About Me
I am a Computer Science PhD Student at Cornell, advised by Immanuel Trummer. My research interests lie in the area of database systems, mainly query optimization and database system tuning. During my PhD, I have interned at Microsoft Research and IBM Research working on similar topics. My work focuses on building query optimizers for federated query execution in distributed query engines, as well as leveraging Large Language Models (LLMs) for automated database system tuning. Prior to Cornell, I was a Senior Software Engineer at TileDB.
Experience
- Cornell University, Ithaca, NY, USA
- Graduate Research Assistant (Aug 2021 – Present)
- Microsoft Research, Redmond, WA, US
- Research Intern (May 2023 – Aug 2023)
- IBM Research, Almaden, CA, US
- Research Intern (May 2022 – Aug 2022)
- TileDB, Boston, MA, US
- Senior Software Engineer (Nov 2019 – Jul 2021)
- Unravel Data Systems, Palo Alto, CA, US
- Software Engineer (Oct 2018 – Oct 2019)
- AISERA, Palo Alto, CA, US
- Data Engineer (Apr 2018 – Sep 2018)
- BEAT, Athens
- Big Data Software Engineer (Sep 2017 – Apr 2018)
- Computing Systems Laboratory (CSLab), NTUA, Greece
- Researcher (Jul 2015 – Aug 2017)
- isMOOD Data Technology Services
- Software Engineer (Apr 2014 – Sep 2015)
Education
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PhD in Computer Science - Cornell University (2021 - Present)
Advisor: Immanuel Trummer - MSc in Computer Science - National and Kapodistrian University of Athens (2018-2021)
Advisors: Yannis Ioannidis, Lefteris Sidirourgos - BSc in Informatics - Ionian University (2012-2017)
Advisor: Dimitrios Tsoumakos
Publications
- V. Giannakouris, I. Trummer, “λ-Tune: Harnessing Large Language Models for Automated Database System Tuning,” SIGMOD 2025, Berlin, Germany, Jun 2025.
- V. Giannakouris, I. Trummer, “DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger,” VLDB 2024, Guangzhou, China, Aug 2024.
- V. Giannakouris, I. Trummer, “Demonstrating λ-Tune: Exploiting Large Language Models for Workload-Adaptive Database System Tuning,” SIGMOD 2024, Santiago, Chile, Jun 2024.
- V. Giannakouris, I. Trummer, “Building learned federated query optimizers,” VLDB 2022 (PhD Workshop), Sydney, Australia, Sep 2022.
- V. Giannakouris, A. Fernandez, A. Simsitsis, and S. Babu, “Cost-Effective, Workload-Adaptive Migration of Big Data Applications to the Cloud,” SIGMOD 2019, Amsterdam, The Netherlands, Jun 2019.
- V. Giannakouris, N. Papailiou, D. Tsoumakos, and N. Koziris, “MuSQLE: Distributed SQL Query Execution Over Multiple-Engine Environments,” BigData 2016, Washington, DC, USA, Dec 2016.
- K. Doka, N. Papailiou, V. Giannakouris, D. Tsoumakos, and N. Koziris, “Mix ’n Match Multi-Engine Analytics,” BigData 2016, Washington, DC, USA, Dec 2016.
- K. Doka, M. Filatov, V. Giannakouris, V. Kantere, N. Koziris, C. Mantas, N. Papailiou, V. Papaioannou, and D. Tsoumakos, “Optimizing, Planning and Executing Analytics Workflows over Multiple Engines,” MEDAL 2016, Bordeaux, France, Dec 2016.
- N. Papailiou, K. Doka, V. Giannakouris, V. Papaioannou, D. Tsoumakos, and N. Koziris, “Robust and Adaptive Multi-Engine Analytics using IReS (demo paper),” BIRTE 2016, New Delhi, India, Mar 2016.
- V. Giannakouris, A. Plerou, and S. Sioutas, “CSMR: A Scalable Algorithm for Text Clustering with Cosine Similarity and MapReduce,” AIAI 2014, Rhodes, Greece, Sep 2014.
Contact
Email: giannakouris.victor@gmail.com.com
LinkedIn: linkedin.com/in/victor