World's Best Scientists 2026 revealed!
Joel Lehman

Joel Lehman

D-Index & Metrics

Computer Science

D-Index
34
Citations
8386
World Ranking
11932
National Ranking
4875

Overview

Joel Lehman is affiliated with OpenAI in the United States and has contributed extensively to the field of computer science, focusing primarily on artificial intelligence. Their work spans several subfields, including artificial intelligence, safety research, sociology and political science, health informatics, and cognitive neuroscience.

The research topics covered by Joel Lehman include:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Topic Modeling
  • Evolutionary Game Theory and Cooperation
  • Natural Language Processing Techniques
  • Ethics and Social Impacts of AI
  • Artificial Intelligence in Healthcare and Education

Joel Lehman has published in a variety of venues, with a frequent presence in:

  • arXiv (Cornell University)
  • Artificial Life
  • ACM Transactions on Evolutionary Learning and Optimization
  • Nature

Some of the recent papers authored or coauthored by Joel Lehman include:

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, 2020, Artificial Life
  • Learning to Continually Learn, 2020, arXiv (Cornell University)
  • Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions, 2020, arXiv (Cornell University)
  • Language Model Crossover: Variation through Few-Shot Prompting, 2024, ACM Transactions on Evolutionary Learning and Optimization
  • The Ethics of Advanced AI Assistants, 2024, arXiv (Cornell University)

Joel Lehman often collaborates with other researchers. Frequent coauthors include:

  • Kenneth O. Stanley
  • Jeff Clune
  • Herbie Bradley
  • Elliot Meyerson
  • Matija Franklin

Best Publications

  • Abandoning objectives: Evolution through the search for novelty alone

    Joel Lehman;Kenneth O. Stanley

  • An intriguing failing of convolutional neural networks and the CoordConv solution

    Rosanne Liu;Joel Lehman;Piero Molino;Felipe Petroski Such

  • Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

    Felipe Petroski Such;Vashisht Madhavan;Edoardo Conti;Joel Lehman

  • Designing neural networks through neuroevolution

    Kenneth O. Stanley;Kenneth O. Stanley;Jeff Clune;Jeff Clune;Joel Lehman;Risto Miikkulainen

  • Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

    Joel Lehman;Kenneth O. Stanley

  • Evolving a diversity of virtual creatures through novelty search and local competition

    Joel Lehman;Kenneth O. Stanley

  • First return, then explore

    Adrien Ecoffet;Adrien Ecoffet;Joost Huizinga;Joost Huizinga;Joel Lehman;Joel Lehman;Kenneth O. Stanley;Kenneth O. Stanley

  • Go-Explore: a New Approach for Hard-Exploration Problems

    Adrien Ecoffet;Joost Huizinga;Joel Lehman;Kenneth O. Stanley

  • Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

    Edoardo Conti;Vashisht Madhavan;Felipe Petroski Such;Joel Lehman

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

    Joel Lehman;Jeff Clune;Dusan Misevic;Christoph Adami

  • A Neuroevolution Approach to General Atari Game Playing

    Matthew Hausknecht;Joel Lehman;Risto Miikkulainen;Peter Stone

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

    Joel Lehman;Jeff Clune;Dusan Misevic;Christoph Adami

  • Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

    Rui Wang;Joel Lehman;Jeff Clune;Kenneth O. Stanley

  • Revising the evolutionary computation abstraction: minimal criteria novelty search

    Joel Lehman;Kenneth O. Stanley

  • Novelty Search and the Problem with Objectives

    Joel Lehman;Kenneth O. Stanley

  • Efficiently evolving programs through the search for novelty

    Joel Lehman;Kenneth O. Stanley

  • Learning to Continually Learn

    Shawn Beaulieu;Lapo Frati;Thomas Miconi;Joel Lehman

  • Safe mutations for deep and recurrent neural networks through output gradients

    Joel Lehman;Jay Chen;Jeff Clune;Kenneth O. Stanley

  • Combining search-based procedural content generation and social gaming in the Petalz video game

    Sebastian Risi;Joel Lehman;David B. D'Ambrosio;Ryan Hall

  • Effective diversity maintenance in deceptive domains

    Joel Lehman;Kenneth O. Stanley;Risto Miikkulainen

  • ES is more than just a traditional finite-difference approximator

    Joel Lehman;Jay Chen;Jeff Clune;Kenneth O. Stanley

  • Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

    Felipe Petroski Such;Aditya Rawal;Joel Lehman;Kenneth Stanley

Frequent Co-Authors

Kenneth O. Stanley
Kenneth O. Stanley University of Central Florida
Jeff Clune
Jeff Clune University of British Columbia
Sebastian Risi
Sebastian Risi IT University of Copenhagen
Risto Miikkulainen
Risto Miikkulainen The University of Texas at Austin
Jian Peng
Jian Peng University of Illinois at Urbana-Champaign
William F. Punch
William F. Punch Michigan State University
Marc Schoenauer
Marc Schoenauer French Institute for Research in Computer Science and Automation - INRIA
Hod Lipson
Hod Lipson Columbia University
Peter Stone
Peter Stone The University of Texas at Austin
François Taddei
François Taddei Université Paris Cité

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