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It's Weirder Than You Could Imagine Cofertility lets women freeze their eggs for free--as long as they give half of them back to the company. What happens next is anyone's guess. The Instagram post, precision-guided by sex and age, had zeroed in on its target. Yuchen Tu is a conser vatory-trained viola player from Chongqing, China, a lover of science fiction, and an aspiring member of the US Army Reserve Officer Training Corps. And late last spring, just after her 24th birthday, she realized she might be the perfect candidate to freeze her eggs--for free. Tu goes by Sue, a name she likes because it reminds her of the 2024 body horror film, whose protagonist tortures herself in pursuit of youth and beauty. To prequalify for free egg freezing, all Sue had to do was take a two-minute quiz. "Not everyone can pass this exam," she remembers thinking. "Oh, it seems like a competition." Sue passed, as have thousands of young women allured by the promise of a company called Cofertility. "The best time to freeze your eggs is when you can least afford it," goes the company's mantra, uttered on repeat by its public face and CEO, onetime Uber employee Lauren Makler. Thus was born Cofertility's mission: to help women like Sue, who had just received her master's degree in classical music and was preparing to embark on a second bachelor's, preserve their future fertility for zero dollars. Instead of paying to freeze and store her eggs with money--the going rate in New York City, where Sue lives, is about $18,000--she would use a currency she already had in abundance: the eggs themselves. Cofertility's clients must, in the company's words, "donate half of the eggs retrieved to intended parents that need the help of an egg donor to have a baby." It's a model called egg sharing, and it hadn't been commercialized widely in the US until Cofertility came along.

ForSight Robotics says its JASPER robotic surgical platform has completed 26 fully robotic-assisted cataract surgeries using surgeon-controlled telemanipulation.

One woman's meth addiction was so bad, the only option left might have been brain surgery. Then a single session of noninvasive, focused ultrasound seemed to do what years of treatment could not. Erin McNulty had been missing for weeks when her mother, Linda, sat down on a chair in her living room, exhausted. Linda had put in her usual seven-day workweek at the antiques shop she runs near Burlington, Vermont. She'd spent her free evenings driving around, trying to track down her daughter. Erin, 45 at the time, had been using methamphetamine for years. Her substance use started in high school--first alcohol, then marijuana, and eventually heroin. Erin's brother used heroin, too. When Linda found out, she started driving her kids to a methadone clinic three hours away in Massachusetts. The methadone helped, but it made Erin feel tired all the time, so she started using cocaine to stay awake. There were stretches of sobriety--she had her daughter during one of them, in 2008.

Researchers at the University of Leeds and collaborators from the University of California San Diego won the Best Paper Award at RoboSoft, the leading international conference focused on soft robotics research. Soft robotics is gaining attention in medical applications because compliant machines can interact more safely with delicate objects and complex anatomy. The award-winning paper describes a 1.8 mm soft growing robot that can be steered magnetically, sense its own shape in real time, and operate without internal pressure. These advances could help improve patient outcomes following minimally invasive procedures. We spoke with lead author Benjamin Calmé about the team's work.

Humanoid robot surgery reached a new milestone as teleoperated robots completed two laparoscopic gallbladder surgeries on pigs for the first time in a UC San Diego preclinical trial.

Classical hypothesis testing frameworks break down in contemporary settings in which null hypotheses are increasingly abstract, the same data are used to both generate and test hypotheses, and minimal assumptions about the underlying data are made. In this work, we propose a new framework for conducting valid hypothesis tests in broad contexts. We propose to add and subtract external noise generated from a symmetric shift-family to our data, $X$, to partition it into two pieces, $X^{(1)}$ and $X^{(2)}$. We provide a generic strategy for orthogonalizing $X^{(2)}$ against $X^{(1)}$ under the null hypothesis $H_0$, then show that testing whether the orthogonalization was successful provides a valid test of $H_0$ under mild assumptions. Remarkably, this framework extends naturally to the post-selection inference setting: we simply select a hypothesis on $X^{(1)}$, then perform orthogonalization under the selected null. As our approach neither requires pre-specification of the selection mechanism, nor is restricted to a small class of data-generating distributions, it dramatically expands the settings for which valid post-selection inference can be conducted. We showcase the flexibility of our proposal in several case studies involving challenging pre-specified null hypotheses and post-selection inference scenarios.

Transfer learning leverages knowledge from related source domains to improve learning in a target domain. Recent theoretical advances cover a broad range of regression settings within (generalized) linear models. Despite their diversity, these methods share two common constraints: they assume a known link function or linear structure and require direct access to raw source data. To move beyond these constraints, we propose a source-data-free transfer learning framework based on the single-index model (SIM). Instead of requiring raw source data, our method transfers only summary statistics derived from a generalized Stein's lemma in a one-time communication. This design preserves privacy and avoids side effects caused by dissimilarities of unknown nonlinear link functions across domains. To capture flexible, unknown nonlinearity, we employ a multilayer perceptron guided by the pre-estimated index from the transferred statistics, which significantly mitigates overfitting. Extensive experiments on synthetic data and a real-world application demonstrate consistent improvements over existing (generalized) linear model-based approaches. The proposed framework thus offers a practical, privacy-preserving, and nonlinear-adaptive solution for transfer learning.

Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems using simple objective functions based on log-likelihoods of a single variable. The concept extends naturally to objective functions operating on vectors - for example, multinomial logistic log-likelihood for multi-class classification, where observations have a score for each class - but popular frameworks approach these functions by either updating one value of the input vectors at a time, or by using a diagonal upper bound on the second derivative. This work extends the usual gradient boosting framework to functions of vector inputs and sketches a simple algorithm that can be used efficiently with histogram-based decision trees.

Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods. In this context, we propose a two-stage Adversarial Contamination-resistant Iterative Hard Thresholding (AC-IHT) algorithm for high-dimensional regression with contamination. Our nonconvex algorithm achieves minimax near-optimal (up to logarithmic terms) estimation by iteratively updating the coefficient vector and the contamination vector with different thresholding scales. We further demonstrate that our AC-IHT estimator is signal-adaptive: under proper signal conditions, it adaptively attains a sharper estimation rate and more accurate support recovery. Moreover, it enjoys the strong oracle property, laying a theoretical foundation for asymptotic inference. Numerical experiments confirm its superior finite-sample performance. Finally, we discuss theoretical extensions of the proposed procedure to generalized linear models and to heavy-tailed noise settings.