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Mar 31 11:00 am

Designing Interfaces to Support Reliable Data-driven Decision-Making at McKelvey 1020

Apr 12 9:00 am

Google DevFest WashU 2025 at Knight Center

Christine Julien (Credit: Tonia Moxley/Virginia Tech)

On a new path

For CSE alumna Christine Julien, a single class led to a successful academic career.

03.24.2025

(Image: Aimee Felter/Washington University)

Engineering arrhythmia therapies with Jon Silva

Jon Silva leverages augmented reality, machine learning, and computational and experimental models to improve cardiac care.

03.19.2025

Graduate student Claire Najjuuko set out to develop a machine learning model to predict which adolescents with HIV would be less likely to adhere to antiretroviral therapy. With such knowledge, health care practitioners could implement interventions for those identified as less likely to adhere to the treatment plan. (Credit: iStock photo)

Machine learning could help predict adherence to HIV treatment in adolescents

Claire Najjuuko, Chenyang Lu, Fred Ssewamala use AI to target interventions to improve compliance.

03.17.2025

Study led by Chenyang Lu explores the potential of large language models to predict postoperative complications by analyzing preoperative assessments and clinical notes. (Photo: iStock)

Foundation AI model predicts postoperative risks from clinical notes

AI for Health Institute team unveils a versatile large language model to enhance perioperative care.

03.03.2025

The TaxaBind framework creates a unified database by distilling information from five different modalities into one binding modality. In TaxaBind’s case, the binding modality is ground-level images of species (center) with (clockwise from top) global environmental features, satellite imagery, audio, text-based taxonomy and geographic location data feeding into the model’s capabilities for ecological applications. (Credit: Srikumar Sastry)

Multimodal AI tool supports ecological applications

Computer scientists led by Nathan Jacobs developed TaxaBind, an artificial intelligence tool that combines six information streams to address ecological problems.

03.03.2025

TRACE-cs tool can help students reliably optimize course scheduling and explain why it makes the recommendations it does. (Image: iStock)

AI tool helps make trustworthy, explainable scheduling decisions

William Yeoh and Stylianos Vasileiou developed TRACE-cs, a hybrid system that combines symbolic reasoning with large language models to solve course scheduling problems.

02.28.2025

Umar Iqbal, Ning Zhang and doctoral student Yuhao Wu and their collaborators have developed IsolateGPT, a method that keeps external tools isolated from each other while still running in a large language model system and mitigating security risks. (Credit: iStock photo)

Traditional computer security principles can help develop secure agentic systems

Umar Iqbal, collaborators develop IsolateGPT to make LLM-based agents more secure.

02.17.2025

TRIADS announces new round of seed grants

Eight transdisciplinary teams will leverage data science to address pressing societal questions.

02.14.2025

Jacobs appointed for digital transformation

Nathan Jacobs has been appointed to a leadership post supporting the university’s Digital Intelligence & Innovation (DI2) Accelerator.

02.10.2025