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Komeil Nasrollahi — Senior Director, Innovation & Venture Partnerships, Siemens Healthineers

Pioneering breakthroughs in healthcare — for everyone, everywhere, sustainably.

Komeil Nasrollahi is a seasoned innovation and business‐development leader currently serving as Senior Director of Innovation & Venture Partnerships at Siemens Healthineers (https://www.siemens-healthineers.com/), where he is charged with forging strategic collaborations, identifying new venture opportunities and accelerating transformative healthcare technologies.

With an academic foundation in industrial engineering from Tsinghua University (and additional studies in the Chinese language) and undergraduate work in civil engineering from Azad University in Iran, Komeil blends technical fluency with global business acumen.

Prior to his current role, Komeil held senior positions driving business engagement and international investment, including leading market‐entry and growth initiatives across China and the U.S., demonstrating a strong ability to navigate cross‐cultural, high‐stakes innovation ecosystems.

In his current role, Komeil works at the intersection of healthcare, technology and venture creation—identifying high-impact innovations that align with Siemens Healthineers’ mission to “pioneer breakthroughs in healthcare, for everyone, everywhere, sustainably.”

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Statistical mechanics for networks of real neurons

Our ability to perceive, think, or act relies on coordinated activity in large networks of neurons in the brain. This review examines recent progress in connecting ideas from statistical physics, such as maximum entropy methods and the renormalization group, to quantitative experiments that record the electrical activity of thousands of neurons simultaneously. This quantitative bridge between the new data and statistical physics models uncovers new, quantitatively reproducible behaviors and makes clear that abstract theoretical principles in studies of the brain can have the level of predictive power that we expect in other areas of physics.

Mind readers: How large language models encode theory-of-mind

Imagine you’re watching a movie, in which a character puts a chocolate bar in a box, closes the box and leaves the room. Another person, also in the room, moves the bar from a box to a desk drawer. You, as an observer, know that the treat is now in the drawer, and you also know that when the first person returns, they will look for the treat in the box because they don’t know it has been moved.

You know that because as a human, you have the to infer and reason about the minds of other people—in this case, the person’s lack of awareness regarding where the chocolate is. In scientific terms, this ability is described as Theory of Mind (ToM). This “mind-reading” ability allows us to predict and explain the behavior of others by considering their mental states.

We develop this capacity at about the age of four, and our brains are really good at it.

A Mathematician’s Model Brings Science Fiction’s Wormholes Closer to Reality

Could a tunnel through space and time—long a dream of science fiction—ever exist in theory? According to Arya Dutta, a Ph.D. student in Mathematics at the Katz School, the answer might be yes, at least on paper.

Accepted for publication in the International Journal of Geometric Methods in Modern Physics, Dutta’s study, “Thin-shell Wormhole with a Background Kalb–Ramond Field,” explored a mathematical model of a wormhole—a hypothetical shortcut through spacetime that could, in theory, connect two distant regions of the universe. “A wormhole allows faster-than-light travel or even time travel,” said Dutta. “It hasn’t been observed yet, but theoretical research has advanced a lot.”

AI model powers skin cancer detection across diverse populations

Researchers at the University of California San Diego School of Medicine have developed a new approach for identifying individuals with skin cancer that combines genetic ancestry, lifestyle and social determinants of health using a machine learning model. Their model, more accurate than existing approaches, also helped the researchers better characterize disparities in skin cancer risk and outcomes.

The research is published in the journal Nature Communications.

Skin cancer is among the most common cancers in the United States, with more than 9,500 new cases diagnosed every day and approximately two deaths from skin cancer occurring every hour. One important component of reducing the burden of skin cancer is risk prediction, which utilizes technology and patient information to help doctors decide which individuals should be prioritized for cancer screening.

Automatic C to Rust translation technology provides accuracy beyond AI

As the C language, which forms the basis of critical global software like operating systems, faces security limitations, KAIST’s research team is pioneering core original technology research for the accurate automatic conversion to Rust to replace it. By proving the mathematical correctness of the conversion, a limitation of existing artificial intelligence (LLM) methods, and solving C language security issues through automatic conversion to Rust, they presented a new direction and vision for future software security research.

The paper by Professor Sukyoung Ryu’s research team from the School of Computing was published in the November issue of Communications of the ACM and was selected as the cover story.

The C language has been widely used in the industry since the 1970s, but its structural limitations have continuously caused severe bugs and security vulnerabilities. Rust, on the other hand, is a secure programming language developed since 2015, used in the development of operating systems and , and has the characteristic of being able to detect and prevent bugs before program execution.

Bacterial Rtc repair system provides new target in fight against resistant infections

The discovery of a new mechanism of resistance to common antibiotics could pave the way for improved treatments for harmful bacterial infections, a study suggests. Targeting this defense mechanism could aid efforts to combat antimicrobial resistance (AMR), one of the world’s most urgent health challenges, researchers say.

The work appears in Nature Communications.

Findings from the study reveal how a repair system inside some bacteria plays a pivotal role in helping them survive commonly used antibiotics. Many of these drugs work by targeting the production of proteins essential for and survival.

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