Seminar: Takanori Fujiwara

Wednesday, May 5, 2021 1:00 pm - 2:15 pm EDT (GMT -04:00)

Comparative Analysis with Intelligent Visual Interfaces

Takanori Fujiwara

Department of Computer Science

University of California, Davis, CA 95616, USA



Comparison—the act of finding similarities and differences between two or more groups within datasets—is rooted as a fundamental analysis task. However, this task is non-trivial when analyzing large network or high-dimensional datasets. In such cases, it is difficult to identify the key contributing factors to the similarities and differences of different groups from all possible relationships or attributes. Representation learning can help address this process by extracting influential factors for a particular aspect within a dataset (e.g., data variance). But, due to their inability to cover a wide range of data types and analysis targets, these existing techniques have limited capabilities for comparative analysis.

In this talk, I will address the challenges of comparative analysis with intelligent visual interfaces that couple interactive visualizations and contrastive learning—a new emerging representation learning scheme that finds salient patterns in one dataset relative to another dataset.  I will demonstrate the effectiveness of these intelligent visual interfaces for network data and high-dimensional data comparisons by analyzing real-world datasets. Finally, I will discuss my future plans to develop a new interactive representing learning method in addition to future research directions that will further expand the field of comparative analysis. 

Biographical Sketch

Takanori Fujiwara is a Ph.D. candidate at the Department of Computer Science at the University of California, Davis where he is a member of the Visualization and Interface Design Innovation research group, advised by Dr. Kwan‑Liu Ma. He works at the intersection of data science and data visualization where his current research focuses on developing techniques in visual analytics, machine learning, and network science to analyze high-dimensional and network data. He is especially interested in how the combination of representation learning and interactive visualization can aid comparative analysis. He has published his research in top-tier visualization venues at the IEEE Transactions on Visualization and Computer Graphics and the IEEE VIS conferences. His work received a Best Paper Honorable Mention at the IEEE VIS in 2019 and the Best Graduate Researcher Award from the Department of Computer Science at UC Davis in 2020. Before UC Davis, he received his Master's degree in Environmental Science and B.E. in Systems Innovation from the University of Tokyo and has worked for Kajima Corporation in Japan.