The Advances of MAchine Learning in THEory & Applications (AMALTHEA) REU Site aims to provide top quality educational experiences to a diverse community of undergraduate students through research participation in the area of Machine Learning (ML). The relevance and importance of ML is not limited to specialized technological innovations, as it was in the past. Nowadays, it also increasingly influences everyday life through its contributions to applications such as voice/face recognition, credit fraud detection, intelligent recommendation systems and many others. Furthermore, ML is inherently multi-disciplinary as it draws from advances in disciplines such as computing, statistics, mathematics, physics, biology and engineering, to a name a few major ones. The project's thrust area is the theory of ML and how it can be integrated and applied to important real-life problems, thus exposing participants to both theory and applications. AMALTHEA involves 10 undergraduate students per year from a broad spectrum of disciplines for 10 weeks in the summer. These participants perform supervised research, whose results are going to impact the field of ML itself, as well as how ML is applied in other scientific disciplines. For this purpose, the faculty mentors have ample expertise in ML and past experience of effectively engaging undergraduate students into state-of-the-art research.
Over its lifespan, the project will directly impact a diverse group of 30 motivated students, the majority of which may not have access to such research participation opportunities otherwise. The participants will be exposed to cutting-edge ML research, as well as professional development activities, such as technical seminars and career-related workshops. Furthermore, AMALTHEA aims to involve overall 10 graduate students in undergraduate teaching and mentoring activities during the summer experiences. Finally, the project's research and education endeavors are supported by its advisory board, which consists of seasoned educators, ML industry practitioners and researchers. Research results will be published in interdisciplinary conferences, and, potentially, technical journals.
The REU Site web site (www.amalthea-reu.org) provides additional information for students as well as educators and researchers in machine learning.