Congratulations to Ragib Ahsan @ragib06 from @EdgesLab who successfully defended his PhD dissertation “Relational causal discovery with cycles” today! Special thanks to the committee Brian Ziebart, Barbara Di Eugenio, @TTulabandhula, and especially @darbour26!
Interested in heterogeneous treatment effect estimation? Come see @chris_tran16’s #KDD2022 presentation on Improving Data-driven HTE Estimation Under Structure Uncertainty which shows how causal feature selection can improve existing ML-based estimators. Today 10am @ Room 208AB.
Very proud of @chris_tran16 delivering an invited talk on “Beyond model-free estimation of heterogeneous treatment effects” at the #KDD2022 Causal Discovery workshop. @EdgesLab
https://t.co/ohA0JfTNTk
Looking forward to talking about causal discovery from social networks at the #KDD2022 Causal Discovery workshop, covering our recent work on heterogeneous peer effects, relational causal model representation & learning, and relational dependence testing.
https://t.co/DUazqaHV78
Interested in social networks and relational data? Ragib Ahsan
@ragib06 will present our paper “Non-parametric inference of relational dependence” at #UAI2022 poster session II today. Apps in inferring social influence & structure learning. Joint work w/ @Zara_ftm & @darbour26
It's a rite of passage for a professor to have their first PhD student defend their thesis. For me, it happened today. Very proud of @chris_tran16 who successfully defended his dissertation "Beyond Model-Free Estimation of Heterogeneous Treatment Effects" 👏🥳
Congratulations @chris_tran16 for successfully defending your Ph.D. dissertation today. It has been a privilege to work alongside you. You have been an inspiration for all of us @EdgesLab. I’m so happy for you and wish you all the best in your future endeavors! 🎉🥳
Interested in causal inference from networks where units can impact each other's outcomes? Today, Ragib Ahsan @ragib06 is presenting Relational Causal Models with Cycles: Representation and Reasoning at #clear22, joint work w/ @darbour26. @EdgesLab
https://t.co/HW9iK5Nwxu
Computer Science Asst. Prof. Elena Zheleva and Andrew Rojecki, assoc. prof. of communications, received a @DARPA award to develop a theoretical framework for detecting and tracking political influence campaigns on social media. @elenadata
.@chris_tran16 presented our paper "Heterogeneous peer effects in the linear threshold model" at #AAAI2022 this week. It looks @ the linear threshold model of info diffusion from a causal perspective & proposes methods for individual threshold estimation.
https://t.co/E2pT4cKYR2
I'm excited to present our work "Heterogeneous Threshold Estimation for Linear Threshold Modeling" today at 1:30pm Pacific as a contributed talk for @mlgworkshop! #KDD2020#MLG2020
Ragib Ahsan from @EdgesLab is presenting his work on "Effectiveness of Sampling Strategies for One-shot Active Learning from Relational Data" at the @mlgworkshop poster session at 11:30am Pacific. #MLG2020#KDD2020
https://t.co/vxXvyTGTBK
@Zara_ftm from @EdgesLab is presenting her work on "Network Experiment Design for Estimating Direct Treatment Effects" at the @mlgworkshop poster session at 11:30am Pacific. #MLG2020
https://t.co/vxXvyTGTBK
@EdgesLab is represented well at @mlgworkshop this year. @chris_tran16 has a contributed talk on "Heterogeneous threshold estimation for linear threshold modeling" at 1:30pm Pacific. Only 4 out of 40 accepted papers were selected for a contributed talk - kudos @chris_tran16!
Very proud of Zahra Fatemi @Zara_ftm whose paper "Minimizing interference and selection bias in network experiment design" received a Best Paper Honorable Mention at #ICWSM2020@ICWSM@UICCS. Perhaps a good time to say a few words about the paper https://t.co/VqPgJXgqcL 1/5
Excited to work on this interdisciplinary research project w/ Barbara Di Eugenio, Liz Marai @UICCS & Andrew Rojecki @CommUIC. We'll combine large-scale data science w/ social science to understand the prevalence, virality & impact of stay-at-home social media attitudes #Covid_19
Congratulations to Zahra Fatemi on her first conference paper "Minimizing interference and selection bias in network experiment design" accepted at @icwsm! We show the inadvertent selection bias created in cluster-based treatment assignment and ways to deal with it.
#ICWSM2020
Elena Zheleva of @UICCS and Houshang Darabi of #UICmie have a new grant from @AnthemInc to apply process mining and machine learning techniques to patient health and claim records. The work could help optimize health outcomes by analyzing data to find personalized interventions.