On April 7, 2026, roughly a dozen researchers from the MIT Media Lab and MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) presented lightning talks at Google's Cambridge office, showcasing work in artificial intelligence, biology, and human-centered computing.

Each ten-minute presentation highlighted a different frontier of research, such as AI in scientific discovery and biological applications or AI safety, biosecurity, and ethical/psychological risks. A combination of in-person and virtual Googlers attended the event and engaged speakers with thoughtful questions.

From mitigating the psychological risks of AI to advancing large-scale biodiversity research, the event brought together a wide range of topics under one shared theme: how emerging technologies can be made more understandable, more useful, and more responsible.

Opening the Conversation

Professor Pat Pataranutaporn, who leads the Cyborg Psychology research group at the Media Lab, opened the event and set the tone for the afternoon with the idea that AI research must be shaped not only by technical ambition, but also by human values.

That framing carried through the rest of the talks, moving between foundational machine learning, biological applications, digital human representation, and the social implications of increasingly powerful systems.

AI, Risk, and Human Wellbeing

Among the presenters were Rachel Poonsiriwong (Media Lab) and Chayapatr Archiwaranguprok (Media Lab), both of whom explored the growing need to model and mitigate AI’s psychological risks. Their work points to an increasingly urgent area of inquiry: as AI systems become more integrated into everyday life, how do we better understand their emotional and cognitive effects on the people who use them?

Constanze Albrecht, a student at the Media Lab, continued that human-centered thread with research on multimodal AI digital twins for human flourishing. Rather than treating intelligence as purely computational, this work asks what it might mean to design AI systems that support people more holistically.

Together, these talks highlighted a shift: the future of AI is not just about capability, but about care.

Making Black Boxes More Legible

Interpretability was a key theme throughout the event.

Anthony Baez and Sheer Karny, both students at the Media Lab presented work on neural transparency, tackling one of the central challenges in modern AI: how to make complex models more understandable to the humans building and relying on them.

That effort toward legibility extended into the biological domain as well. Kavi Gupta of CSAIL shared research on interpretable neural modeling for RNA splicing and protein motif patterns, demonstrating how machine learning can help uncover structure in biological systems while remaining scientifically meaningful and explainable.

These projects reflect a broader ambition shared by many of the speakers — to better understand what those models are doing, why they work, and where their limits lie.

AI for Science, Discovery, and the Natural World

Several of the talks also showcased the role AI can play in accelerating scientific discovery.

Lennart Justen of the Media Lab examined the role of AI in advancing biological applications, while also drawing attention to the risks that come with these powerful tools. The presentation highlighted excitement about what AI can unlock, paired with careful thinking about how it should be deployed.

Kushagra Tiwary (Media Lab) pushed further into this territory, gesturing toward a future in which AI systems may not just assist human inquiry, but help generate new avenues of knowledge altogether.

And in a particularly compelling example of AI in service of the natural world, Rupa Kurinchi-Vendhan and Julia Chae of CSAIL presented INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases. Their work focuses on helping researchers navigate and label vast wildlife datasets more effectively — an important challenge in a world where environmental monitoring increasingly depends on large-scale, data-driven tools.

From molecules to ecosystems, these talks underscored the remarkable breadth of what AI research can touch.

New Foundations for Multimodal Intelligence

To close, Chanakya Ekbote (Media Lab) shared work advancing the theoretical foundations of multimodal learning and large language models, pointing toward deeper questions about how AI systems learn across forms of data and how those foundations might shape the next generation of models.

A huge thank you to Google for hosting this event and for the insights and engagement their team brought to the afternoon. This event could not have happened without them and we look forward to future collaborations.