HeyJaredWayne Lewis
- Indexed articles, last 90 days
- 4
- Latest publication
- Sep 18, 2026
- Outlet visibility, for UCLA
- Top 1M sites
- Earliest in this view
- Jul 22, 2026
Latest articles
Toward physical AI: When the hardware becomes the neural network (opens the original)
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Digital computing using silicon chips has transformed nearly every aspect of modern life and enabled the remarkable growth of artificial intelligence. But as AI models scale up, that growth comes with increasing demands for energy, water and computing infrastructure. At the same time, many emerging applications — from satellites and robots to distributed sensors — need to process information where it is generated, often with limited power and connectivity. UCLA helped blaze the trail for a compl
Update the textbooks: UCLA-led research revises theory of how crystals form (opens the original)
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The most common scientific approach for thinking about how condensation, freezing and other phase transitions begin is based in classical nucleation theory, which was developed about a century ago. Thousands of experiments have supported a key equation describing how initial ordered seeds, called nuclei, form within disordered matter. Now, new UCLA-led research proposes a revision to classical nucleation theory. The study, published in Nature Materials, used tiny spheres, or nanoparticles, made
UCLA bioengineer Jason Zhang deploys AI to create proteins never before seen in nature (opens the original)
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The decades-old scientific quest to create brand new proteins has been turbocharged in the era of artificial intelligence. A key building block for life, a protein can serve as structural support, messenger, catalyst or transport system depending on the amino acids that make it up and on its resulting 3D shape. That versatility makes designed proteins an expected game changer in medicine, industry and research. Among today’s vanguard in generative AI–enabled protein design is Jason Zhang, an ass
Biomedical imaging, autonomous vehicle sensors may get boost from AI designed for physical signals (opens the original)
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A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within “complex media,” which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve upon an existing imaging technique. In tests with standard calibration images obscured by complex media, the new system more than doubled the signal-to-noise ratio c
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