“It is better to know how to learn than to know” – Dr. Seuss
I am a Ph.D. candidate (ABD) of Informatics at the college of Information Sciences and Technology (IST) at the Pennsylvania State University. I further hold a Ph.D. minor in Statistics and a Master of Science in Information Sciences and Technology from Penn State. I am advised by Dr. Vasant Honavar (IST) and Dr. Aleksandra Slavkovic (Statistics). Before joining Penn State, I received a Master’s degree in artificial intelligence and a Bachelor’s degree in computer software engineering.
I specialize in machine learning, data science, and artificial intelligence. During Ph.D., I have a focus on the following areas: (i) Causal inference in machine learning, (ii) Fairness and interpretation in machine learning, and (iii) Applying machine learning to health care. I am a member of the Artificial Intelligence Research Lab, a fellow at the Biomedical Big Data Training Program, and an AI Student Ambassador at Intel Co. You can find my CV here.
Google Scholar page: Click here.
Media Coverage: The paper “Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality” made its way to the news at (non-exhaustive):
Link to a short video of me at the College of IST at Penn State: Meet Aria Khademi
- I received an M.S. degree in Information Sciences and Technology from Penn State (Spring 2020).
- Presented my work on “Algorithmic Bias in Recidivism Prediction: A Causal Perspective” at AAAI-20 (Feb 2020).
- I was recognized as a top 5% Excellent Reviewer for Neurips 2019 Workshop on Machine Learning for Health (Nov 2019).
- My extended abstract “Algorithmic Bias in Recidivism Prediction: A Causal Perspective” was accepted to AAAI (Oct 2020).
- My paper “Personalized Sleep Parameters Estimation from Actigraphy: A Machine Learning Approach” has been accepted for publication in Nature and Science of Sleep (Oct 2019).
- Successfully passed my proposal defense (Aug 2019).
- My request for a graduate minor in Statistics at Penn State got approved (June 2019).
- My paper “Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality” got accepted to WWW-2019.
- My article on the future of AI research got published on Intel’s Medium (Click here).
- Became an AI Student Ambassador for Intel (July 2018).
- After a long time of procrastination, finally wrote about my favorite teachers, under education on the left (July 2018).
- Attended the workshop by Center for Causal Discovery at Carnegie Mellon University (June 2018).
- Started writing about my favorite movies in the movie blog on the left (June 2018).
- Successfully passed 32 credits in courses for PhD (May 2018).
- Served as a judge for Penn State’s undergraduate poster exhibition (Apr 2018).
- Started writing about music theory in the music blog on the left (Apr 2018).
- Presented my work on sleep-wake prediction to the Biomedical Big Data to Knowledge spring retreat (Apr 2018).
- My paper on personalized sleep-wake prediction from actigraphy was published in BHI 2018 (Mar 2018).
- Khademi, A., Honavar, V. (2020) Algorithmic bias in recidivism prediction: A causal perspective. In: Proceedings of the AAAI Conference on Artificial Intelligence, Student Abstract Program (Acceptance rate: 48%).
- Khademi, A., El-Manzalawy, Y., Master, L., Buxton, O. M., Honavar, V. G. (2019). Personalized sleep parameters estimation from actigraphy: A machine learning approach. In: Nature and Science of Sleep, Volume 11, pp. 387-399.
- Khademi, A., Lee, S., Foley, D., Honavar, V. (2019). Fairness in algorithmic decision making: An excursion through the lens of causality. In: Proceedings of the 2019 Conference on The World Wide Web (WWW-19) pp. 2907-2914 (Acceptance rate: 20%, See Media Coverage above).
- Khademi, A., EL-Manzalawy, Y., Buxton, O., Honavar, V. (2018). Toward personalized sleep-wake prediction from actigraphy. In: IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) pp. 414-417 (Oral presentation acceptance rate: 14%).
- Khademi, A., Honavar, V. (2020) A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution.
Paper reviewer: NeurIPS-20, ICML-20, ACM CHIL-20 (Program Committee), NeurIPS-19, PLOS ONE, WMSCI-19.
Invited judge: Penn State official undergraduate poster exhibition (2018).
I play music, read books, watch movies, and exercise in my free time. Read my music and movie blogs on the left if you will.