The University of Washington site of IFDS is funding six Research Assistants in Winter quarter 2021 to collaborate across disciplines on IFDS research. Each one is advised by a primary and a secondary adviser, who are members of IFDS.
Kristof Glauninger (Statistics) works with Zaid Harchaoui (Statistics), Virginia E. Armbrust (Oceanography) and François Ribalet (Oceanography) on statistical modeling for marine ecology. He focuses on statistical inference questions arising from phytoplankton population modeling. He is also interested in optimal transport and machine learning.
Alec Greaves-Tunnell (Statistics) works with Zaid Harchaoui (Statistics), Ali Shojaie (Biostatistics), and Azadeh Yazdan (Bioengineering) on distributionally robust learning for brain science and engineering. He is also interested in sequence models and time series in general, with applications to language processing and music analysis.
Adhyyan Narang (ECE) works with Maryam Fazel (ECE) and Lilian Ratliff (ECE). So far, he has worked to provide theoretical answers to foundational questions in learning from data: such as generalization of overparameterized models and robustness to adversarial examples. More recently, he is interested in providing guarantees for optimization in uncertain online environments in the presence of other agents.
Swati Padmanabhan (ECE) works with Yin Tat Lee (Computer Science and Engineering) and Maryam Fazel (ECE) on designing a faster algorithm for the optimal design problem. She is also interested in semidefinite programming in general. She has developed several semidefinite programming in different settings which are fastest known in their setting.
Omid Sadeghi (ECE) works with Maryam Fazel as well as Lillian Ratliff (ECE). He is interested in the design and analysis of online optimization algorithms with budget constraints, and with stochastic or adversarial inputs. His work includes online resource allocation with submodular utility functions.
Zhihan Xiong (Computer Science and Engineering) works with Maryam Fazel (Electrical and Computer Engineering) and Kevin Jamieson (Computer Science and Engineering) as well as
Lalit Jain (Business School). His current work addresses optimal experimental design in a streaming setting where the measurement budget is limited and the quality of measurements vary unpredictably over time.