Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Events on February 7, 2024

Ghulam Jilani Quadri and Hyeon Jeon Presents:

Multiple Talks

February 7, 2024 at 12:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.


Ghulam Jilani Quadri: Driving an Effective Data Exploration through a Perception-Based Framework

Abstract: Data has evolved from purely scientific artifacts to a core part of public reasoning, decision-making, and communication. For example, during the COVID-19 pandemic, data were used to justify actions and drive understanding at levels ranging from the Centers for Disease Control to journalists at the New York Times to citizen scientists on Twitter. These efforts universally relied on visualizations to provide an accessible and effective way to explore and communicate data. However, how we represent data can dramatically influence the conclusions that we draw from that data. While visualization design depends on the visual channels used, visualization types, or visual tasks, we need a concrete human-centered system to understand the intersection of these factors to create a task-optimized visualization. In this talk, we address this gap by developing perceptual frameworks built on empirical models, data-driven metrics, and topology-based systems that consider design decisions and the task to maximize statistical task efficacy. This talk will describe systems, techniques, experiments, and user studies to model visualization design optimization and data transformation for low-level visual tasks. Further, the talk will elaborate on utilizing the framework to provide less ambiguous data presentations, leading to better quality and higher confidence in decision-making. Finally, the talk will introduce the ongoing and future work on driving effective data exploration using the proposed perceptual and conceptual models through cognitive and perception science.

Bio: Ghulam Jilani Quadri is a tenure-track Assistant Professor at the School of Computer Science at the University of Oklahoma. Ghulam was previously a Postdoctoral Research Associate and CRA/CCC/NSF Computing Innovation Fellow in the Department of Computer Science at the University of North Carolina-Chapel Hill, working with Dr. Danielle Albers Szafir. Quadri earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2021, advised by Dr. Paul Rosen. He holds an M.S. in Computer Science from the University of South Florida and a B.E. in Computer Engineering from the University of Mumbai. Quadri's research lies at the intersection of Information Visualization, HCI, ML Models, and perception & cognition. His primary goal is to create a perceptual and human-centered framework to optimize visualization design, improving decision-making quality and confidence, while providing objective guidance for designers. His research contributions have received significant support, funding, and recognition, including honorable mentions at the VAST Challenge 2017, an NSF Computing Innovation Fellowship in 2021, the IEEE VGTC Best Dissertation Award in 2022, and honorable mentions for the Best Paper Award at IEEE VIS 2023.

Hyeon Jeon: Making High-dimensional Data Analysis More Reliable

Abstract: Data often lies in high-dimensional (HD) space. However, our displays only cover a limited dimensionality, which is typically two. Visualizing high-dimensional data thus incorporates a process of “compressing” or “summarizing” the data into low-dimensional (LD) space, which makes visualizations less reliable. For example, visualizations may miss important aspects of the HD data or show distorted representations. In this talk, we will explore how we can overcome such reliability issues, focusing on the algorithms for reducing the dimensionality of HD data while maintaining the original structure of data (i.e., dimensionality reduction algorithms). Our discussion will cover the entire HD data analysis pipeline, which includes four key stages: designing algorithms, validating outputs, visualizing results, and perceiving patterns. We will conclude the talk by highlighting ongoing challenges in enhancing the reliability of HD data analysis and visualization.

Bio: Hyeon Jeon is a Ph.D. Student at the Department of Computer Science and Engineering, Seoul National University, Seoul, Korea. He is under the supervision of Prof. Jinwook Seo, working as a member of the Human-Computer Interaction Laboratory. His research interests span the field of Visual Analytics and Machine Learning. He is currently working on developing new techniques that support reliable data analysis. His research was published in prestigious visualization and HCI venues, including TVCG, VIS, PacificVIS, and IUI. Before starting his Ph.D. program, he received a B.S. degree in Computer Science and Engineering from the Pohang University of Science and Technology (POSTECH), Pohang, Korea.

Posted by: Nathan Galli