Machine Learning | AI Consulting

Ashkan Farhangi

United States

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Biography

Ashkan Farhangi is a highly accomplished doctoral researcher in Machine Learning with a strong focus on deep learning, cloud-based AI systems, and anomaly detection. His extensive research contributions, including publications and US patents, reflect his deep expertise and innovative approach in the field. As an adjunct professor, Ashkan actively shapes the next generation of AI experts while continuing to serve on program committees for leading conferences. His work has earned him multiple awards, confirming his reputation as a thought leader in machine learning and AI.

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.