This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.

This research uses the Manhattan maze to study rapid learning and memory in mice. The study demonstrates that mice can acquire complex navigation sequences after only a few rewards, retain memories overnight, and generalize learned strategies to new mazes. The findings provide insights into few-shot learning, memory formation, and adaptive intelligence.

This neuroscience research investigates how the human brain constructs and adapts goals. Using fMRI and a dynamic decision-making game, the study identifies neural activity in the prefrontal cortex and anterior cingulate cortex associated with goal selection, valuation, and adaptation. The findings may help develop AI systems better aligned with human goals.

This research develops rigorous mathematical foundations for consensus-based optimization algorithms, where large groups of interacting particles collaboratively search for optimal solutions. Using mean-field theory and propagation of chaos, the work proves long-term stability and improves optimization methods for applications including robotics, aircraft design, and drug discovery under real-world constraints.

This research uses natural language processing techniques to uncover evolutionary relationships between ancient proteins. By analyzing contextual patterns among amino acids, the new computational tool can identify connections between proteins that diverged billions of years ago, helping scientists reconstruct the history of early microbial life and Earth’s biological evolution.

This research develops a fast, space-efficient method for assessing fall risk in older adults using wearable sensors and AI analysis of a seated foot-tapping task. Early findings show that slower, inconsistent tapping predicts higher fall risk. The approach could improve prevention strategies, reduce healthcare costs, and help older adults maintain independence.

Generative AI chatbots are predictive systems that generate human-like responses without true understanding. Using large datasets, they model word relationships similarly to weather forecasting. While effective, they can produce convincing inaccuracies, or “hallucinations.” This research emphasizes interpreting AI realistically—as probabilistic tools with limitations—rather than attributing human cognition to them.

This research critiques AI-based classroom monitoring, arguing that while algorithms can measure behavior, they cannot interpret meaning. It proposes the “Augustinian limit,” where AI supports logistics but human judgment guides interpretation. The framework protects authentic learning moments, emphasizing that true education relies on human insight, not just data-driven evaluation.

This research investigates why many organizations fail to implement AI effectively, focusing on readiness rather than technology. In automotive after-sales services, it identifies gaps between systems and AI ambitions. The study develops a framework aligning people, processes, and capabilities, helping organizations achieve sustainable and successful AI adoption.

This research investigates asthma’s underlying mechanisms, focusing on airway fibrosis and the extracellular matrix. Using Raman spectroscopy, researchers generate molecular “barcodes” of lung tissue. Artificial intelligence is then applied to analyze complex data, aiming to identify key biological drivers of asthma and move beyond temporary treatments toward deeper understanding and potential long-term solutions.