data analytics

George Mason University competes in 2023 FAA Challenge

Rena Malai — Fri, 30 Jun 2023 17:57:38 +0000

George Mason University competes in 2023 FAA Challenge Rena Malai Fri, 06/30/2023 - 13:57

In This Story

Earlier this week, students from George Mason University's data analytics engineering program competed in the 2023 FAA Data Challenge Forum, using AI strategies to navigate air travel solutions and improvements.

Led by advisor Isaac Gang, associate professor in Mason's College of Engineering and Computing (CEC), the team tied second place alongside Duke University for their proposal called Modernized Flight Risk Assessment Tool for General Aviation Pre-Flight Planning.

“Congratulations to the Mason data analytics engineering program student team - Hunter Walden, Kathleen Hill, Chi Quinn, Erick Torres - and their advisor Dr. Isaac Gang for being one of 10 finalists selected to compete for the $25,000 top prize in the 2023 FAA Data Challenge Forum,” said Bernard Schmidt, acting director of the MS data analytics engineering program within CEC.

The FAA Data Challenge focuses on the use of AI/Machine Learning and advanced analytics to address aviation-related problems and opportunities. The challenge provides an opportunity for university students to be involved in FAA’s mission, and offer creative, unique solutions for current and future challenges and serve as a pathway for students to become future transportation leaders.

The challenge this year was held at the MITRE campus in McLean, Va.

Topics

data analytics

Aviation

The future at Fuse: Data analytics

Teresa M Donnellan — Mon, 13 Feb 2023 19:50:50 +0000

The future at Fuse: Data analytics Teresa M Donnellan Mon, 02/13/2023 - 14:50

In This Story

James Baldo

Bernard Schmidt

Kenneth Comer

Mason Square is home to data analytics classes from several College of Engineering and Computing (CEC) departments. This makes it promising locale for George Mason University’s multidisciplinary Data Analytics Engineering Program. The Data Analytics Engineering master of science program is the second largest in CEC, with more than 700 students enrolled in classes across three Mason campuses. Currently, three sections of the capstone course DAEN 690 meet at Mason Square.

“Mason Square is a perfect place to hold courses, especially for individuals who are returning students or lifelong learning adults, who are government employees or contractors,” says interim program director Bernard Schmidt.

Starting with the Fall 2023 semester, Schmidt said the goal is to schedule as many sections of DAEN courses, including the capstone course, at Mason Square as is offered at the Fairfax Campus.

“This offers the greatest opportunity to not only maximize the utilization of Mason Square, but it also offers more course availability options as a way to encourage those returning students and lifelong learning adults located in Northern Virginia to apply to the DAEN program and enroll in DAEN program courses,” he says.

Students can earn an MS in Data Analytics Engineering or a certificate in Data Analytics. Both options require core classes in applied information technology, computer science, operations research, and statistics. The master’s further includes a capstone data analytics project and a concentration in a specialized technical area. Currently, students can choose from 13 different concentrations ranging from health data analytics to financial engineering.

Ken Comer, who teaches the core course OR 531 Analytics and Decision Analysis, says these students’ aim is to develop the types of tools that do the data analytic work for a client and consistently provide meaningful and useful results.

“The engineer designs. It's someone else who operates, so in order to get there, of course, the students have to learn the techniques that they're going to have [in order] to create as the engineer,” he says, citing the three techniques that can be embedded in a data analytic tool: optimization, simulation, and data analysis.

The study of data analytics is more than simply using data visualization tools, Comer says. He notes one problem with data science can be that the scientist gives the client copious insights into the client’s data, but the client may not actually feel helped in deciding.

“[Students] get a lot of tools in how to curate data, how to manipulate it, store it, move it around, secure it, and a variety of other things,” says Comer, “But this is the only class where they get to … learn how to create a useful product out of the data—something that would help somebody, that is important for somebody who has to make a decision.”

In his syllabus, Comer explains, “Every problem we will work will be focused on the allocation of resources or some other important decision that might be experienced in the course of business, industry, or government operations. Some examples are: If I’m offered additional resources, how much should I pay for them? What is the mix of production decisions that maximizes profit? What sequence should I use for a multi-step process? How much should I save to ensure an 80% chance of having a set sum at retirement?”

He further writes, “In order to receive proper credit, you will need to answer the question at hand. Your boss or your customer cannot be expected to search around your spreadsheet for the numerical answer.”

James Baldo, the program director currently on sabbatical, says he is excited that working students at Mason Square might be able to apply their newly acquired skills immediately in the workplace. “It's going to be a hub of industry partnerships down there with the university,” he predicts.

Topics

College of Engineering and Computing

Data Analytics Engineering

Campus News

Avyuct brings clarity to data

Rena Malai — Mon, 21 Feb 2022 15:54:54 +0000

Avyuct brings clarity to data Rena Malai Mon, 02/21/2022 - 10:54

Rajesh Aggarwal, adjunct professor of data analytics engineering in the College of Engineering and Computing at George Mason University, has always been fascinated by data.

“I’ve been working with data science for a long time,” says Aggarwal. “I was working on data models on my own, and decided it was an opportunity to create a company around it.”

Combining his data science training with AI and machine learning, Aggarwal started Avyuct in 2017. A Sanskrit word meaning ‘crystal clear' he says the tagline of Avyuct is exactly that—bringing crystal clear clarity to data.

“At Avyuct we build models using data science techniques and work with small satellite coordination, object detection, and anomaly detection,” Aggarwal says.

Anomaly detection---which tracks any unusual discrepancies in data—is used by a variety of industries, says Aggarwal. It’s a practical tool for targeting underlying issues within a product, concept, or project and it can be a key factor in avoiding problems and determining the best solutions.

“Anomaly detection is used by pharmaceutical companies to detect defective pills, tech companies to detect security threats, manufacturing industries to detect machine defects…it’s also an important tool to incorporate in the area of data science,” he says.

“Right now, we have a couple interesting projects,” Aggarwal says. “We are working to develop the coordination of small satellites for optimal data collection on the ground, and an anomaly detection model.”

Avyuct started as a part-time venture for Aggarwal, as he explored through data concept models in his spare time. Today, the company has grown to include full- time employees, and when space and time allows, interns.

Aggarwal plans to recruit interns during summer 2022 at Avyuct. Those interested in the opportunity can check any open positions on the Mason career portal.

For more information on Avyuct, visit https://avyuct.com.

Topics

data

data analytics

College of Engineering and Computing

Data Analytics Engineering

Confronting and combatting algorithm bias at CoNECD conference

Anonymous — Wed, 24 Mar 2021 12:19:31 +0000

Confronting and combatting algorithm bias at CoNECD conference Anonymous (not verified) Wed, 03/24/2021 - 08:19

In This Story

James Baldo

Algorithms help us make hard decisions every day. Credit card companies, job boards, and more use fast-thinking algorithms to fairly decipher who fits their chosen criteria. But sometimes, they aren’t always as fair as they appear.

At the annual Collaborative Network for Engineering and Computing Diversity (CoNECD) conference in January, the Director of the MS Data Analytics Engineering program, James Baldo, presented how algorithm biases arise and where data analysts and algorithm creators could make changes to increase fairness.

“I looked at algorithm bias from a high-level technical perspective to show the audience that yes, algorithms can be biased, but that there is more to it than a yes or no analysis,” says Baldo.

There are numerous aspects of algorithms that could hold bias. The data used for the algorithm, the core of the algorithm itself, and even the people interpreting the algorithm’s data could be where bias sneaks into the decision-making process, says Baldo.

“Algorithms use artificial intelligence and are designed by computational data that may have an inherent and unintentional bias,” he says. “Employment decisions are a good example. If an algorithm is using a sample of data to sift through applicants for a software engineering job that doesn’t include many women, the algorithm could unintentionally sift women out.”

Baldo says there have been numerous studies on algorithm bias. Still, he shared his thoughts at the conference because he felt it was important for conference attendees to understand algorithm bias’s root causes.

“One slide I presented discussed how we achieve fairness with algorithms. We need to look at the data and try and detect biases in it. We can train people who interpret the data and educate them on the best practices,” says Baldo. “I mainly wanted my presentation to raise awareness, and I wanted to take some of the mystery out of algorithms.”

From a data perspective, Baldo works to embed these best practices into the data analytics engineering program. “We are trying to embed education on the effects of algorithm bias into the MS program. Since it is an interdisciplinary program, we have to work together to figure out how to do that collaboratively.”

Baldo sees algorithm bias awareness and prevention as crucial for building the data analytics workforce. “The social fabric of this is very important, and we have a responsibility as engineers to address it, and this was a start.”

Topics

Data Analytics Engineering

data analytics

algorithm bias