Updates
2023
Our paper “ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees” has been accepted to WSDM 2024. The code is available on GitHub.
I started working as an AI Engineer in the Privacy-Preserving ML team at Sony AI in Zurich!
I successfully passed my private PhD defense!
Our new preprint titled ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees is available on Arxiv.
We are organizing an in-person workshop Privacy and Fairness in AI for Health on 27 March 2023 in The Alan Turing Institute, London, UK. Registration is free and open to all.
I was invited to give a talk on “Deep Learning on Graphs with Differential Privacy” at Imperial-X, Imperial College London. The slides can be downloaded from here.
I started as a visiting PhD student of the Safe and Ethical AI programme at The Alan Turing Institute to continue my research on trustworthy graph ML at the intersection of privacy and adversarial robustness.
2022
The slides and demo notebooks for the course “An Introduction to Trustworthy Machine Learning” are available here.
Our paper “GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation” with Ali Shahin Shamsabadi, Aurélien Bellet, and Daniel Gatica-Perez has been accepted to USENIX Security Symposium (USENIX Security 2023). Our code is available on GitHub.
I will lecture an online short course on “Trustworthy Machine Learning” on 23-24 November 2022, 10:00–13:00 CET. If you are interested to attend, please drop me an email.
I was invited to give a remote talk about privacy-preserving machine learning on graphs for the course “Socially Responsible AI” by Lu Cheng at University of Illinois at Chicago. The slides are available here.
I was invited to give a remote talk about our work “GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation” at L3S Research Center. The slides can be downloaded from here.
I was awarded a travel grant of €500 to attend CISPA Summer School in Trustworthy AI, Saarbrücken, Germany.
Our new preprint titled GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation is available on Arxiv.
I started my research internship at Brave Software working on decentralized privacy-preserving machine learning for ads and news recommendation.
2021
Our paper “Locally Private Graph Neural Networks” has been shortlisted as a finalist in CSAW Applied Research Competition.
I was invited to give a talk about our work “Locally Private Graph Neural Networks” at Graph Neural Networks User Group Meetup run by Amazon Web Services and NVIDIA AI. The recording is available on Youtube and the slides can be downloaded from here.
I presented our recent CCS paper “Locally Private Graph Neural Networks” at AI4Media Workshop on Explainability, Robustness and Privacy in AI. Slides can be downloaded from here.
Our paper “Locally Private Graph Neural Networks” with Daniel Gatica-Perez has been accepted to the ACM Conference on Computer and Communications Security (CCS 2021).
I delivered a remote talk on our recent work, “Locally Private Graph Neural Networks” at Twitter Machine Learning Seminar. You can download the slides from here.
2020
I delivered a remote talk on privacy-preserving deep learning over graphs in Information Processing and Communcations Lab at Imperial College London. You can download the slides from here.
Our recent paper on integrating differential privacy with graph neural networks is out! Check it out on arXiv.
I successfully passed my PhD candidacy examination!
Our paper “A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics”, with Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, and Hamed Haddadi, has been accepted to IEEE Internet of Things Journal (Impact Factor: 9.9).
2019
Our paper “Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks”, with Sogol Bazargani, Jiawei Zhang, and Hamid R. Rabiee, has been accepted to ACM Transactions on Knowledge Discovery from Data (Impact Factor: 2.0).
I started as a research assistant at the Social Computing group in Idiap Research Institute, Martigny, Switzerland.
I was offered a PhD admission to the electrical engineering program at EPFL.
2018
MIT Technology Review has selected the report on our paper about world cuisines as one of the best of 2018 articles.
I was offered a research assistanship position at IBM-Illinois Center for Cognitive Computing Systems Research.
I was offered a PhD admission to the computer science program at University of Illinois at Urabana-Champaing.
2017
I am invited by Department of Computer Engineering at Sharif University of Technology as a guest lecturer to teach Fudamentals of Programming course for Fall 2017.
2016
Our paper “Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web”, with Sina Jafarzadeh, Seyed Ali Ossia, Hamid R. Rabiee, Yelena Mejova, Hamed Haddadi, Mirco Musolesi, Emiliano De Cristofaro, and Gianluca Stringhini, has been accepted to the 26th International World Wide Web Conference (WWW 2017), Perth, WA, Australia.
Our paper “Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web” has been covered by MIT Tech Review, Independent, France 24, Sciences et Avenir, PromptCloud, ReachMD, and NEXO.
I gratuated from Sharif University of Technology with an M.Sc degree in information technology engineering, networked systems.
Our paper “Predicting Anchor Links between Heterogeneous Social Networks”, with Hamid R. Rabiee and Ali Khodadadi, has been accepted to the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016), San Francisco, CA, USA.
2014
I started as a research assistant in Data Science and Machine Learning Lab at Sharif University of Technology.
I have been admitted to Sharif University of Technology for MSc program in information technology engineering.