Magdalini Eirinaki
Professor
Program Director, MS in Artificial Intelligence
Computer Engineering Department
Preferred: magdalini.eirinaki@glass-ink.com
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 College of Engineering at 菠菜网lol正规平台. She also serves as the program director of the MS in Artificial Intelligence program. Her research interests span a broad range of machine learning, data mining, social graph mining, recommender systems, and deep learning applications. 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 serves as the General Chair of IEEE BDS 2024, steering committee of the Silicon Valley Women in Engineering conference series (most recently WiE 2024), and guest editor of FGCS's special issue on Big Data Computing Service and Machine Learning Applications. She has recently served as Reproducibility Chair of ACM RecSys 2023, and Senior PC of ACM WSDM 2023 and TheWebConf 2023. She has previously served as guest editor, organization committee member, program committee member, or reviewer in numerous conferences and journals, including JDSA, TWEB, BDR, TKDE, DKE, SIGIR, RecSys, BigData, DSAA, ICDM, and CIKM, among others.
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 菠菜网lol正规平台 distinguished faculty mentor award in 2015, 2019, 2020, 2022, and 2023.
Links
- Selected publications (DBLP, Google Scholar)
- Interviews
News
- (4/2024) Our paper "Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems" has been accepted for publication in IEEE Access. An earlier pre-print version is available via arXiv.
- (3/2024) Congratulations to our lab's student Derek Lilienthal for being selected as a finalist in the 2024 菠菜网lol正规平台 Grad Slam.
- (11/2023) Our paper "Using Graph Neural Networks for Social Recommendations" was published in Algorithms (SI: New Trends in Algorithms for Intelligent Recommendation Systems)
- (9/2023) Our paper "Prompt Recommendations for AI Art" was presented and will appear in the proceedings of IEEE AIKE 2023.
- (8/2023) Our paper "Deep Learning in AI Medical Imaging for Stroke Diagnosis" was presented at the 2023 BMES annual conference.
- (7/2023) Our paper "Distress Signal Recognition Using Pose Estimation and Neural Networks" was presented and will appear in the proceedings of IEEE MobileCloud 2023.
- (7/2023) Dr. Eirinaki gave an interview to Aarna Sahu for her "Aarna's News" podcast. Listen here.
- (6/2023) Dr. Eirinaki gave a keynote on "Recommender Systems: Methods, Challenges, and Opportunities" at the 5th Summit on Gender Equality in Comuting (GEC 2023).
- (5/2023) Our paper "Reinforcement Learning for Autonomous Network Defence using MARL and Self-Play" got the best paper award at the SVCC 2023 conference.
- (5/2023) Congratulations to our lab's students, James Guzman and Surabhi Gupta, who won 2nd place in the Computer Science & Engineering category of the CSU research competition for their project “Deep Learning in AI Medical Imaging for Stroke Diagnosis.”