Dr. Bilal Akin wins 2026 O'Donnell Award in Engineering for transformative work in EV energy systems and industrial automation
Grant and Award Announcement
Updates every hour. Last Updated: 11-Dec-2025 08:13 ET (11-Dec-2025 13:13 GMT/UTC)
Groundbreaking electrical engineer Bilal Akin, Ph.D., Professor in the Department of Electrical and Computer Engineering at The University of Texas at Dallas, is the recipient of the 2026 Edith and Peter O’Donnell Award in Engineering from TAMEST. He was chosen for his cutting-edge advancement of sustainable and high-efficiency energy conversion systems for electric vehicles (EVs) and industrial automation.
Researchers from Okayama University and Tohoku University have discovered that targeting collagen signaling through the discoidin domain receptor 1 (DDR1) enhances drug delivery and reverses therapy-induced resistance in pancreatic cancer. Their study shows that DDR1 inhibition improves macromolecular drug penetration and mitigates fibrosis triggered by MEK inhibitors, offering new hope for more effective treatment strategies.
Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer’s disease, addressing a critical gap in Alzheimer’s care: significant underdiagnosis, particularly among underrepresented communities.
Researchers have reported initial findings from a public-private partnership between the ECOG-ACRIN Cancer Research Group and Caris Life Sciences to improve recurrence risk assessment in early-stage breast cancer using artificial intelligence (AI). They are pairing ECOG-ACRIN’s extensive clinical trial expertise and biorepository resources with Caris’ comprehensive MI Cancer Seek® whole exome and whole transcriptome profiling, whole slide imaging, and advanced machine learning platforms. New multimodal–multitask deep learning algorithms were trained on histopathologic imaging, clinical data, and molecular profiling data from over 4,000 patient cases in the biorepository of the groundbreaking TAILORx cancer clinical trial, one of the world’s largest such resources. Analyses of these AI-driven models demonstrated they were more effective than existing methods for assessing recurrence risk. This research highlights the potential of AI to support more personalized treatment decisions in early-stage breast cancer. Such a level of multimodal integration is unprecedented at this scale in the prognostication of early breast cancer.