Abstract

Cybercrime is often approached as a technical problem, but its global distribution reflects broader social, institutional, and governance conditions. This talk examines cybercrime as a socio-technical system through two complementary studies. First, using FireHOL IP blocklist data, I compare a Generalized Linear Model (GLM) with several non-linear machine learning approaches to identify the global drivers of cybercrime. The Random Forest Regressor achieves the strongest predictive performance, showing that cybercrime is shaped by complex, non-linear relationships that are not well captured by conventional linear approaches. Socio-economic and governance variables—including poverty rate, adult population, government effectiveness, and rule of law—emerge as especially important predictors, highlighting that cybercrime is not driven by technical infrastructure alone. Second, I examine how U.S. criminology and criminal justice doctoral programs are preparing scholars to study this increasingly complex domain. A review of 43 Ph.D. programs shows that only about half offer cybercrime-related coursework, with just six maintaining dedicated laboratories. Cybercrime is typically treated as an elective and often lacks technical or interdisciplinary depth. Taken together, these findings reveal a mismatch between the complexity of cybercrime and the current structure of doctoral training. The talk argues for a more integrated approach that connects computational methods, social science theory, and interdisciplinary training to better understand and respond to cybercrime.

Speaker Bio

Dr. Ling Wu is an Associate Professor in the Department of Criminology and Criminal Justice at the University of Alabama. Her research focuses on cybercrime, cyber victimization, and the social and structural dimensions of digital crime. Her work applies quantitative and computational approaches to examine how technological, social, and institutional factors shape cybercrime risks and patterns.

Abstract

In the area of explainable artificial intelligence, Symbolic Regression (SR) has emerged as a promising approach by discovering interpretable mathematical expressions that fit data. However, SR faces two main challenges: most methods are evaluated on scientific datasets with well-understood relationships, limiting generalization, and SR primarily targets single-output regression, whereas many real-world problems involve multi-target outputs with interdependent variables. To address these issues, we propose multi-task regression GINN-LP (MTRGINN-LP), an interpretable neural network for multi-target symbolic regression. By integrating GINN-LP with a multi-task deep learning, the model combines a shared backbone including multiple power-term approximator blocks with task-specific output layers, capturing inter-target dependencies while preserving interpretability. We validate multi-task GINN-LP on practical multi-target applications, including energy efficiency prediction and sustainable agriculture. Experimental results demonstrate competitive predictive performance alongside high interpretability, effectively extending symbolic regression to broader real-world multi-output tasks.

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). He received Ph.D. in computer engineering at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) large language models; (3) interpretable AI; (4) natural language processing.

Abstract

The development of brain-penetrant kinase inhibitors remains a major challenge in central nervous system (CNS) oncology and neurodegeneration due to restrictive blood–brain barrier (BBB) transport and efflux mechanisms. We established an AI-enabled, computer-aided drug design (CADD) platform that integrates structure-based modeling, molecular dynamics, kinase selectivity profiling, and pharmacokinetic (PK) optimization to rationally engineer selective, BBB-competent kinase inhibitors with translational potential. Our approach combines orthosteric and allosteric strategies to maximize target engagement while minimizing off-target toxicity and ABC transporter–mediated efflux.

In oncology, we identified highly potent VEGFR2 inhibitors (HA46, HA47; IC₅₀ = 1.3 and 0.72 nM) that significantly suppressed angiogenesis in HUVEC assays and reduced tumor growth in xenograft models without detectable systemic toxicity. Concurrently, selective HER2 inhibitors and dual HER2/VEGFR2-targeted analogs demonstrated robust in vivo efficacy in HER2-positive breast cancer models, with enhanced antitumor activity in combination regimens. BBB permeability was optimized through precise modulation of lipophilicity (LogP/LogD), conformational flexibility, and efflux liability, validated by PAMPA-BBB assays and predictive in silico modeling.

Beyond oncology, repurposed kinase scaffolds exhibited potent antibacterial activity against multidrug-resistant pathogens (MRSA, VRE), underscoring platform versatility. Furthermore, selective modulation of the tau–Fyn signaling axis attenuated Aβ-induced inflammatory responses, supporting therapeutic potential in Alzheimer’s disease.

Collectively, this integrated discovery framework delivers first-in-class, brain-penetrant kinase inhibitors with strong preclinical efficacy, favorable safety profiles, and clear translational trajectories for CNS malignancies, brain metastases, and neurodegenerative disorders.

Speaker Bio

Dr. Hamed I. Ali got his Ph.D. degree in Japan in 2007. He completed his postdoctoral training at TAMU-Rangel College of Pharmacy, then was promoted to Instructor, Lecturer, Assistant Professor, and then Associate Professor. Dr. Ali has several years of expertise in designing, synthesizing, and biologically screening selective tyrosine kinase inhibitors for targeting aggressive metastatic breast cancer. Through his successful scholarly activity, he garnered substantial extramural/intramural funding. He has mentored more than 30 undergraduate, graduate, and Pharm-D students. Accordingly, he received the “Faculty Research Award” in 2023 for demonstrating sustained and impactful scholarship at RCOP. Dr. Ali also demonstrated excellence in teaching Medicinal Chemistry/Pharmacology for Pharm-D and graduate students, being recognized as “Teacher of the Year” in 2017 and 2021 and “Teaching Team of the Year” for 10 consecutive years at RSOP. He received the “Texas A&M University-Distinguished Achievement Award” in 2019.

Abstract

Background: “Pathogenic” or “Likely Pathogenic” mutations in DNA repair genes such as Brca1, Brca2, and Palb2 in the BRCA repair pathway or can lead to an increased risk of developing breast, ovarian, prostate, and pancreatic cancers. Other mutations may be found in the patient population, yet whether they play a role in increasing the risk of developing cancer is unknown; these are called Variants of Undetermined Significance (VUS). The impact of these variants’ expression on cell proliferation, apoptosis, oxidative stress factors, and internalization in certain cell types is unclear. Key DNA repair genes such as BRCA1 and BRCA2 have shown to be important for hematopoiesis in animal models, and some studies have observed differential affects among certain progenitor subsets. Further, several questions remain about the prevalence and impact of DNA repair gene variants in certain tissues. Characterizing the phenotypic impacts of these variants in different cell-based models will provide more information about relevant molecular targets.

Speaker Bio

Dr. Victoria Mgbemena is an Associate Professor in the Department of Biology, Marvin D. and June Samuel Brailsford College of Arts and Sciences, Prairie View A&M University. 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, and Preventative Care. She is interested in the following mechanisms: Cell-Cell Communications, modulation of cell metastatic potential by DNA repair pathways.

Abstract

Indole and phenol metabolites- small molecules that come from the breakdown of tryptophan, phenylalanine, and tyrosine- have significant physiological effects. Understanding the sources of these metabolites is crucial for designing effective therapeutic strategies to control their concentrations. Using stable isotope tracing together with gnotobiotic and antibiotic-treated mouse models, we re-examine the prevailing assumption that indole and phenol metabolites originate exclusively from gut microbial metabolism. Our results reveal substantial host contributions to the circulating pools of many of these metabolites.

For phenols that do require gut microbial input, we identify distinct upstream protein sources: host-secreted proteins (such as mucins) are the source for inflammatory phenols, while digestion-resistant dietary proteins are the source for phenols linked with healthy outcomes. We further demonstrate that targeted dietary interventions can selectively alter the circulating concentrations of these phenol classes by reshaping microbial access to their preferred protein substrates. Collectively, these studies highlight the importance of understanding the relative contributions of host and microbial metabolism to physiologically active metabolites.

Speaker Bio

Jenna AbuSalim is an MD/PhD trainee at Rutgers Robert Wood Johnson Medical School Princeton University, where she completed her PhD in Molecular Biology in the lab of Joshua D. Rabinowitz, MD, PhD. Her research in this lab focused on the biochemical interplay between diet, the gut microbiome, and host metabolism. Prior to her doctoral training, she conducted cancer metabolism research as a post-baccalaureate researcher at the National Cancer Institute and Emory University in the lab of Aparna Kesarwala, MD, PhD. She earned her undergraduate degree from Indiana University, completing a Bachelor of Science in Music with an outside field in Chemistry.