&ldquo;MouseScholar: Evaluating an Image+Text Search System for Biocuration&rdquo;, Juan Trelles Trabucco, Carla Floricel, Cecilia Arighi, Hagit Shatkay, Daniela Raciti, Martin Ringwald, and G. Elisabeta Marai is an accepted paper at 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023). Of the 1084 paper submissions submitted to BIBM this year, only 211 regular papers were accepted (acceptance rate: 19.5%). 
Project Description: 
In this work, we instantiate and evaluate a first-in-kind, hybrid image+text document search system for biocuration (the process of analyzing biological or biomedical articles to organize biological data into data repositories using taxonomies and ontologies). The system, MouseScholar, leverages an image modality taxonomy derived in collaboration with biocurators, in addition to figure segmentation, and classifiers components as a back-end and a streamlined front-end interface to search and present document results. We formally evaluated the system with ten biocurators on a mouse genome informatics biocuration dataset and collected feedback. The results demonstrate the benefits of blending text and image information when presenting scientific articles for biocuration. 
Congratulations EVL research team on being accepted for publication with this prestigious Conference!

EVLers Juan Trelles Trabucco, Carla Floricel and Professor Elisabeta Marai Biocuration Research Recognized by IEEE BIBM 2023

EVl graduate student Hal Brynteson&rtsquo;s research in fluid dynamics will be showcased in Washington, DC at the Gallery of Fluid Motion during the 76th Annual Meeting of the APS Division of Fluid Dynamics. The Gallery of Fluid Motion is a visual record of the aesthetic and science of contemporary fluid mechanics shared with researchers in the discipline, as well as the general public. Submissions are judged on an annual basis for their visual qualities and scientific interest. 
Brynteson&rsquo;s poster entitled &ldquo;Effects of employing liquid-liquid emulsions on heat transfer within a turbulent Rayleigh-Benard convection&rdquo; (Authors: H. Brynteson, A. Moradi Bilondi, N. Scapin, L. Brandt, P. Mirbod, M.E. Papka) will be presented November 20th.

EVL Researcher Hal Brynteson&rsquo;s Work Showcased at the Gallery of Fluid Motion

PhD student Ke Fan&rsquo;s research in profiling and visualizing performance data at scale is recognized with a best poster nomination at SC23. &ldquo;Two-phase IO Enabling Large-scale Performance Introspection&rdquo; focuses on developing an effective and interactive exploration of the profiles of large-scale parallel programs, which remain a challenge due to the high I/O overheads of profiles and the difficulties in scaling downstream visualization tools. 
The poster presents Viveka, a lightweight end-to-end system for profiling and visualizing the performance of MPI-based applications. 
- A simple data format for the generated logs that minimizes metadata, leading to a smaller storage footprint and faster load times. (on average 3x more lightweight than Caliper). 
- Development of a custom two-phase data aggregation system to scale parallel I/O (of performance data) to high core counts, ensuring minimal overhead. 
- A lightweight web-based visualization dashboard that is capable of performing interactive analysis of performance data collected at high process counts. 
- A case study on real parallel applications to demonstrate the efficacy of our profiling and visualization system. 
Funding in part by NSF RII Track-4 award 2132013, NSF PPoSS planning award 2217036, NSF PPoSS large award 2316157 and, NSF collaborative research award 2221811. Compute hours provided on Theta Supercomputer Argonne Leadership Computing Facility&rsquo;s Directors Discretionary (DD) program.

EVL Affiliated Faculty Sidharth Kumar&rsquo;s Advisee Recognized with Best Poster Nomination at SC23

EVL&rsquo;s professor Marai is part of UIC&rsquo;s brain research now funded by a $5 million grant from the National Institutes of Health. The project incorporates brain tissue research at UIC, Northwestern University, Lurie Children’s Hospital, Rush University, and the University of Chicago, utilizing the INTUITION data platform that combines tissue data with clinical, functional, genetic, and 3D imaging information. The goal of the project is to assist clinicians in treating patients and help researchers better understand the mechanisms of brain disorders by developing a new resource for clinical care and research on epilepsy, brain tumors, and neurological disorders. 
The project is led by Dr. Jeffrey Loeb, the John S. Garvin Endowed Chair in Neurology at UIC, professor, and head of neurology and rehabilitation in the College of Medicine. INTUITION was initially supported by funding from the Discovery Partners Institute to create I-BRAIN, an initiative exploring commercialization opportunities around brain tissue data and research. Other UIC researchers include Dr. Anna Serafini, Biswajit Maharathi, Fozia Mir and Dr. Ahmad Daher from the Department of Neurology and Rehabilitation; Dr. Francesco Pucci from the Department of Neurosurgery; and Dr. Tibor Valyi-Nagy from the Department of Pathology. 
For the full article see <a href="https://today.uic.edu/nih-grant-expands-uic-brain-bank-into-citywide-effort-to-study-epilepsy-brain-cancer/?utm_source=mailpoet&utm_medium=email&utm_campaign=today%2B02.14.23">UIC today</a>.

EVL Professor G. Elisabeta Marai Part of Research Team Awarded NIH Funding to Expand UIC&rsquo;s Brain Bank

EVL Research Associate Professor Luc Renambot joins collaborators from the University of Hawaii Manoa and Virginia Tech to present the latest advances in SAGE3 with a tutorial session at the <a href="https://iss2023.acm.org/">ACM ISS 2023</a>. 
SAGE3, the newest and most advanced generation of the Smart Amplified Group Environment, is an open-source software designed to facilitate collaboration among scientists, researchers, students, and professionals across various fields. This tutorial aims to introduce attendees to the capabilities of SAGE3, demonstrating its ability to enhance collaboration and productivity in diverse settings, from co-located office collaboration to remote collaboration to both at once, with diverse displays, from personal laptops to large-scale display walls. Participants will learn how to effectively use SAGE3 for brainstorming, data analysis, and presentation purposes, as well as installation of private collaboration servers and development of custom applications. 
Presenters: 
Jesse Harden, Virginia Tech 
Nurit Kirshenbaum, University of Hawaii at Manoa 
Roderick S. Tabalba Jr., University of Hawaii at Manoa 
Jason Leigh, University of Hawaii at Manoa 
Luc Renambot, University of Illinois Chicago, EVL 
Chris North, Virginia Tech 
In-person and on-line Sunday Nov 5, 3 to 4:30PM, and Wed. Nov 8 1PM to 2:30 PM.

SAGE3 Tutorial Sessions at ACM Interactive Surfaces and Spaces Conference (ACM ISS 2023)

EVL alumnus Khairi Reda presents his research at EVL tech meeting: 
Friday, November 3, 2023, 12PM - 1PM 
EVL Continuum, Room 2068 ERF 
Towards Trustworthy Visual Analytics 
Abstract: Interactive visualization tools, such as Tableau, Jupyter notebooks, and R with ggplot, have become vital instruments in the data science ecosystem. These systems empower users to explore data and search for new patterns that might lead to insights. However, these tools can also expose analysts to noise and random data fluctuations. Analysts can frequently misinterpret these spurious patterns and mistake them for reliable effects. This phenomenon has led to a &ldquo;replication crisis,&rdquo; with many previously published results failing to replicate, eroding trust in the scientific process. 
In this talk, Reda will discuss his research on reducing the risk of spurious visualizations and describe techniques we have developed to operationalize analyst hypotheses and beliefs in the visual analytics process. Empirical results showing that these unobtrusive interactions can promote good analytical practices and reduce the incidence of false discoveries will be discussed. Finally, Reda will show how using visual inference as a benchmark can provide a complementary account of visualization utility, leading to new insights into visualization design beyond what can be learned from traditional &ldquo;graphical perception&rdquo; studies. 
Bio: Khairi Reda is an Associate Professor in the Luddy School of Informatics & Computing at Indiana University Indianapolis. He is an alumnus of EVL, having earned a PhD in CS from the University of Illinois at Chicago. His research employs a variety of methods, weaving together techniques from human-computer interaction, machine learning, and design thinking to investigate new approaches for visualizing and interacting with data.

EVL Alumnus Khairi Reda Presents his Research &ldquo;Towards Trustworthy Visual Analytics&rdquo;

EVL alumni Tommy Marrinan, Victor Mateevitsi and Mike Papka (also EVL Professor) are recognized at <a href="https://sciviscontest2023.github.io/">2023 IEEE VIS SciVis Contest</a> for their research in the visualization of neuronal network simulations of plasticity changes in the human brain. 
The first award was for Best Workflow; enabling a scientific workflow was identified as one of the 4 main tasks for this year&rsquo;s contest. The goal of this component was to combine the functionality of the other tasks into a combined framework and interface that enables experts to explore the data and investigate several questions interactively. Which tools help the users to mark, select, and analyze the data on different levels of complexity and transfer information among these levels? 
The second award was for 1st Place Overall; judges took into account how the submission addressed all 4 main tasks, as well as any additional features. The judging was based on a short paper description of the visualization system and an 8-minute video demo. 
Congratulations, Tommy, Victor, and Mike! 
See <a href="https://insidehpc.com/2023/11/alcf-team-wins-first-place-and-best-workflow-at-2023-ieee-scivis-contest/">Inside HPC</a>, &ldquo;ALCF Team Wins First Place and Best Workflow at 2023 IEEE SciVis Contest&rdquo;

EVL Alumni Win Best Workflow and 1st Place Overall at 2023 IEEE SciVis Contest

EVL affiliated faculty member and Computer Science Assistant Professor Sidharth Kumar and his graduate students Landon Dyken and Will Usher are recognized with the Best Paper Award at the 13th IEEE Symposium on Large Data Analysis and Visualization. This year&rsquo;s LDAV 2023 focus is on Big Data Analysis and Visualization, specifically targeting methodological innovation, algorithmic foundations, and possible end-to-end solutions. 
&ldquo;Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes&rdquo; (Usher, W., Dyken, L., Kumar, S.) proposes a new GPU algorithm for implicit isosurface rendering that progressively traverses rays through the volume and decompresses data on-demand to minimize its memory footprint. Up to 5.7× reductions in overall memory use and 8.4× reductions in data decompressed compared to the state of the art in memory constrained isosurface extraction is acheived, without sacrificing interactivity. At 1280×720, the Richtmyer-Meshkov averages 264ms per-pass and 1.2s total on an RTX 3080. 
Congratulations team!

EVL Affiliated Faculty Sidharth Kumar&rsquo;s Advisees Recognized with Best Paper Award at LDAV 2023

The EVL research team lead by Professor Liz Marai and graduate research assistants Carla Floricel and Andrew Wentzel has their visual analytics work published at the IEEE Transactions on Visualization and Computer Graphics session at the IEEE Vis 2023 Conference held in Melbourne, Australia, October 22- 27, 2023. &ldquo;Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining&rdquo; (authors: Carla Gabriela Floricel, Andrew Wentzel, Abdallah Mohamed, Clifton David Fuller, Guadalupe Canahuate, G. Elisabeta Marai) is an interactive analysis system to support sequential rule mining (SRM) model builders who work in cancer symptom research. SRM is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. Our system facilitates mechanistic knowledge discovery of treatment-related toxicities (i.e., &ldquo;roses have thorns&rdquo;) in large-scale cohort data. 
This work was developed by visual computing researchers at EVL in collaboration with data mining specialists at the University of Iowa, and clinicians at the MD Anderson Cancer Center in Texas, and it is supported by funding from the US National Institutes of Health (NIH awards NCI-R01-CA258827 and NLM-R01-LM012527), and US National Science Foundation (NSF awards CDSE-1854815 and CNS-1828265).

EVL Research Team Publishes Their Visual Analytics Research for Oncological Care

The National Science Foundation has awarded EVL a Major Research Instrumentation (MRI) grant to build the Data Observation and Computation Collaboratory (DOCC) in the new Computer Design Research and Learning Center (CDRBC) building. The Data Observation and Computation Collaboratory (DOCC, &rsquo;the dock&rsquo;) is a much-needed visual data science instrument for UIC science and engineering research and research training. DOCC provides high-performance computing, advanced networking, high-speed data storage, interactive high-resolution displays and software and services for Artificial Intelligence / Machine Learning (AI/ML), data analytics, data simulations, visualization, and virtual-reality requirements. Given how varied modern data-science application requirements can be, DOCC is designed as a configurable, heterogeneous computing environment that prioritizes real-time, interactive, collaborative applications and remote data streaming, but can be scheduled off-hours for large, batch-mode processing. 
DOCC will empower users to work with their data and their colleagues in real-time, test codes, view a &rsquo;window in the oven&rsquo;to watch large simulations run, and simultaneously view results along with related data and ensemble runs on large display walls. DOCC has a visual front end that enables users to easily access familiar collaboration tools, scientific notebooks, AI/ML workflows, and popular visualization software. 
Alternatively, users can add new AI/ML models or create them from scratch. And because DOCC will be connected to the campus research network, data and/or computer codes can be ported to remote computer and cloud resources located on campus, regionally, nationally, or internationally. 
A room-sized instrument, DOCC will be located in the high-trafficked atrium of the new CDRLC, home to the UIC Computer Science Department, part of the College of Engineering, situated across from an auditorium and multi-purpose event space. This location will promote UIC data-science research initiatives and the instrument&rsquo;s collaboration potential. DOCC will also be used to conduct campus research meetings, programs and training initiatives. 
Award Number (FAIN): 2320261 
Period of Performance: 10/01/2023 - 09/30/2026 
Project Title: Research Infrastructure: MRI: Track 2 Acquisition of Data Observation and Computation Collaboratory (DOCC)

EVLers Juan Trelles Trabucco, Carla Floricel and Professor Elisabeta Marai Biocuration Research Recognized by IEEE BIBM 2023

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