Dr. Farhangi is currently a Research Scientist at OSCE AI, with a strong focus on deploying large-scale cloud-based AI systems and deep learning models. His areas of expertise include GenAI, MLOps, Recommender Systems, and Agentic AI. His extensive contributions include publications, US patents as well as serving as a program committee member for AI conferences such AAAI.
OSCE AI is large-scale platform supporting thousands of students worldwide in preparing for their most critical clinical examination. Developed in close collaboration with a dedicated team of medical educators, each case is meticulously crafted and continuously updated to reflect real-world exam standards. The platform delivers highly personalized learning experiences and in-depth analytics, allowing learners to track progress, identify strengths and weaknesses, and receive targeted feedback for improvement.
Expert-reviewed OSCE stations designed and maintained by a team of medical professionals
Personalized dashboards and progress tracking for every learner
Comprehensive analytics to highlight learning gaps and guide focused practice
Scalable infrastructure supporting thousands of concurrent users globally
Continuous content updates to match evolving exam requirements and best practices
Publications
• 2024: Adaptive Anomaly Prediction Models PDF BibTeX
• 2023: AA-Forecast: Anomaly-Aware Forecast for Extreme Events PDF BibTeX
@article{farhangi2023aa,
title={AA-forecast: anomaly-aware forecast for extreme events},
author={Farhangi, Ashkan and Bian, Jiang and Huang, Arthur and Xiong, Haoyi and Wang, Jun and Guo, Zhishan},
journal={Data Mining and Knowledge Discovery},
volume={37},
number={3},
pages={1209--1229},
year={2023},
publisher={Springer}
}
• 2022: Protoformer: Embedding Prototypes for Transformers PDF BibTeX
@inproceedings{farhangi2022protoformer,
title={Protoformer: Embedding prototypes for transformers},
author={Farhangi, Ashkan and Sui, Ning and Hua, Nan and Bai, Haiyan and Huang, Arthur and Guo, Zhishan},
booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
pages={447--458},
year={2022},
organization={Springer}
}
• 2022: A Novel Deep Learning Model For Hotel Demand and Revenue Prediction amid COVID-19 PDF BibTeX
@inproceedings{farhangi2022novel,
title={A novel deep learning model for hotel demand and revenue prediction amid COVID-19},
author={Farhangi, Ashkan and Huang, Arthur and Guo, Zhishan},
booktitle={Proceedings of the 55th Hawaii International Conference on System Sciences},
year={2022}
}
• 2022: Leveraging Data Analytics to Understand the Relationship Between Restaurants’ Safety Violations and COVID-19 Transmission PDF BibTeX
@article{huang2022leveraging,
title={Leveraging data analytics to understand the relationship between restaurants’ safety violations and COVID-19 transmission},
author={Huang, Arthur and de la Mora Velasco, Efr{\'e}n and Farhangi, Ashkan and Bilgihan, Anil and Jahromi, Melissa Farboudi},
journal={International Journal of Hospitality Management},
volume={104},
pages={103241},
year={2022},
publisher={Elsevier}
}
• 2019: A Deep Learning Strategy for I/O Scheduling in Storage Systems PDF BibTeX
@inproceedings{farhangi2019work,
title={Work-in-progress: a deep learning strategy for I/O scheduling in storage systems},
author={Farhangi, Ashkan and Bian, Jiang and Wang, Jun and Guo, Zhishan},
booktitle={2019 IEEE Real-Time Systems Symposium (RTSS)},
pages={568--571},
year={2019},
organization={IEEE}
}
Program Committee:
I am an active program committee member for AAAI, ICONIP conferences. I am also a reviewer for the following conferences and journals: EMSOFT, TPDS, RTSS, RTAS, RTCSA, DAC, TNNLS, ICIST.
