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 13, 2023

Qianwen Wang

Qianwen Wang, Postdoctoral Fellow, Harvard University Presents:

Interpreting and Steering AI Explanations with Interactive Visualizations

February 13, 2023 at 10:00am for 1hr
Evans Conference Room

Click here for Zoom Link
Meeting ID: 815 5643 6020 Passcode: 744203

Bio: Qianwen Wang is a Postdoctoral Fellow at Harvard University. Her research strives to facilitate the communication and collaboration between users and AI through interactive visual tools, with a special interest in their applications in solving biomedical challenges. Her research has made contributions to visualization, human-computer interaction, and bioinformatics, as demonstrated by 18 publications in top-tier venues (IEEE VIS, TVCG, ACM CHI, Bioinformatics, ISMB). She has received two best abstract awards from BioVis ISMB, one honorable mention from IEEE VIS, and one Best paper award from IMLH@ICML. She is an awardee of the HDSI Postdoctoral Research Fund. Her Research has been covered by MIT News and Nature Technology Features. She serves as the abstract chair for the ISMB BioVis COSI, the Poster Chair for PacificVis, and an organizer for Visualization in Biomedical AI workshop@IEEE VIS.


Artificial Intelligence (AI) has advanced at a rapid pace and is expected to revolutionize many biomedical applications. However, current AI methods are usually developed via a data-centric approach regardless of the usage context and the end users, posing challenges for domain users in interpreting AI, obtaining actionable insights, and collaborating with AI in decision-making and knowledge discovery.

As a visualization researcher, I aim to address this challenge by combining interactive visualizations with interpretable AI, with a particular focus on biomedical applications.  In this talk, I will first discuss the prospects for interactive visualization in the application of biomedical AI. I will then present two methodologies for achieving this goal: 1) visualizations that explain AI models and predictions and 2) interaction mechanisms that integrate user feedback into AI models. I will demonstrate how interactive visual explanations can facilitate AI applications via real-world case studies. Despite some challenges, I will conclude on an optimistic note: interactive visual explanations should be indispensable for Human-AI collaboration in biomedical applications. The methodology discussed can be applied generally to other applications where human-AI collaborations are involved, assisting domain experts in data exploration and insight generation with the help of AI.

Posted by: Deb Zemek

Dr. Yair Rivenson Presents:

Virtual biomarkers - an emerging high throughput research tool

February 13, 2023 at 12:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.

Posted by: Mitra Alirezaei