Sergey Levine
E164770
Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Sergey Levine canonical | 10 |
How this entity was disambiguated
This entity first appeared as the object of triple T1413908 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sergey Levine Context triple: [John Schulman, coAuthorWith, Sergey Levine]
-
A.
Pieter Abbeel
Pieter Abbeel is a Belgian-American computer scientist and professor at UC Berkeley known for his influential work in robotics and deep reinforcement learning.
-
B.
Shane Legg
Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
-
C.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
D.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
E.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sergey Levine Target entity description: Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
-
A.
Pieter Abbeel
Pieter Abbeel is a Belgian-American computer scientist and professor at UC Berkeley known for his influential work in robotics and deep reinforcement learning.
-
B.
Shane Legg
Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
-
C.
Ilya Sutskever
Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
-
D.
Demis Hassabis
Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
-
E.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
person ⓘ researcher ⓘ university professor ⓘ |
| almaMater | Stanford University ⓘ |
| citizenship |
United States
ⓘ
linked to:
United States of America
|
| employer | University of California, Berkeley ⓘ |
| fieldOfStudy | computer science ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
deep learning ⓘ deep reinforcement learning ⓘ machine learning ⓘ reinforcement learning ⓘ robot learning ⓘ robotics ⓘ |
| hasAcademicAdvisor |
Pieter Abbeel
ⓘ
Vladimir Koltun NERFINISHED ⓘ |
| hasHIndex | very high in machine learning and robotics research community ⓘ |
| hasRole | principal investigator of a research lab at UC Berkeley ⓘ |
| knownFor |
deep reinforcement learning research
ⓘ
deep visuomotor policies ⓘ end-to-end learning for robotics ⓘ guided policy search ⓘ imitation learning ⓘ large-scale robot learning ⓘ learning from demonstration in robotics ⓘ model-based reinforcement learning methods ⓘ model-free reinforcement learning methods ⓘ offline reinforcement learning ⓘ robot learning algorithms ⓘ robotics research ⓘ |
| languageSpoken | English ⓘ |
| memberOf |
Berkeley Artificial Intelligence Research Lab
ⓘ
Robotics research community ⓘ |
| notableStudent | Chelsea Finn ⓘ |
| notableWork |
research on deep visuomotor policies for robotic control
ⓘ
research on guided policy search for robotics ⓘ research on large-scale data-driven robot learning ⓘ |
| occupation | professor at University of California, Berkeley ⓘ |
| researchInterest |
autonomous robotic manipulation
ⓘ
learning from human feedback ⓘ multi-task reinforcement learning ⓘ representation learning for control ⓘ robotic control ⓘ scalable robot learning ⓘ unsupervised reinforcement learning ⓘ vision-based control ⓘ |
| workInstitution |
UC Berkeley College of Engineering
ⓘ
linked to:
Berkeley Engineering
UC Berkeley Department of Electrical Engineering and Computer Sciences ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Sergey Levine Description of subject: Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
Referenced by (10)
Full triples — surface form annotated when it differs from this entity's canonical label.