Emergency Response Time (ERT) is crucial for urban safety, measuring cities' ability to handle medical, fire, and crime emergencies. In NYC, medical ERT increased 72% from 7.89 minutes in 2014 to 14.27 minutes in 2024, with half of delays due to Emergency Vehicle (EMV) travel times. Each minute's delay in stroke response costs 2 million brain cells, while cardiac arrest survival drops 7-10% per minute. This dissertation advances EMV facilitation through three contributions. First, EMVLight, a decentralized multi-agent reinforcement learning framework, integrates EMV routing with traffic signal pre-emption. It achieved 42.6% faster EMV travel times and 23.5% improvement for other vehicles. Second, the Dynamic Queue-Jump Lane system uses Multi-Agent Proximal Policy Optimization for coordinated lane-clearing in mixed autonomous and human-driven traffic, reducing EMV travel times by 40%. Third, an equity study of NYC Emergency Medical Services revealed disparities across boroughs: Staten Island faces delays due to sparse signalized intersections, while Manhattan struggles with congestion. Solutions include optimized EMS stations and improved intersection designs. These contributions enhance EMV mobility and emergency service equity, offering insights for policymakers and urban planners to develop safer, more efficient transportation systems.

Across New Jersey, reports of mysterious drone sightings have been rising for weeks, with people contacting authorities and posting on social media about aerial vehicles behaving strangely, especially at night. The reports have spread in New York City as well, with alleged sightings in Staten Island, Brooklyn, and Queens. The United States Federal Aviation Administration imposed a temporary ban in New Jersey this week on flying drones over the Army's Picatinny Arsenal in Wharton and a golf course owned by US president-elect Donald Trump in Bedminster. While the mystery has become a growing sensation, virtually no information has been available about whether the sightings are connected or represent anything out of the ordinary. Vague and noncommittal statements from state and federal authorities have only complicated the matter and fueled public intrigue.

A social media user filmed what appeared to be drones flying over the Fairfield train station. Several drones were allegedly spotted in the skies above a Connecticut suburb on Thursday night, adding to recent drone sightings that have perplexed locals and raised questions about possible national security and public safety concerns. A social media user on X posted videos of possible drones in Fairfield, 55 miles northeast of New York City. Drones hovering over New Jersey and near Staten Island, New York in recent weeks have raised concerns amid a lack of clarity over their origin. A social media user said she filmed several drones hovering over Fairfield, Connecticut on Thursday night.

The field of neuroscience and the development of artificial neural networks (ANNs) have mutually influenced each other, drawing from and contributing to many concepts initially developed in statistical mechanics. Notably, Hopfield networks and Boltzmann machines are versions of the Ising model, a model extensively studied in statistical mechanics for over a century. In the first part of this chapter, we provide an overview of the principles, models, and applications of ANNs, highlighting their connections to statistical mechanics and statistical learning theory. Artificial neural networks can be seen as high-dimensional mathematical functions, and understanding the geometric properties of their loss landscapes (i.e., the high-dimensional space on which one wishes to find extrema or saddles) can provide valuable insights into their optimization behavior, generalization abilities, and overall performance. Visualizing these functions can help us design better optimization methods and improve their generalization abilities. Thus, the second part of this chapter focuses on quantifying geometric properties and visualizing loss functions associated with deep ANNs.

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.

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