Spring into Science 2026 Registration
Please register for Spring into Science below. The event is on May 21, 2026 at the David Rubenstein Treehouse, 20 Western Ave, Boston, MA 02134.
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Affiliate Faculty
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I am not affiliated with the Kempner Institute but I am interested in learning more
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Discussion Groups
Please register for your top 3 discussion group topics! We guarantee that you will be placed in one of your selections for the event.
First Choice:
Please select your first choice
AI for Science beyond Disciplines: Challenges and Frontiers
AI Interpretability, Fairness, and Safety
Automating Research with Agents: Zeitgeist and Possible Futures
Biologically-Inspired Learning Rules: Bridging Neuroscience and AI for Enhanced Computational Methods
Communication in Multi-Agent systems
Dendritic Computations and Their Role in Credit Assignment
How Can LLMs Accelerate (your) Research?
How—if at all—Do We Use Biological Brains to Develop Intelligent Systems?
Human-Computer Interaction
Major Axes of Differences Between AI and Biological Intelligence
Mathematical Theories in Neuroscience
Model-Based RL
Neural AI: The Structural Relationship Between Natural and AI Models
Performance Is Relative: Efficiency Across the Cluster
Problem Solving and Reasoning in Naturalistic Domains: Humans, LLMs, and Crosstalk
What Are Commercial Models Missing to Model More Diverse Human Behavior?
What Does Scaling Mean in Your Research?
What Should a Modern Training in Cognitive Science/Neuroscience/Science of Machine Intelligence Look Like?
N/A (Kempner Staff only)
Second Choice:
Please select your second choice
AI for Science beyond Disciplines: Challenges and Frontiers
AI Interpretability, Fairness, and Safety
Automating Research with Agents: Zeitgeist and Possible Futures
Biologically-Inspired Learning Rules: Bridging Neuroscience and AI for Enhanced Computational Methods
Communication in Multi-Agent systems
Dendritic Computations and Their Role in Credit Assignment
How Can LLMs Accelerate (your) Research?
How—if at all—Do We Use Biological Brains to Develop Intelligent Systems?
Human-Computer Interaction
Major Axes of Differences Between AI and Biological Intelligence
Mathematical Theories in Neuroscience
Model-Based RL
Neural AI: The Structural Relationship Between Natural and AI Models
Performance Is Relative: Efficiency Across the Cluster
Problem Solving and Reasoning in Naturalistic Domains: Humans, LLMs, and Crosstalk
What Are Commercial Models Missing to Model More Diverse Human Behavior?
What Does Scaling Mean in Your Research?
What Should a Modern Training in Cognitive Science/Neuroscience/Science of Machine Intelligence Look Like?
N/A (Kempner Staff only)
Third Choice:
Please select your third choice
AI for Science beyond Disciplines: Challenges and Frontiers
AI Interpretability, Fairness, and Safety
Automating Research with Agents: Zeitgeist and Possible Futures
Biologically-Inspired Learning Rules: Bridging Neuroscience and AI for Enhanced Computational Methods
Communication in Multi-Agent systems
Dendritic Computations and Their Role in Credit Assignment
How Can LLMs Accelerate (your) Research?
How—if at all—Do We Use Biological Brains to Develop Intelligent Systems?
Human-Computer Interaction
Major Axes of Differences Between AI and Biological Intelligence
Mathematical Theories in Neuroscience
Model-Based RL
Neural AI: The Structural Relationship Between Natural and AI Models
Performance Is Relative: Efficiency Across the Cluster
Problem Solving and Reasoning in Naturalistic Domains: Humans, LLMs, and Crosstalk
What Are Commercial Models Missing to Model More Diverse Human Behavior?
What Does Scaling Mean in Your Research?
What Should a Modern Training in Cognitive Science/Neuroscience/Science of Machine Intelligence Look Like?
N/A (Kempner Staff only)
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