Karthika Mohan

Research Interests

My research interests lie in the intersection of Artificial Intelligence and Causal Inference. My research agenda for the next decade is motivated by the belief that in order to bring machines closer to human levels of intelligence, machine learning algorithms need to deal with the causal component of human reasoning.

Awards

• Google Outstanding Graduate Student Research Award
  Commencement Award (2017-2018), CS Department, UCLA

Editorial Board Memberships

• Journal of Causal Inference Link
  Role: Associate Editor

Grants

• Inference with Incomplete Data Link
  National Science Foundation (NSF)

Tutorials

• Graphical Models for Causal Inference Link
  With Judea Pearl
  Uncertainty in Artificial Intelligence (UAI), 2012

Selected Publications and Technical Reports

• Causal Graphs for Constructing Fair Data
  Currently Under Review
• Causal Graphs for Missing Data: A Gentle Introduction
  Invited Chapter in Judea Pearl's ACM Turing Award Book, Forthcoming
• Graphical Models for Processing Missing Data PDF BIB
  Joint work with Judea Pearl
  Forthcoming, Journal of American Statistical Association (JASA)
• A Novel Approach to Handling Hard Missing Data Problems PDF BIB
  Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, AAAI Spring Symposium (2019)
• Causal Discovery in the Presence of Missing Data PDF BIB
  Joint work with Ruibo Tu, Cheng Zhang, Paul Ackermann, Hedvig Kjellström and Kun Zhang
  In Proceedings of Artificial Intelligence and Statistics (AISTATS), 2019
• Estimation with Incomplete Data: The Linear Case PDF BIB
  Joint work with Felix Thoemmes and Judea Pearl
  In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2018
  Oral presentation

• Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data PDF BIB
  Joint work with Guy Van den Broeck, Arthur Choi, Adnan Darwiche, and Judea Pearl
  In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015
  Oral presentation; Acceptance Rate: 28/292 or 9%
• Missing Data as a Causal and Probabilistic Problem PDF BIB
  Joint work with Ilya Shpitser and Judea Pearl
  In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015
  Oral presentation; Acceptance Rate: 28/292 or 9%
• Graphical Representation of Missing Data Problems PDF BIB
  Joint work with Felix Thoemmes
  Structural Equation Modeling: A Multidisciplinary Journal, January 2015
• Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data PDF BIB
  Joint work with Judea Pearl
  In Advances in Neural Information Processing Systems 27 (NIPS), 2014
• On the Testability of Models with Missing Data PDF BIB
  Joint work with Judea Pearl
  In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
  Oral presentation; Acceptance Rate: 22/335 or 6.5%
• Recoverability and Testability of Missing Data: Introduction and Summary of Results PDF BIB
  Joint work with Judea Pearl
  UCLA Cognitive Systems Laboratory, Technical Report (R-417), 2014
• Graphical Models for Inference with Missing Data PDF BIB
  Joint work with Judea Pearl and Jin Tian
  In Advances in Neural Information Processing Systems 26 (NIPS), 2013
  Oral spotlight presentation; Acceptance Rate: 72/1420 or 5%

Teaching Experience

• C++ Preparatory Online Course for Financial Engineering, Summer 2012
  Teaching Assistant and Grader
  Anderson School of Management, UCLA
• CS-31: Introduction to Computer Science-I, Summer 2012
  Teaching Assistant and Grader
  Department of Computer Science, UCLA
• CS-32: Introduction to Computer Science-II, Winter 2012, Spring 2012
  Teaching Assistant and Grader
  Department of Computer Science, UCLA

Invited Talks

• Causal Graphs for Missing Data: A Gentle Introduction
  • Invited Speaker at AI Rising Stars Symposium, University of Southern California, 2019
• Graphical Models for Missing Data: Recoverability, Testability and Recent Surprises!
  • Foundations and New Horizons for Causal Inference, Mathematical Research Institute of Oberwolfach, 2019
  • Workshop on the Foundations of Causal Discovery and Inference, Carnegie Mellon University, 2018
  • Seminar Series of the Johns Hopkins Bloomberg School of Public Health, 2016
  • Microsoft Research, Redmond, 2016
  • Seminar Series of the Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine (LSHTM), London, 2015
• Missing Data as a Causal Inference Problem
  • West Coast Experiments, 2017
  • "Modern Methods for Missingness", New England Statistical Symposium, 2018
• The Essential Role of Causal Knowledge in Artificial Intelligence
  • Keynote Speaker at Artificial Intelligence Technology (AITech), 2018
• Recovering Information from Missing Data using Graphical Causal Models
  • "Graphical Models and Causality", Computational and Methodological Statistics, 2015

Workshop / Grant Review Meeting Talks

• Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
  Joint work with Guy Van den Broeck, Arthur Choi and Judea Pearl
  • Causal Modeling & Machine Learning Workshop
    International Conference on Machine Learning (ICML), 2014
• Transportability and Bias-Variance Trade-off
  Joint work with Jennifer Wortman Vaughan and Judea Pearl
  • Domain Adaptation Workshop
    Neural Information Processing Systems (NIPS), 2011
• Advances in Causal Reasoning
  Joint work with Judea Pearl
  • Knowledge Representation, Reasoning and Learning for Understanding Scenes and Events
    ONR MURI 2013-2014 Annual Meeting, Los Angeles, California
• Recent Results on Transportability and Missing Data
  Joint work with Judea Pearl
  • Knowledge Representation, Reasoning and Learning for Understanding Scenes and Events
    ONR MURI 2012-2013 Annual Meeting, Los Angeles, California

Reviewing Experience (Conferences)

• Knowledge Representation and Reasoning (KR), 2014
• International Conference on Machine Learning (ICML), 2014, 2017, 2018
• International Conference on Data Engineering (ICDE), 2015
• Association for the Advancement of Artificial Intelligence (AAAI), 2015, 2020
• European Conference on Artificial Intelligence (ECAI), 2016
• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2016, 2017, 2018
• Uncertainty in Artificial Intelligence (UAI), 2016, 2017, 2018
• Neural Information Processing Systems (NIPS), 2016, 2017, 2018
• International Joint Conference on Artificial Intelligence (IJCAI), 2018
• Artificial Intelligence and Statistics (AISTATS), 2020

Reviewing Experience (Journals)

• Journal of the Royal Statistical Society
• Journal of Machine Learning Research
• ACM Transactions on Intelligent Systems and Technology
• Statistics in Medicine
• Psychological Methods
• Biometrical Journal
• Journal of Educational and Behavioral Statistics
• Journal of Politics
• Journal of the American Statistical Association

Miscellaneous

• Chairperson of the Student Committee for 'Prospective Student Visit Day', 2015
• Coordinator of meetings and activities of the Women in CS group at UCLA, 2011-2016
• Reviewer of PhD and MS applications, UCLA, 2014, 2015
• Graduate Student Volunteer for Society of Women Engineers (GradSWE)