With a deep passion for physics, Shiraz Minwalla investigates the complexities of black holes, grey galaxies, and beyond.

In a research world defined by scale and AI-driven discovery, small colleges are proving impact isn’t measured in dollars. At places like Wellesley and Bowdoin, close mentorship, collaborative problem-solving, and a readiness to experiment with new tools are shaping the next generation of scientists to navigate a rapidly evolving research landscape.

More scientists are leaving academia, trading tenure-track hurdles for the speed and flexibility of industry. For physicist Elizabeth Frank, that shift meant moving from mapping Mercury to mining the Moon — swapping publication bottlenecks for the fast, interdisciplinary problem-solving of space startups, and using AI to revive data gathered half a century ago.

At the intersection of artificial intelligence and fundamental physics, the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) is preparing scientists to learn, think and understand at the deepest levels. In its latest week-long Summer School program, students explored frontier challenges through lectures, tutorials, and collaborative hackathons, testing how AI can shape the future of physics, and how physics can push the boundaries of AI.

At Columbia University, quantum breakthroughs emerge from a culture of shared labs, ideas, and materials. What began as informal collaboration has become a scalable model for cross-disciplinary science that powers advances in programmable quantum systems.

A new kind of network is emerging. One that could enable ultra-secure communication, safeguarded by the laws of physics. As the race to build a quantum internet accelerates, nations are vying for digital sovereignty and want to be the first to build the unbreakable internet.

She’s one of the internet’s sharpest scientific voices: equal parts physicist, critic, and communicator. In this candid conversation, Sabine Hossenfelder reflects on AI, the flood of low-impact theory papers, and how a scientific culture ripe for reform could finally be ready for change.

Hidden prompts buried in preprints show how easily large language models (LLMs) can be manipulated, exposing a deep vulnerability in science’s quality-control system. As artificial intelligence (AI) becomes part of the scientific review process, traceability and transparency must become new norms, not afterthoughts.

The Flatiron Institute stands out for its bold model: full-time scientists, open-source tools, and a mission to accelerate discovery through computation. Its success across fields—from astrophysics to neuroscience—reflects the power of that approach.