Andreas Kipf

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Andreas Kipf

Andreas Kipf

@andreaskipf

Professor @utn_nuremberg. Formerly @AWS, @MIT_CSAIL, @TU_Muenchen. Focused on building efficient, easy-to-use data systems.

Germany Katılım Haziran 2009
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Andreas Kipf
Andreas Kipf@andreaskipf·
Excited to share that I’ll start as Professor of Data Systems @utn_nuremberg in early 2024! My research will explore the intersection of data systems and ML. I’ll soon announce PhD and postdoc positions in my group.
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ACM SIGMOD/PODS (@sigmodconf.bsky.social)
📣 🏆2026 SIGMOD Research Highlight Awards 🏆: sigmod.org/sigmod-awards/… DPconv: Super-Polynomially Faster Join Ordering @mihail_sto, @andreaskipf dl.acm.org/doi/10.1145/36… Automating Vectorized Distributed Graph Computation Wenyue Zhao, Yang Cao, Peter Buneman, Jia Li, @ntarmos dl.acm.org/doi/10.1145/36… AnyBlox: A Framework for Self-Decoding Datasets Mateusz Gienieczko, @maxikuschewski, Thomas Neumann, Viktor Leis, @JanaGiceva dl.acm.org/doi/10.14778/3… Rel: A Programming Language for Relational Data @molhamaref, Paolo Guagliardo, @gkastrinis, Leonid Libkin, Victor Marsault, Wim Martens, Mary McGrath, Filip Murlak, Nathaniel Nystrom, Liat Peterfreund, Allison Rogers, Cristina Sirangelo, Domagoj Vrgoč, David Zhao, Abdul Zreika dl.acm.org/doi/10.1145/37… MEMPHIS: Holistic Lineage-based Reuse and Memory Management for Multi-backend ML Systems @ArnabPhani, @matthiasboehm7 openproceedings.org/2025/conf/edbt… Diva: Dynamic Range Filter for Var-Length Keys and Queries Navid Eslami, @IoanaBercea, @niv_dayan dl.acm.org/doi/10.14778/3… The Key to Effective UDF Optimization: Before Inlining, First Perform Outlining Samuel Arch, Yuchen Liu, Todd C. Mowry, @pateljm, @andy_pavlo dl.acm.org/doi/10.14778/3… Output-sensitive Conjunctive Query Evaluation @ShaleenDeep, @HangdongZ79542, Austen Z. Fan, Paraschos Koutris dl.acm.org/doi/10.1145/36… Output-Optimal Algorithms for Join-Aggregate Queries Xiao Hu dl.acm.org/doi/10.1145/37… Differentially Private Substring and Document Counting Giulia Bernardini, @philipbille, @li_rtz, Teresa Anna Steiner dl.acm.org/doi/10.1145/37… Congratulations to all the authors👏 👏 💐 #SIGMOD2026 #ACM #researchhighlight #SIGMODawards
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Mihail Stoian
Mihail Stoian@mihail_sto·
Can your cloud database predict underprovisioning before it even happens? Meet ◒ xBound, the very first framework for join size lower bounds. xBound tells you how many tuples your SQL query will produce *at least*. Brought to you by Microsoft GSL & @utndatasystems.
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Andreas Kipf
Andreas Kipf@andreaskipf·
Looking forward to great discussions and catching up with everyone at VLDB!
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Andreas Kipf
Andreas Kipf@andreaskipf·
Our lab is excited to be presenting two papers at VLDB 2025 in London this week! 🇬🇧
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Yuki
Yuki@y_m_asano·
Today we release Franca, a new vision Foundation Model that matches and sometimes outperforms DINOv2. The data, the training code and the model weights (with intermediate checkpoints) are open-source, allowing everyone to build on this. Methodologically, we introduce two new SSL components, one is a multi-granularity SK clustering loss that utilizes Matryoshka representations and a quick post-pretraining scheme to remove unwanted spatial biases. This is the result of a close and fun collaboration @valeoai (in France) and @FunAILab (in Franconia)
Shashank@shawshank_v

Can open-data models beat DINOv2? Today we release Franca, a fully open-sourced vision foundation model. Franca with ViT-G backbone matches (and often beats) proprietary models like SigLIPv2, CLIP, DINOv2 on various benchmarks setting a new standard for open-source research🧵

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Andreas Kipf
Andreas Kipf@andreaskipf·
The Data Systems Lab is seeking a motivated PhD candidate to join our team and work on foundation models for data compression.
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Andreas Kipf
Andreas Kipf@andreaskipf·
Off to SIGMOD 2025 in Berlin! 🚄 Here’s our schedule: Today, 4:20 PM: 💡 Redbench: A Benchmark Reflecting Real Workloads (aiDM) Wed, 2:00 PM: 🏆 DPconv: Super-Polynomially Faster Join Ordering Thu, 2:30 PM: ❄️ Pruning in Snowflake: Working Smarter, Not Harder Come say hi! 👋
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Andreas Kipf
Andreas Kipf@andreaskipf·
Parachute takes semi-join filtering to the next level! Congrats to my PhD student @mihail_sto and thanks to our co-authors from MIT for initiating the project four years ago. See you in London! 🇬🇧
Mihail Stoian@mihail_sto

Delighted to announce that Parachute 🪂 will appear at @VLDBconf! 🇬🇧 Compared to regular semi-join filtering, Parachute removes dangling tuples in a bi-directional manner by precomputing fingerprint columns. Dangling tuples ⏬ = Join pruning ⏫. 📎 arxiv.org/abs/2506.13670

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Andreas Kipf
Andreas Kipf@andreaskipf·
Thrilled to share that we've received the Best Demonstration Award 🏆 at EDBT 2025! Congratulations to my students @mihail_sto and Ping-Lin Kuo for their excellent work and dedication over the past few weeks—well deserved! Paper: openproceedings.org/2025/conf/edbt…
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Mihail Stoian
Mihail Stoian@mihail_sto·
Check out our poster tomorrow at EDBT Demo 🇪🇸! 🔥Update: Virtual v0.2 now supports S3 and 🤗 Parquet files - try it out! pip install virtual-parquet.
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Andreas Kipf
Andreas Kipf@andreaskipf·
We just released Redbench, a new benchmark that contains 30 analytical SQL workloads that can be used to benchmark workload-driven optimizations. Go check it out! GitHub: github.com/utndatasystems…
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