Join us for faculty plenary lectures at 10:00 AM.
Schedule of Faculty Plenary Lectures will be posted by September 1, 2026
ASDRP Summer 2026 Research Symposium & Expo
Schedule of Faculty Plenary Lectures will be posted by September 1, 2026
In an era of abundant AI resources, translating complex models into practical solutions remains challenging, yet it offers immense opportunities for aspiring young engineers. To harness these opportunities, our research lab focuses on applying data science and AI to tackle real-world problems across financial engineering, risk mitigation, automated decision-making, and beyond. In quantitative finance, we engineer effective stock and cryptocurrency trading strategies by leveraging sentiment signals extracted from social media data. We optimize investment portfolio performance using genetic algorithms and reinforcement learning. In risk management, we apply Vision Language Models (VLMs) to detect frauds in checks. Finally, to improve everyday user experiences with generative AI, we developed a multi-phase evaluation framework that benchmarks leading LLMs (such as ChatGPT, Gemini, and DeepSeek), identifies model vulnerabilities, and proposes strategies to build more reliable, context-aware, and user-centric AI systems.
Nature provides an extraordinary starting point for cancer drug discovery, but synthetic chemistry offers the opportunity to reprogram, refine, and expand the biological function of natural products. The Njoo Lab explores this interface through the design and synthesis of small molecules that interrogate oncogenic signaling and translate chemical structure into therapeutic function. Our work includes natural-product-derived inhibitors of the Wnt/β-catenin pathway, including semisynthetic andrographolide analogs whose activity can be dramatically altered through targeted modification of the scaffold. Recent studies demonstrate that subtle changes in C-19 functionalization, including the incorporation of silicon, can profoundly alter both anticancer potency and biological mechanism. Beyond conventional medicinal chemistry, we investigate how replacing canonical elements with silicon and fluorine can create new chemical and biological behavior. Organosilicon substitution provides access to anticancer molecules with altered physicochemical properties and cellular activity, while fluorine offers a powerful tool for molecular design, mechanistic interrogation, and the development of new therapeutic scaffolds. Together, these efforts illustrate a central philosophy of the Njoo Lab: use organic chemistry not simply to make molecules, but to make molecules matter, connecting molecular design and mechanistic insight to the discovery of new cancer therapeutics.
Adaptation and evolution are strongly influenced by environmental conditions that affect genome stability, gene regulation, and DNA repair. However, how external stresses interact with DNA to shape evolution remains incompletely understood. Here, we propose an integrated framework to investigate the relationship between environmental stress, gene regulation, DNA repair, CRISPR-induced DNA damage, and bacterial evolution. Using Escherichia coli as a model system, we examine how different conditions and antibiotic exposure influence mutation frequency, antibiotic resistance, and cellular stress responses. In parallel, CRISPR-Cas9 provides a controlled system for generating DNA double-strand breaks, while a MuGam-GFP-based reporter enables visualization of DNA fragmentation and DNA damage in living cells. By combining phenotypic measurements, fluorescence-based DNA-damage detection, gene-expression analysis, and genomic approaches, we aim to identify pathways that determine how bacteria respond to different external factors. We hypothesize that external stress alters regulatory and DNA-repair networks, thereby influencing mutation patterns and the persistence of resistant variants. Understanding these interactions may improve our ability to predict evolution in general, identify mechanisms of genome instability, and develop strategies to limit the emergence of antibiotic resistance, multidrug resistance and unintended genetic changes including diseases.
Biomaterials are materials designed to interact with biological systems for medical and biological applications. Calcium phosphate, a major inorganic component of bone and teeth, has been widely used in bone repair, dental materials, coatings, and drug delivery because of its biocompatibility. In nature, however, biological materials are often composed of multiple components. Bone, for example, is a natural hybrid material consisting mainly of CaP and collagen. This combination provides properties that cannot be achieved by either component alone. This presentation will introduce the basic properties and applications of CaP and other inorganic biomaterials, followed by their limitations and the concept of hybrid materials. The potential of biopolymers and hydrogels will then be discussed, with examples including modified calcium phosphate nanoparticles, methacrylated gelatin and hyaluronic acid, and photocrosslinked hydrogels. Methods used to characterize their chemical and physical properties will also be introduced.
This talk presents highlights of studies conducted at ASDRP. We are investigating the potential of the prediction of mechanical properties across three distinct material systems: nanostructures, bulk copper, and 3D-printed polylactic acid (PLA). For nanostructures, machine learning is used to identify and characterize the morphology. Nanoindentation can be correlated to microhardness to macroscopic strength. Bulk mechanical properties of copper are evaluated through tensile and hardness testing to determine if grain size influences the linear correlation coefficients between hardness and strength. The mechanical behavior of 3D-printed PLA is characterized under two varying parameters—such as print orientation—to determine their effect on stiffness, strength, and failure modes. Comparative analysis across these materials highlights critical relationships between structure and mechanical response, potentially providing predictive insights applicable to materials design and additive manufacturing optimization. The topic of interfacial free energy measurements from dihedral angle of twin boundaries in copper will also be discussed.