Abstract

Statistical facts are powerful as they help our society derive insights from data and form decisions. How can we design algorithmic tools to discover facts from massive data? When we are inundated with too many facts, how do we make our algorithms discern trivial from important facts? When statistics become outright lies, how can we build computing tools to mitigate falsehood? For more than a decade, the Innovative Data Intelligence Research Laboratory (IDIR) has been building data-driven fact-checking and fact-finding systems in searching for answers to these questions. The projects are part of the Lab’s inter-disciplinary research program in computational journalism. This talk will present a high-level, intuitive overview of these projects.

Speaker Bio

Dr. Chengkai Li is a Professor and Associate Chair in the Department of Computer Science and Engineering at The University of Texas at Arlington. His research interests encompass various domains of big data intelligence and data science, including natural language processing, knowledge graphs and the semantic web, databases, data mining, and machine learning. His work extends to applications in computational journalism, data-driven fact-checking, smart agriculture, public health, and transportation. At UTA, he directs the Innovative Data Intelligence Research (IDIR) Lab and serves as co-director of the Center for Artificial Intelligence and Big Data (CARIDA). Dr. Li earned his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, and holds an M.Eng. and a B.S. in Computer Science from Nanjing University.

Abstract

Personalized adaptive learning (PAL) stands out by closely monitoring individual students’ progress and tailoring their learning paths to their unique knowledge and needs. A crucial technique for effective PAL implementation is knowledge tracing, which models students’ evolving knowledge to predict their future performance. Recent advancements in deep learning have significantly enhanced knowledge tracing through Deep Knowledge Tracing (DKT). However, there is limited research on DKT for Science, Technology, Engineering, and Math (STEM) education at Historically Black Colleges and Universities (HBCUs). This study builds a comprehensive dataset to investigate DKT for implementing PAL in STEM education at HBCUs, utilizing multiple state-of-the-art (SOTA) DKT models to examine knowledge tracing performance. These findings have significant implications for faculty members and academic advisors, providing valuable insights for identifying students at risk of academic underperformance before the end of the semester. Furthermore, this allows for proactive interventions to support students’ academic progress, potentially enhancing student retention and graduation rates.

Speaker Bio

Dr. Xishuang Dong is a member of CRI Center for Computational Systems Biology and CREDIT, and Associate Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.

Abstract

This presentation focuses on building urban climate resilience using spatial data science to address climate risks such as hurricanes, heatwaves, and flooding. The research incorporates urban sensing, traditional GIS data, and near real-time urban big data to enhance decision-making for preparedness, response, recovery, and mitigation. Key areas of study include post-disaster damage assessment using deep learning techniques, mobility prediction in extreme weather conditions, and the socioeconomic disparities in mobility recovery. Through collaborations with various stakeholders, including governmental agencies and NGOs, we provide valuable insights into urban resilience strategies.

Speaker Bio

Dr. Wei Zhai is an Assistant Professor of Urban and Regional Planning at the University of Texas at San Antonio, focusing on urban resilience and urban science. His research interests center on how to make cities smart and resilient in the face of hurricane, flooding, extreme heat, etc. He is currently working on two NSF-funded projects focused on urban digital twins and community resilience, as well as collaborating on two heat mitigation research projects with the City of San Antonio.

Abstract

Most cancers are epithelial in origin and can invade other tissues, and we seek to understand: 1)How germline and somatic mutations may contribute to pathogenesis and perturbed immunity in reproductive cancers. Prostate cancer is the second-leading cause of death among cancer cases in US men. The American Cancer Society estimates that there will be over 190,000 new cases of prostate cancer in the US in 2020. It is estimated that one out of 9 men will receive a prostate cancer diagnosis in their lifetime. A study examining molecular mechanisms of health disparities identified 362 differentially expressed genes in signaling pathways regulating tumor aggressiveness. Risk factors which contribute to cancer include advanced age, smoking, ethnicity, inheritance, and to a certain degree—although less clear—diet, obesity, inflammation and chemical exposure. The goal of our current projects is to determine the role of variants of undetermined significance on prostate cancer cell gene expression, apoptosis and proliferation. We will use the All of Us Research Hub to identify variants. Although known variants of pathogenicity have been linked to disease for prominent DNA repair genes BRCA1 and BRCA2, certain rare variants have not been elucidated for functionality. The objectives are to express clinical variants of BRCA2 in prostate cancer cells and examine gene expression, homologous recombination, apoptosis, and cell proliferation. In a future direction, we aim to determine the responsiveness of cancer cells with variant mutations to current targeted therapies.

Speaker Bio

Victoria Mgbemena is an Assistant Professor in the Department of Biology. She received a Ph.D. from the University of Texas Health Science Center at San Antonio, where she studied Host-Pathogen Interactions. She completed her Postdoctoral studies in Hematology/Oncology at UT Southwestern Medical Center, where she studied the role of a DNA repair gene in hematopoiesis.

Dr. Mgbemena’s research topics interests include: Inheritance of rare diseases, Reproductive Health, Applications for developing Personalized Medical Approaches, Health Disparities, and Preventative Care. She is interested in the following mechanisms: Cell-Cell Communications, modulation of cell metastatic potential by DNA repair pathways.

Abstract

Food production is the main driver of biodiversity loss, species extinction, river pollution, and will be critical to keeping the climate within safe limits. Many people and businesses don’t realize their daily food choices can be such a big cause of and solution to our most critical environmental issues. This lecture is given through the Sustainable Food Management Program which seeks to reduce wasted food and its associated impacts over the entire life cycle, starting with the use of natural resources, manufacturing, sales, and consumption, and ending with decisions on recovery or final disposal. EPA works to promote innovation and highlight the value and efficient management of food as a resource. Through the sustainable management of food, we can help businesses and consumers save money, provide a bridge in our communities for those who do not have enough to eat, and conserve resources for future generations. The Sustainable Food Management Program is managed by the Office of Resource Conservation and Recovery under the authority of the Resource Conservation and Recovery Act (RCRA).

Speaker Bio

Stephen Sturdivant is an environmental engineer at the Environmental Protection Agency’s Region 6 Sustainable Management of Food Program. Stephen graduated with a mechanical engineering degree from the University of North Texas and has been with the EPA since 2006.