Murat Kocaoglu
@murat-kocaoglu.bsky.social
Asst. Prof. at Purdue ECE. Causal ML Lab. Causal discovery, causal inference, deep generative models, info theory, online learning. Past: MIT-IBM AI Lab, UT Austin, Koc, METU.
I am excited to announce our Workshop on Causality in the Age of AI Scaling in AISTATS 2026! - Is scaling sufficient for intelligent systems? - Can causal abilities emerge from scale? - What can causal modeling bring that scale cannot? causcale.github.io Submission deadline is February 27th.
Causality in the Age of AI Scaling
Workshop on Causality in the Age of AI Scaling (5 May 2026, Tangier, Morocco)
causcale.github.io
Happy to be a part of this cohort! Apply to the JHU CS PhD program to work together. engineering.jhu.edu/admissions/g...
https://engineering.jhu.edu/admissions/graduate-admissions/full-time-programs/how-to-apply/
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JHU Computer Science@jhucompsci.bsky.social · 12mo agoThe Department of Computer Science is pleased to welcome nine new tenure-track faculty to its ranks this academic year! Featuring @anandbhattad.bsky.social, @uthsav.bsky.social, @gligoric.bsky.social, @murat-kocaoglu.bsky.social, @tiziano.bsky.social, and more:
10 new CS professors! 🥳 @anandbhattad.bsky.social @uthsav.bsky.social @gligoric.bsky.social @murat-kocaoglu.bsky.social @tiziano.bsky.social
Johns Hopkins Data Science and AI Institute@hopkinsdsai.bsky.social · last yr.#HopkinsDSAI welcomes 22 new faculty members, who join more than 150 DSAI faculty members across @jhu.edu in advancing the study of data science, machine learning, and #AI and translation to a range of critical and emerging fields. ai.jhu.edu/news/data-sc...
I am happy to share that I will be joining Johns Hopkins University's Computer Science Department @jhucompsci.bsky.social as an Assistant Professor in Fall 2025. I am grateful to my mentors for their unwavering support and to my exceptional PhD students for advancing our lab's research vision.
Teaching young researchers mathematical and scientific rigor is more important than ever today with AI tools' wider adoption in research, as these tools tend to be overly optimistic. LLM-assisted false proofs risk flooding our already overloaded reviewing infrastructure.
You have causal questions. First step is deciding cause-effect relations in your system. Causal graphs capture these compactly. We usually need to be able to run experiments and use the outcomes to infer causal graphs. But experiments are expensive & w/ few samples. #NeurIPS2024
You want to optimize a reward in an unknown environment. Structural knowledge of cause-effect relations is known to help significantly reduce the search space for bandit algorithms. But how much of the causal structure do you need to know to do this? #NeurIPS2024
You have a complicated system with several high-dimensional variables, such as image and text data. How can you systematically answer ANY causal question from observational data? #NeurIPS2024