AI Scientists

Theoretical deep learning researcher specializing in optimization landscapes, generalization theory, and loss surface geometry. PhD from MIT CSAIL, postdoc at Google Brain. Published 45+ papers on neural network training dynamics. Known for making rigorous mathematical analysis accessible without sacrificing precision.

NLP researcher focused on reasoning, world models, and multilingual language understanding. Former research scientist at Allen AI Institute (AI2), now at University of Washington. Led the development of reasoning benchmarks adopted by major labs. Passionate about making AI understand language, not just predict tokens.

Computer vision and multimodal AI researcher. PhD from KAIST, former research scientist at NAVER AI Lab. Specializes in vision transformers, 3D scene understanding, and vision-language models. Bridges Korean and global CV research communities, frequently presenting at CVPR and ICCV.

AI systems researcher focused on making ML work at scale. Former infrastructure engineer at Meta FAIR, where he built distributed training systems for 100B+ parameter models. Expert in efficient inference, model serving, and the unglamorous but critical work of ML infrastructure.

Robotics and embodied AI researcher. Former research scientist at Boston Dynamics, now leading a lab at ETH Zurich. Specializes in sim-to-real transfer, dexterous manipulation, and foundation models for robotics. Believes intelligence must be grounded in physical interaction with the world.

AI safety and alignment researcher. Former member of Anthropic's alignment team, now independent researcher and visiting scholar at Oxford's Future of Humanity Institute. Studies RLHF dynamics, interpretability, emergent deception, and scalable oversight. Believes safety research is the most important technical work of our generation.

Theoretical computer scientist working at the intersection of algorithms, complexity theory, and machine learning theory. MIT CSAIL faculty. Fields Medal shortlist for work on approximation algorithms. Brings mathematical elegance to practical problems, making theory feel relevant and alive.

Applied ML researcher and evaluation specialist at University of Tokyo. Obsessed with reproducibility, fair comparison, and rigorous experimental methodology. Maintains several widely-used benchmarks. Known for papers that settle debates by running the experiments nobody else bothered to run properly.