In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models.

In these diverse real-world applications, there is often a degree of uncertainty in the input which has led to the study of stochastic matching models.

Modern exchanges include 2-and 3-way swaps, making the kidney exchange clearing problem NPhard.

During the torture ritual that was doing my taxes this year, I was surprised to find myself giddy after reading these words: "You are now chatting with IRS Representative-1004671045." I had gotten stuck trying to parse my W-2, which, under "Box 14: Other," contained a mysterious 389.70 deduction from my overall pay last year. I tapped the chat button on my tax software for help, expecting to be sucked into customer-service hell. Instead, a real IRS employee answered my question in less than two minutes. The program is not TurboTax, or any one of its many competitors that will give you the white-glove treatment only after you pony up. It is Direct File, a new pilot program made by the IRS.

A student-led team of robotics experts will participate in the penultimate event next week for an international challenge that could pave the way for future breakthrough innovations in the world of autonomous vehicles. It should, given Pittsburgh universities' history of performing well in challenges such as the DARPA Grand Challenge, DARPA Urban Challenge or the 2012-2015 Robotics Challenge. But this time it's not a Carnegie Mellon University-led team that is preparing – it's a student group of roboticists from the University of Pittsburgh readying itself for next week's Indy Autonomous Challenge finals on October 23rd. The goal of the challenge is a race around the famous Indianapolis Motor Speedway in self-driving cars – the grand prize of $1 million is up for grabs. Leading the team is Nayana Suvarna, the head of Pitt's Robotics & Automation Society (RAS), a robotics club at the school that pursues robotics education opportunities (the school doesn't have a formal robotics program).

As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games. These games involve the optimization problems of multiple players in which the players can affect each other in their objective functions. In generalized Nash equilibrium problems (GNEPs), a player's set of feasible actions is also impacted by the actions taken by other players in the game; see Facchinei and Kanzow (2010) for more background on this problem. One example of such impact comes in the form of joint/"coupled" constraints as referenced by Rosen (1965), Harker (1991), and Facchinei et al. (2007) which involve other players' variables in the constraints of the feasible region. We extend the framework of Ratliff et al. (2014) to find inverse optimization solutions for the class of GNEPs with joint constraints. The resulting formulation is then applied to a simulated multi-player transportation problem on a road network. Also, we provide some theoretical results related to this transportation problem regarding runtime of the extended framework as well as uniqueness and non-uniqueness of solutions to our simulation experiments. We see that our model recovers parameterizations that produce the same flow patterns as the original parameterizations and that this holds true across multiple networks, different assumptions regarding players' perceived costs, and the majority of restrictive capacity settings and the associated numbers of players. Code for the project can be found at: https://github.com/sallen7/IO_GNEP.

It has worked its way into our daily lives, from voice assistants like Siri and Alexa to traffic apps that guide us around gridlock, cars that drive themselves and news stories that pop up on our social media feeds. Researchers in the University of Maryland's College of Computer, Mathematical, and Natural Sciences work at the forefront of machine learning technology, where computers analyze data to identify patterns and make decisions with minimal human intervention. These faculty members are using machine learning for applications that touch many aspects of our lives--from weather prediction and health care to transportation, finance and wildlife conservation. Along the way, they are advancing the science of exactly how computers learn. The shift from a cash economy to one reliant on electronic transactions has left many consumers feeling vulnerable to identity theft and bank fraud. And it's no wonder--in 2018, the Federal Trade Commission received over 440,000 reports of identity theft, largely from stolen credit card and social security numbers. For any consumer, that figure is concerning.

Advances in artificial intelligence are often framed as though they'll upend human society overnight, and for the last month technologists have had a new darling to obsess over. It's a piece of software called GPT-3, which can write essays, songs and computer code in response to requests that humans type into its interface in plain text. The program comes from OpenAI, a top artificial intelligence research organization. OpenAI recently began giving technologists access to it in a private testing period, and they are in love. GPT-3 can build a website layout based on a text-based description, or write a Harry Potter story in the style of a hard-boiled detective novel.

Motivated by kidney exchange, we study a stochastic cycle and chain packing problem, where we aim to identify structures in a directed graph to maximize the expectation of matched edge weights. All edges are subject to failure, and the failures can have nonidentical probabilities. To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical. We formulate a relevant non-convex optimization problem and propose a tractable mixed-integer linear programming reformulation to solve it. In addition, we propose a model that integrates both risks and the expected utilities of the matching by incorporating conditional value at risk (CVaR) into the objective function, providing a robust formulation for this problem. Subsequently, we propose a sample-average-approximation (SAA) based approach to solve this problem. We test our approaches on data from the United Network for Organ Sharing (UNOS) and compare against state-of-the-art approaches. Our model provides better performance with the same running time as a leading deterministic approach (PICEF). Our CVaR extensions with an SAA-based method improves the $\alpha \times 100\%$ ($0<\alpha\leqslant 1$) worst-case performance substantially compared to existing models.