Office of Data and Strategic Analytics AI and Data Analytics Faculty Fellows Program

2023-2024 Lead AI Faculty Fellow

The Lead AI Faculty Fellow plays a pivotal role in steering the direction and successful execution of all AI-related use cases within the Faculty Fellows Program. This position actively provides technical and critical leadership and guidance to the Fellows as they execute and conduct their AI projects.

Michal Brylinski

Associate Professor
Department of Biological Sciences/Center for Computation & Technology

Dr. Michal Brylinski is an Associate Professor in the Department of Biological Sciences and an Associate Director for Machine Learning and Data Science in the Center for Computation and Technology at LSU. He has secured over $1 million in research funding for developing and implementing advanced AI-based systems to support a wide range of projects in the fields of biology and biomedicine and has published in more than 95 peer-reviewed journals. Dr. Brylinski was a 2020 semifinalist for the IBM Watson AI XPRIZE and was awarded the LSU Rainmaker Mid-Career Scholar Award in 2019 and the LSU Alumni Association Rising Faculty Research Award in 2017. 

Dr. Brylinski’s research areas include the development of novel tools for the modeling and analysis of biological networks, integrating many algorithms from different research areas such as Structural Bioinformatics, Functional Genomics, Cheminformatics, and Pharmacogenomics, for the purpose of computer-aided drug discovery and design.

2023-2024 AI/Data Analytics Faculty Fellows

Teaching and Learning Team
Use Case: Problem Sets Generation Tool Targeted at LSU Gateway Courses with High DFW Rates

To address the high DFW rates in specific LSU gateway courses (College Algebra - MATH 1021 and Calculus I - MATH 1550), Drs. Jennifer Qian, Supratik Mukhopadhyay, and David Saccardi are developing a Problem Sets Generation Tool. The AI tool will utilize advanced algorithms and machine learning to create personalized problem sets, adapting the difficulty level to each student's progress for each course. Accessible through a mobile app and integrated into LSU Moodle for learning analytics, the tool aims to reduce DFW rates significantly and contribute to the long-term success and diversity of students in STEM and humanities majors at LSU.

Jennifer Qian

Professor of Professional Practice
Lutrill & Pearl Payne School of Education

Dr. Jennifer Qian leads and teaches in the Educational Technology program in the School of Education. Prior to joining LSU, she served as the doctoral research chair in the Doctor of Education program at Northeastern University, Boston. Dr. Qian has secured $2.4 million in research funding for innovative educational initiatives – recently serving as co-principal investigator to the NSF grant for the Future of Work at the Human-Technology Frontier Program with an award of approximately $1 million. 

Dr. Qian’s research areas include technology-enabled academic innovation, digital transformation, technology leadership, and emerging technologies. She also serves as the Editor-in-Chief of the International Journal of Innovative Teaching and Learning in Higher Education. 

Supratik Mukhopadhyay

Professor
Department of Environmental Sciences/Center for Computation & Technology

Dr. Supratik Mukhopadhyay has led the team that developed the DeepDrug AI Platform to the semifinals of the AI Intelligence XPrize in 2020. He has secured more than $8 million in research grants as principal/co-principal investigator, has published around 125 refereed papers in high-impact journals, and has been awarded four US patents with eight pending. He also launched the world’s first AI-discovered nutraceutical in 2021 and co-founded Ailectric, LLC, an artificial intelligence startup focusing on sound, text, and image analytics. 

Dr. Mukhopadhyay’s research areas include data analytics and artificial intelligence for social good with applications to education, agriculture, drug discovery, wellness, biomedical imaging, coast and environment, transportation systems, video analytics, and robotics. He has developed AI and data analytics solutions that have utilized diverse and complex datasets ranging from geometry problems, natural language texts, satellite imagery, biomedical images, molecular fingerprints, graphs, full motion videos, and time series.

David Saccardi

Assistant Professor 
School of Music

Dr. David Saccardi is an Assistant Professor of Music Education and the Director of the Translational Research in Music Lab, an organization that seeks to bridge the gap between research in music teaching and learning and its applications in K-12 music classrooms. He also directs graduate research, teaches courses in the psychology of music, graduate string pedagogy, quantitative analysis, and experimental design, and teaches courses in the undergraduate music teacher preparation sequence.

Dr. Saccardi's research focuses on the intersection of music and science, with collaborations across LSU's campus, including cognitive psychology and biomedical science, and involvement in the LSU Multidisciplinary Initiative for Neuroscience Discovery (MIND). His work explores topics such as music cognition, biological markers of music cognition, psychology of music, and music education. His research has been published in multiple music education journals, and he serves on the editorial board for American String Teacher.

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Student Success and Workforce Needs Team
Use Case: Integration and Deployment of Novel AI Tools for Rapid Assessment of Student Success

Drs. Yu “April” Chen, Mostafa Elseifi, and Thanos Gentimis are developing a novel AI tool that will assist both incoming and current students in making critical academic decisions, such as selecting or changing majors, based on predictions of academic success, completion and graduation rates, time to completion, salary expectations, and post-graduation employability. Additionally, these AI models will provide valuable support for academic advisors, career counselors, and financial counselors, enabling them to offer evidence-based guidance to students. The team is focused on implementing these models within a mobile application, and where distinct patterns and behaviors emerge among different student populations, separate AI models will be developed.

Yu “April” Chen

Judith Walker Gibbs Endowed Associate Professor
Lutrill & Pearl Payne School of Education

Dr. Yu “April” Chen was awarded an NSF CAREER grant last year for a predictive analytics project on transfer students, totaling approximately $500,000. She has secured more than $8 million in research awards, her scholarly work has been featured in prominent higher education journals, and she serves on the editorial board for the Journal of The First-Year Experience and Students in Transition. 

Her research combines advanced statistical and qualitative methods to investigate community college transfer students, STEM success among underrepresented student groups, and the experiences of international students. 

Mostafa Elseifi

Occidental Chemical Corporation Distinguished Professor
Department of Civil & Environmental Engineering

Dr. Mostafa Elseifi has over 25 years of academic and industrial experience in Civil Engineering. During his tenure at LSU, he has completed 30 research projects sponsored by NSF, US Dept of Transportation, LaDoTD, LA Board of Regents, and the National Cooperative Highway Research Program, for a total of $8.8 million. He has authored/coauthored 117 refereed publications in leading journals. Dr. Elseifi also serves on the East Baton Rouge Parish Engineer and Surveyor Selection Board. 

His research interests include constitutive modeling and analysis of flexible pavement, laboratory characterization of hot-mix asphalt, tire-pavement interaction, and pavement instrumentation, and the digitization and implementation of machine learning applications, including virtual and data analytics emerging technologies.

Thanos Gentimis

Assistant Professor
Department of Experimental Statistics

Dr. Thanos Gentimis is an Assistant Professor in Experimental Statistics and also the leading member of the Digital and Precision Agriculture Initiative in the College of Agriculture, using machine learning algorithms to analyze large datasets in the agricultural field. He has also served as the Head of the Big Data and Health Informatics Lab at Florida Polytechnic and was a consultant for the USDA Risk Management Agency,  using machine learning for risk prediction in farm management. He has published in 20 peer-reviewed journals, has been awarded two patents, and has secured $675,000 in research grants. He is currently serving as chair of a multi-state project for AI in Agroecosystems with more than 15 partner universities. 

His field of expertise is in mathematics, computer science, statistics, data analytics, machine learning, and neural networks.

Mahmood Jasim

Assistant Professor
Division of Computer Science and Engineering

Dr. Mahmood Jasim's research interests include designing, developing, and evaluating decision-support systems for inclusive collection and analysis of large-scale public-generated data to enable effective and efficient data-driven decision-making to address complex socio-technical problems. His interdisciplinary research is spread across human-computer interactions (HCI), information visualization, and social computing. Dr. Jasim currently serves as the director of the Computer Human Interaction & Visualization Experience (CHIVE) Lab at LSU. He is the recipient of several best paper awards including CSCW 2020, DIS 2021 (Honorable Mention), and EuroVIS 2022 and has built decision-support systems that have been deployed and used across multiple cities in the US to facilitate hybrid town hall meetings. 

 

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University Infrastructure and Facilities Team
Use Case: Optimizing Indoor Environmental Conditions with Advanced Data Analytics

Drs. Yimin Zhu and Supratik Mukhopadhyay (see above) are applying data analytics to improve campus facility maintenance and planning. By integrating data from various sources and using machine learning techniques, such as recurrent neural networks, this project seeks to predict associations between indoor environmental conditions and maintenance schedules, building conditions, and weather. The project's outcomes will support better maintenance, retrofitting decisions, and provide information that will help to inform and improve decision making.

Yimin Zhu

Pulte Homes Endowed Professorship in Construction Management
Bert S. Turner Department of Construction Management

Dr. Yimin Zhu is cross trained in construction management, management, and computer science. He has authored/co-authored 84 peer-reviewed journal articles and 86 peer-reviewed conference articles and has secured over $6 million in research awards during his tenure at LSU. 

Dr. Zhu’s research interests are sustainability in the built environment and informatics including systems integration of construction software applications; stochastic time; cost and environmental impact optimization of construction processes; and concept learning in construction education. 

 

 

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Training and Development in Artificial Intelligence 

Dr. Roberto Fritsche-Neto will be providing LSU faculty and graduate students with a series of workshops on basic data mining and modeling techniques. Specifically, he will be presenting on the principles of data preparation and quality control, supervised and unsupervised learning, neural networks, convolution neural networks, tuning parameters, and model evaluation (confusion matrix, accuracy, etc.). Each topic will be accompanied by practical hands-on exercises drawn from real-world applications.

Roberto Fritsche-Neto

Assistant Professor
School of Plant, Environmental and Soil Sciences/H. Rouse Caffey Rice Research Station

Dr. Roberto Fritsche-Neto is an Assistant Professor of Quantitative Genetics/Prediction-Based Breeding at the LSU Rice Research Station. In the last 12 years, he has published more than 126 papers relating data science to genetics and plant breeding, released four varieties/germplasm panels, and obtained approximately $862,000 in grants to fund his research.

Dr. Fritsche-Neto’s research aims to expedite the crop breeding process, increase genetics gains, and reduce costs and labor. To these ends, he proposes new breeding methods and integrates genomic prediction, enviromics, omics, crop-growth models, and high-throughput phenotyping.