91猎奇

Professor & Associate Chair of Graduate Affairs
Computer Engineering Department

Email

Preferred: magdalini.eirinaki@sjsu.edu

Telephone

Preferred: (408) 924-3828

Office: ENG 283F

Education

• PhD in Computer Science (Informatics), Athens Univ of Econ & Business, 2006
• MSc in Advanced Computing, Imperial College, London, United Kingdom, 2000
• BSc in Computer Science (Informatics), University of Piraeus, Greece, 1998

 

• Diploma in Music (Piano), Greek Ministry of Education, cum laude, 2006

Bio

Dr. Magdalini Eirinaki is a Professor at the Computer Engineering Department of the at 91猎奇. She also serves as the Associate Chair of Graduate Affairs. Her research interests span a broad range of machine learning, recommender systems, deep learning applications, social graph mining, and generative AI. She has published several papers in refereed journals and international conference proceedings in the above areas (links to selected publications are included below). 

Prof. Eirinaki is an external member of the of Harokopio University. She also serves on the steering committee of the Silicon Valley Women in Engineering conference series. She is also serving in multiple senior roles in journals and conference in her research area.

Prof. Eirinaki is the recipient of the 2019 Newnan Brothers Award for Faculty Excellence, the 2017 Applied Materials Award for Excellence in Teaching and received the 91猎奇 distinguished faculty mentor award in 2015, 2019, 2020, 2022, and 2023. 

Her research is funded by NSF, CAHSI/Google, CA Learning Lab, EU Horizon (Marie Slodowska-Curie Actions), and IBM. 

Links

 

• Selected publications (, , )
• Interviews
  • (in Greek) (8/2022)
  •  (12/2018) 

 

Recent News

• (7/2025) Our paper 鈥淢ultimodal Benchmarking and Recommendation of Text-to-Image Generation Models鈥� (with K. Wanaskar (MS AI '24), and G. Jena) got the best student paper award at the , July 2025, Tucson, AZ
• (6/2025) Our poster paper 鈥淐loudsweeper: Leveraging Large Language Models to Personalize Sensitive Archive Search鈥�, to appear in the Proceedings of the , June 2025, Santa Clara, CA 
• (5/2025) Dr. Eirinaki gave a talk on 鈥淗yperpersonalization through data鈥�,  to MBA student delegation from the University of Applied Sciences and Arts Northwestern Switzerland (through International Gateways, 91猎奇)
• (5/2025) Our special issue on Big Data Computing Service and Machine Learning Applications is published. Read our for an overview of the 19 papers covering a broad array of topics related to ML. 
• (5/2025) Martin Alvarez-Lopez (MS Software Engineering '25) presented his paper "The Impact of Tree Data on Urban Heat Island Mapping: A San Jose Case Study" at the .
• (4/2025) Tanvi Guttula (BS Software Engineering '24) and Johnathon Lu (BS Software Engineering '24) presented their paper "LifeTone: Personalized Skin Care Analysis at your Fingertips" at the . 
• (3/2025) Our paper "Cloudsweeper: Leveraging Large Language Models to Personalize Sensitive Archive Search" was accepted as a poster and will appear in the proceedings of the
• (2/2025) Our project "" was awarded an by the State of California (CA Learning Lab).
• (11/2024) Our paper "" appears in proceedings of IEEE Big Data 2025.
• (9/2024) Dr. Eirinaki and Dr. Potika participate in the "MUSIT" ("MUlti-Sensor Inferred Trajectories") project, that was selected for funding by EU's Horizon/Marie Sklodowska-Curie Actions. 
• (9/2024) IBM donated $50K of cloud credit to Dr. Eirinaki's lab via the IBM SkillsBuild initiative, to develop an LLM-based project focused on sustainability.
• (8/2024) Dr. Eirinaki was elected to serve as an external member of the Board of Administration of Harokopio University. 
• (7/2024) Our project "" (with Dr. Y. Liu and Dr. W. Wu) got selected for funding by NSF. 
• (7/2024) Our paper is published in IEEE Access. 
• (7/2024) Our project "Cloudsweeper: Leveraging Language Models to Personalize Sensitive Archive Search" (with Dr. C. Kanich, UIC) got selected for funding by the .
• (5/2024) Our paper "Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems" is published in . An earlier . 

 

Funded by: