News & Events

Events

Upcoming CSE Events

The CTCN Annual Public Lecture on Minds and Machines at Jeffrey T. Fort Neuroscience Research Building Auditorium

WashU researchers Eunhye Ahn and Yevgeniy Vorobeychik are teaming up to study the multifaceted dynamics of families involved in child protection systems. The interdisciplinary team, combining expertise in social work and computer science, is supported by a Here and Next seed grant.

Researchers harness AI to study family involvement with child protection systems

AI-driven research is helping reveal the full picture of families in the child welfare system, paving the way for more comprehensive and compassionate interventions.

04.09.2025

Amazon smart speaker Echo records user voice commands to train its AI assistant Alexa. Going forward, consumers will not have the option to opt out of those recordings being processed in the Amazon cloud. (Photo: Shutterstock)

WashU Expert: How Alexa is listening

Users of Amazon’s smart speaker lose opt-out option for voice command recordings.

03.26.2025

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

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

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

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