Andrew Forney, PhD

Associate Professor, Department of Computer Science, Loyola Marymount University

Director, Applied Cognitive Technologies (ACT) Lab

Advisory Board Member, Abstract

 Education

University of California, Los Angeles; Los Angeles, CA

2018

Ph.D. in Computer Science, Artificial Intelligence (Major), Information & Data Management (Minor), and Statistics (Minor)

2015

M.S. in Computer Science, Artificial Intelligence


Loyola Marymount University; Los Angeles, CA

2012

B.S. in Computer Science (Major), Psychology (Major), and Pure Mathematics (Minor)

Summa Cum Laude

Full CV (PDF)

 Publications & Presentations

Selected Publications

  • Forney, A., & Mueller, S. (2022). Causal Inference in AI Education: A Primer. Journal of Causal Inference.

  • Cinelli, C., Forney, A., & Pearl, J. (2022). A Crash Course on Good and Bad Controls. Journal of Sociological Methods and Research.

  • Kitamura, M., Korpusik, M., & Forney, A. (2022). Pacman Trainer: Classroom-Ready Deep Learning from Data to Deployment. In American Society for Engineering Education Annual Conference Content (ASEE-2022).

  • Browne, A., & Forney, A. (2022). Exploiting Causal Structure for Transportability in Online, Multi-Agent Environments. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems.

  • Forney, A. & Kim, S. (2020). Redefining Retention in STEM Education: New Perspectives on a Student-centered Metric of Success. In Proceedings of the 2020 Conference of the American Society for Engineering Education.
      Educational Research and Methods Division (ERM) Best Paper Finalist (Top 4).

  • Forney, A. & Bareinboim, E., (2019). Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. In Proceedings of the 34th International Conference of the Association for the Advancement of Artificial Intelligence.

  • Forney, A. (2018). A Framework for Empirical Counterfactuals, or for All Intents, a Purpose (Doctoral dissertation, University of California, Los Angeles).

  • Forney, A., Pearl, J., & Bareinboim, E. (2017). Counterfactual Data-Fusion for Online Reinforcement Learners. In Proceedings of the 34th International Conference on Machine Learning, 2017.
      Nominated for "Most Visionary Paper" award at TiRL-AAMAS.

  • *Bareinboim, E., *Forney, A., & Pearl, J. (2015). Bandits with Unobserved Confounders: A Causal Approach.
    UCLA Cognitive Systems Laboratory, Technical Report (R-460), 2015.
    In Proceedings of the 28th Annual Conference on Neural Information Processing Systems, 2015.

    * The authors contributed equally.

  • Gilbert, R. & Forney, A. (2014). Can Avatars Pass the Turing Test? Intelligent Agent Perception in a 3D Virtual Environment. International Journal of Human-Computer Studies, 73, 30-36. DOI: 10.1016/j.ijhcs.2014.08.001

  • Gilbert, R. & Forney, A. (2013). The Distributed Self: Virtual Worlds and the Future of Human Identity. In D. Powers & R. Teigland (eds.) The Immersive Internet: Reflections on the Entangling of the Virtual with Society, Politics and the Economy. Palgrave-Macmillan, 23-37.

Selected Presentations

  • Browne, A., & Forney, A. (May, 2022). Exploiting Causal Structure for Transportability in Online, Multi-Agent Environments. Presented at the 21st International Conference on Autonomous Agents and Multiagent Systems, Virtual.

  • Forney, A. & Kim, S. (June, 2020). Redefining Retention in STEM Education: New Perspectives on a Student-centered Metric of Success. Presented at the 2020 Conference of the American Society for Engineering Education, Virtual.

  • Forney, A., (December, 2019). A Gentle Introduction to the Causal Hierarchy and Counterfactual Randomization. Invited talk at the Annual Global Innovative Methods with Big Data and Artificial Intelligence (IM DATA) Conference, Pasadena, CA.

  • Forney, A. & Bareinboim, E., (January, 2019). Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. Presented at the 34th International Conference of the Association for the Advancement of Artificial Intelligence, Honolulu, HI.

  • Forney, A., Pearl, J., & Bareinboim, E., (2017, August). Counterfactual Data-Fusion for Online Reinforcement Learners. Presented at the 34th International Conference on Machine Learning, Sydney, Australia. Presented at the the Transfer in Reinforcement Learning Workshop in the 16th International Conference on Autonomous Agents and Multiagent Systems.

Teaching

Dates

Course

School

Springs 2019-2023

CMSI 498/432/4320: Cognitive Systems Design

LMU

Springs 2019-2023, Falls 2022-2023

CMSI 282/2120: Algorithms

LMU

Spring 2018, Falls 2018-2023

CMSI 485/3300: Artificial Intelligence

LMU

Spring 2018

CMSI 387: Operating Systems

LMU

Falls 2016-2021

CMSI 281/2120: Data Structures

LMU

Falls 2015, 2017, 2018

CMSI 185: Introduction to Programming

LMU

Falls 2015-2016

CS 495: CS TA Training Program [TA]

UCLA

Spring 2014, Winter 2015, Spring 2015, Winter 2016

CS 161: Fundamentals of Artificial Intelligence [TA]

UCLA

Winter 2014, Summer 2014, Summer 2015

CS 32: Introduction to Programming II (Data Structures & Algorithms) [TA]

UCLA

Fall 2013, Fall 2014

CS 31: Introduction to Programming I (Intro C++) [TA]

UCLA

Fall 2011 - Fall 2012

Keck Lab Teaching Assistant: General CS Course Tutor [TA]

LMU

 Awards & Honors

Date

Award

School

2023

Seaver Interdisciplinary Seed Grant Recipient

LMU

2021

Honors Program Honorable Mentor Award

LMU

2015 & 2016

UCLA Computer Science Head TA

UCLA

2012

Dr. Rose Patricia Walsh Research Award

LMU

2012

Outstanding Psychologist (Highest GPA in Major)

LMU

2012

Outstanding Computer Scientist (Highest GPA in Major)

LMU

2012

Rains Undergraduate Research Fellowship

LMU

2011

Rev. Kilp, S. J. Scholarship

LMU

2008 - 2012

Dean's List (All Semesters)

LMU

2008

Pedro Arrupe Scholarship

LMU