JEFF CLUNEASSOCIATE PROFESSOR, COMPUTER SCIENCE, UNIVERSITY OF BRITISH COLUMBIACANADA CIFAR AI CHAIR, VECTOR INSTITUTESENIOR RESEARCH ADVISOR, DEEPMINDJCLUNE@GMAIL.COM
JEFFCLUNE.COM
GOOGLE SCHOLAR: HTTPS://TINYURL.COM/JEFFCLUNEEDUCATION

Ph.D. in Computer Science, Michigan State University. 2010. 4.0 GPA.
M.A. in Philosophy, Michigan State University. 2005. 4.0 GPA.
Honors B.A. in Philosophy, University of Michigan. 1999. 3.9 GPA.

ACADEMIC & RESEARCH POSITIONSAssociate Professor, Computer Science, University of British Columbia 2021 - present
Canada CIFAR AI Chair and Faculty Member, Vector Institute, 2021 - present
Senior Research Advisor, DeepMind, 2023 - present
Research Team Leader, OpenAI 2020 - 2022
Harris Associate Professor, Computer Science, University of Wyoming, 2017 – 2019
Senior Research Manager & Founding Member, Uber AI Labs, 2017 – 2019 (We created Uber AI Labs
after Uber acquired our startup Geometric Intelligence)
Assistant Professor, Computer Science, University of Wyoming, 2013 – 2017
Consultant, Geometric Intelligence, a deep learning startup (acquired by Uber), 2015 – 2016
Visiting Scientist, Cornell University, Department of Mechanical and Aerospace Engineering, 2012 –2013. Advisor: Hod Lipson
Postdoctoral Fellow, Cornell University, Department of Mechanical and Aerospace Engineering, 2010 –2012. Advisor: Hod Lipson
Postdoctoral Scientist, BEACON Center for the Study of Evolution in Action, Michigan State University,
Summer 2010. Advisor: Charles Ofria
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JOURNAL PUBLICATIONS● Kudithipudi et al. (2022) Biological Underpinnings for Lifelong Learning Machines. Nature MachineIntelligence. 4:196–210● Ecoffet A*, Huizinga J*, Lehman J, Stanley KO, Clune J (2021) First return, then explore. Nature.
*co-first authors● Huizinga J, Clune J (2021) Evolving Multimodal Robot Behavior via Many Stepping Stones with the
Combinatorial Multi-Objective Evolutionary Algorithm. Evolutionary Computation.
● Lehman J, Clune J, Misevic D et al. (2020) The surprising creativity of digital evolution: A collection of
anecdotes from the evolutionary computation and artificial life research communities. Artificial Life.
● Norouzzadeh M, Morris D, Beery S, Joshi N, Jojic N, Clune J (2020) A deep active learning system forspecies identification and counting in camera trap images. Methods in Ecology & Evolution.
● Tabak MA, Norouzzadeh M, Wolfson D, Newton E, Boughton R, Ivan J, Odell E, Newkirk E, Conrey
R, Stenglein J, Iannarilli F, Erb J, Brook R, Davis A, Lewis J, Walsh D, Beasley J, VerCauteren K, Clune
J, Miller R (2020) Improving the accessibility and transferability of machine learning algorithms for
identification of animals in camera trap images: MLWIC2. Ecology and Evolution.
● Stanley K, Clune J, Lehman J, Miikkulainen R (2019) Designing neural networks throughneuroevolution. Nature Machine Intelligence. 1:1: 24-35.
● Nguyen A, Yosinski J, Clune J. (2019) Understanding Neural Networks via Feature Visualization: A
survey, in Samek W, Montavon G, Vedaldi A, Hansen LK, Müller K (Eds.), Interpretable AI:
Interpreting, Explaining and Visualizing Deep Learning. Springer. (book chapter)● Norouzzadeh M, Nguyen A, Kosmala M, Swanson A, Palmer MS, Parker C, Clune J (2018)
Automatically identifying, counting, and describing wild animals in camera-trap images with deeplearning. Proceedings of the National Academy of Sciences (PNAS). 115:25. (cover article)● Tabak MA, Norouzzadeh MS, Wolfson DW, Sweeney SJ, VerCauteren KC, Snow NP, Halseth JM, Di
Salvo PA, Lewis JS, White MD, Teton B, Beasley JC, Schlichting PE, Boughton RK, Wight B, Newkirk
ES, Ivan JS, Odell EA, Brook RK, Lukacs PM, Moeller AK, Mandeville EG, Clune J, Miller RS (2018)
Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in
Ecology and Evolution. 2018:00:1–6.
● Huizinga J, Stanley K, Clune J (2018) The emergence of canalization and evolvability in an open-ended,interactive evolutionary system. Artificial Life. 24:3: 157-181.
● Velez R, Clune J (2017) Diffusion-based neuromodulation can eliminate catastrophic forgetting in
simple neural networks. PLoS ONE.
● Helms L, Clune J (2017) Improving HybrID: How to best combine indirect and direct encoding inevolutionary algorithms. PLoS ONE 12(3): e0174635.
● Kouvaris K, Clune J, Kounios L, Brede M, Watson R (2017) How evolution learns to generalise: Using
the principles of learning theory to understand the evolution of evolvable developmental organization.
PLoS Computational Biology. 13(4): e1005358.
● Stanton C, Clune J (2016) Curiosity Search: Producing Generalists by Encouraging Individuals to
Continually Explore and Acquire Skills Throughout Their Lifetime. PLoS One. 11(9): e0162235

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● Mengistu H, Huizinga J, Mouret JB, Clune J (2016) The evolutionary origins of hierarchy. PLoS
Computational Biology. 12(6): e1004829.
● Nguyen A, Yosinski J, Clune J (2016) Understanding Innovation Engines: Automated Creativity and
Improved Stochastic Optimization via Deep Learning. Evolutionary Computation Journal.
● Taylor T, Auerbach JE, Bongard J, Clune J, Hickinbotham S, Ofria C, Mizuki O, Risi S, Stanley KO,
Yosinski J (2016) WebAL comes of age: A review of the first 21 years of Artificial Life on the web.
Artificial Life. 22: 364–407.
● Cully A, Clune J, Tarapore D, Mouret JB (2015) Robots that can adapt like animals. Nature.
521.7553: pp. 503-507. (cover article)● Ellefsen K, Mouret JB, Clune J (2015) Neural modularity helps organisms evolve to learn new skills
without forgetting old skills. PLoS Computational Biology. 11(4): e1004128. (cover article)● Clune J, Baptiste-Mouret J-B, Lipson H (2013) The evolutionary origins of modularity. Proceedings of
the Royal Society B. 280: 20122863. Winner: Top 5 Most Cited Papers of 2013.
● Clune J, Pennock RT, Ofria C, Lenski RE (2012) Ontogeny tends to recapitulate phylogeny in digitalorganisms. The American Naturalist. 180: E54–E63.
● Clune J, Stanley KO, Pennock RT, Ofria C (2011) On the performance of indirect encoding across thecontinuum of regularity. IEEE Transactions on Evolutionary Computation. 15(3): 346-367.
● Clune J, Goldsby H, Ofria C, Pennock RT (2011) Selective pressures for accurate altruism targeting:
Evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory. Proceedings of theRoyal Society B. 278: 666-674.
● Clune J, Misevic D, Ofria C, Lenski RE, Elena SF, and Sanjuán R (2008) Natural selection fails to
optimize mutation rates for long-term adaptation on rugged fitness landscapes. PLoS Computational
Biology. 4(9): e1000187. (cover article)

PEER REVIEWED CONFERENCE PUBLICATIONS● Beaulieu S, Clune J, Cheney N (2023) Continual learning under domain transfer with sparse synaptic
bursting. AutoML Workshop.
● Baker B, Akkaya I, Zhokhov P, Huizinga J, Tang J, Ecoffet A, Houghton B, Sampedro R, Clune J(2022) Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos. NeurIPS(25.6% acceptance rate) Oral presentation (1.76% acceptance rate of all submissions)● Kanitscheider I, Huizinga J, Farhi D, Guss WH, Houghton B, Sampedro R, Zhokhov P, Baker B,
Ecoffet A, Tang Jie, Klimov O, Clune J (2021) Multi-task curriculum learning in a complex, visual,
hard-exploration domain: Minecraft. https://arxiv.org/abs/2106.14876● Beaulieu S, Frati L, Miconi T, Lehman J, Stanley KO, Clune J*, Cheney N* (2020) Learning toContinually Learn. Proceedings of the European Conference on Artificial Intelligence. (26.8%
acceptance rate)● Wang R, Lehman J, Rawal A, Zhi J, Li Y, Clune J*, Stanley KO* (2020) Enhanced POET: Open-ended
Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions.
International Conference on Machine Learning (ICML). *co-senior authors

3● Petroski Such F, Rawal A, Lehman J, Stanley KO*, Clune J* (2020) Generative teaching networks:
accelerating neural architecture search by learning to generate synthetic training data. International
Conference on Machine Learning (ICML). *co-senior authors● Rawal A, Lehman J, Petroski Such F, Clune J*, Stanley KO* (2020) Synthetic Petri Dish: A Novel
Surrogate Model for Rapid Architecture Search. NeurIPS Workshop on Meta-Learning.
https://arxiv.org/abs/2005.13092 *co-senior authors● Ecoffet A, Clune J, Lehman J (2020) Open Questions in Creating Safe Open-ended AI: Tensions
Between Control and Creativity. Proceedings of the Artificial Life Conference (ALIFE).
● Colas C, Huizinga J, Madhavan V, Clune J (2020) Scaling MAP-Elites to Deep Neuroevolution.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
● Wang R, Lehman J, Clune J*, Stanley KO* (2019) Paired Open-Ended Trailblazer (POET): Endlessly
Generating Increasingly Complex and Diverse Learning Environments and Their Solutions.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) *co-senior authors(Best paper award)● Gajewski A, Clune J, Stanley KO, Lehman J (2019) Evolvability ES: scalable and direct optimization of
evolvability. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
● Petroski Such F, Madhavan V, Liu R, Wang R, Samuel Castro P, Li Y, Schubert L, Bellemare M, Clune
J, Lehman J (2019) An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep
Reinforcement Learning Agents. International Joint Conference on Artificial Intelligence (IJCAI)(18% acceptance rate).
● Miconi T, Rawal A, Clune J, Stanley KO (2019) Backpropamine: training self-modifying neuralnetworks with differentiable neuromodulated plasticity. International Conference on Learning Systems(ICLR) (31% acceptance rate).
● McAllister R, Kahn G, Clune J, Levine S (2019) Robustness to out-of-distribution inputs via
task-aware generative uncertainty. International Conference on Robotics and Automation (ICRA).
● Miconi T, Clune J, Stanley KO (2018) Differentiable plasticity: Training plastic neural networks withbackpropagation. International Conference on Machine Learning (ICML).
● Stanton C, Clune J (2018) Deep curiosity search: Intra-life exploration improves performance on
challenging deep reinforcement problems. NeurIPS Deep Reinforcement Learning Workshop.
● Petroski Such F, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (2017) Deep neuroevolution:
Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement
learning. NeurIPS Deep Reinforcement Learning Workshop.
● Lehman J, Chen J, Clune J, Stanley KO (2017) ES is more than just a traditional finite-difference
approximator. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
● Lehman J, Chen J, Clune J, Stanley KO (2017) Safe mutations for deep and recurrent neural networks
through output gradients. Proceedings of the Genetic and Evolutionary Computation Conference(GECCO).
● Nguyen A, Clune J, Bengio Y, Dosovitskiy A, Yosinski J (2016) Plug & Play Generative Networks:
Conditional Iterative Generation of Images in Latent Space. Computer Vision and Pattern Recognition(CVPR). Awarded spotlight oral presentation (~10% spotlight acceptance rate).
● Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J (2016) Synthesizing the preferred inputs forneurons in neural networks via deep generator networks. Advances in Neural Information Processing
Systems (NeurIPS). (23% acceptance rate).
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● Nguyen A, Yosinski J, Clune J (2016) Multifaceted Feature Visualization: Uncovering the different
types of features learned by each neuron in deep neural networks. ICML Workshop on Visualization
for Deep Learning. Selected for oral presentation. Winner: Best Workshop Paper (14% award
acceptance rate).
● Norouzzadeh M, Clune J (2016) Neuromodulation improves the evolution of forward
models. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
● Velez R, Clune J (2016) Identifying core functional networks and functional modules within artificial
neural networks via subsets regression. Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO).
● Mengistu H, Lehman J, Clune J (2016) Evolvability Search: Directly selecting for evolvability in orderto study and produce it. Proceedings of the Genetic and Evolutionary Computation Conference(GECCO).
● Huizinga J, Mouret JB, Clune J (2016) Does aligning phenotypic and genotypic modularity improvethe evolution of neural networks? Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO).
● Tarapore D, Clune J, Cully A, Mouret JB (2016) How do different encodings influence theperformance of the MAP-Elites algorithm? Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO).
● Lehman J, Risi S, Clune J (2016) Creative Generation of 3D Objects with Deep Learning andInnovation Engines. Proceedings of the International Conference on Computational Creativity.
● Li Y, Yosinski J, Clune J, Lipson H, Hopcroft J (2015) Convergent Learning: Do different neuralnetworks learn the same representations? International Conference on Learning Representations(ICLR). Selected for oral presentation (5.7% oral acceptance rate). Also selected for oral presentation
at the Neural Information Processing Systems (NeurIPS) Feature Extraction Workshop (6.7% oral
acceptance rate).
● Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks throughDeep Visualization. International Conference on Machine Learning (ICML) Deep Learning
Workshop.
● Nguyen A, Yosinski J, Clune J (2015) Innovation Engines: Automated Creativity and Improved
Stochastic Optimization via Deep Learning. Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO). Winner: Best Paper Award (3% award acceptance rate).
● Nguyen A, Yosinski J, Clune J (2015) Deep Neural Networks are Easily Fooled: High Confidence
Predictions for Unrecognizable Images. Computer Vision and Pattern Recognition (CVPR). Awarded
oral presentation (3% oral acceptance rate). Overall acceptance rate: 25%.
● Gurbhoo N, Kubichek RF, Muknahallipatna S, Clune J (2015) A Method to Improve Signal Quality in
Wireless Ad-Hoc Networks with Limited Mobility. Proceedings of the International Conference on
Computing, Networking, and Communications.
● Yosinski J, Clune J, Bengio Y, and Lipson H (2014) How transferable are features in deep neuralnetworks? Advances in Neural Information Processing Systems (NeurIPS). Selected for oralpresentation (1% oral acceptance rate).
● Li J, Storie J, Clune J (2014) Encouraging Creative Thinking in Robots Improves Their Ability to Solve
Challenging Problems. Proceedings of the Genetic and Evolutionary Computation Conference(GECCO). 193-200.

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● Velez R, Clune J (2014) Novelty Search Creates Robots with General Skills for Exploration.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). 737-744.
● Huizinga J, Mouret JB, Clune J (2014) Evolving Neural Networks That Are Both Modular andRegular: HyperNeat Plus the Connection Cost Technique. Proceedings of the Genetic andEvolutionary Computation Conference (GECCO). 697-704.
● Cheney N, Clune J, Lipson H (2014) Evolved Electrophysiological Soft Robots. Proceedings of the
Artificial Life Conference (ALIFE).
● Coleman O, Blair A, Clune J (2014) Automated Generation of Environments to Test the General
Learning Capabilities of AI Agents. Proceedings of the Genetic and Evolutionary Computation
Conference (GECCO). 161-168.
● Clune J, Chen A, Lipson H (2013) Upload Any Object and Evolve It: Injecting Complex Geometric
Patterns into CPPNs for Further Evolution. Proceedings of the IEEE Congress on Evolutionary
Computation (CEC). 3395-3402.
● Cheney N, MacCurdy R, Clune J, Lipson H (2013) Unshackling evolution: evolving soft robots withmultiple materials and a powerful generative encoding. Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO). 167-174.
● Lee S, Yosinski J, Glette K, Lipson H, Clune J. 2013. Evolving gaits for physical robots with theHyperNEAT generative encoding: the benefits of simulation. Applications of Evolutionary
Computing. 540-549. Springer.
● Lohmann S, Yosinski J, Gold E, Clune J, Blum J, Lipson H (2012) Aracna: An Open-Source
Quadruped Platform for Evolutionary Robotics. Proceedings of the Artificial Life Conference.
387-392. Best presentation award.
● Clune J, Lipson H (2011) Evolving three-dimensional objects with a generative encoding inspired bydevelopmental biology. Proceedings of the European Conference on Artificial Life. 144-148.
● Yosinski J, Clune J, Hidalgo D, Nguyen S, Cristobal Zagal J, Lipson H (2011) Evolving robot gaits in
hardware: the HyperNEAT generative encoding vs. parameter optimization. Proceedings of theEuropean Conference on Artificial Life. 890-897.
● Suchorzewski M, Clune J (2011) A novel generative encoding for evolving modular, regular andscalable networks. Proceedings of the Genetic and Evolutionary Computation Conference. 1523-1530.
● Clune J, Beckmann BE, McKinley PK, Ofria C (2010) Investigating whether HyperNEAT produces
modular neural networks. Proceedings of the Genetic and Evolutionary Computation Conference.
635-642.
● Clune J, Beckmann BE, Pennock RT, Ofria C (2009) HybrID: A hybridization of indirect and direct
encodings for evolutionary computation. Proceedings of the European Conference on Artificial Life.
Vol. 2: 134: 141.
● Goldsby HJ, Knoester DB, Clune J, McKinley PK, Ofria C (2009) The evolution of division of labor.
Proceedings of the European Conference on Artificial Life. Vol. 2: 10-18.
● Clune J, Pennock RT, and Ofria C (2009) The sensitivity of HyperNEAT to different geometricrepresentations of a problem. Proceedings of the Genetic and Evolutionary Computation Conference.
675-682. Best paper award.
● Goldsby HJ, Goings S, Clune J, and Ofria C (2009) Problem decomposition using indirect reciprocity
in evolved populations. Proceedings of the Genetic and Evolutionary Computation Conference.
105-112.
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● Clune J, Beckmann BE, Ofria C, and Pennock RT (2009) Evolving coordinated quadruped gaits with
the HyperNEAT generative encoding. Proceedings of the IEEE Congress on Evolutionary Computing.
2762-2771.
● Clune J, Ofria C, and Pennock RT (2008) How a generative encoding fares as problem-regularity
decreases. Proceedings of the 10th International Conference on Parallel Problem Solving From Nature.
358-367.
● Clune J, Ofria C, and Pennock RT (2007) Investigating the emergence of phenotypic plasticity indigital organisms. Proceedings of the European Conference on Artificial Life. 74-83.
● Clune J, Goings S, Goodman ED, and Punch W (2005) Investigations in meta-GAs: panaceas or pipe
dreams? Proceedings of the Genetic and Evolutionary Computation Conference. 235-241.
● Goings S, Clune J, Ofria C, and Pennock RT (2004) Kin-Selection: The rise and fall of kin cheaters.
Proceedings of the Ninth Conference on Artificial Life. 303-308.

PUBLICATIONS ON ARXIV●● Ecoffet A, Huizinga J, Lehman J, Stanley KO*, Clune J* (2019) Go-Explore: a New Approach for
Hard-Exploration Problems. https://arxiv.org/abs/1901.10995 **co-senior authors● Clune J (2019) AI-GAs: AI-generating algorithms, an alternate paradigm for producing generalartificial intelligence. https://arxiv.org/abs/1905.10985● Zhang X, Clune J, Stanley KO (2017) On the relationship between the OpenAI evolution strategy andstochastic gradient descent. https://arxiv.org/abs/1712.06564● Kounios L, Clune J, Kouvaris K, Wagner GP, Pavlicev M, Weinreich D, Watson R (2016) Resolving theparadox of evolvability with learning theory: How evolution learns to improve evolvability on rugged
fitness landscapes. arXiv preprint arXiv:1612.05955● Mouret JB, Clune J (2015) Illuminating search spaces by mapping elites. arXiv
http://arxiv.org/abs/1504.04909

GRANTS & FELLOWSHIPS (NUMBERS BEFORE 2020 ARE USD, AFTER ARE CAD)

● 2023: Schmidt Futures: $672,000 CAD● 2023: OpenPhilanthropy Grant: $134,000 CAD● 2022: NSERC Discovery Grant: $205,000 CAD● 2021: Canada CIFAR AI Chair & Vector Faculty Membership: substantial, but confidential amount● 2021: Research gift from private donor: $16,000 CAD● 2018: Overcoming Catastrophic Forgetting and Rapidly Adapting via Selective Plasticity Driven by
Diffusion-Based Neuromodulation. PI. DARPA Lifelong Learning Machines Program. With Nick
Cheney. $1,669,969

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● 2018: NSF Collaborative Research: Framework: Software: HDR: Building the Twenty-First Century
Citizen Science Framework to Enable Scientific Discovery Across Disciplines. Co-PI with Sarah
Benson-Amram at UW. $85,284 to UW.
● 2017: Collaboration on Intelligent Machines. Grant from the Norwegian government to Jim Torresen
to collaborate with scientists in other countries including myself. Co-PI. $400,000 total, but none
directly to UW.
● 2016: Oak Ridge Directors Discretion Award $26,848 value.
● 2016: Ellbogen Next Generation Program $1,500● 2015: NSF CAREER Award. $507,465● 2015: NASA WSG Faculty Research Initiation Grant. $20,000● 2015: UW CEAS Active Learning Initiative. $30,180● 2015: XSEDE Supercomputing Research Allocation. Value: $26,848● 2015: Berry Center Art in Conservation Grant. $5,000● 2015: NVIDIA donation of two Titan GPU cards $2,000● 2015: Co-PI on grant establishing UW as an NVIDIA GPU Research and Education Center. Includes
hardware donation valued at $4,150● 2015: UWEFE funds to buy robots. $1,317● 2015: Three private donations to Laramie Robotics Club. $1,600● 2015: External collaborator donation. $1,600● 2014: Ellbogen K-12 engineering initiative. $3,000● 2014: UW summer graduate research enhancement award. $5,000● 2014: Nvidia hardware donation for research. ~$11,500 (value of donated Nvidia Tesla K40 graphics
card and the matching hardware donated by the advanced research computing center)● 2014: XSEDE computing allocation. ~$4000● 2014: Private donation from UW Alumni to my lab + Google match. $16,000● 2014: iRobot donated 3 Roomba robots to Laramie Robotics Club. ~$2,000● 2014: Modular Robotics donated a robot kit. $149.
● 2014: Associated Students of U. Wyoming funded the Laramie Robotics Club. $965.
● 2013-2015: Co-author of successful Graduate Mentoring Initiative proposal at U. Wyoming. The
award consisted of one funded Ph.D. graduate assistantship for two years, or $21,060.
● 2014-2016: Co-author of successful Graduate Mentoring Initiative proposal at U. Wyoming. The
award consisted of one funded Ph.D. graduate assistantship for two years, or $21,060.
● 2013: UW Engineering Fund for Enrichment. $2,541● 2013: Private donation from UW alumni to my lab. $10,000● 2012: Co-author of funded DARPA grant titled "Matter Compiler: Visual Interaction for Rapid
Exploratory Design for Manufacturing." $824,000● 2010-2011: NSF Postdoctoral Research Fellowship in Biology. $123,000.
● 2010: Co-author of a successful funding request to the BEACON Center for the Study of Evolution in
Action titled “Open-Ended Evolution of Ecologies of Digital Organisms in 3-Dimensions.” The grantcovered the cost of one graduate student salary for one year.
● 2010: Research Travel Fellowship, Michigan State University
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● 2010: Genetic and Evolutionary Computation Conference Travel Award● 2009: European Conference on Artificial Life Student Fellowship● 2009: Council of Graduate Students Conference Grant● 2006: NSF Graduate Research Fellowship, Honorable Mention● 2005: Quantitative Biology and Modeling Initiative Research Fellowship, Michigan State University● 2003-2005: Dean's Recruitment Fellowship, Michigan State University● Won by Students I Advised● 2023: Four Year Fellowship (4YF) from UBC CS. Shengran Hu. $72,800● 2018: U. Wyoming’s Outstanding Dissertation of 2018 award. Anh Nguyen. $3,000● 2016: Wyoming Space Grant. Richard Yang. $5,000● 2015: EPSCoR Grant. Richard Yang. $1,800● 2015: Wyoming Research Scholars Program. Richard Yang. $7,300● 2015-2020: CEAS Excellence Fellowship. Christopher Stanton. $225,000● 2015-2020: CEAS Excellence Fellowship. Anh Nguyen. $225,000● 2014: ACM Women in Computing. Jingyu Li. $600
● 2014: Wyoming Space Grant. Roby Velez. $20,000● 2014: Wyoming Space Grant. Tyler Hughes. $5,000● 2014: EPSCoR Grant. Tyler Hughes. (declined) $3,500● 2014: McNair Scholars Program. Tyler Hughes. $3,600● 2014: ACM Women in Computing Scholarship. Jingyu Li. $600
● 2013:Minority and Women’s Assistantship. $31,590 (1.5 years of Ph.D. support)

SELECTED AWARDS & DISTINCTIONS● 2023: SIGEVO Impact Award, recognized one paper in 2023 that was published in the GECCO
conference 10 years earlier that was both highly cited and deemed to be seminal by the SIGEVO
Executive Committee.
● 2023: Norwegian Artificial Intelligence Research Consortium, Publication of the decade 2012-2022
for the paper: Ellefsen, Mouret, and Clune. "Neural modularity helps organisms evolve to learn new
skills without forgetting old skills." PloS computational biology (2015)● 2023: Invitee, Private Conversation with US Senate Majority Leader Chuck Schumer on how tobest regulate AI● 2023: Named “One of the people to watch in Canadian AI”, by The Logic● 2022: Oral presentation, NeurIPS. 1.76% acceptance rate of all submissions. For Learning to Act by
Watching Unlabeled Online Videos.
● 2022: Appointed a Kavli Fellow by the US National Academy of Sciences. For those that are“distinguished young scientists” and “future leaders in science.”● 2022: Dean of Science teaching commendation letter for 340: Machine Learning and Data Mining. For
having some of the highest teaching evaluation scores in the Faculty of Science.
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● 2022: Dean of Science teaching commendation letter for 532J: Never-ending RL. For having some ofthe highest teaching evaluation scores in the Faculty of Science.
● 2021: Outstanding Paper of 2020. International Society for Artificial Life. For “"The surprisingcreativity of digital evolution.”● 2019: Presidential Early Career Award for Scientists and Engineers (PECASE). “The PECASE is thehighest honor bestowed by the United States Government to outstanding scientists and engineers whoare beginning their independent research careers and who show exceptional promise for leadership inscience and technology.” – White House press release● Outstanding Publication of the Decade 2004-2014 (awarded in 2019), International Society for
Artificial Life, for “The Evolutionary Origins of Modularity” (Clune, Mouret, & Lipson. Proc. Royal
Society B 2013)● 2019: Best Paper Award, GECCO 2019. For “POET: Endlessly Generating Increasingly Complex andDiverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer.”● 2018: Invited by the White House to the White House AI Summit (served as Uber’s representative)● 2017: Early Tenure● 2016: Distinguished Young Investigator Award, International Society for Artificial Life. All
students, postdocs, and pre-tenure faculty in the field worldwide were eligible.
● 2016: Outstanding Paper of 2015, International Society for Artificial Life. For our paper ‘Robots
that can adapt like animals’, which was featured on the cover of Nature.
● 2016: Winner: Best Workshop paper (14% award acceptance rate) for the paper: Nguyen A, Yosinski J,
Clune J (2016) Multifaceted Feature Visualization: Uncovering the different types of features learned byeach neuron in deep neural networks. ICML Workshop on Visualization for Deep Learning. Also
selected for oral presentation.
● 2016: Top 50 most downloaded papers of 2016, PLoS Computational Biology● 2016: La Recherche prize from La Recherche magazine. For the top paper of 2015 in the field of"information science” (for "Robots that can adapts like animals”, Nature 2015). One award is given peryear in each of 12 scientific fields.
● 2016: Best Student Video, AAAI Video Awards. For “Deep Neural Networks are Easily Fooled” (wonby my Ph.D. student Anh Nguyen)● 2015: CVPR Community Top Paper. $3,000 cash prize.
● 2015: Altmetrics’ analyses reveal our paper “Deep Neural Networks are Easily Fooled” to be the 63rd
most impactful scientific paper worldwide from any discipline in 2015
● 2015: Popular Science “Best of What's New” Award Winner● “The award is Popular Science’s top prize, and the 100 winners—chosen from among thousands ofnominees—are each a revolution in their respective fields.”- Cliff Ransom, Editor-in-Chief, Popular
Science● 2015: “Top Prof” award from the University of Wyoming Mortar Board (senior honors society)● 2015: Best paper award. Genetic and evolutionary computation conference. For the paper “Innovation
Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning.”● 2015: Winner: Virtual Creatures Competition, Genetic and Evolutionary Computation Conference● 2015: Winner: Best Long Video & Most Educational Video. Deep Neural Networks are Easily Fooled.
IJCAI 2015 Video competition.
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● 2015: Winner: Most Entertaining Video. Unshackling evolution: evolving soft robots with multiple
materials. IJCAI 2015 Video competition.
● 2014: Winner, Best Video, AAAI Video Competition, “Evolving Modular, Regular ANNs”● 2014: Winner, GECCO Virtual Creatures Competition (Judged by Karl Sims, Peter Bentley, Auke Jan
Ijspeert, Mark Bedau, & Sebastian Risi)● 2014: Author of Top 5 Most Cited Publications in 2013 for the journal Proc. Royal Society B.
● 2013: Winner, Most Entertaining Video, AAAI Video Competition● 2012: Winner, Visualizing Evolution Contest, Genetic & Evolutionary Computation Conference● 2012: Student Paper of the Year award from The American Naturalist, runner-up (For Ontogeny Tends
to Recapitulate Phylogeny in Digital Organisms).
● 2012: AAAI Video Award Finalist● 2011: EndlessForms.com voted one of the top 35 websites in the 3D printing industry● 2011: Finalist in the Evolutionary Art Competition (one of four), Genetic and Evolutionary
Computation Conference● 2009: Best Paper Award, Genetic and Evolutionary Computation Conference● 1999: Angell Scholar, University of Michigan● 1995: Branstrom Award, University of Michigan

CHAIRING, TUTORIALS, & SERVICE WITHIN MY FIELD

Reviewer:▪ Funding agencies: National Science Foundation, European Research Council (ERC), Templeton
Foundation, NSERC.
▪ Journals: PNAS, Nature Communications, Science Advances, PLoS Biology, PLoS One, IEEE
Transactions on Neural Networks, Journal of Machine Learning Research, IEEE Transactions on
Evolutionary Computation, Evolutionary Computation, Neural Computation, Adaptive Behavior,
Artificial Life Journal, Astrobiology, Soft Computing, Leonardo▪ Conferences: NeurIPS 2023 Workshop Proposals, SIGGRAPH Conference, Genetic andEvolutionary Computation Conference, International Conference on Intelligent Robots andSystems, Artificial Life Conference, EvoStar Conference, AAAI AI Video Competition, VirtualCreatures Competition, ICML Workshop on Visualization for Deep Learning● 2023: Co-organizer, Workshop on Learning from Diverse, Offline Data (L-DOD), ICRA 2023
● 2020: Co-organizer, NeurIPS Meta-Learning Workshop● 2020: Co-organizer, ICLR Workshop, Beyond “tabula rasa” in reinforcement learning: agents that
remember, adapt, and generalize.
● IEEE Task Force Evolutionary-Developmental Systems and Robotics, 2018-Present● 2013-2017: Elected to the Board of Directors of the International Society of Artificial Life
● 2016: NeurIPS committee to accept/reject workshop proposals● 2015-2016: Vice chair, IEEE Technical Committee on Cognitive and Developmental Systems EvoDevo
TaskForce●11

● 2014. Co-organizer. Workshop On Artificial Life And The Web. Artificial Life Conference.
● 2013: Co-organizer, AAAI Symposium, How Should Intelligence be Abstracted in AI Research:
MDPs, Symbolic Representations, Artificial Neural Networks, or _____? Featured 38 participants andfive keynotes, each of which is a major leader of their respective fields (Andrew Ng, Stanford; Georg
Striedter, UC Irvine; Randall O’reilly, University of Colorado Boulder; Risto Miikkulainen, UTAustin; Gary Marcus, NYU; Pierre-Yves Oudeyer, Inria France).
● 2010 & 2011: Co-chair, Generative and Developmental Systems Track, Genetic and Evolutionary
Computation Conference● 2010: The Avida Digital Evolution Platform, Tutorial, Genetic and Evolutionary Computation
Conference● 2009: Leveraging the Avida Digital Evolution Platform for Research in Evolving Cooperation, Tutorial,
European Conference on Artificial Life

LIGHTLY PEER REVIEWED VIDEO PUBLICATIONS

Note: To date, videos of our research have been viewed over 1,700,000 times.
● Nguyen A, Yosinski J, Clune J (2016) 2016: Best Student Video, AAAI Video Awards. For “DeepNeural Networks are Easily Fooled”● Cully A, Clune J, Tarapore D, Mouret JB (2016) Robots that can adapt like natural animals. AAAI
video competition. Nominated for best video, best student video, and best robot video.
● Ellefsen K, Mouret JB, Clune J (2016) Neural modularity helps organisms evolve to learn new skills
without forgetting old skills. AAAI video competition. Nominated for best student video.
● Nguyen A, Yosinski J, Clune J (2015) Deep Neural Networks are Easily Fooled. IJCAI 2015 Video
competition. Winner: Best Long Video & Most Educational Video.
● Huizinga J, Mouret JB, Clune J (2014) Evolving neural networks that are both modular and regular.
AAAI Video Competition. Winner: Best Video.
● Li J, Storie J, Clune J (2014) Encouraging creative thinking in robots improves their ability to solve
challenging problems.
● Cheney N, MacCurdy R, Clune J, Lipson H (2013) Unshackling evolution: evolving soft robots withmultiple materials. Winner: Most Entertaining Video at both the 2013 AAAI Video Competition
and the 2015 IJCAI Video competition.
● Clune J (2013) Evolving gaits for legged robots. AAAI Video Competition.
● Clune J, Mouret JB, Lipson H (2013) The evolutionary origins of modularity: video summary. AAAI
Video Competition.
● Clune J, Yosinski J, Doan E, Lipson H (2012) Automating user creation of 3D printable objects using
evolutionary algorithms based on developmental biology. AAAI Video Competition. Finalist: BestVideo Award.

NON-PEER REVIEWED PUBLICATIONS

12

● Bradley H, Dai A, Zhang J, Clune J, Stanley K, Lehman J (2023) Quality Diversity Through AI
Feedback, Carper AI Blog
● Petroski Such F, Rawal A, Lehman J, Stanley KO, Clune J (2019) Generative Teaching Networks:
Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data. Uber
Engineering Blog.
● Petroski Such F, Madhavan V, Liu R, Wang R, Li Y, Clune J, and Lehman J (2019) Creating a Zoo of
Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning. Uber
Engineering Blog.
● Wang R, Lehman J, Clune J*, Stanley KO* (2019) POET: Endlessly Generating Increasingly Complex
and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer.
*co-senior authors Uber Engineering Blog.
● Ecoffet A, Huizinga J, Lehman J, Stanley KO (2018) Montezuma’s Revenge Solved by Go-Explore, a
New Algorithm for Hard-Exploration Problems (Sets Records on Pitfall, Too). Uber Engineering Blog.
● Miconi T, Clune J, Stanley K (2018) Differentiable Plasticity: A New Method for Learning to Learn.
Uber Engineering Blog.
● Petroski Such F, Stanley K, Clune J (2018) Accelerating Deep Neuroevolution: Train Atari in Hours ona Single Personal Computer. Uber Engineering Blog.
● Wang R, Clune J, Stanley KO (2018) VINE: An Open Source Interactive Data Visualization Tool for
Neuroevolution. Uber Engineering Blog.
● Stanley K, Clune J (2017) Welcoming the Era of Deep Neuroevolution. Uber Engineering Blog.
● Lehman J, Clune J, Risi S. An Anarchy of Methods: Current Trends in How Intelligence Is Abstracted
in AI. IEEE Intelligent Systems. 2015. 29 (6), 56-62. (invited)● Ellefsen K, Clune J (2015) The surprising creativity of digital evolution. Laboratory News
● Burns G, Gil Y, Liu Y, Villanueva-Rosales N, Risi S, Lehman J, Clune J, Lebiere C, Rosenbloom P S,
van Harmelen F, Hendler J A, Hitzler P, Janowic K, Swarup S. Reports on the 2013 AAAI Fall
Symposium Series. AI Magazine. 2014. 35 (2), 69-74. (invited)

PRESS COVERAGE (SELECTED ARTICLES)● Can We Stop Runaway A.I.? New Yorker. 2023
● These 4 free apps can help you identify every flower, plant and tree around you. Washington Post. 2023.
● ‘I hope I’m wrong’: Why some experts see doom in AI. The Globe and Mail. 2023.
● The people to watch in Canadian AI, The Logic, 2023
● Interview on QR Calgary Radio with Rob Breakenridge, 2023
● Machines that think like humans: Everything to know about AGI and AI Debate 3. ZDNet. 2023.
● TalkRL Podcast. Jeff Clune. ~Hour-long interview. 2023
● Interview on AI scams. A Little More Conversation With Ben O’Hara-Byrne. Global News. 2023.
● DeepMind AI learns simple physics like a baby. Nature. 2022.
13

● This Week in Machine Learning (TWIML) Podcast. Jeff Clune: Accelerating Intelligence with AI-Generating
Algorithms. 2022.
● BBC Brazil. Google's LaMDA: The interactions that create a sense of `self-awareness' in robots. 2022.
● What we learned about AI and deep learning in 2022. VentureBeat. 2022.
● Scientists Increasingly Can’t Explain How AI Works. Vice. 2022.
● MIT Tech Review. 2022. A bot that watched 70,000 hours of Minecraft could unlock AI’s next big thing.
Coverage of VPT also in TechTimes, TechCrunch, Gizmodo, Singularity Hub, and TechRadar.
● PC Mag. OpenAI Taught a Neural Network How to Play Minecraft. 2022.
● Vector Institute. New Vector Faculty Member Jeff Clune’s quest to create open-ended AI systems. 2022.
● MIT Tech Review. 2021. AI is learning how to create itself.
● BBC News. 2021. AI conquers challenge of 1980s platform games● Scientific American. 2021. Machine Learning Pwns Old-School Atari Games● NewScientist. 2021. AI smashes video game high scores by remembering its past success● VentureBeat. 2021. How AI trained to beat Atari games could impact robotics and drug design● VentureBeat. 2020. OpenAI’s Jeff Clune on deep learning’s Achilles’ heel and a faster path to AGI● VentureBeat. 2020. Uber details Fiber, a framework for distributed AI model training● VentureBeat. 2019. Uber creates AI to generate data for training other AI models● Wired. 2019. A Sobering Message About the Future at AI's Biggest Party● Science. 2019. This artificial intelligence teaches robots to walk—by creating custom obstacle courses● Nature. 2019. Why deep-learning AIs are so easy to fool
● Nature Machine Intelligence. 2019. Moving beyond reward prediction errors● Communications of the ACM. 2019. Lifelong learning in artificial neural networks● MIT Technology Review. 2018. Uber has cracked two classic ’80s video games by giving an AI algorithm a
new type of memory.
● Wired. 2018. When Bots Teach Themselves to Cheat● BBC. 2018. The AI developed to identify and count wild animals● MIT Technology Review. 2018. AI is helping wildlife biologists identify rare beasts on the Serengeti● New York Times. 2018. Google Researchers Are Learning How Machines Learn● New Scientist. 2018. 8 hilarious ways AI has outsmarted us to get the job done
● Science. 2018. Artificial intelligence can ‘evolve’ to solve problems● Nature. 2017. Astronomers explore uses for AI-generated images● Wired. 2017. AI Will Make Forging Anything Entirely Too Easy
● Slate. 2017. The Fake-Image Arms Race
● BBC. 2017. Tiny changes can cause AI to fail.
● New Scientist. 2017. Deep learning tells giraffes from gazelles in the Serengeti.
● MIT Technology Review. 2017. The Dark Secret at the Heart of AI● The Verge. 2017. Magic AI: These Are the Optical Illusions That Trick, Fool, and Flummox Computers● The Verge. 2016. Artificial Intelligence Is Going to Make It Easier Than Ever to Fake Images and Video14

● Coverage of our startup Geometric Intelligence being acquired by Uber occurred in Wired, Fortune, the
New York Times, MIT Tech Review, BBC, Bloomberg, Wall Street Journal, Tech Crunch, and manymore.
● Nature News, Nature News Podcast. 2016. Can we open the black box of AI?
● KurzweilAI.net. 2016. Hierarchies exist in the brain because of lower connection costs.
● Press coverage of 2015 Nature cover paper “Robots that can adapt like animals.”● Two commentaries in Nature and one in Science● Coverage in BBC, Washington Post, The Guardian, Nature News, NBC News, NPR’s Science
Friday, The Atlantic, Fast Company, MIT Technology Review, BBC Inside Science, BBC World
Service, The Economist, CNBC, Popular Science, Christian Science, Discover, Wired, Daily Mail,
Voice of America, Le Monde, Liberation, Gizmodo, IFL Science, IEEE Spectrum, The Times, The Verge,
Live Science, The Register, BFM Business, Wyoming Public Media, Vice, and many more.
● Popular Science. 2016. See the difference one year makes in artificial intelligence research an improved way oflearning about neural networks.
● Popular Science. 2016. This sculpture was designed and 3d printed by an AI artist.
● Motherboard. 2016. When AI goes wrong, we won’t be able to ask it why● HowStuffWorks. 2016. Are you actually an android?
● The Economist. 2015. Rise of the machines.
● Scientific American Mind. 2015. Do androids dream?
● Reddit Ask Me Anything (AMA) hosted by PLoS. Over 1,000 comments/questions and 42,000+ uniqueparticipants.
● Communications of the ACM. 2015. Teaching computers with illusions.
● Laramie Boomerang. 2016. UW professor’s research lights up scientific, media interest.
● Nautilus Magazine. 2015. Artificial intelligence is already weirdly inhuman.
● Wyoming Public Media. 2016. Programming Through Trial And Error, With An Emphasis On Error.
● Motherboard magazine. 2015. How automating evolution could give machines imaginations.
● BBC World Service. 2015. Why computers forget. Also on interviewed for BBC Four.
● The Atlantic. 2015. Teaching a computer not to forget.
● Popular Science. 2015. To build multi-tasking robots, mimic the human brain.
● MIT Technology Review. 2014. Smart” Software can be tricked into seeing what isn’t there.
● Wired. 2014. Simple pictures that state-of-the-art AI still can’t recognize. Rebroadcast on Slate.
● Slashdot (homepage). 2014. Research highlights how AI sees and how it knows what it's looking at. Also #1
article on HackerNews.
● The Atlantic. 2014. How to fool a computer with optical illusions● New Scientist. 2014. Optical illusions fool computers into seeing things● BBC (featured on homepage). 2014. Robot learns to keep going with broken leg.
● IEEE Spectrum. 2014. Hexapod robot gets even better at being indestructible.
● Slashdot (homepage). 2014. Robot with broken leg learns to walk again in under 2 minutes.
● CNET Video. 2014. Tomorrow daily. (Show featured segment on robot damage recovery).
● Discover News. 2013. Evolution helps build better robots.
15

● NBC News. 2013. Squishy virtual creatures 'evolve' walking behaviors. The story was also featured on
YouTube’s Most Popular Videos page, MSNBC.com, and the front pages of Digg.com, Hacker News,
BuzzFeed, and Reddit Science.
● Discover. 2013. Watch This: Squishy Robots Evolve for Speed.
● National Geographic. 2013. The parts of life, by Carl Zimmer.
● BBC. 2013. 3D printing your thoughts.
● Through the Wormhole with Morgan Freeman. 2013. Are Robots the Future of Human Evolution?
● Popular Science. 2013. 175,000 ways to walk.
● Fast Company: 2013. With evolved brains, robots creep closer to animal-like learning.
● NPR (National Public Radio). 2012. Academic Minute – Evolution and Embryology.
● Houston Chronicle. Laramie club teaches students to control robots. 2013.
● Cornell Chronicle. 2013. Scientists find ‘holy grail’ of evolving modular networks.
● MIT Technology Review. 2012. Computer scientists reproduce the evolution of evolvability.
● ScienceDaily. 2013. Engineers solve a biological mystery and boost artificial intelligence.
● The New Scientist (featured on cover). 2011. Darwin’s robots: A holistic, evolutionary approach meansthat robots could learn to design themselves.
● MSNBC.com. 2011. Intelligent design: Users power evolution in 3-D Web printing.
● Slashdot. 2011. Crowdsourcing speeds evolution of 3D printable objects.
● IEEE Spectrum. 2013. Bizarre Soft Robots Evolve to Run.
● The New Scientist (featured on cover). 2010.
● Main article: Artificial life forms evolve basic intelligence● Editorial: Digital evolution and the meaning of life
● US News & World Report. 2010. New MSU research sheds light on how we become altruistic.
● The Daily Telegraph. 2010. Computer-simulated life forms evolve intelligence.
● Slashdot. 2010. Artificial life forms evolve basic memory, strategy.
● MIT Technology Review. 2011. 3-D design simplified: a new website could accelerate the adoption of 3-D
printing.
● Science. 2006 (vol. 311). Darwin’s Place on Campus Is Secure—But Not Supreme.
● NSF Highlight. 2013. Modular Biological Networks Avoid Costly Connections.
● Servo Magazine. 2013. Aracna: 3D printed, open-source robot.
● Communications of the ACM. 2010. ‘EndlessForms’ uses the Web to breed 3D printable objects.
● KurzweilAI.net. 2010. Artificial life forms evolve basic intelligence.
● Miami Herald/Houston Chronicle/Laramie Boomerang 2013. Laramie club teaches area students to control
robots.
● Gizmodo. 2014. Watch this bot with a broken leg learn to walk straight again.
● UW News. 2013. Visiting doctoral student from Norway works to evolve robots to learn better.
● Discover. 2005 (cover article). Testing Darwin.
● Mechanical Engineering. 2012. Design in the age of 3-D printing.
● ZDNet.com. 2013. Fabricated: The New World of 3D Printing.
● SigEvolution, ACM's newsletter on Genetic and Evolutionary Computation. Cover article.
16

● ScienceDaily.com. 2012. Why do organisms build tissues they seemingly never use?
● ScienceDaily.com. 2011. No technical know-how needed: Endless Forms Web site helps users ‘breed’ 3-D
printable objects.
● ScienceDaily.com. 2010. Research sheds light on altruism.
● Hacker News (front page). 2011. Breed 3D printable objects, no technical know-how needed.
● Research also covered in the following: Lansing State Journal, Jerusalem Post, Cornell Daily Sun, Innovation
News Daily, LiveScience.com, PhysOrg.com, eCampusNews, NewsWise.com, BigThink.com, Business News
Daily, Cornell Chronicle, State News, Shapeways blog, Thingiverse blog, Carl Zimmer’s blog, 3Dprinter.net,
PlasticsToday.com, Biota Live Podcast, Impact Radio, City Pulse, OneIndia.com, Computerra.com,
TodayOnline.com, Creativity Online, Heise.de, ZeitNews.org, MyScience.cc, TheHighLow.com, and 50+
other media outlets.
Comments from me on work by others or trends in AI● Online romance scammers may have a new wingman — artificial intelligence. CBC. 2023 (Also ran in the
Globe and Mail and the Vancouver Sun)
● Big Tech Is Now Developing Powerful AI Brains for Real-World Robots. Vice. 2023
● Two years after DALL-E debut, its inventor is “surprised” by impact. VentureBeat. 2023

INVITED TALKS

Keynotes: CORL 2021, AutoML 2022, Beijing Academy of Artificial Intelligence Conference, NICE 2022,
Linux Foundation North America Open Source Summit, Mexican Conference on AI, BEACON Congress,
Genetic Programming in Theory & Practice, GECCO EvoRL workshop.
MIT, Cambridge, Yale, Berkeley, University of Michigan, DeepMind, MIT, MILA, Carnegie Mellon,
Stanford, University of Southern California, ICLR 2019 Plenary Debate, ICML 2019 Tutorial, NYU,
University of California Santa Barbara, AGI Debate 3, University of British Columbia, University ofColorado Boulder, University of Arizona, University of Oregon, OpenAI, Cornell, CIFAR Board of
Directors, Santa Fe Institute, Cold Spring Harbor Laboratory, Toyota Research Institute, Amazon,
University of California Irvine, Michigan State University, Queen Mary London (via Skype), IST Austria,
Pierre & Marie Curie University, American University, Colorado State University, Center for a New American
Security, Sharif University of Technology, Wyoming Global Technology Summit, Canterbury University,
Sentient Corporation, University of Hawaii, University of Puerto Rico, Paris Descartes Medicine Faculty:
Center for Interdisciplinary Research, University of Lausanne, Swiss AI Lab (IDSIA), Samsung AI Research,
ReWork Deep Learning Summit, ReWork Deep Learning for Robotics Summit, ReWorkAI Summit West,UC Merced, Visipedia workshop, Origins Project / Bulletin of Atomic Scientists (at Arizona State University),
Schwartz Reisman Institute (U. Toronto), Open Data Science Conference, Deep Learning Classics andTrends, Fields Institute for Research in Mathematical Sciences (U. Toronto), BC Children's Hospital
Research Institute, Open AI Robotics Symposium.
Workshops: International Workshop on Intrinsically-Motivated Open-ended Learning (IMOL2023),
Workshop on Reincarnating Reinforcement Learning at ICLR (panelist), NeurIPS 2023 Agent Learning in
17

Open-Endedness Workshop, RSS'23 Workshop: Environment Generation for Generalizable Robots, CoRL
2022 Language + Control Workshop, International Conference on Intelligent Robots and Systems (IROS2022), ICLR 2022 AI for Earth and Space Science, ICLR 2021 Neural Architecture Search Workshop,
NeurIPS Deep RL Workshop 2018, NeurIPS Meta-Learning Workshop 2019, NeurIPS workshop on
Biological and Artificial Reinforcement Learning 2019, ICML 2020 Continual Learning Workshop, ICLR
2020 BeTR-RL Workshop, 2022 ICLR AI for Earth Sciences Workshop, CVPR 2021 Workshop on
Continual Learning in Computer Vision, ICML 2019 workshop on Multi-Task and Lifelong Reinforcement
Learning, NeurIPS Emergent Communications Workshop.

OUTREACH*
2023: Guest on MIT’s Teach Me Something video series (twice)*2013-2019: Founded and directed the Laramie Robotics Club, a weekly meeting where local
middle and high school students had fun playing with robots and learned to love programing and STEM
(science, technology, engineering, & math). Around 20 6th-12th grade students and 20 volunteer mentors
regularly attended. We also regularly did outreach activities teaching STEM and robotics to local middle
and high schools, scout groups, and other youth organizations visiting campus. Major leadership,
direction, and curriculum development was performed by my PhD students, especially Roby Velez.
*
Led team that built EndlessForms.com, a website where non-technical users can design 3-D,
printable objects with evolutionary algorithms based on concepts from developmental biology. The site
also enables the public to learn about evolution and see its ability to create complexity. To date, over 2
million objects have been evaluated by nearly 40 thousand visitors from over 140 countries and all 50 US
states. Video tours of the site have been viewed nearly 10,000 times.
*
One of three designers and developers of Avida-ED, a software package used in university biology
classes to teach evolution. Avida-ED enables students to conduct research in experimental evolution by
testing evolutionary hypotheses and getting immediate feedback. Avida-ED was discussed in Sciencemagazine (2006: 311) and has been used in many universities worldwide. The NSF grant overseer for
Avida-ED described it as "one of the most successful science education materials projects with which I am
acquainted. The product is excellent, dissemination is already successful, and the assessment plan isoutstanding."*
Featured in a University of Wyoming advertising campaign called “Can one university make an
impact?” The year-long campaign included billboards at the Denver International Airport. The ads
showed me working with graduate students in my lab and were used to encourage students to attend UW
and participate in scientific research.
*
Presenter to Upward Bound at Laramie High School, a program to inspire low-income students
to pursue science.

18

ADVISING

▪
▪

▪

▪
▪
▪

▪

▪
▪

Currento Ben Norman, PhD track student, University of British Columbia
Received a PhD for which I was the sole advisoro Joost Huizinga. Now a Research Scientist at OpenAI.
o Anh Nguyen. Now an Assistant Professor of Computer Science and Software Engineering at
Auburn University. Winner of U. Wyoming’s Outstanding Dissertation of 2018 award.
o Henok Mengistu. Now a Data Science Manager with Accentureo Roby Velez. Now an Assistant Professor in Electrical Engineering and Computer Science at
Milwaukee School of Engineeringo Mohammad (Arash) Norouzzadeh. Now Machine Learning Research Engineer at Bosch
Received a Master’s degree for which I was the sole advisor
o Christopher Stanton. Now a Senior Computer Support Specialist, U. Wyoming Foundationo Lucas Helms. Now in IT at U. Wyoming.
Postdoc
o Nick Cheney. Now an Assistant Professor at the University of Vermont
Visitorso Kai Olav Ellefsen (visited from Norway), now Associate Professor at University of Oslo
Researchers from my Team at Uber AI Labso Felipe Petroski Such. Now at OpenAI.
o Joost Huizinga. Now in my group at OpenAI.
o Adrien Ecoffet (co-managed by Ken Stanley). Now in my group at OpenAI.
o Ashley Edwards. Now at DeepMind.
o Edoardo Conti. Now Co-founder of ML startup (after Facebook AI Research).
o Vashisht Madhavan. Now Research Engineer at Element, Inc.
o John Sears. Now Head of Data Science and Analytics at KeepTruckin.
Undergraduates I mentored in research (an unofficial role)o Jingyu Li. Graduated from MIT with a full-ride undergraduate scholarship. Now work on
self-driving at Lyft.
o Tyler Hughes, undergraduate, now with Google
o Richard Yang, undergraduate, received MS in CS from Stanford. Now Machine Learning
Engineer at Adobe
External committee member for:o Jason Yosinski, Cornell University
I have served as a committee member for dozens of students, both at the universities I have been aprofessor at and others around the world. Due to the volume (many per year), I am not listing them
individually.

19

SERVICE

●
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Contributor, World Economic Forum Working Group on Safe Systems and Technologies, 2023
Generative AI Working Group (UBC-wide, organized by UBC CIO), 2023
Faculty Membership and Hiring Committee (off-cycle and on), Vector Institute, 2022-2023
Faculty Search, Computer Science Department, UBC, 2022-2023
Faculty Search, Computer Science Department, UBC, 2021-2022
Faculty Affairs Committee, Computer Science Department, UBC, 2021-2022
Postdoctoral hiring committee, Vector Institute, 2021-2022
Co-leader of a Dialog and Listening Topic for the University Strategic Planning Effort, 2016
U. Wyoming Computer Science Department: Faculty Search Committee (two openings), 2016
U. Wyoming Computer Science Department: Graduate Program Improvement Committee,
2013-2016
U. Wyoming: Faculty Advisory Committee on high-performance computing cluster for campus-wide
research computing, 2013-2016
U. Wyoming Computer Science Department: Graduate Assistantship Award Committee, 2013-2016
Robotics/AI and Diplomacy Board Member, The Lifeboat Foundation, 2013-2016
Computer Science and Engineering Graduate Student Association, Liaison to Computer Science
Department Faculty Meetings, MSU, 2009-2010
Computer Science and Engineering Advisory Committee, MSU, 2009-2010
College Hearing Board, MSU, 2009-2010
Panelist & Speaker, Graduate Student Orientation and Recruitment
Founder, Meteorite, University of Michigan Undergraduate Journal of Philosophy
Associate Editor, Michigan Journal of Political Science, 1996-1998

PATENTS GRANTED●●●Petroski Such F, Clune J, Stanley KO, Conti E, Madhavan V, Lehman J (2020) Scalable parameterencoding of artificial neural networks obtained via an evolutionary process. US Patent No.
US20190188553A1.
Lehman J, Stanley KO, Clune J (2020) Training of artificial neural networks using safe mutations
based on output gradients. US Patent No. US10699195B2
Conti E, Madhavan V, Clune J, Petroski Such F, Lehman J, Stanley KO (2021) Training neuralnetworks using evolution based strategies and novelty search. US Patent No. US11068787B2

INTERESTS

20

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▪
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▪

Travel (over 55 countries on 6 continents)
Sports (surfing, kitesurfing, rock climbing, hockey, whitewater kayaking, ultimate frisbee, hiking,mountain climbing, rock climbing, mountain biking, running)
Literature (Borges, Kundera, Calvino, Penn Warren, Dostoyevsky, DeLillo, Marquez, Card, Tolkien,
Tolstoy, Carroll, Pirsing, Stephenson)
Spanish language (fluent at one time)21