Last week, I had the opportunity to attend—and volunteer at—a remarkable event at the University of Cambridge titled “Exploring Interdisciplinary Frontiers: Cognitive Science, Computational Modeling, and Artificial Intelligence.” This two-day conference brought together some of the most influential researchers working at the nexus of AI, neuroscience, psychology, and philosophy. It left me not only full of new questions, but deeply energized by the diversity of conversations happening at the edges of multiple fields.
Volunteering gave me a front-row seat—not just to the talks, but to the people. It opened the door to some incredible hallway chats, spontaneous brainstorms, and genuine moments of connection that reminded me why I love this work in the first place.
As a writer with nearly a decade of experience in tech and science communication, I’ve always been drawn to the big, bridging questions: How does the brain learn? What can machines teach us about ourselves? And how can science stay human in the age of AI? This conference touched on all of these—and more—with curiosity, humility, and an eye toward the future.
Keynote Highlights on Interdisciplinary AI and Neuroscience
Contextual Intelligence & Biological Learning
Keynote: Prof. Máté Lengyel
Prof. Máté Lengyel opened the conference with a compelling framework for biological continual learning—a theory of how the brain determines when and how to learn based on context. His work challenges the idea of memory as simple accumulation. Instead, he introduced the idea of contextual inference, where the brain updates memories proportional to their relevance in a given situation—a concept modeled through examples like spontaneous and evoked recovery and a dual-date memory model.
He also touched on the difference between explicit and implicit learning, showing how our brains constantly navigate between conscious recall and unconscious adaptation. As someone immersed in information through code, content, and cognition, this reframed memory for me as something far more active, selective, and dynamic than we usually think.
Rodents in VR and Hybrid Computation
Keynote: Prof. Mayank Mehta
One of the most unexpectedly captivating talks came from Prof. Mayank Mehta, whose lab is placing rodents in virtual environments to better understand how the brain constructs representations of space, time, and causality.
Through concepts like visually evoked vectorial selectivity and neural variability, he argued that brain function is not purely digital or analog, but hybrid—a nuance that’s often missed in mainstream AI discourse. He introduced his REPP (Repeats Every Period Paradigm) as a model for learning driven by structured input and noise, drawing on the emergent properties of Hebbian learning.
It got me thinking: perhaps we need to stop treating ambiguity as something to engineer out of our systems. In both brains and machines, complexity and redundancy may be the point—not the problem.
Empathy, Trust, and Social AI
Keynotes: Prof. Emily Cross & Dr. Frank Pollick
Human–AI relationships took center stage in talks by Prof. Emily Cross and Dr. Frank Pollick. Cross shared her research into social bonding with robots, asking what makes artificial agents feel relatable, even trustworthy. Her lab’s work—some of which takes place with robots embedded on university campuses—examines how we respond emotionally to machines that mimic us.
Pollick built on this, asking whether we trust AI the way we trust tools, or people. His findings suggest that transparency, design choices, and perceived intent are key factors in how we judge intelligent systems. These questions feel especially urgent right now, as AI becomes embedded not just in our workflows, but in our lives.
The flash talks extended this thread—touching on cultural differences in facial expressions, the role of sarcasm and laughter in autism, and embodied AI in human-robot interaction. I particularly enjoyed Di Fu’s exploration of what it truly means to design a robot that feels “human”—and whether that’s always the goal.
AI in Clinical Neuroscience
Keynote: Prof. Li Su
Prof. Li Su’s talk provided a powerful bridge between theory and practice. His lab is applying AI to analyze brain function and improve clinical diagnostics—from speech processing in schizophrenia, to modeling cognitive decline using large-scale neural data. His work was a clear reminder that interdisciplinary research isn’t just advancing science—it’s changing how we care for people.
Coffee, Curiosity, and Community
While the talks were insightful, some of the most valuable moments came between them—during coffee breaks, over lunch, and in the quiet corners of the venue. As a volunteer, I had the chance to meet PhD students, postdocs, visiting faculty, and other communicators, all of whom were generous with their time and ideas.
These informal conversations often went beyond research, touching on everything from funding challenges to collaboration across labs to what it takes to cultivate leadership in interdisciplinary science. It was refreshing to feel part of a space where curiosity was not just welcome, but foundational.
For me, it was also a powerful reminder that attending a research conference as a non-academic can be just as meaningful, sometimes even more so. With the right mix of humility and curiosity, there’s room for writers, designers, and communicators to contribute not just by reporting science but by shaping how it’s shared, questioned, and evolved.
What’s Next
A few topics I’m excited to explore:
- How AI can (and can’t) mirror brain function
- Trust, transparency, and the emotional design of intelligent systems
- Creative roles for communicators in scientific communities
- Why showing up as a “non-expert” at academic events can be a superpower
For now, I’m just grateful to have been in the room—to listen, to learn, to ask questions, and to meet so many people doing thoughtful work at the intersection of brain and machine.
Let’s Stay Connected
If you’re working at the crossroads of technology, cognition, or communication—or just love asking big questions—reach out. I’d love to hear from you.
Interested in interdisciplinary AI and neuroscience? Follow my insights on LinkedIn and explore how mind meets machine in real-world research.