEOR offices in the Nguyen Engineering building one day in 2014, with no appointment. Late faculty member Andy Loerch, who also served in the military, asked if he could help Burke. The two bonded.

鈥淔rom that moment on, I thought, 鈥楾his is a great place for me,鈥欌€� Burke said.

Loerch inspired Burke to enroll at George Mason and became his mentor. When he finished his program, Loerch and Ariela Sofer, then department chair, encouraged him to create a class on sports analytics as an adjunct.

Burke loves the role. 鈥淚 really enjoy it and enjoy sharing what I know. You don鈥檛 know something unless you can teach it, so it forces me to nail down the basics and examine assumptions and go back to first principles.鈥� He said he appreciates the opportunity to connect with fellow SEOR faculty members. 鈥淭he creative friction when we鈥檙e together gives me great ideas.鈥�

聽

Topics

Big Data

data analytics

Sports Analytics

Systems Engineering and Operations Research

Adjunct Faculty

Alumni

New scalable computing technique will make analyzing Big Data easier聽

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

New scalable computing technique will make analyzing Big Data easier聽 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 麻豆国产 statistics professor Lily Wang hopes to change that.聽聽

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 麻豆国产, 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.聽聽

鈥淚n the past, we knew there were insights hidden in the data, but due to computing limitations, we couldn鈥檛 access them,鈥� said Lily Wang. 鈥淣ow, 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.鈥澛�

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

鈥淭his 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,鈥� said Wang. Large datasets often exhibit heterogeneous and dynamic patterns, requiring new approaches to capture meaningful relationships.聽

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.聽

鈥淏oth datasets have been challenging to analyze in the past due to their size and complexity,鈥� explained Wang. 鈥淏ut through scalable and distributed learning techniques, we鈥檙e now able to handle large-scale heterogeneous data across the entire United States.鈥澛�

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.聽聽

鈥淵ou 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,鈥� Wang said. 鈥淭his method allows us to analyze millions of data points simultaneously without the need for supercomputers.鈥澛�

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.聽聽

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).聽

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.鈥澛�

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

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

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

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

In This Story

Abolfazl Safikhani

George Mason 麻豆国产 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.聽

Safikhani earned bachelor鈥檚 and master鈥檚 degrees in mathematics before earning a doctorate in statistics.聽

鈥淚 decided to do a PhD in statistics because throughout the master鈥檚 I had become more and more interested in connecting real world problems to data. And I'm very happy that I made that decision,鈥� he said.聽

Abolfazl Safikhani

Along with a former colleague at the 麻豆国产 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.聽

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鈥檚 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.聽

Another of Safikhani鈥檚 projects considers land use and value specifically concerning the price of residential real estate. Safikhani鈥檚 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 鈥渜uant guy,鈥� Safikhani said, he thought there could be a better way: applying the idea of transfer learning.聽

鈥淭he 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,鈥� Safikhani explained. 鈥淪o, 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.鈥澛�

He continued, 鈥淚f 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.鈥澛�

Safikhani is working with a colleague from the 麻豆国产 of California 鈥� 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.聽聽

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鈥揹isrupting tools.聽

鈥淚t 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,鈥� he said. 鈥淎nd 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.鈥澛�

Topics

Applied Statistics

Department of Statistics

Statistics Faculty

Computational statistics

Big Data

big data analytics

real estate entrepreneurship

Artificial Intelligence

Research

Big data may lead to safer roadways, lower emissions

Teresa Donnellan — Mon, 23 Oct 2023 16:26:19 +0000

Big data may lead to safer roadways, lower emissions Teresa Donnellan Mon, 10/23/2023 - 12:26

In This Story

Shanjiang Zhu

Anand Vidyashankar

Once, transportation officials made decisions based on household surveys performed roughly once per decade, which asked selected households to record their travel behavior on a given day. With the advent of smartphones, similar data became available roughly every few minutes. Now, with an increasing number of connected vehicles on the road, that data is available in nearly real time. CEIE professor Shanjiang Zhu is embracing this shift, exploring the capabilities of researchers with this massive amount of data.聽

With funding from the Virginia Department of Transportation (VDOT), Zhu and his research team, Anand N. Vidyashankar from the department of statistics and Chenfeng Xiong from the Civil and Environmental Engineering department at Villanova 麻豆国产, will reconcile the travel data from three different sources鈥攕urveys, smartphones, and connected vehicles鈥攊nto invaluable travel information.聽

"In the past, we tried to understand travel behavior, which is critical for future investment decisions and also transportation policy, based on survey data,鈥� Zhu explained. 鈥淏ased on that, you understand, on average, where people have traveled, in what mode, with whom, and spent how much time there, uh what is the purpose for the trip, etc. Using that information, you can develop a model that basically can predict future scenario, like how congested the network could be in 2040; and that drives all the investment decisions and policy debates.鈥� This method introduces problems of timeliness, as it can skip major events such as the COVID-19 pandemic, and human error, as people would not necessarily remember every detail of their travel on a given day.聽

The introduction of widespread smartphone use about ten years ago made the available data much denser, said Zhu, resulting in about one data point every three to five minutes. Each time a person鈥檚 smartphone app calls for location service, their location is automatically registered. Nevertheless, this method introduced a bias problem, as not everyone owns a smartphone and not everyone uses them often. 聽

About a year ago, Zhu鈥檚 team won a competition hosted by VDOT to make the best possible use of connected vehicle data, basically newer vehicles like those with an 鈥淪OS鈥� button installed. One drawback to the data currently is connected vehicles currently make up a relatively small share of vehicles on the road.聽

鈥淏ut we have ways to make corrections from a statistical perspective, and then this gives you a much more accurate picture of traffic on the road,鈥� said Zhu, adding 鈥淥n average, it's one data point every three seconds. With such data, the accuracy and timeliness of travel demand models could be greatly improved.鈥� Zhu noted his colleague Vidyashankar will be reviewing the data fusion to ensure a rigorous statistical approach.聽

The new data also opens the door for new safety studies, Zhu said, adding safety studies are currently based mainly on police reports after an accident has occurred. By using alternate data, such as how often a car鈥檚 brake deceleration rate exceeds a certain threshold or how hard a driver turns the steering wheel, dangerous locations might be addressed before an accident occurs. Zhu is interested in exploring the topic further using the dataset resulting from his current project.聽聽聽

Zhu foresees data from connected vehicles becoming increasingly important as more and more people adopt the technology. He said, 鈥淣ow we are investing in the methodology part and seeing how we can make this connection more productive, to improve the driving environment, to make our roads safer, to make the driving experience better, and also to reduce our energy consumption and emissions."聽

Topics

Department of Civil Environmental and Infrastructure Engineering (CEIE)

transportation engineering

Big Data

big data analytics

Transportation Policy Operations and Logistics

Research

CEC faculty research

NSF CAREER Award funds faculty member鈥檚 mission to expand cloud鈥檚 capabilities

Anonymous — Tue, 15 Jun 2021 13:03:49 +0000

NSF CAREER Award funds faculty member鈥檚 mission to expand cloud鈥檚 capabilities Anonymous (not verified) Tue, 06/15/2021 - 09:03

In This Story

Yue Cheng

Social media platforms and dozens of other web and mobile apps generate countless聽amounts of data. And with a constant flux of data聽comes a continuous need for high-performance and highly scalable ways to store and access this data. Yue Cheng, an assistant professor in the Department of Computer Science, won a National Science Foundation CAREER Award to meet the needs of the big data industry.

Cheng and his research team will use the nearly $580,000 award 鈥淗arnessing Serverless Functions to Build Highly Elastic Cloud Storage Infrastructure鈥� to develop a scalable and cost-effective cloud computing storage system using serverless computing.

鈥淲e are researching new ways of using emerging serverless computing capabilities to build a high-performance cloud storage infrastructure,鈥� says Cheng. 鈥淪erverless computing is the next generation of cloud.鈥澛�

Cheng鈥檚 new infrastructure, InfiniStore, will reduce the cost and need for manual storage management, says Cheng.聽

Conventional cloud computing uses virtual machines (VM) that are rented out to cloud users. 鈥淭he user pays for whatever resources they need, and while it gives great flexibility, the VM-based model isn鈥檛 the most effective nor elastic. Cloud service providers still charge for space and capacity that is reserved but not in use. Cloud users also must manually start or stop virtual machines to best suit their needs.鈥澛�

Serverless computing is the solution to cost and resource issues under the conventional VM model. Because of its inherent elasticity, it can scale up and down autonomously. Since it doesn鈥檛 require a space reservation, like with VMs, Cheng can implement a new cloud storage pricing model based on usage.聽

鈥淟et鈥檚 say at one point in time there are no requests, and no cloud function is launched. And a later point in time, there is a huge spike in data access requests. The elastic storage would automatically trigger thousands of cloud functions, each serving as a tiny little data storage unit, to serve this spike of data access requests, and the cloud user is only charged when the data stored in cloud functions are accessed,鈥� says Cheng.聽

Cheng鈥檚 project includes collaborations with industry leaders like NetApp and IBM Research and fellow researchers at Mason. In addition to the development of InfiniStore, the grant also consists of an educational plan centered around teaching serverless computing and increasing access for undergraduate students to participate in leading research.聽

鈥淲e will design a new cloud service called InfiniCloud, which is a digital and interactive notebook service that allows students and educators to implement, write, and deploy serial and parallel Python programs at any scale,鈥� says Cheng. 鈥淲e will also start an outreach plan to promote diversity in computing and engage undergraduate students in computing research.鈥澛�

Cheng stresses the importance of advancing computing capabilities and allowing students and educators access too. 鈥淭his project will support big data storage, which is extremely important, but I am also committed to introducing undergrad students to this state-of-the-art field in cloud computing,鈥� says Cheng.聽

鈥淵ue鈥檚 NSF CAREER Award comes at a time of nearly universal reliance on cloud services to support critical enterprise functions. His research will realize significant improvements in the ways that cloud resources are allocated to end-users, through innovations in cloud storage technology,鈥� says Department of Computer Science Chair David Rosenblum. 鈥淎nd the educational plan he has devised will give Mason students significant hands-on learning opportunities with this important area of computing technology.鈥澛�

聽

Topics

cloud computing

Big Data

Awards

Mason offers new Data Analytics Credential to prepare students for careers in big data

Anonymous — Thu, 11 Mar 2021 16:47:33 +0000

Mason offers new Data Analytics Credential to prepare students for careers in big data Anonymous (not verified) Thu, 03/11/2021 - 11:47

In This Story

Liza Wilson Durant

Brett Hunter

George Mason 麻豆国产 is offering a new Data Analytics Credential to help undergraduates hone their skills in handling big data.

The new credential is the second one offered in partnership with the Greater Washington Partnership鈥檚 Capital CoLAB. Mason launched the Digital Technology Credential in 2019 to support additional skills in data analysis, visualization, and cybersecurity for non-engineers. More than 150 students are currently enrolled in that program.

鈥淭he new Data Analytics Credential will enable hundreds of Mason students to offer their specialized data analytics skills to employers who are seeking to meet the talent shortfall in data science and analytics,鈥� says Liza Wilson Durant, associate dean for strategic initiatives and community engagement for the Volgenau School of Engineering.聽

Liza Wilson Durant says the new credential is an exciting showcase of our industry partners engaging with students from multiple disciplines. Photo by Evan Cantwell.

The credential focuses on data storage and management and will make students more marketable, including for jobs as data analysts and data scientists, says Brett Hunter, associate chair of the Department of Statistics.

The credential is designed for undergraduates in statistics, computing, information technology, and data science who want to acquire聽the data analytics skills needed by high-profile employers in the metropolitan areas of Baltimore, Washington, D.C., and Richmond.

Developed with the Greater Washington Partnership鈥檚 Capital CoLAB, the credential equips students across disciplines with the specialized data analytics聽skills that the Greater Washington Partnership employers have specified to be most important to their operations. The Capital CoLAB (Collaborative of Leaders in Academia and Business) is an action-oriented partnership of employers and academic institutions that executes initiatives to develop the talent needed for the jobs of today and tomorrow.

By enrolling in either the Digital Technology Credential or Data Analytics Credential programs, students will have direct access to opportunities and engagement with some of the largest employers in the region, including Amazon, Capital One, and Northrop Grumman.

鈥淚t is exciting to see our industry partners directly engaged with our students and reinforcing the demand for the skills they are acquiring by offering them internships and other experiential learning opportunities,鈥� says Durant.

Many students in statistics, computational data science, computer science, and information science and technology will only need to take one to three additional undergraduate courses to earn the credential, Hunter says. They can use some of their elective courses to do that.

鈥淭he credential is not another degree, but essentially a badge you can put on your LinkedIn profile or electronic resume,鈥� he says. 鈥淚 think most statistics undergraduates will take advantage of the opportunity to communicate their skills in this new way.鈥澛�

The badge associated with the Data Analytics Credential also represents an innovation for students and employers. 鈥淢icro-credentials like the Data Analytics badge reflect the 鈥榥ew currency鈥� in denoting skill achievement and helps our students move more easily from college to career,鈥� says Marc Austin, Executive Director of Professional Development and Academic Ventures who leads Mason鈥檚 Continuing and Professional Development unit which administers the new digital badge.

More than 50 students have already enrolled in the new Data Analytics Credential program.

Students who are working towards earning the credential will receive several exclusive benefits from area companies, including:

Access to a student portal with paid internship and event opportunities.
Invitations to participate in annual internship fairs with employers looking to recruit students.
Access to professional development webinars and other ad-hoc opportunities.

Mason students can learn more and enroll in the new Data Analytics Credential here.

Topics

data analytics

Digital Technology Credential Program

Biostatistics

Big Data

Statistics seniors receive award at American Statistical Association national data challenge

Martha Bushong — Thu, 21 Jan 2021 12:09:10 +0000

Statistics seniors receive award at American Statistical Association national data challenge Martha Bushong Thu, 01/21/2021 - 07:09

Topics

Volgenau School of Engineering

Four seniors majoring in statistics鈥撯€�Emily Litzenberg, Kate Lang, Nate Mulugeta, and Shannon Connor鈥撯€搑eceived an honorable mention for the best use of external data at the American Statistical Association鈥檚 (ASA) Fall Data Challenge 2020.

For the national competition, called Get Out the Vote,聽teams of high school and undergraduate students submitted their recommendations on聽how to increase voter turnout聽using聽voting behavior聽data from聽the Census Bureau and the Bureau of Labor Statistics. High school and undergraduate students were judged separately.

Mason鈥檚 team looked at the data from 2000 to 2016 to see if there was an increase in registered voters who didn鈥檛 vote because they didn鈥檛 like either candidate in the presidential race.聽鈥淲e found there was a rise in people who didn鈥檛 vote over the years because of dislike for the candidates,鈥� Connor says.

The group was then tasked with coming up with a way to improve voter turnout, and they suggested ranked-choice voting in which voters rank candidates by preference on their ballots, she says.

Connor says the project gave the students a chance to work closely on a statistical analysis. 鈥�I learned a lot about how to go from start to finish with a statistical group project.鈥�

Elizabeth Johnson, an associate professor of statistics and the group鈥檚 sponsor, says, 鈥淭his national competition allows students to work in teams to investigate current issues using real data, They experience the whole research process of creating a research question before collecting, analyzing, and reporting on data. I am very proud of their accomplishment in a very competitive field."聽

Four senior students from the Department of Statistics received an honorable mention for the best use of external data at the American Statistical Association鈥檚 (ASA) Fall Data Challenge 2020. Pictured from the left are聽Shannon Connor, Nate Mulugeta, and Emily Litzenberg. Kate Lang is not pictured.

Photo credit:

Photos provided

New Screening Tool Can Tell the Difference Between COVID-19 and the Flu, and It鈥檚 Evidence-Based and Free

dhawkin — Thu, 02 Jul 2020 20:51:31 +0000

New Screening Tool Can Tell the Difference Between COVID-19 and the Flu, and It鈥檚 Evidence-Based and Free dhawkin Thu, 07/02/2020 - 16:51

Data analytics work finds keys to music, murder

Martha Bushong — Wed, 06 Sep 2017 17:22:24 +0000

Data analytics work finds keys to music, murder Martha Bushong Wed, 09/06/2017 - 13:22

Data Analytics Adds Classes in Arlington

dallen21 — Mon, 03 Nov 2014 20:12:26 +0000

Data Analytics Adds Classes in Arlington dallen21 Mon, 11/03/2014 - 15:12

聽

To increase access and availability for our students, we will be offering classes at Mason's Arlington Campus for students enrolled in the聽MS in Data Analytics Engineering. Beginning in January 2015 we will offer AIT 580, OR 531, and STAT 515 in Arlington. The campus is located on Fairfax Blvd. and is convenient to the Metro's Virginia Square (GMU) station on the Orange and Silver Lines. To register for classes聽visit our website.聽

Big Data

From punts to proof: George Mason adjunct helped solve the NFL鈥檚 鈥�4th-down problem鈥�

4th Down Bot

Read Burke's reflection

Topics

New scalable computing technique will make analyzing Big Data easier聽

In This Story

Topics

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

In This Story

Topics

Big data may lead to safer roadways, lower emissions

In This Story

Topics

NSF CAREER Award funds faculty member鈥檚 mission to expand cloud鈥檚 capabilities

In This Story

Topics

Mason offers new Data Analytics Credential to prepare students for careers in big data

In This Story

Topics

Statistics seniors receive award at American Statistical Association national data challenge

Topics

New Screening Tool Can Tell the Difference Between COVID-19 and the Flu, and It鈥檚 Evidence-Based and Free

Data analytics work finds keys to music, murder

Data Analytics Adds Classes in Arlington

From punts to proof: George Mason adjunct helped solve the NFL鈥檚鈥�4th-down problem鈥�