The new president of the Los Angeles Police Commission, Teresa Sánchez-Gordon, is a former L.A. County judge who says her upbringing in an undocumented family gives her a unique perspective on the current challenges facing the LAPD.

This article introduces a novel, geometric approach for multi-manifold clustering (MMC), i.e. for clustering a collection of potentially intersecting, d-dimensional manifolds into the individual manifold components. We first compute a locality graph on d-simplices, using the dihedral angle in between adjacent simplices as the graph weights, and then compute infinity path distances in this simplex graph. This procedure gives a metric on simplices which we refer to as the largest angle path distance (LAPD). We analyze the properties of LAPD under random sampling, and prove that with an appropriate denoising procedure, this metric separates the manifold components with high probability. We validate the proposed methodology with extensive numerical experiments on both synthetic and real-world data sets. These experiments demonstrate that the method is robust to noise, curvature, and small intersection angle, and generally out-performs other MMC algorithms. In addition, we provide a highly scalable implementation of the proposed algorithm, which leverages approximation schemes for infinity path distance to achieve quasi-linear computational complexity.

A'Fox News @ Night' panel gives their closing thoughts after the fourth night of anti-ICE protests in Los Angeles. A Los Angeles-based federal judge appointed by former President Joe Biden recently issued a temporary restraining order, restricting the Los Angeles Police Department (LAPD) from using less-lethal munitions (LLMs) on journalists covering immigration protests. The order, signed by Judge Hernan Vera on Thursday, also prevents the LAPD from detaining or restricting the movements of journalists. Vera cited at least 35 "troubling" incidents between June 6 and 19, where police allegedly exposed journalists to LLM, tear gas and other physical force to block them from covering conflict zones. Los Angeles Police Department (LAPD) officers move in on demonstrators in front of LA City Hall during a protest against federal immigration sweeps in downtown Los Angeles, California, on June 8, 2025.

Citing successes other police departments across the country have seen using drones, the Los Angeles Police Commission said it would allow the LAPD to deploy unmanned aircraft on routine emergency calls. The civilian oversight body approved an updated policy Tuesday allowing drones to be used in more situations, including "calls for service." The new guidelines listed other scenarios for future drone use -- "high-risk incident, investigative purpose, large-scale event, natural disaster" -- and transferred their command from the Air Support Division to the Office of Special Operations. Previously, the department's nine drones were restricted to a narrow set of dangerous situations, most involving barricaded suspects or explosives. Bryan Lium told commissioners the technology offers responding officers and their supervisors crucial, real-time information about what type of threats they might encounter while responding to an emergency.

Thousands of demonstrators took to the streets of Los Angeles County over the weekend to protest immigration raids by Immigration and Customs Enforcement. In response, federal, state, and local authorities have all escalated a brutal crackdown against the anti-ICE demonstrators, with law enforcement deploying tear gas, bean bags, and rubber bullets against protesters and journalists alike, and President Donald Trump activating state National Guard members in defiance of Governor Gavin Newsom's wishes. "This is a chilling statement." On Sunday, according to the Los Angeles Times, the Los Angeles Police Department made another escalation, flying over a group of demonstrators in downtown LA in a helicopter and announcing, "I have all of you on camera. I'm going to come to your house."

Discovering physical laws from data is a fundamental challenge in scientific research, particularly when high-quality data are scarce or costly to obtain. Traditional methods for identifying dynamical systems often struggle with noise sensitivity, inefficiency in data usage, and the inability to quantify uncertainty effectively. To address these challenges, we propose Langevin-Assisted Active Physical Discovery (LAPD), a Bayesian framework that integrates replica-exchange stochastic gradient Langevin Monte Carlo to simultaneously enable efficient system identification and robust uncertainty quantification (UQ). By balancing gradient-driven exploration in coefficient space and generating an ensemble of candidate models during exploitation, LAPD achieves reliable, uncertainty-aware identification with noisy data. In the face of data scarcity, the probabilistic foundation of LAPD further promotes the integration of active learning (AL) via a hybrid uncertainty-space-filling acquisition function. This strategy sequentially selects informative data to reduce data collection costs while maintaining accuracy. We evaluate LAPD on diverse nonlinear systems such as the Lotka-Volterra, Lorenz, Burgers, and Convection-Diffusion equations, demonstrating its robustness with noisy and limited data as well as superior uncertainty calibration compared to existing methods. The AL extension reduces the required measurements by around 60% for the Lotka-Volterra system and by around 40% for Burgers' equation compared to random data sampling, highlighting its potential for resource-constrained experiments. Our framework establishes a scalable, uncertainty-aware methodology for data-efficient discovery of dynamical systems, with broad applicability to problems where high-fidelity data acquisition is prohibitively expensive.

On any given day, Los Angeles police officers record roughly 8,000 interactions with the public on body-worn cameras. Most of the footage goes unseen. The city spent millions on the cameras to help provide transparency and accountability, but LAPD officials say they don't have enough personnel to monitor the countless hours of recordings. The department has also struggled to keep tabs on whether officers are turning off their cameras in violation of department rules -- as members of a disbanded gang unit from the Mission division are suspected of doing in order to cover up thefts, unlawful searches, and other alleged misconduct. A recent internal report suggested lapses in body-cam activation are more widespread than the department has previously let on, and that its system for auditing compliance falls short.

The Los Angeles Police Department is considering changing department policy to increase random reviews of body camera recordings that don't involve arrests or the use of force, according to an email from Chief Michel Moore to his senior staff. Moore wrote that "it's in our best interest to strengthen and reinforce" the number of incidents involving patrol and gang officers that are reviewed roughly every 30 days, "which more closely aligns with other agencies." "Additionally, I support expanding the incidents that qualify beyond those in which no enforcement action was taken," Moore said in the Oct. 19 email, a copy of which was reviewed by The Times. Internal LAPD reports indicate the problem of police officers flouting department body camera policy is more widespread than officials have been telling the public. Moore's directive comes months after revelations that members of a scandal-ridden gang unit were routinely turning off their body-worn cameras in violation of department policy.

Researchers will use artificial intelligence to analyze the tone and word choice that LAPD officers use during traffic stops, the department announced Tuesday, part of a broader study of whether police language sometimes unnecessarily escalates public encounters. Findings from the study, conducted by researchers from USC and elsewhere, will be used to help train officers on how best to navigate encounters with the public and to "promote accountability," said Cmdr. Machine learning, she said at a meeting of the Board of Police Commissioners, "is in its infancy, but will undoubtedly become a profound element in officer training in the future." Over three years, researchers will review body camera footage from roughly 1,000 traffic stops, then develop criteria on what constitutes an appropriate interaction based on public and office feedback and a review of the department's policies, according to Benjamin A.T. Graham, an associate professor of international relations at USC and one of the study's authors. These criteria will then be fed into a machine learning program, which will "learn" how to review videos on its own and flag instances where officers cross the line, Graham said.

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways. Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges. Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them. The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility. However, often the public is never informed of how these systems operate and resultant institutional decisions are made. A ``black box'' effect of automated decision-making systems reduces the public's perceptions of integrity and trustworthiness. The result is that the public loses the capacity to identify, challenge, and rectify unfairness or the costs associated with the loss of public goods or benefits. The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system. We formulate an automated decision-making system as a data science lifecycle (DSL) and examine the implications of fiduciary responsibility within the context of the DSL. Fiduciary responsibility within DSLs provides a methodology for addressing the public's lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public. We posit that fiduciary responsibility manifests in several contexts of a DSL, each of which requires its own mitigation of sources of mistrust. To instantiate fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive policing case study is examined.