Deniz Altınbüken
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Deniz Altınbüken is a Research Engineer at Google DeepMind who focuses on enhancing system performance and efficiency through machine learning techniques (ML for Systems). Her particular work includes consensus protocols, bug detection, caching, indexing, and query optimization.[1]
Education
[edit]Altınbüken graduated from Koç University in 2009 with a degree in Computer Engineering.[2] She earned her Ph.D. in Computer Science from Cornell University in 2017, where her research centered on building large-scale, evolving distributed systems, with a specialization in consensus protocols and self-adapting systems.[3] Altınbüken is now a member of the Advisory Board of Koç University.[4]
Career contributions
[edit]Altınbüken's has delivered talks and lectures on ML for Systems at institutions including the University of California, Berkeley, and the University of Illinois Urbana-Champaign sharing insights on designing, implementing, and deploying machine learning solutions in system architectures.[5] She has delivered invited talks at institutions including the presentation of "The Tip of the Iceberg: How to make ML for Systems work"[3] at the UC Berkeley Sky Seminar, in 2022 sharing insights from her experiences in building and deploying ML solutions in system architectures.[3] She was also invited to speak at the "COVID-19 Döneminde Doğru Bilgiye Ulaşmak" ("Accessing Accurate Information During the COVID-19 Period") event organized by Koç University.[2]
Her conference recognition includes the paper "Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor,"[6] co-authored by Altınbüken, abd presented at the 29th ACM Symposium on Operating Systems Principles (SOSP) in 2023.[7] Altınbüken has also been active in the academic community through her involvement in organizing workshops and serving on program committees. Altınbüken has served in program chair positions including ATC'25,[8] FAST'23,[9] ACM SoCC'22.[10] She co-chaired the 2nd Workshop on Practical Adoption Challenges of ML for Systems in Industry (PACMI) at the MLSys 2023 conference.[11] She has also been a Program Committee member of the International Workshop on Cloud Intelligence / AIOps[12] since 2024.[13]
Selected publications
[edit]- Robbert Van Renesse, Deniz Altinbuken "Paxos made moderately complex" (2015)[14]
- Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou (Steve)Li, Andy Ly, Christopher Olston "Learned Indexes for a Google-scale Disk-based Database" (2020)[15]
- Sishuai Gong, Dinglan Peng, Deniz Altınbüken, Pedro Fonseca, Petros Maniatis "Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor" (2023)[6]
- Kensen Shi, Deniz Altinbüken, Saswat Anand, Mihai Christodorescu, Katja Grünwedel, Alexa Koenings, Sai Naidu, Anurag Pathak, Marc Rasi, Fredde Ribeiro, Brandon Ruffin, Siddhant Sanyam, Maxim Tabachnyk, Sara Toth, Roy Tu, Tobias Welp, Pengcheng Yin, Manzil Zaheer, Satish Chandra, Charles Sutton "Natural Language Outlines for Code: Literate Programming in the LLM Era." (2024)[16]
References
[edit]- ^ "Deniz Altınbüken". research.google. Retrieved 2025-01-03.
- ^ a b "Deniz Altınbüken'09 ile Covid -19 Döneminde Doğru Bilgiye Ulaşmak - Koç Üniversitesi" (in Turkish). 2020-04-22. Retrieved 2025-01-04.
- ^ a b c "Sky Seminar: "The Tip of the Iceberg: How to make ML for Systems work" with Deniz Altınbüken". RISE Lab. Retrieved 2025-01-03.
- ^ "Advisory Board". Computer Science and Engineering. 2019-10-30. Retrieved 2025-01-04.
- ^ "CS 591: UIUC Systems Reading Group". systems-seminar-uiuc.github.io. Retrieved 2025-01-04.
- ^ a b "Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor" (PDF). googleapis.com. 12 September 2023. Retrieved 2025-01-03.
- ^ "dblp: Deniz Altinbüken". dblp.org. Retrieved 2025-01-04.
- ^ "USENIX ATC '25". USENIX. 2023-09-21. Retrieved 2025-01-04.
- ^ "FAST '23". USENIX. 2021-02-09. Retrieved 2025-01-04.
- ^ "2022 ACM Symposium on Cloud Computing". acmsocc.org. Retrieved 2025-01-04.
- ^ "2nd Workshop on Practical Adoption Challenges of ML for Systems in Industry". mlsys.org. Retrieved 2025-01-04.
- ^ "Cloud Intelligence / AIOps The Workshop". cloudintelligenceworkshop.org. Retrieved 2025-01-04.
- ^ "Organizers". cloudintelligenceworkshop.org. Retrieved 2025-01-04.
- ^ "Paxos Made Moderately Complex" (PDF). mit.edu. 25 March 2011. Retrieved 2025-01-03.
- ^ Abu-Libdeh, Hussam; Altınbüken, Deniz; Beutel, Alex; Chi, Ed H.; Doshi, Lyric; Kraska, Tim; Xiaozhou; Li; Ly, Andy (2020-12-23), Learned Indexes for a Google-scale Disk-based Database, arXiv:2012.12501, retrieved 2025-01-04
- ^ Shi, Kensen; Altınbüken, Deniz; Anand, Saswat; Christodorescu, Mihai; Grünwedel, Katja; Koenings, Alexa; Naidu, Sai; Pathak, Anurag; Rasi, Marc (2024-08-09), Natural Language Outlines for Code: Literate Programming in the LLM Era, arXiv:2408.04820, retrieved 2025-01-04