Mariya Toneva joins the tenure-track faculty at our institute starting September 2022. Mariya’s research is at the intersection of machine learning, natural language processing, and neuroscience. Her group bridges language in machines with language in the brain, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems.

Prior to joining MPI-SWS, Mariya conducted research as a C.V. Starr Fellow at the Princeton Neuroscience Institute. She received her Ph.D. in a joint program between Machine Learning and Neural Computation from Carnegie Mellon University, and her B.S. in Computer Science and Cognitive Science from Yale University.

Two MPI-SWS faculty, Maria Christakis and Elissa Redmiles, have earned highly competitive Google Research Scholar awards. Maria Christakis's award was given for her research on metamorphic specification and testing of machine-learning models and Elissa Redmiles's award was given for her research on aligning technical data privacy protections with user concerns.

The Google Research Scholar Program provides unrestricted gifts of up to $60,000 to support research at institutions around the world and is focused on funding world-class research conducted by early-career professors. Award proposals go through an internal, merit-based review process and selected faculty can receive a Google Research Scholar award only once in their career. Award recipients are assigned a liaison at the company to share findings with and as a point of contact for further collaboration.

MPI-SWS researchers Jiarui Gan, Rupak Majumdar, Goran Radanovic, and Adish Singla have received an Outstanding Paper Award Honorable Mention at AAAI 2022, for their paper "Bayesian Persuasion in Sequential Decision-Making."

The AAAI conference is one of the leading international venues for AI research, covering all sub-areas of the field. AAAI 2022 received more than 9000 submissions, of which 1370 were accepted for publication. Of these 1370 papers, only three papers were selected for an outstanding paper award. Of these three outstanding papers, two were authored by researchers at the Saarland Informatics Campus.

Adish Singla, head of the MPI-SWS Machine Teaching Research group, has been awarded a 2021 ERC Starting Grant. Over the next five years, his project TOPS will receive funding of 1.5 million euros for research on "Machine-Assisted Teaching for Open-Ended Problem Solving: Foundations and Applications". Read more about the TOPS project below.

ERC grants are the most prestigious and the most competitive European-level awards for ground-breaking scientific investigations. This year, less than 10% of all ERC Starting Grant applicants across all scientific disciplines received the award, with only 23 awardees in Computer Science across all of Europe! You can find more information about ERC Starting Grants awarded this year at https://erc.europa.eu/news/StG-recipients-2021.

The TOPS Project

Computational thinking and problem solving skills are essential for everyone in the 21st century, both for students to excel in STEM+Computing fields and for adults to thrive in the digital economy. Consequently, educators are putting increasing emphasis on pedagogical tasks in open-ended domains such as programming, conceptual puzzles, and virtual reality environments.

When learning to solve such open-ended tasks by themselves, people often struggle. The difficulties are embodied in the very nature of tasks being open-ended: (a) underspecified (multiple solutions of variable quality), (b) conceptual (no well-defined procedure), (c) sequential (series of interdependent steps needed), and (d) exploratory (multiple pathways to reach a solution). These struggling learners can benefit from individualized assistance, for instance, by receiving personalized curriculum across tasks or feedback within a task. Unfortunately, human tutoring resources are scarce, and receiving individualized human-assistance is rather a privilege. Technology empowered by artificial intelligence has the potential to tackle this scarcity challenge by providing scalable and automated machine-assisted teaching. However, the state-of-the-art technology is limited: it is designed for well-defined procedural learning, but not for open-ended conceptual problem solving.

The TOPS project will develop next-generation technology for machine-assisted teaching in open-ended domains. We will design novel algorithms for assisting the learner by bridging reinforcement learning, imitation learning, cognitive science, and symbolic reasoning. Our theoretical foundations will be based on a computational framework that models the learner as a reinforcement learning agent who gains mastery with the assistance of an automated teacher. In addition to providing solid foundations, we will demonstrate the performance of our techniques in a wide range of pedagogical applications.