Ozlem Garibay Archives | �鶹ԭ�� News

�鶹ԭ�� Rises to No. 36 Best National Universities Ranking by Washington Monthly for 2023

Fri, 15 Sep 2023 20:21:41 +0000

�鶹ԭ�� again is recognized among the nation’s most impactful institutions for students and communities — advancing into the top 40 Best National Universities and the top 20 for social mobility in Washington Monthly’s annual rankings released today.

Washington Monthly’s rankings are based on what institutions “do for our country” — contributions to the public good in three broad categories: social mobility, research and promoting public service. All of those categories align well with �鶹ԭ��’s mission and commitments to unleashing potential by empowering all students to succeed, and serving as a talent pipeline to industries critical to our region and state.

�鶹ԭ�� rose 11 spots from last year to rank No. 36 among Best National Universities. �鶹ԭ�� also ranks No. 16 nationally among peer public universities, advancing 26 spots in the past two years.

�鶹ԭ�� also moved into the top 20 among universities nationally for social mobility and advanced to No. 9 for Best Bang for Your Buck School in the Southeast.

“At �鶹ԭ��, our mission is to unleash potential through access to high-quality education and the opportunity to earn success,” �鶹ԭ�� President Alexander N. Cartwright says. “In doing so, we are not just transforming individual lives, but uplifting our entire community, driving success and prosperity for generations to come.”

In the social mobility category, �鶹ԭ�� jumped to No. 20, up 17 spots from last year and 38 from 2021. �鶹ԭ�� also rose 14 spots in the past two years to move into the top ten among Best Bang for Your Buck schools in the Southeast for the first time.

“We’re proud to see �鶹ԭ�� climb in the social mobility rankings,” says �鶹ԭ��’s Senior Vice President for Student Success Paul Dosal. “We believe firmly in the transformative power of higher education. When we admit students, their success is our top priority as we support them through graduation and help them pursue high-impact careers.��For the thousands and thousands of limited-income, first-generation students and underrepresented minorities we serve, that means we give them the chance to unleash their potential and improve their lives.”

As a metropolitan research university that empowers faculty to find innovative solutions to local and global challenges, �鶹ԭ�� ranks No. 105 for Research — eight spots higher than 2021. �鶹ԭ��’s dedicated efforts to supporting our community and engaging students and alumni in opportunities to give back is recognized with a No. 163 ranking for Service.

Prioritizing Student Success to Drive Social Mobility

�鶹ԭ��’s efforts to accelerate student success and enhance well-being prepare our graduates to lead enriched and fulfilling lives, and to have the knowledge, skills, and aptitudes that align with the workforce of the future.

The number of students who received Pell Grants and graduated was also considered, with �鶹ԭ�� producing 5,166 in the last year. �鶹ԭ�� is the second-highest producer of graduates with Pell Grants across all institutions Washington Monthly ranked this year.

“A higher education degree is the ticket to a better life,” Dosal says. “My life changed for the better because I earned a college degree. I am proud that we can give these same opportunities to other students, who find �鶹ԭ�� is an institution that cares deeply and sincerely about promoting social mobility and economic prosperity for our state and country.”

Investing in Critical Research to Benefit Humankind

�鶹ԭ�� aspires to be the No. 1 provider of diverse talent and fuel the nation’s talent pipeline across critical areas, many of which are STEM fields that rely on evolving research. Washington Monthly considers the number of science and engineering Ph.D.s awarded by institutions, and �鶹ԭ�� awarded 217 in the last year.

�鶹ԭ�� alumni work for major companies such as Lockheed Martin and Siemens, which are key partners that provide experiential learning experiences for our students. Recently engineering alum Raghu Kancherla ’19PhD received the Dilip R. Ballal Early Career Engineering Award by the American Society of Mechanical Engineers’ International Gas Turbine Institute. Kancherla, in part, is honored for developments he made at �鶹ԭ�� that have been used in academia, industry and government agencies to advance supercritical CO2 combustion technology.

His mentor, Professor Subith Vasu, also received the award in 2017 — which highlights how students benefit from the expertise and guidance of �鶹ԭ��’s world-renowned faculty to make their own impact on the world.

Research funding also contributes to the overall ranking. Among the innovations developed at �鶹ԭ�� are:

An artificial intelligence (AI) model, developed by students, researchers and faculty members Ozlem Garibay ’01MS ’08PhD and Sudipta Seal, that accelerates the development of life-saving medicines that otherwise take billions of dollars and decades to produce.
The first large-scale and multicolor alternative to pigment-based colorants, which can contribute to energy-saving efforts — reducing costs and helping to reduce global warming. This novel technology was created by Professor Debashis Chanda.
Engineered tissue with human cells to study how to fight diseases transmitted by mosquitos — the world’s deadliest animal. This was created by College of Medicine researchers Bradley Jay Willenberg with Mollie Jewett working with a University of Tennessee faculty member.

Dedicated Efforts to Serving Others

�鶹ԭ�� fosters a culture of innovation, public service and collaboration, and aims to be a model for civil discourse.

Students’ enrollment in service-oriented majors, such as social work and public administration, was also considered — with about one in five �鶹ԭ�� students pursuing a field with a focus on helping others. Institutions that received the Carnegie Community Engagement Classification — which �鶹ԭ�� received in 2015 — also received points toward their ranking. �鶹ԭ�� received six points for voter engagement, the highest number institutions received for this factor.

Students’ engagement in ROTC programs contributed to the ranking. �鶹ԭ�� is home to the Fighting Knights Battalion, an Army ROTC program that hosted its 15th annual Iron Knight Challenge, which recruits students to the program, this spring. Air Force Reserve Officer Training Corps Detachment 159, which is also known as the Flying Knights and celebrated 50 years at �鶹ԭ�� last year, is also a part of Knight Nation.

The number of alumni active with the Americorps and Peace Corps contribute to Washington Monthly’s ranking — with 229 Knights engaged in volunteer work nationally and internationally.

�鶹ԭ�� Researchers Are Advancing AI-assisted Drug Discovery

Beatriz Nina Ribeiro Oliveira — Wed, 02 Aug 2023 13:31:11 +0000

�鶹ԭ�� researchers are advancing AI-assisted drug screening technology with a new method that not only improves their own model’s predictive ability but also that of seven other state-of-the-art models.

This new method can be beneficial in accelerating the development of life-saving medicines that otherwise take billions of dollars and decades of time to produce.

The results were published recently in the journal Briefings in Bioinformatics.

Their new model, BindingSite-AugmentedDTA, uses their previously reported model, AttentionsiteDTI, as the first step of a two-step prediction approach.

“A unique aspect of our approach is that it can be easily integrated with any deep learning-based prediction model, which allows for improved performance compared to using the prediction models alone,” says study co-author Ozlem Garibay ’01MS ’08PhD, an assistant professor in the Department of Industrial Engineering and Management Systems.

“By integrating our approach with other state-of-the-art deep learning-based drug-target-affinity prediction models, we have shown significant improvement in prediction performance across multiple metrics,” Garibay says. “This integration makes it a powerful tool for drug discovery research.”

How it Works

The researcher’s AttentionsiteDTI model is a classification model specifically designed to determine two key aspects. First, it identifies whether a drug compound binds with a target protein, and second, it determines the specific binding site on the protein where the drug compound interacts.

Their improved BindingSite-AugmentedDTA model follows a two-step prediction approach in which the first step uses the AttentionsiteDTI model to identify the specific binding site on the protein.

In the second step, a regression prediction model is integrated to estimate the binding strength, or affinity, between the drug molecule and the identified protein binding site.

Garibay says that this combined approach enhances the accuracy of drug target affinity predictions by reducing the search space of potential-binding sites of the protein in the first step, thus making the binding affinity prediction more efficient and accurate.

The researchers validated the prediction power of their model through in-vitro experiments and used it to successfully predict binding affinity values between FDA-approved drugs and key proteins of SARS-CoV-2.

They also showed improved performance of state-of-the-art predictive models, such as GraphDTA, DGraphtDTA and DepGS, in finding the most probable binding sites of proteins when AttentionSiteDTI was included in the models compared to when it wasn’t.

Next Steps

The researchers are working on a Python package that includes most of the drug-target interaction and drug-target affinity models and datasets, which is highly customizable.

“This will enable further high-quality research in the community by providing a convenient tool for researchers to develop and evaluate their models,” Garibay says.

They also plan to make their largest model available online for inference.

“This will facilitate fast drug screening for biology and pharmaceutical researchers with limited computer science knowledge — allowing them to easily predict drug-target binding affinities and identify potential drug candidates,” Garibay says. “This can potentially accelerate the drug discovery process and lead to the development of new treatments for various diseases.”

About the Team

Ozlem Garibay is an assistant professor of Industrial Engineering and Management Systems, part of �鶹ԭ��’s College of Engineering and Computer Science, where she directs the Human-Centered Artificial Intelligence Research Lab. Prior to that, she served as the director of research technology. Her areas of research are big data, social media analysis, social cybersecurity, artificial social intelligence, human-machine teams, social and economic networks, network science, STEM education analytics, higher education economic impact and engagement, artificial intelligence, evolutionary computation and complex systems. She earned her master’s and doctorate in computer science from �鶹ԭ��.

Mehdi Yazdan-Jahromi is a third-year doctoral student in computer science at �鶹ԭ��. His current research interests include computer vision, drug–target interaction and algorithmic fairness.

Niloofar Yousefi’17PhD is a postdoctoral research associate at �鶹ԭ��’s Complex Adaptive Systems Laboratory in the College of Engineering and Computer Science. Her research areas include machine learning, artificial intelligence and statistical learning theory to develop data analytics solutions with more transparency and explainability.

Collaborators:

Aida Tayebi is a third-year doctoral student at �鶹ԭ��. Her current research interests include algorithmic fairness and bias mitigation techniques in DTI.

Elayaraja Kolanthai is a postdoctoral research associate at the �鶹ԭ�� Department of Materials Science and Engineering. His current research interests include the development of nanoparticles, layer-by-layer antimicrobial/antiviral nanoparticle coatings, polymer composites for tissue engineering, and gene/drug delivery methodologies.

Craig Neal’14 ‘16MS ‘21PhD is a postdoctoral research associate at the �鶹ԭ�� Department of Materials Science and Engineering. His current research interests include wet chemical synthesis and surface engineering of nanoparticles for biomedical applications and electrochemical devices, and electroanalysis of nanomaterials and bio-nano interactions.

Sudipta Seal is currently the chair of the Department of Materials Science and Engineering at �鶹ԭ�� as well as a Pegasus Professor and a University Distinguished Professor. He joined the Advanced Materials Processing and Analysis Center (AMPAC) at �鶹ԭ�� in 1997. He has been consistently productive in research, instruction and service to �鶹ԭ�� since 1998. He has served as the nano initiative coordinator for the vice president of research and commercialization. He served as the director of AMPAC and the NanoScience Technology Center from 2009 to 2017.

Research Study: BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing

Researchers Identify 6 Challenges Humans Face with Artificial Intelligence

Beatriz Nina Ribeiro Oliveira — Mon, 27 Mar 2023 14:20:02 +0000

A �鶹ԭ�� professor and 26 other researchers have published a study identifying the challenges humans must overcome to ensure that artificial intelligence is reliable, safe, trustworthy and compatible with human values.

The study, “Six Human-Centered Artificial Intelligence Grand Challenges,” was published in the International Journal of Human-Computer Interaction.

Ozlem Garibay ’01MS ’08PhD, an assistant professor in �鶹ԭ��’s Department of Industrial Engineering and Management Systems, was the lead researcher for the study. She says that the technology has become more prominent in many aspects of our lives, but it also has brought about many challenges that must be studied.

For instance, the coming widespread integration of artificial intelligence could significantly impact human life in ways that are not yet fully understood, says Garibay, who works on AI applications in material and drug design and discovery, and how AI impacts social systems.

The six challenges Garibay and the team of researchers identified are:

Challenge 1, Human Well-Being: AI should be able to discover the implementation opportunities for it to benefit humans’ well-being. It should also be considerate to support the user’s well-being when interacting with AI.

Challenge 2, Responsible: Responsible AI refers to the concept of prioritizing human and societal well-being across the AI lifecycle. This ensures that the potential benefits of AI are leveraged in a manner that aligns with human values and priorities, while also mitigating the risk of unintended consequences or ethical breaches.

Challenge 3, Privacy: The collection, use and dissemination of data in AI systems should be carefully considered to ensure protection of individuals’ privacy and prevent the harmful use against individuals or groups.

Challenge 4, Design: Human-centered design principles for AI systems should use a framework that can inform practitioners. This framework would distinguish between AI with extremely low risk, AI with no special measures needed, AI with extremely high risks, and AI that should not be allowed.

Challenge 5, Governance and Oversight: A governance framework that considers the entire AI lifecycle from conception to development to deployment is needed.

Challenge 6, Human-AI interaction: To foster an ethical and equitable relationship between humans and AI systems, it is imperative that interactions be predicated upon the fundamental principle of respecting the cognitive capacities of humans. Specifically, humans must maintain complete control over and responsibility for the behavior and outcomes of AI systems.

The study, which was conducted over 20 months, comprises the views of 26 international experts who have extensive backgrounds in AI technology.

“These challenges call for the creation of human-centered artificial intelligence technologies that prioritize ethicality, fairness and the enhancement of human well-being,” Garibay says. “The challenges urge the adoption of a human-centered approach that includes responsible design, privacy protection, adherence to human-centered design principles, appropriate governance and oversight, and respectful interaction with human cognitive capacities.”

Overall, these challenges are a call to action for the scientific community to develop and implement artificial intelligence technologies that prioritize and benefit humanity, she says.

The group of 26 experts include National Academy of Engineering members and researchers from North America, Europe and Asia who have broad experiences across academia, industry and government. The group also has extensive educational backgrounds in areas ranging from computer science and engineering to psychology and medicine.

Their work also will be featured in a chapter in the book, Human-Computer Interaction: Foundations, Methods, Technologies, and Applications.

Five �鶹ԭ�� faculty members co-authored the study:

Gavriel Salvendy, a university distinguished professor in �鶹ԭ��’s College of Engineering and Computer Science and the founding president of the Academy of Science, Engineering and Medicine of Florida.

Waldemar Karwowski, a professor and chair of the Department of Industrial Engineering and Management Systems and executive director of the Institute for Advanced Systems Engineering at the �鶹ԭ��.

Steve Fiore, director of the Cognitive Sciences Laboratory and professor with �鶹ԭ��’s cognitive sciences program in the Department of Philosophy and Institute for Simulation & Training.

Ivan Garibay, an associate professor in industrial engineering and management systems and director of the �鶹ԭ�� Artificial Intelligence and Big Data Initiative.

Joe Kider, an associate professor at the IST, School of Modeling, Simulation and Training and a co-director of the SENSEable Design Laboratory.

AI-based Screening Method Could Boost Speed of New Drug Discovery

Robert Wells — Thu, 22 Sep 2022 19:24:58 +0000

Developing life-saving medicines can take billions of dollars and decades of time, but �鶹ԭ�� researchers are aiming to speed up this process with a new artificial intelligence-based drug screening process they’ve developed.

Using a method that models drug and target protein interactions using natural language processing techniques, the researchers achieved up to 97% accuracy in identifying promising drug candidates. The results were published recently in the journal Briefings in Bioinformatics.

The technique represents drug–protein interactions through words for each protein binding site and uses deep learning to extract the features that govern the complex interactions between the two.

“With AI becoming more available, this has become something that AI can tackle,” says study co-author Ozlem Garibay, an assistant professor in �鶹ԭ��’s Department of Industrial Engineering and Management Systems. “You can try out so many variations of proteins and drug interactions and find out which are more likely to bind or not.”

The model they’ve developed, known as AttentionSiteDTI, is the first to be interpretable using the language of protein binding sites.

The work is important because it will help drug designers identify critical protein binding sites along with their functional properties, which is key to determining if a drug will be effective.

The researchers made the achievement by devising a self-attention mechanism that makes the model learn which parts of the protein interact with the drug compounds, while achieving state-of-the-art prediction performance.

The mechanism’s self-attention ability works by selectively focusing on the most relevant parts of the protein.

The researchers validated their model using in-lab experiments that measured binding interactions between compounds and proteins and then compared the results with the ones their model computationally predicted. As drugs to treat COVID are still of interest, the experiments also included testing and validating drug compounds that would bind to a spike protein of the SARS-CoV2 virus.

Garibay says the high agreement between the lab results and the computational predictions illustrates the potential of AttentionSiteDTI to pre-screen potentially effective drug compounds and accelerate the exploration of new medicines and the repurposing of existing ones.

“This high impact research was only possible due to interdisciplinary collaboration between materials engineering and AI/machine learning and computer scientists to address COVID related discovery” says Sudipta Seal, study co-author and chair of �鶹ԭ��’s Department of Materials Science and Engineering.

Mehdi Yazdani-Jahromi, a doctoral student in �鶹ԭ��’s College of Engineering and Computer Science and the study’s lead author, says the work is introducing a new direction in drug pre-screening.

“This enables researchers to use AI to identify drugs more accurately to respond quickly to new diseases, Yazdani-Jahromi says. “This method also allows the researchers to identify the best binding site of a virus’s protein to focus on in drug design.”

“The next step of our research is going to be designing novel drugs using the power of AI,” he says. “This naturally can be the next step to be prepared for a pandemic.”

The research was funded by �鶹ԭ��’s internal AI and big data seed funding program.

Co-authors of the study also included Niloofar Yousefi, a postdoctoral research associate in �鶹ԭ��’s Complex Adaptive Systems Laboratory in �鶹ԭ��’s College of Engineering and Computer Science; Aida Tayebi, a doctoral student in �鶹ԭ��’s Department of Industrial Engineering and Management Systems; Elayaraja Kolanthai, a postdoctoral research associate in �鶹ԭ��’s Department of Materials Science and Engineering; and Craig Neal, a postdoctoral research associate in �鶹ԭ��’s Department of Materials Science and Engineering.

Garibay received her doctorate in computer science from �鶹ԭ�� and joined �鶹ԭ��’s Department of Industrial Engineering and Management Systems, part of the College of Engineering and Computer Science, in 2020. Previously, she worked for 16 years in information technology for �鶹ԭ��’s Office of Research.

Article title: AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification

Awards Presented to Artificial Intelligence, Big Data Projects for COVID-19 Research

Zenaida Gonzalez Kotala — Mon, 31 Aug 2020 03:33:35 +0000

Five research teams using Artificial Intelligence and Big Data have been awarded a total of $185,000 to conduct COVID-19-related research.

Establishing the awards was the first act of �鶹ԭ��’s new Artificial Intelligence & Big Data Initiative announced this summer. The intent of the program is to seed the development of research projects that use these tools to answer big research questions. The money will help to generate preliminary results that university leaders hope will lead to external research funding.

The competition initially announced that $175,000 would be available to interdisciplinary groups that offered innovative projects. But there was so much interest that the Office of Research, College of Engineering and Computer Science, College of Sciences, and College of Business, who are co-sponsoring the grants, decided to add another $10,000 to the fund. A total of 23 teams put forth competitive applications.

“There were many really interesting and innovative projects that hold a lot of promise in the mix,” says Debra Rienhart, associate vice president for Research and Scholarship. “It was very difficult to select just a handful. We can’t wait to see where these projects go.”

The applications were vetted by a selection team made up of faculty, researchers and administrators with knowledge about AI and Big Data.

The selected teams are tackling a variety of challenges from perception of COVID-19 and new technology to new sensors that may be able to detect and potentially learn how to defeat the virus.

The projects are:

COVID-19 and Medical AI Adoption: The Role of Technology Receptivity

The COVID-19 pandemic has changed many aspects of everyday life. The researchers hope to find out if the pandemic has changed consumer adoption of AI and predictive algorithms in the medical field. They also seek to identify the optimal human-AI interface.

Team leaders: Xin He, Lin Boldt and Sona Klucarova, College of Business

Amount Award: $28,000

Artificial Intelligence-assisted discovery of complex polymeric nanofilms designed to trap and kill the COVID-19 virus for personal protection equipment applications

Quickly developing, identifying, designing and fabricating novel nanomaterials to help produce more effective personal protection equipment to keep healthcare workers and teachers safe during the pandemic.

Team leaders: Ozlem Garibay and Sudipta Seal, College of Engineering and Computer Science

Amount: $40,000

High-Dimensional Analysis for Spectroscopy of Exhaled Gas from COVID-19 Patients

Develop a highly sensitive monitor to test exhaled gases for markers that indicate COVID infection. The monitor would use AI to distinguish the symptomatic and asymptomatic individuals directly based on the spectroscopy of the breath. The instrument could potentially also estimate the concentration of markers in the exhaled air, learn their patterns and test for changes between phases as the disease develops.

Team Leaders: Mengyu Xu, College of Sciences, and Subith Vasu, College of Engineering and Computer Science

Amount: $40,000

Genetically Modified Optical Sensors for Low Cost, High Throughput Detection and Screening for COVID-19

Create optical sensors that allow quick, on-the-spot detection of the virus and capability to measure immune response against the virus. The sensors would provide a fast, easy alternative to current serology tests, which serve to screen for the presence of antibodies to derive insights regarding immune response against the infection.��

Team Leaders: Debashis Chanda, NanoScience Technology Center, and Mubarak Shah, College of Engineering and Computer Science

Amount: $40,000

AEM: An AI-Powered and Epidemiology-Informed Modeling System for Accurate COVID-19 Prediction and Analysis

Further develop an analytic tool that takes data about COVID infections from across the nation and uses AI to predict the spread of the disease. The project, which combines math and computer engineering, creates models informed by 10 deep-neural networks. This modeling system has already received some attention from media outlets.

Team Leader: Liqiang Wang, College of Engineering and Computer Science, and Shunpu Zhang, College of Sciences

Amount: $37,000

After teams are done with their work, they will present a report to the vice president of Research within six months of completion. All teams will also present their findings during the College of Engineering and Computer Science Seminar Series and at the COVID-19 Artificial Intelligence & Big Data Seed Funding Research Forum in 2021.