"One of the dreams I had as a kid was about the first day of school, and being able to build and be creative, and it was the happiest day of my life. And at MIT, I felt like that dream became reality," says Ballesteros. Growing up in the suburban town of Spring, Texas, just outside of Houston, Erik Ballesteros couldn't help but be drawn in by the possibilities for humans in space. It was the early 2000s, and NASA's space shuttle program was the main transport for astronauts to the International Space Station (ISS). Ballesteros' hometown was less than an hour from Johnson Space Center (JSC), where NASA's mission control center and astronaut training facility are based.

Each year, a small group of PhD students are chosen to participate in the AAAI/SIGAI Doctoral Consortium. This initiative provides an opportunity for the students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. During 2024, we met with some of the students to find out more about their research and the doctoral consortium experience. They also shared their advice for prospective PhD students. Changhoon Kim completed his PhD in Computer Engineering at Arizona State University.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we hear from Pulkit Verma, recent PhD graduate from Arizona State University. I recently completed my PhD in Computer Science from School of Computing and Augmented Intelligence, Arizona State University. My research focuses on safe and reliable behavior of AI agents.

Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering inherent explainability through paths associating recommended items and seed items, non-experts could not easily understand these explanations. A popular alternative is to convert graph-based explanations into textual ones using a template and an algorithm, which we denote here as ''template-based'' explanations. Yet, these can sometimes come across as impersonal or uninspiring. A novel method would be to employ large language models (LLMs) for this purpose, which we denote as ''LLM-based''. To assess the effectiveness of LLMs in generating more resonant explanations, we conducted a pilot study with 25 participants. They were presented with three explanations: (1) traditional template-based, (2) LLM-based rephrasing of the template output, and (3) purely LLM-based explanations derived from the graph-based explanations. Although subject to high variance, preliminary findings suggest that LLM-based explanations may provide a richer and more engaging user experience, further aligning with user expectations. This study sheds light on the potential limitations of current explanation methods and offers promising directions for leveraging large language models to improve user satisfaction and trust in recommender systems.

No one knows just many green jobs will be created in the United States over the next decade, but there's little disagreement that the demand will create a huge opportunity for the next generation of workers. Thanks to the Inflation Reduction Act of 2022, an estimated 800 billion will flow from the federal government over the next 10 years to fund massive clean energy programs, from solar and wind installations to energy-efficient buildings to business and neighborhood microgrid power production. Billions more in state and federal money will help build out an electrified transportation system, including electric cars, trucks, trains and the infrastructure to support them. High school and university educators are reporting increasing interest in "green" careers: jobs that help address global warming and other environmental issues, with enough of a future to pay the bills and then some. Given the drumbeat of bad news on a changing climate, it might even be considered a matter of survival.

In a series of interviews, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. The Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. In this latest interview, we met Bálint Gyevnár and found out about his work creating explanations for AI-based decision-making systems. I was lucky enough to study at this university for the last eight years, so I've done my undergraduate and master's here as well. I really enjoy Edinburgh – it's a very stimulating environment and there's a lot of academic discussion which prompted me to stay here.

Colleges across the US have added AI courses to their curriculum as companies scramble to find skilled employees and students look for higher paying fields. While much of the world sees the tech as the way of the future, some people have cautioned students to not gamble tens of thousands of dollars on technology that seems to evolve each day. Tiffany Hsieh, who works in the development of AI told, DailyMail.com: 'While there is plenty of data telling us about the number of skills that will be affected by Generative AI, much of that data doesn't tell us about the nature of the impact on those skills. But a graduate student majoring in the tech at New York's Yeshiva University said he believes AI is here to stay and the degree would be necessary for him to become a machine language engineer, which pays at least 160,000 a year.

The AAAI/SIGAI Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. This year, 30 students were selected for this programme, and we've been meeting them and talking about their research. In this interview, Raffaele Galliera, tells us about his work on deep reinforcement learning for communication networks. My name is Raffaele Galliera and I'm a PhD student in the Intelligent Systems and Robotics program at the University of West Florida, located in Pensacola. It's a joint program between the University of West Florida and the Institute for Human and Machine Cognition (IHMC), which is a nonprofit organization based in Pensacola.

Angelique Yameogo is studying for a PhD at the University of South Brittany in France. Her thesis is focused on fake news analysis using data science techniques. She has worked with several companies in Burkina Faso as an artificial intelligence engineer and mobile developer. She is skilled in HTML, CSS, JavaScript, pandas, sci-kit-learn, NLTK and others. Through networking, you can also access hidden opportunities and keep abreast of trends and developments in your field.

"There are a lot of folks around the world, especially younger folks, contemplating whether to pursue a STEM degree or not. There might be self-doubt about whether their background becomes a barrier, or they might think tech is intimidating and whether they'll be able to do it. Even if one person who doesn't have enough resources or facing challenges, gathers the courage from examples like this, not just mine, it would bring a sense of achievement for me because I feel like there should be nothing that should stop a true learner." Pratibha serves on the AnitaB AI committee and has worked both in natural language processing and deep learning. She has founded a start-up in video analytics that was selected for the Target Accelerator program.