Three CSE Faculty Members Received NSF Career Awards
Three CSE assistant professors, Brian Kulis, Arnab Nandi, and Anastasios Sidiropoulos received National Science Foundation Faculty Early CAREER Development (CAREER) Awards, one of the highest national awards junior faculty can achieve. The CAREER award selection is based on an outstanding research record and plan, a strong effort of integration of education, and research within the context of the mission of each academic institution. The grant winner receives more than $400,000 for the research and education project. Twenty-nine CSE faculty members have earned CAREER Awards since the program started in 1995.
The newest three CSE faculty members are working in three different but very important areas: machine learning, databases, and computer science theory.
Dr. Brian Kulis is working in the area of artificial intelligence, focusing on machine learning. His CAREER project is “Rich and Scalable Optimization for Modern Bayesian Nonparametric Learning”. His research focuses on data analysis and machine learning, and the goal is to develop new tools and algorithms for rich analysis of very large-scale data. As part of the project, he is developing algorithms for problems such as analyzing the evolution of communities in large networks, image classification, and automatic determination of topics in document collections.
Kulis says of the honor, “This award will provide support for several graduate students, and will make it possible for my group to design and analyze new methods for large-scale data analysis based on Bayesian nonparametric models. I am thankful to the National Science Foundation for their ongoing support of my research.”
Arnab Nandi is working in the area of databases focusing on data analytics and interactive query interfaces. His CAREER project is “Querying Beyond Keyboards: Gesture-driven Querying of Databases”. Computing devices that use non-traditional methods such as gestures to interact with data are becoming more popular. While decades of research in databases have gone into making databases more performant, the focus has typically been on large-scale pipelines, and not end users. Research in human-computer interaction and visualization has recently been investigating data management concepts for user interfaces. In order to bridge this gap, this project takes a new approach towards enabling interactive and gestural querying of data. This includes a new query model featuring the concept of a "query intent", and an expressive "gestural query language," allowing users to use gestures as the sole mode of data interaction. The project looks into methods for "intent interpretation," allowing the system to better recognize the user's query intent during the gesture and "feedback generation," allowing the system to provide feedback during the gesture articulation. All components will be designed while keeping interactivity in mind, in order to maintain a low-latency loop and ensure a fluid user experience.
An Ohio State College of Engineering faculty member since 2012, Nandi is also a founder of The STEAM Factory, a collaborative interdisciplinary research and public outreach initiative, and faculty director of Ohio State’s OHI/O Hackathon.
Anastasios (Tasos) Sidiropoulos is working in the area of theoretical computer science, focusing on computational geometry and graph theory. His CAREER project is “Geometric frontiers in algorithm design”. The analysis of complex data sets is a task of increasing importance for science and engineering. Even though in many applications there is an abundance of raw inputs, extracting meaningful information can often be a major computational challenge. Over the recent years, geometric methods have become an indispensable tool towards this goal. The reason behind this development is the fact that a data set endowed with pairwise similarities can be naturally interpreted as a geometric space. Such data sets include DNA sequences, statistical distributions, collections of news articles, and so on. Under this interpretation, several important data analytic questions can be understood as geometric computational problems. The main algorithmic challenges in this context occur in high-dimensional, or more generally, complex metric spaces. This project aims at resolving some of the main problems inherent in the analysis of such geometric data sets, and thus enabling improved solutions for a variety of computational tasks.
"I am very grateful for all the support I have received from my mentors, colleagues, family, and friends, as well as the department, and OSU in general," said Tasos.