Michal Valko
Bulding something new · Research at Inria, Lectures at ENS/MVA · Ex: Llama at GenAI, Meta; Gemini and BYOL at Google DeepMind
San Francisco, California
Overview
Work Experience
Chief Models Officer, Member of the Founding Team, Member of Technical Staff
2025 - Current
Developing the next generation of language models
Principal Llama Engineer
2024 - 2024
new policy-gradient algorithms for Llama 3 and 4 post-training, improving math, reasoning, and code skills
Meta is a social technology company that enables people to connect, find communities, and grow businesses.
Raised $25,607,817,488.00 from ValueAct Capital.
Senior Staff Research Scientist
2021 - 2024
reinforcement learning with human feedback, online NashMD learning and offline IPO alignment for Gemini 2
Staff Research Scientist
2019 - 2021
BYOL for self-supervised learning, BYOL-Explore & BYOL-Hindsight for world models, BGRL for SSL graph embeddings
External Lecturer (CEV)
2014 - 2023
Teaching the graduate course "Graphs in Machine Learning" for the Master 2 program MVA - Mathématiques / Vision / Apprentissage.
Experienced Junior Scientist
2013 - 2023
new Monte-Carlo tree search techniques, solving open problem in online kernel and graph sparsification
Junior Scientist
2012 - 2013
Researcher in SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. Designing algorithms that would require as little human supervision as possible.
postdoctoral researcher
2011 - 2012
bandits, inverse reinforcement learning. Semi-supervised learning. composing learning for artificial cognitive systems
Research Assistant
2006 - 2011
Statistical anomaly detection methods for identification of unusual outcomes and patient management decisions. I combined max--margin learning with distance learned to create and anomaly detector, which outperforms the hospital rule for Heparin Induced Thrombocytopenia detection. I later scaled the system for 5K patients with 9K features and 743 clinical decisions per day. At the recent study, from 222 alerts 50\% were highly relevant. Mass-spec: I built a framework for the cancer prediction from high--throughput proteomic and genomic data sources. I found a way to merge heterogeneous data sources: My fusion model was able to predict pancreatic cancer from Luminex combined with SELDI with 91.2% accuracy.
Teaching Assistant
2005 - 2005
I was a TA for "Introduction of Programming" for Prof. Wizzard. Giving recitations, helping students and grading. http://www.cs.pitt.edu/~michal/hp/ta2061-0007
Graduate Research Intern
2010 - 2010
Large Scale Semi-Supervised Learning. Multi-manifold learning. I parallelized online harmonic solver to process 1 TB of video data in a day. I am working on the multi-manifold learning that can overcome changes in distribution. I am showing how the online learner adapts as to characters' aging over 10 years period in Married ... with Children sitcom. The research was part of Everyday Sensing and Perception (ESP) project.
An ecosystem of software & hardware vendors, integrators and solution providers focused on adoption of NFV and SDN-based solutions.
Intel Research Grad Intern
2009 - 2009
I extended graph-based semi-supervised learning to the structured case and demonstrated on handwriting recognition and object detection from video streams. Regularized harmonic function solution: The algorithm outputs a confidence of inference and uses it for learning. I came up with an online algorithm that on the real-world datasets recognizes faces at 80-90% precision with 90% recall.
An ecosystem of software & hardware vendors, integrators and solution providers focused on adoption of NFV and SDN-based solutions.
Research Assistant
2003 - 2006
Computer modeling of plausible neural network systems: I was modelling basic learning function at the level of synapses. I designed a model that is able to adapt to the regular frequencies with different a rate as the time flows. I used genetic programming to find biologically plausible networks that distinguish different gamma distribution and provided explanation of the strategies evolved.
Education
PhD.
2005 - 2011
Habilitation à Diriger des Recherches (HdR)
2015 - 2016
MSc.
2000 - 2005