**Short Bio**

I
received my B.S. degree in Electrical - Electronics Engineering with a minor
degree in Physics from the Middle East Technical University in 2010, and M.S. degree
from the Koc University, Turkey in 2012 under the supervision of Prof. Ozgur B.
Akan and Ph.D. degree from The University of Texas at Austin in 2018 under the
supervision of Prof. Alex Dimakis and Prof. Sriram Vishwanath. I am currently a
Research Staff Member in the MIT-IBM Watson AI Lab in IBM Research, Cambridge,
Massachusetts. My current research interests include causal inference,
generative adversarial networks, and information theory.

**Research**

My current research focuses
on machine learning in general and causal inference and learning algorithms
from data in particular. Specifically, I have been developing algorithms for
learning causal graphs from observational and experimental data using tools
from information theory and graph theory.

**News**

I will be starting as an assistant professor at Purdue University in the ECE department in January 2021 and will be looking for motivated Ph.D. students to work on fundamental problems in causal inference with machine learning applications starting Fall 2021. Please apply here, mention my name in your application package and email me your resume.

· Our paper
titled “Characterization and Learning of Causal Graphs with Latent Variables
from Soft Interventions” is accepted at NeurIPS’19 available **here**.

·
Our paper
titled “Sample Efficient Active Learning of Causal Trees” is accepted at
NeurIPS’19.

·
I gave an invited talk in
the WHY-19 Symposium on CausalGAN. Website and slides are **here**.

·
I gave a talk on the Shannon
Channel on entropic methods for causal inference, you can watch it **here**.

·
Our paper titled
“Experimental Design for Cost-Aware Learning of Causal Graphs” is accepted at NeurIPS’18.
A preprint is available **here**.

·
A new preprint titled
“Entropic Causal Inference with Latent Variables” is available **here**.

·
Our paper titled “CausalGAN:
Learning Causal Implicit Generative Models with Adversarial Training” is
accepted at ICLR’18. A preprint is available **here**.

·
Our paper titled
“Experimental Design for Learning Causal Graphs with Latent Variables” is
accepted at NIPS’17. A preprint is available **here**.

·
A new preprint titled
“CausalGAN: Learning Causal Implicit Generative Models with Adversarial
Training” is available here. Tensorflow implementation
is available **here**.

·
Our paper titled
“Cost-Optimal Learning of Causal Graphs” is accepted at ICML’17. A preprint is
available **here**.

·
Our paper titled “Entropic
Causality and Greedy Minimum Entropy Coupling” is accepted at ISIT 2017. A
preprint is available **here**.

·
A new preprint titled
“Sparse Quadratic Logistic Regression in Sub-quadratic Time” is available **here**.

·
A new preprint titled
“Cost-Optimal Learning of Causal Graphs” is available **here**.

·
A new preprint titled
“Entropic Causality and Greedy Minimum Entropy Coupling” is available **here**.

·
Our paper titled
"Contextual Bandits with Latent Confounders: An NMF Approach" is
accepted at AISTATS 2017. A preprint is available **here**.

·
Python code is available for
the entropy-based causal inference algorithm of "Entropic Causal
Inference" paper **here**.

·
Our paper titled
"Entropic Causal Inference" is accepted at AAAI 2017. A preprint is
available **here**.

·
We have a preprint available
on contextual bandits with unobserved confounders **here**.

·
We are organizing a student
seminar series within WNCG. You can reach the schedule **here**.