Dr. Been Kim
Senior Staff Research Scientist
Sources checkedSenior Staff Research Scientist at Google DeepMind. World-renowned authority on machine learning interpretability and explainability. Completed Ph.D. at MIT. Pioneer in developing methods that translate high-dimensional deep representation manifolds into concepts humans understand naturally.
Areas of focus
Professional niches
Proof of Work
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Invented TCAV (Testing with Concept Activation Vectors), an interpretability framework that uses directional derivatives in activation space to quantify how much a human-understandable concept (e.g., stripes on a zebra) contributes to a model's prediction.
Requires user-provided exemplars for concept definitions; high-level abstract concepts without clear visual or lexical exemplars can be difficult to vectorize cleanly.
Context: InceptionV3, ResNet internal activation layers, linear classifiers in latent embedding manifolds.
View missionRelative Representations Enable Non-Degenerate Latent Space Alignment
Co-authored breakthrough research demonstrating that representations can be compared across disparate neural networks by computing pairwise angles and similarities to anchor points, proving latent geometries are invariant across architectures.
Choice and distribution of anchor points can introduce variance in representation reconstruction accuracy across domain shifts.
Context: Cross-model embedding alignment across Vision Transformers, CNNs, and language models without weight access.
View mission