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第437行： − <nowiki>##</nowiki>将第一人称换为“埃利亚斯·巴伦博伊姆”，超链接可附上+ − + − + − + − + − + − + − * I am joining the Computer Science Department at Columbia University.+ − + − + − * Our paper "Causal Identification under Markov Equivalence" (with Amin Jaber and Jiji Zhang, link) was selected as the Best Student Paper Award (1 out 337 papers) at the Uncertainty in Artificial Intelligence conference (UAI-18).+ − + − * I am thankful for IBM's generous gift ($50k) and support to our research and collaboration. − − − * Our work on solving big data's fusion problem and combining massive sets of research data just appeared at the Proceedings of the National Academy of Sciences (PNAS), see story and paper. − * I am honored to be selected by IEEE Intelligent Systems as one of AI's 10 To Watch (story, pdf). − − * Our paper "Recovering from selection bias in causal and statistical inference" was selected as a notable paper in computing in 2014, to appear in the ACM Computing Reviews' 19th Annual Best of Computing (see full list here). − * I will join the Computer Science Department at Purdue as an Assistant Professor in the Fall/2015. − * I was selected as the 2014 Edward K. Rice Outstanding Doctoral Student. This award is given to a single PhD student in all engineering and applied sciences majors at UCLA. − * Our paper "Recovering from Selection Bias in Causal and Statistical Inference" (link) just received the best paper award (1 out 1406 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-14). − * I am honored that I was selected as the "Outstanding Graduating PhD Student" (commencement award), Computer Science, UCLA. − * I received the "Google Outstanding Graduate Research Award", Computer Science, UCLA. − * I am honored to be selected as one of the 2014 Dan David Scholars for "outstanding achievement and future promise" in the field of Artificial Intelligence (citation here). − * I am co-organizing an ICML-14 workshop on Causal Modeling & Machine Learning (with B. Scholkopf, K. Zhang, JJ. Zhang), consider submitting your work, link. − * I am a guest editor (with J. Pearl, B. Scholkopf, K. Zhang, J. Li) of ACM Transactions on Intelligent Systems and Technology on "Causal Discovery and Inference". See the call for papers. − * With Judea Pearl, I gave a tutorial on "Causes and Counterfactuals: Concepts, Principles and Tools" at NeurIPS 2013. The video (with slides) is available online, link (requires HTML5). − * The video of my talk on meta-transportability in AISTATS-2013 is now available here.
无编辑摘要
<nowiki>##</nowiki>（描述所著文章及书籍的要旨与意义，选取一下这个人的经典文献介绍一下也可以）
<nowiki>##</nowiki>（描述所著文章及书籍的要旨与意义，选取一下这个人的经典文献介绍一下也可以）
=== 2021: ===
=== 2021 ===
'''Partial Counterfactual Identification from Observational and Experimental Data'''
'''Partial Counterfactual Identification from Observational and Experimental Data'''
''Columbia CausalAI Laboratory, Technical Report (R-61)'', Jun, 2020.
''Columbia CausalAI Laboratory, Technical Report (R-61)'', Jun, 2020.
=== 2020: ===
=== 2020 ===
'''General Transportability of Soft Interventions: Completeness Results'''
'''General Transportability of Soft Interventions: Completeness Results'''
''Columbia CausalAI Laboratory, Technical Report (R-52)'', Nov, 2019.
''Columbia CausalAI Laboratory, Technical Report (R-52)'', Nov, 2019.
=== 2019: ===
=== 2019 ===
'''Causal Inference and Data-Fusion in Econometrics'''
'''Causal Inference and Data-Fusion in Econometrics'''
== 近期报道 ==
== 近期报道 ==
* 与 J. Pearl, Y. Bengio, B. Scholkopf, T. Sejnowski 共同组织 NeurIPS-21 的 Workshop "Causal Inference & Machine Learning: Why now?" (WHY-21) [https://why21.causalai.net/ 链接].
* 与 Juan Correa, Duligur Ibeling, Thomas Icard 共同完成 ''ACM special volume in honor of Judea Pearl'' 一书中的 ''On Pearl’s Hierarchy and the Foundations of Causal Inference'' 章节 [https://causalai.net/r60.pdf 链接].
* I am co-organizing with J. Pearl, Y. Bengio, B. Scholkopf, T. Sejnowski the NeurIPS-21 Workshop "Causal Inference & Machine Learning: Why now?" (WHY-21), consider submitting your work (link).
* 在 ICML-20 发布tutorial ''Causal Reinforcement Learning''，关注因果推断和强化学习的交叉领域 [http://crl.causalai.net/ 链接].
* Our chapter "On Pearl’s Hierarchy and the Foundations of Causal Inference" (with Juan Correa, Duligur Ibeling, Thomas Icard) will appear at an ACM special volume in honor of Judea Pearl and is now available online (link).
* 在哥伦比亚大学发表演讲 Causal Data Science，关注因果推断和数据科学的交叉领域 [http://crl.causalai.net/ 链接].
* The slides and videos of my tutorial at ICML-20 on the intersection of causal inference and reinforcement learning, which I have been calling "causal reinforcement learning" (CRL), are now available online (link).
* 与 Sanghack Lee and Juan Correa 共同完成的论文 ''General Identifiability with Arbitrary Surrogate Experiments'' 被评为 UAI-19 会议的最佳论文奖 (1/450).
* The video of my talk at Columbia University on "causal data science" -- the intersection of causal inference and data science -- is now available online (link).
* 与 J. Pearl, B. Scholkopf, C. Szepesvari, S. Mahadevan, P. Tadepalli 共同组织 AAAI-19 春季研讨会 ''Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI'' (WHY-19) [http://why19.causalai.net/ 链接].
* Our paper "General Identifiability with Arbitrary Surrogate Experiments" (with Sanghack Lee and Juan Correa, pdf) was selected as the Best Paper Award (1 out 450 papers) at the Uncertainty in Artificial Intelligence conference (UAI-19).
* 与 Amin Jaber and Jiji Zhang 共同完成的论文 ''Causal Identification under Markov Equivalence'' 被评为 UAI-18 会议的最佳学术论文奖 (1/337).
* 与 Juan Correa and Jin Tian 共同完成的论文 ''Generalized Adjustment Under Confounding and Selection Biases'' 被评为 AAAI-18 会议的优秀论文奖荣誉提名 (2/3800).
* I am co-organizing with J. Pearl, B. Scholkopf, C. Szepesvari, S. Mahadevan, P. Tadepalli the AAAI-19 Spring Symposium "Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI" (WHY-19), consider submitting your work (link).
* 成为 Journal of Causal Inference 期刊编委会成员 [https://www.degruyter.com/view/j/jci 链接].
* I am thankful for Adobe's generous gift ($50k) and support to our research.
* 协办第7届 UAI Causality Workshop: Learning, Inference, and Decision-Making [https://causalai.net/causal-uai17/ 链接].
* 共同主办 2016 ACM SIGKDD Workshop on Causal Discovery [http://nugget.unisa.edu.au/CD2016/index.html 链接].
* Our paper "Generalized Adjustment Under Confounding and Selection Biases" (with Juan Correa and Jin Tian, link) just received the Outstanding Paper Award Honorable Mention (2 out 3800 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-18).
* 共同主办 2016 UAI Workshop on Causation: Foundation to Application [http://people.hss.caltech.edu/~fde/UAI2016WS/ 链接].
* I am joining the Editorial Board of the Journal of Causal Inference (link), consider submitting your work.
* I am co-organizing the 7th UAI Causality Workshop: Learning, Inference, and Decision-Making (link), consider submitting your work.
* I am co-organizing the 2016 ACM SIGKDD Workshop on Causal Discovery (link) and the 2016 UAI Workshop on Causation: Foundation to Application (link), consider submitting your work.
== 相关链接 ==
== 相关链接 ==
<nowiki>##</nowiki>（包含个人wiki、采访、谷歌学术个人主页等）
<nowiki>##</nowiki>（包含个人wiki、采访、谷歌学术个人主页等）