Sotabase

Career

· Postdoctoral Researcher, Carnegie Mellon University2024–
· Instructor: 6.S091, Causality, Massachusetts Institute of Technology2023–
· M.Eng., Electrical Engineering and Computer Science, Massachusetts Institute of Technology2019–
· PhD Candidate, Electrical Engineering and Computer Science, Massachusetts Institute of Technology2019–2024
· Teaching Assistant: 6.437, Inference and Information, Massachusetts Institute of Technology2019–
· B.S., Electrical Engineering and Computer Science, Massachusetts Institute of Technology2018–
· Teaching Assistant: 6.438, Algorithms for Inference, Massachusetts Institute of Technology2018–
· Data Scientist, nference2018–2018
· Research Assistant, MIT IDSS2017–2018
· Research Intern, NECSI2017–2017

Publications (50)

Permutation-Based Causal Structure Learning with Unknown Intervention Targets
Conference on Uncertainty in Artificial Intelligence · 2019
98
cited
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Neural Information Processing Systems · 2023
89
cited
Linear Causal Disentanglement via Interventions
International Conference on Machine Learning · 2022
83
cited
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
International Conference on Artificial Intelligence and Statistics · 2019
73
cited
Causal Structure Learning: A Combinatorial Perspective
Foundations of Computational Mathematics · 2022
60
cited
Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing
Nature Communications · 2020
55
cited
Ordering-Based Causal Structure Learning in the Presence of Latent Variables
International Conference on Artificial Intelligence and Statistics · 2019
47
cited
Active learning for optimal intervention design in causal models
Nature Machine Intelligence · 2022
43
cited
Active Structure Learning of Causal DAGs via Directed Clique Tree
Neural Information Processing Systems · 2020
39
cited
DCI: Learning Causal Differences between Gene Regulatory Networks
bioRxiv · 2020
35
cited
Direct Estimation of Differences in Causal Graphs
Neural Information Processing Systems · 2018
34
cited
Unpaired Multi-Domain Causal Representation Learning
Neural Information Processing Systems · 2023
30
cited
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
2021
28
cited
Causal Imputation via Synthetic Interventions
CLEaR · 2020
26
cited
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
CLEaR · 2022
26
cited
Matching a Desired Causal State via Shift Interventions
Neural Information Processing Systems · 2021
19
cited
EXPERIMENTAL ANALYSIS OF ASPECTS OF THE CASCADE-CORRELATION LEARNING ARCHITECTURE
1991
15
cited
Size of Interventional Markov Equivalence Classes in Random DAG Models
International Conference on Artificial Intelligence and Statistics · 2019
12
cited
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
CLEaR · 2024
11
cited
Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample
2021
7
cited
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Chandler Squires | Researcher Profile | Sotabase | Sotabase