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I'm a Research Scientist at OpenAI since 2021, leading a team trying to solve reasoning in neural networks, as one of the foundational researchers of the "strawberry" team (o1, o3, GPT-5 Thinking, etc).
Before that, I did a PhD in machine learning at the Max Planck Institute for Intelligent Systems and ETH Zürich, as a doctoral fellow of the Center for Learning Systems, where I was co-supervised by Bernhard Schölkopf and Thomas Hofmann. The topic of my PhD was out-of-distribution generalization with deep learning. I was also an Ellis student.
During my PhD, I spent time at DeepMind in London, and Google X in Mountain View.
Also, as the final step of a professorship interview process at MIT started in 2020, I gave an invited seminar talk. My research plan proposed to use GPT for reasoning [slides] and was called nonsense by a large part of the committee 💖
I just want to understand the mind and create intelligence.
News
09/2024: Foundational research for OpenAI o1/o3, where I introduced, generalized, and scaled up reward modeling, environments, and ██ ████ the algorithm for general reasoning. Gave a talk here about my early 2021 work on "think step by step"
12/2023: My 2016 paper was awarded the 2023 IEEE Signal Processing Society Best Paper Award
11/2023: Started a new team at OpenAI, still focused on reasoning
05/2023: Policymaker engagement, including technical briefings to bi-partisan senior U.S. Senate staff working on AI regulation (including the Senate Majority Leader’s) and to the Gov of California
03/2023: Contributed making GPT-4 that we just released
10/2021: Started as a Research Scientist at OpenAI, in the RL team with John Schulman, then moved to Algorithms with Mark Chen, then 🍓 with Jerry Tworek
09/2021: I defended my PhD on the 13th of September 🤖 My thesis is here.
Thanks to my examination committee Bernhard Schoelkopf, Yoshua Bengio, Thomas Hofmann.06/2021: I opened a blog that I will quickly abandon :)
05/2021: Neural Symbolic Regression that Scales has been accepted at ICML 2021
03/2021 Gave that invited Seminar talk at MIT mentioned above
01/2021: Learning explanations that are hard to vary and A teacher-student framework to distill future trajectories have been accepted at ICLR 2021
11/2020: A Seq2Seq approach to Symbolic Regression accepted at two NeurIPS 2020 Workshops: ”Learning Meets Combinatorial Algorithms” and ”Knowledge Representation & Reasoning Meets Machine Learning”
08/2020: I (remotely) attended the MIT - Center for Brains Minds and Machines, Summer Course 2020
02/2020: Started my year at ETH Zurich with Thomas Hofmann
06/2019: Joined DeepMind in London for a summer internship, our project Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning is now on arXiv
03/2019: Finally created this website, and added old news to make it look like it didn't take me 2 years to do it
11/2018: Generalization in anti-causal learning accepted at the NeurIPS18 Workshop on Critiquing and Correcting Trends in Machine Learning
10/2018: Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models accepted at NeurIPS 2018
05/2018: Learning Independent Causal Mechanisms accepted at ICML 2018
05/2018: Tempered Adversarial Networks accepted at ICML 2018
04/2018: I will join Google X in Mountain View for a summer internship. By the end, I will have worked on a project exploring automated design for electromagnetic devices, with applications ranging from integrated photonics to metasurfaces, designed on ultra-large-scale simulators
11/2017: Learning Independent Causal Mechanisms spotlight at NIPS 2017 workshop on Learning Disentangled Representations
11/2017: ConvWave accepted at NIPS 2017 workshop on Deep Learning for Physical Sciences
09/2017: Avoiding Discrimination Through Causal Reasoning accepted at NIPS 2017
06/2017: Participated in the Machine Learning Summer School in Tübingen, as student and official master of games for social activities.
06/2017: Participated in the Google Machine Learning Summer Summit at Google Zürich
03/2017: Started my PhD at the Max Planck Insitute for Intelligent Systems in Tübingen and ETH Zürich
07/1789: First spark of the French Revolution
Deep Learning Beyond the Training Distribution
Giambattista Parascandolo
Thesis committee: Bernhard Schoelkopf, Yoshua Bengio, Thomas Hofmann.
PhD Thesis - ETH Zurich and Max Planck Institute for Intelligent Systems Tuebingen (2021)
[PDF]
A Seq2Seq approach to Symbolic Regression
Luca Biggio, Tommaso Bendinelli, Aurelien Lucchi, Giambattista Parascandolo
NeurIPS 2020 Workshop ”Learning Meets Combinatorial Algorithms”
NeurIPS 2020 Workshop ”Knowledge Representation & Reasoning Meets Machine Learning”
[paper]
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Giambattista Parascandolo*, Lars Buesing*, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber (* equal contribution)
submitted
Generalization in anti-causal learning
Niki Kilbertus*, Giambattista Parascandolo*, Bernhard Schölkopf (* alphabetical order)
NeurIPS 2018 Workshop on Critiquing and correcting trends in machine learning
[paper]
Learning Independent Causal Mechanisms
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Schölkopf
ICML 2018
also at: NIPS 2017 Workshop on Learning Disentangled Representations - Spotlight presentation
Tempered Adversarial Networks
M. S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf
ICML 2018
also at: ICLR 2018 Workshop
[paper]
ConvWave: Searching for Gravitational Waves with Fully Convolutional Neural Nets
Timothy Gebhard*, Niki Kilbertus*, Giambattista Parascandolo, Ian Harry, Bernhard Schölkopf (* equal contribution)
NeurIPS 2017 Workshop on Deep Learning for Physical Sciences
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features,
Sharath Adavanne, Giambattista Parascandolo, Pasi Pertilä, Toni Heittola, Tuomas Virtanen
Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 - 1st place in the challenge.
[paper]
Acoustic Scene Classification Using Convolutional Neural Networks
Michele Valenti, Aleksandr Diment, Giambattista Parascandolo, Stefano Squartini, Tuomas Virtanen
International Joint Conference on Neural Networks (IJCNN) 2017 - Best Student Poster Intel Award
IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2016)
[paper]
Low-latency sound source separation using deep neural networks
Gaurav Naithani, Giambattista Parascandolo, Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen
IEEE Global Conference on Signal and Information Processing (GlobalSIP) 2016
[paper]
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Emre Cakir*, Giambattista Parascandolo*, Toni Heittola, Heikki Huttunen, Tuomas Virtanen (*equal contribution)
IEEE/ACM Transactions on Audio, Speech, and Language Processing (Journal)
[paper]
Recurrent neural networks for polyphonic sound event detection in real life recordings
Giambattista Parascandolo, Heikki Huttunen, Tuomas Virtanen
ICASSP 2016 - Best student paper of its special session
[paper]
Miscellaneous
Miscellaneous
Co-organized the Hackathon at the CLS Max Planck - ETH retreat 2018, and photographer for the 2017 retreat in the Alps
Reviewed for NeurIPS, ICML, ICLR
Coding and technical interviews for PhD candidates applying to the CLS and IMPRS programs
In my free time I play the piano, volleyball, football, volleypong, and with words. I also like to collect photons on film and DSLR sensors.
Contact
Contact
g b [a t] o p e n a i [d o t] c o m
g b [a t] o p e n a i [d o t] c o m
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