The Curious Engineer

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Kyunghyun Cho

kyunghyuncho.me

AI / MLScienceEssays / personal

’15’20’25
Posts
88 from 19 Feb 2014 to 12 May 2026
Posting
now and then (3 in the last year)
Kind
personal blog
Feed
https://kyunghyuncho.me/feed/

2026 3

  1. Teaching <Fundamentals of Machine Learning>
  2. The Irrational Decision by Recht [2026]
  3. Leaving Prescient Design/Genentech/Roche

2025 6

  1. MyChartExplorer: a vibe-coding journey toward accessing my own medical records
  2. Drug Discovery may be in the Cold War Era
  3. Global AI Frontier Lab at New York University
  4. Glen de Vries Professor of Health Statistics
  5. Softmax forever, or why I like softmax
  6. Bye, Felix

2024 10

  1. Amortized Mixture of Gaussians (AMoG): A Proof of Concept for “Learning to X”, or how I re-discovered simulation-based inference
  2. Stochastic variational inference for low-rank stochastic block models, or how i re-discovered SBM unnecessarily
  3. i sensed anxiety and frustration at NeurIPS’24
  4. <The Atomic Human> by Neil Lawrence
  5. An outrageous idea: a society-level forever clinical trial
  6. Continued musing on DPO
  7. Fixing DPO but I have a dinner reservation …
  8. A random thought on retrieval-augmented generation
  9. Gradient-based planning, mapping and execution
  10. Gradient-based trajectory planning

2023 7

  1. A short thought on watermarking
  2. Follow-up donation after 2 years
  3. Expectile regression
  4. Defining emergence
  5. When do duplicates/frequencies matter in classification?
  6. Three faces of sparsity: nonlinear sparse coding
  7. Are JPEG and LM similar to each other? If so, in what sense, and is this the real question to ask?

2022 10

  1. [NeurIPS’22] Chasing reviewers
  2. Donation to “청포도” 보호종료아동을 위한 커뮤니티 케어 센터
  3. My opening statement at the ICML 2022 Debate
  4. [TMLR] how to check the difference between revisions
  5. [TMLR] how to set your availability as a reviewer/action editor
  6. Reading others’ reviews
  7. %ec%99%b8%ed%95%a0%eb%a8%b8%eb%8b%88
  8. [NeurIPS 2022] How to request a reduced load for reviewers
  9. How to think of uncertainty and calibration … (2)
  10. How to think of uncertainty and calibration …

2021 8

  1. Manifold mixup: degeneracy?
  2. The 8-th SW Welcomes Girls
  3. Supporting female researchers and researchers from under-represented groups, together with CIFAR
  4. Restricted Boltzmann machines or contrastive learning?
  5. Ho-Am Prize & Lim Mi-Sook Scholarship (임미숙 장학금) at KAIST
  6. Ho-Am Prize & 백규고전학술상 (Baek-Gyu Award)
  7. Ho-Am Prize & Scholarship for Macadamia at Aalto University
  8. Donation to the Hanmaum education volunteer corp

2020 10

  1. 2020년 가을 NYU
  2. Aalto University School of Science Alumnus of the Year 2020
  3. A few QA’s from the course F’20 <Deep Learning>
  4. Scaling laws of recovering Bernoulli
  5. Giving thanks: Samsung AI Researcher of the Year Award and Donation to Mila
  6. A foreword written for the K-12 AI education material by Busan Metropolitan City Office of Education
  7. Creating an encyclopedia from GPT-3 using B̶a̶y̶e̶s̶’̶ ̶R̶u̶l̶e̶ Gibbs sampling
  8. Social impacts & bias of AI
  9. Soft k-NN
  10. Google Faculty Award: 2020

2019 8

  1. NYU Center for Data Science: What is intelligence?
  2. a short note on “Rebooting AI” by Marcus & Davis
  3. Discrepancy between GD-by-GD and GD-by-SGD
  4. Sharing some good news and some bad news
  5. Best paper award at the AI for Social Good Workshop (ICML’19)
  6. BERT has a Mouth and must Speak, but it is not an MRF
  7. Are we ready for self-driving cars?
  8. On the causality view of “Context-Aware Learning for Neural Machine Translation”

2017 4

  1. Best Paper Award at ISMIR 2017
  2. CIFAR Azrieli Global Scholar 2017
  3. Lecture note “Brief Introduction to Machine Learning without Deep Learning”
  4. Google Faculty Award: 2016

2016 5

  1. Best paper runner-up at NAACL’16
  2. [Closed] A Post-Doctoral Researcher Position in Deep Learning for Medical Image Analysis
  3. Google Faculty Award: Fall 2015
  4. to arXiv or not to arXiv
  5. DeepMind Q&A Data

2015 8

  1. Lecture Note for “NLP with Distributed Representation” on arXiv Now
  2. Lost in Interpretability
  3. Summer School in Helsinki, Finland
  4. Brief Summary of the Panel Discussion at DL Workshop @ICML 2015
  5. Joining NYU this Autumn
  6. Quick Introduction to Natural Language Processing with Neural Networks
  7. Deep Learning Workshop at ICML 2015
  8. Area Chair at NIPS 2015

2014 9

  1. Oxford and Cambridge
  2. Visit to Google DeepMind
  3. Talk at IBM Watson R&D Center (NLP PIC Seminar)
  4. Deep Learning Tutorial at KAIST
  5. Moving to Montreal
  6. I have defended my thesis!
  7. Research Visit to the University of Montreal
  8. Back in Finland
  9. Ph.D. Thesis Defense on 21 March 2014