Mihail Stoian

I am first-year PhD student in the newly-founded Data Systems Lab at UTN, advised by Andreas Kipf. My main interest is in query optimization for cloud databases.

Previously, I worked as a student research assistant in the TUM database group (Prof. Thomas Neumann) and in the DAML group (Prof. Stephan Günnemann).

During my studies I did two industry internships at Oracle Labs and Amazon Redshift.

Whenever I see a problem in other areas that is similar to a database problem, I cannot leave it unsolved (see my blog).

[Google Scholar]

Preprints

TSP Escapes the $O(2^n n^2)$ Curse [proof-of-concept]
Mihail Stoian

TL;DR. First improvement on the $O(2^n n^2)$-time algorithm for the Traveling Salesman Problem by Bellman and Held-Karp, designed in 1962.
Did Fourier Really Meet Möbius? Fast Subset Convolution via FFT
Mihail Stoian

TL;DR. There is no need for Zeta/Moebius transforms in fast subset convolution. FFT suffices. Even in the same running time.
Sinking an Algorithmic Isthmus: $(1 + \varepsilon)$-Approximate Min-Sum Subset Convolution
Mihail Stoian

TL;DR. First proposal for approximate min-sum subset convolution. This results in out-of-the-box exp-time $(1 + \varepsilon)$-approximations for prize-collecting Steiner tree, min-cost $k$-coloring, protein networks, and more applications in computational biology.
Corra: Correlation-Aware Column Compression
Hanwen Liu, Mihail Stoian, Alexander van Renen, Andreas Kipf

TL;DR. Are you still using FOR-, Delta-, RLE-encodings? Correlation-aware column encodings can compress your data even better!
Group Privacy Amplification and Unified Amplification by Subsampling for Rényi Differential Privacy
Jan Schuchardt, Mihail Stoian*, Arthur Kosmala*, Stephan Günnemann
On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond [code]
Mihail Stoian, Richard Milbradt, Christian B. Mendl

Publications

2023

Fast Joint Shapley Values [code] [talk]
Mihail Stoian
SRC @ SIGMOD 2023.
Faster FFT-based Wildcard Pattern Matching [code] [talk]
Mihail Stoian
SRC @ SIGMOD 2023.

2022

Concurrent Link-Cut Trees [code] [talk]
Mihail Stoian | Advised by Jana Giceva and Philipp Fent
SRC @ SIGMOD 2022.

2021

Towards Practical Learned Indexing [code] [talk]
Mihail Stoian, Andreas Kipf, Ryan Marcus, and Tim Kraska.
AIDB @ VLDB 2021.
Benchmarking Learned Indexes [blog] [code] [leaderboard]
Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, and Tim Kraska.
VLDB 2021.

2020

RadixSpline: A Single-Pass Learned Index [code] [talk]
Andreas Kipf^*, Ryan Marcus^*, Alexander van Renen^*, Mihail Stoian, Alfons Kemper, Tim Kraska, and Thomas Neumann.
aiDM @ SIGMOD 2020.

2019

SOSD: A Benchmark for Learned Indexes [code]
Andreas Kipf^*, Ryan Marcus^*, Alexander van Renen^*, Mihail Stoian, Alfons Kemper, Thomas Neumann, and Tim Kraska.
ML For Systems @ NeurIPS 2019.