Principal Machine Learning Engineer | Ex-Meta

Ashkan Farhangi, PhD

United States

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Biography

Ashkan Farhangi is a Doctoral Principal Machine Learning Engineer with a strong focus on large-scale cloud-based AI systems and deep learning models. His extensive contributions include publications and US patents, as well as serving as a program committee for AAAI. He also serves as an adjunct professor, teaching various courses ranging from cloud to deep learning fundamentals for the next generation.

Publications

• 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, Huang, Arthur, and Guo, Zhishan},
  booktitle={Proceedings of the 55th Hawaii International Conference on System Sciences},
  year={2022}
}

• 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}
}

• 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}
}

US Patents

• Learning Model for Recognizing Complex Medical Data

Area

His area of expertise lies in large-scale ML systems, Large Language Models (LLMs), cloud-based AI solutions, Anomaly Prediction, NLP, and XAI.

Program Committee

He is an active program committee member for AAAI and ICONIP conferences. He also serves as a reviewer for the following conferences and journals: EMSOFT, TPDS, RTSS, RTAS, RTCSA, DAC, TNNLS, and ICIST. Please consider submitting or attending.

Adjunct Professor

EEL 4798 Massive Storage & Big Data
EEL 4781 Computer Communication Networks

Talks

• "Can Artificial Intelligence Address the Burden Associated with Scoring Narrative Assessments?" International Association of Medical Science Educators Annual Meeting (IAMSE), Oral Session, June 2022.