Martin Aumüller
Curriculum Vitae: June, 2022
Email:
Address:

Birth:
Citizenship:
Website:maau@itu.dk
IT University of Copenhagen
Algorithms Group
Rued Langgaards Vej 7
Office 4B05
DK-2300 København S
Denmark
January 12, 1986 in Gera, Germany
Germany
http://www.itu.dk/people/maau

Areas of interest
Broad interests:

algorithms and data structures, algorithm engineering,algorithmic fairness & privacy

Specific interests:• design of randomized algorithms for massive data sets
• analysis of algorithms• algorithm engineering for sorting & similarity search algorithms• algorithmic privacy using differential privacy

Academic positions
Jun. 2022–
Jul. 2021–current
Aug. 2018–Jun. 2020
Sep. 2017–May 2018
Jan. 2016–Jul. 2018
Apr. 2010–Dec. 2015

Affiliated with Providentia at University of Copenhagenhttps://www.rasmuspagh.net/providentia/
Associate Professor in Computer Science at IT University of Copenhagen
Assistant Professor in Computer Science at IT University of Copenhagen (8 months 40% part-time paternity leave)
Affiliated with BARC (Basic Algorithms Research Center in Copenhagen), http://barc.ku.dk/
Postdoctoral researcher in the ERC funded project “Scalable Similarity Search” of Prof. Rasmus Pagh at ITU Copenhagen (3 months
100% paternity leave)
Research and Teaching Assistant at the Chair of Complexity Theory
and Efficient Algorithms (headed by Prof. Martin Dietzfelbinger),
Ilmenau University of Technology, Germany

2Martin Aumüller

Education
Apr. 2010–Jun. 2015

Oct. 2004 – Mar. 2010

Dr. rer. nat. in Theoretical Computer Science (equivalent to a Ph.D.),
Ilmenau, University of Technology,
Dissertation: On the Analysis of Two Fundamental Randomized Algorithms: Multi-Pivot Quicksort and Efficient Hash Functions,
Reviewer: Martin Dietzfelbinger, Rasmus Pagh, Philipp Woelfel.
Grade: summa cum laude (“graduated with highest honors”)
Dipl.-Inf. in Computer Science (equivalent to a Master’s degree),Ilmenau, University of Technology,
Major: Computer Science, Minor: Mathematics,
Thesis: An Alternative Analysis of Cuckoo Hashing with a Stash
and Realistic Hash Functions.
Grade: 1.2 (“graduated with distinction”)

Research Key Metrics• Citation count: 417∗
• H-Index: 10∗• Orchid: http://orcid.org/0000-0002-7212-6476(∗ according to https://scholar.google.dk/citations?user=TNJYIYoAAAAJ)

Publications
Editorial work
2020

S. Satoh, L. Vadicamo, A. Zimek, F. Carrara, I. Bartolini, M. Aumüller, B. Jónsson, Rasmus Pagh, Similarity Search and Applications - 13th International Conference, SISAP 2020

Martin Aumüller

Journal publications
2022
2021
2021
2019
2018
2016
2015
2014

M. Aumüller, S. Har-Peled, S. Mahabadi, R. Pagh, F. Silvestri, Sampling a Near Neighborin High Dimensions - Who is the Fairest of Them All? ACM Trans. Database Syst. 47(1):4:1-4:40.
M. Aumüller, S. Har-Peled, S. Mahabadi, R. Pagh, F. Silvestri, Fair near neighbor search
via sampling, ACM SIGMOD Record 50.
M. Aumüller, M. Ceccarello, The Role of Local Dimensionality Measures in Benchmarking
Nearest Neighbor Search. Information Systems.
M. Aumüller, E. Bernhardsson, A. Faithfull, ANN-Benchmarks: A Benchmarking Tool for
Approximate Nearest Neighbor Algorithms, Information Systems.
M. Aumüller, M. Dietzfelbinger, C. Heuberger, D. Krenn, H. Prodinger, Dual-Pivot
Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths, Combinatorics,
Probability, and Computing. S
M. Aumüller, M. Dietzfelbinger, P. Klaue, How Good is Multi-Pivot Quicksort?, ACM
Transactions on Algorithms 13(1).
M. Aumüller, M. Dietzfelbinger, Optimal Partitioning for Dual-Pivot Quicksort, ACM
Transactions on Algorithms 12(2).
M. Aumüller, M. Dietzfelbinger, P. Woelfel, Explicit and Efficient Hash Families Suffice for
Cuckoo Hashing with a Stash, Algorithmica (70), 2014. Special Issue on Selected Papers
from ESA 2012.

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Martin Aumüller

Peer-reviewed conference papers
AISTATS 2022
EDBT 2022
CCS 2021
PODS 2020
SISAP 2020
SISAP 2020
ESA 2019
ALENEX 2019
SISAP 2019

PODS 2018
SISAP 2017
SODA 2017
AofA 2016
ICALP 2013
ESA 2012
ESA 2009

M. Karppa, M. Aumüller, R. Pagh, DEANN: Speeding up Kernel-Density Estimation
using Approximate Nearest Neighbor Search
M. Aumüller, M. Ceccarello, Implementing Distributed Similarity Joins using Locality Sensitive Hashing
M. Aumüller, C. Lebeda, R. Pagh, Differentially Private Sparse Vectors with Low
Error, Optimal Space, and Fast Access.
M. Aumüller, R. Pagh, F. Silvestri, Fair Near Neighbor Search: Independent Range
Sampling in High Dimensions.
M. Aumüller, A. Bourgeat, J. Schmurr, Differentially Private Sketches for Jaccard
Similarity Estimation.
M. Aumüller, M. Ceccarello, Running Experiments with Confidence and Sanity.
M. Aumüller, T. Christiani, R. Pagh, M. Vesterli, PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors.
M. Aumüller, N. Hass, Simple and Fast BlockQuicksort using Lomuto’s Partitioning
Scheme.
M. Aumüller, M. Ceccarello, The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search.
Invited to Information Systems.
♣ Best Paper Award ♣M. Aumüller, T. Christiani, R. Pagh, F. Silvestri, Distance-sensitive Hashing.
M. Aumüller, E. Bernhardsson, A. Faithfull, ANN-Benchmarks: A Benchmarking
Tool for Approximate Nearest Neighbor Algorithms,
Invited to Information Systems
T. D. Ahle, M. Aumüller, R. Pagh, Parameter-free Locality Sensitive Hashing forSpherical Range Reporting
M. Aumüller, M. Dietzfelbinger, C. Heuberger, D. Krenn, H. Prodinger. Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot
Quicksort.
M. Aumüller, M. Dietzfelbinger, Optimal Partitioning for Dual Pivot Quicksort.
M. Aumüller, M. Dietzfelbinger, P. Woelfel, Explicit and Efficient Hash Families
Suffice for Cuckoo Hashing with a Stash.
Invited to Algorithmica.
M. Aumüller, M. Dietzfelbinger, M. Rink, Experimental Variations of a Theoretically Good Retrieval Data Structure.

Peer-reviewed workshop papers
2019

M. Aumüller, M. Ceccarello, Benchmarking Nearest Neighbor Search: Influence of Local
Intrinsic Dimensionality and Result Diversity in Real-World Datasets

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Martin Aumüller

Other
NeurIPS 2021
MM 2020
SEA 2020

H. Simhadri, G. Williams, M. Aumüller, M. Douze, A. Babenko, D. Baranchuk, Q. Chen,
L. Hosseini, R. Krishnaswamy, G. Srinivasa, S. Subramanya, J. Wang, Results of the
NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Q. Truong, H. Lauw, M. Aumüller, N. Nitta, Reproducibility Companion Paper: Visual
Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN .
M. Aumüller, Algorithm Engineering for High-Dimensional Similarity Search Problems(Invited Talk).

Theses2015
2010

On the Analysis of Two Fundamental Randomized Algorithms: Multi-Pivot Quicksort andEfficient Hash Functions, Dissertation, TU Ilmenau.
An Alternative Analysis of Cuckoo Hashing with a Stash and Realistic Hash Functions,
Master’s thesis, TU Ilmenau.

Presentations
Invited talks
2020

Algorithm Engineering for High-Dimensional Similarity Search Problems, Symposium on
Experimental Algorithms (SEA 2020), virtual.

Conference presentations
2020
2020
2019
2019
2019
2018
2017
2013
2012

Differentially Private Sketches for Jaccard Similarity Estimation, SISAP 2020, virtual, October 2020.
Fair Near Neighbor Search: Independent Range Sampling in High Dimensions, PODS 2020,
virtual, June 2020.
The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search,
SISAP 2019, Newark, October 2019.
PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors, ESA 2019,
Munich, September 2019.
Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and
Result Diversity in Real-World Datasets, EDML 2019 (co-located with SDM 2019), Calgary, May 2019.
Distance-Sensitive Hashing, PODS 2018, Houston, June 2018
Parameter-free Locality Sensitive Hashing for Spherical Range Reporting, SODA 2017,
Barcelona, January 2017
Optimal Partitioning for Dual Pivot Quicksort, ICALP 2013, Riga, July 2013
Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, ESA 2012,
Ljubljana, September 2012Martin Aumüller

Invited presentations
2021
2018
2017
2016
2015
2015
2014
2011

Local Intrinsic Dimensionality and Nearest Neighbor Search, WSDM 2021, Conferenceon Web Search and Data Mining, March 2021.
Recent Advances in Quicksort, Institute Seminar, University of Padova, October 2018.
Distance-sensitive Hashing, Dagstuhl, Seminar 17181, May 2017
News on Multi-Pivot Quicksort, Dagstuhl Seminar 16101, March 2016
Optimal Partitioning for Multi-Pivot Quicksort, Algorithm seminar, ITU Copenhagen,
October 2015
Strong Randomness Properties of (Hyper-)Graphs Generated by Simple Hash Functions,
Analysis of Algorithms 2015, Strobl, Austria, June 2015
Optimal Partitioning for Multi-Pivot Quicksort, Dagstuhl Seminar 14091, February 2014
Strong Randomness Properties of Graphs and Hypergraphs Generated by Simple Hash
Functions, Research seminar, Ilmenau, December 2011

Workshop presentations
2016
2013
2010

Multi-Pivot Quicksort: Comparison-Optimal Algorithms and Beyond, ARCO’16, April
2016
Optimal Partitioning for Dual Pivot Quicksort, 66. Theorietag der Fachgruppe Algorithmen und Komplexität, Hannover, Germany, June 2013
Cuckoo Hashing with a Stash and Realistic Hash Functions, 60. Theorietag der Fachgruppe Algorithmen und Komplexität, Kiel, Germany, June 2010

Advising
IT University of Copenhagen
PhD students• 2020–ongoing, Christian Janos Lebeda, co-supervised with Rasmus Pagh.
Master thesis advising• I. Larsen, A. Madsen, “An Empirical Comparison of Differentially Private Similarity Estimation Techniques”, 2022.
• L. Berthelsen, E. Crome, S. Jensen, “Graph-Based Similarity Search Algorithms”, 2022.
• E. Lemming, J. Nielsen, O. Schiermer, “ 2d k-nearest neighbors search using shallowcuttings”, 2022.
• R. Pontoppidan, M. Vagnholm, “Similarity Joins via LSH and LSF”, 2021.
• M. Edvardsen, J. Homann, “Privacy-Preserving Similarity Search”, 2020.
• M. Kopcik, “Machine Learning Framework for Solving Nearest Neighbor Search”, 2020.
• A. Ensing, “Graph-based Similarity Search”, 2020.
• M. Drasbeck, “Using Differential Privacy on Danish Data”, 2020.
• O. Kristiansen, “Similarity Search in Large Image Data Sets”, 2019.
• A. Vosmaer, “Machine Learning Based Indexing Techniques for Finding Approximate
Nearest Neighbors”, 2019.

6Martin Aumüller• A. Bourgeat, J. Schmurr, “Privacy-Preserving Similarity Search”, 2019, (Paper SISAP
2020).
• F. Bakke, S. Petursson, “Locality Sensitive Hashing on the GPU” , 2019.
• F. Buch, A. Lauridsen, “Frequent Item Mining - Introducing a hybrid differential privacy
model”, 2019.
• K. Nielsen, “Evaluation of improved ANN framework”, 2018.
• D. Leszkowicz, “Interactive Data Visualization for a better understanding of Recommender Systems”, 2018.
• V. Limbean, “Audio Feature Extraction and Fingerprinting”, 2018
• N. Hass, “Design and experimental evaluation of Multi-Pivot BlockQuickSort on Lomuto
based partitioning”, 2017, (Paper ALENEX 2019).
• R. Dobre, C. Matrakou, R. Themsen, “Image similarity search using Locality Sensitive
Hashing (LSH)”, 2016
Bachelor thesis advising• B. Larsen, J. Porsgaard, “Learning a locality sensitive hashing scheme in euclidean space”,
2022.
• A. Christensen, I. Matic, “Reproducibility in Algorithm Engineering”, 2022.
• T. Poulsen, V. Thomsen, “Sketching Techniques in PUFFINN”, 2022.
• C. Rüdinger, G. Brygger, W. Bugge, “Engineering Low-Dimensional Similarity Search”,
2021.
• B. Kehler, D. Engggard, “Outlier Detection Using LSH Variants”, 2021.
• M. Peterson, “Introduction to the adaptive sorting algorithms Peek Sort and Power Sort
in C#”, 2019.
• F. Stauning, M. Krøse, “Implementing multi-pivot quicksort algorithms in C#”, 2018.
Student project advising• I. Hemmingsen,“Visualizing data using Python”, 2018.
• M. Rasmussen, N. Hass, “Investigating branch-free and equal-element aware multi-pivot
quicksort variants”, 2016

TU Ilmenau
Master thesis advising• P. Klaue, “Optimal Partitioning for Multi-Pivot Quicksort”, 2014, (Paper TALG 13).
Bachelor thesis advising• D. Knacker, “Theoretical Considerations in Route Planning Algorithms”, 2014.
• A. Chemissov, “Performance Evaluation of Efficient Hashing Methods”, 2014.
• A. Seifert, “Modern Algorithms for Route Planning”, 2012.

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Martin Aumüller

Teaching
At ITU Copenhagen
Spring 2022
Spring 2022
Autumn 2021
Summer 2021
Spring 2021
Autumn 2020
Autumn 2020
Summer 2020
Autumn 2019
Autumn 2019
Autumn 2018
Autumn 2018
Spring 2018
Autumn 2017
Spring 2017
Autumn 2016
Spring 2016

Algorithmic Fairness, Accountability, and Ethics (graduate level)
Algorithmic Problem Solving (undergraduate level)
Introduction to Programming (undergraduate level)
Algorithms and Data Structures (undergraduate level)
Algorithmic Problem Solving (undergraduate level)
Applied Algorithms (graduate level)
Introduction to Programming (undergraduate level)
Algorithms and Data Structures (summer university) (undergraduatelevel)
Introduction to Programming (undergraduate level)
Applied Algorithms (graduate level)
Introduction to Programming (undergraduate level)
Applied Algorithms (graduate level)First-Year Project: Map of Denmark. Visualization, Navigation,
Searching, and Route Planning (undergraduate)
Programming workshop (graduate level)
Algorithm design project (graduate level)
Programming workshop (graduate level)
Advanced algorithm seminar (graduate level)

At TU Ilmenau (teaching in German)
2015
2010–2015
2010–2015
2010–2014
2011, 20132011–20122010

Algorithms and Data Structures∗ : tutorial Summer 2015
Efficient Algorithms 2∗ : tutorial Winter 2010–2015
Project seminar: supervised students writing literature papers on
topics in algorithms, data structures and complexity theory(13 students supervised)
Efficient Algorithms∗ : main instructor in Summer 2012,
tutorial Summer 2010–2014
Complexity Theory∗ : tutorial Summer 2011 and 2013
Approximation Algorithms∗ : tutorial Winter 2011 and 2012
Computability and Complexity: tutorial Winter 2010

As a student T.A.
2006–2007

tutorials for foreign students on introductory programming courses( indicates that around 20% of lectures were given as a replacement for the official teacher)∗Awards and Prizes
PrizesOct. 2015
Oct. 2013

Awarded “Lehrpreis 2015” from Technische Universität Ilmenau(one out of ten university-wide awards for excellent teaching)Awarded “Lehrpreis 2013” from Technische Universität Ilmenau

9Martin Aumüller

Awards∗Dec. 2020
Oct. 2019
Dec. 2014
Dec. 2012
Dec. 2011

SIGMOD Research Highlight for PODS 2020 paper (Invited toCACM).
Best Paper Award at SISAP 2019.
2 Awards: “best tutorial” (“Efficient Algorithms” & “Efficient Algorithms 2”)
3 Awards: “best lecture” (“Efficient Algorithms”) and “best tutorial”(“Efficient Algorithms” & “Efficient Algorithms 2”)
1 Award: “best tutorial” (“Efficient Algorithms”)(∗ teaching awards based on mandatory faculty-wide student evaluations)

Affiliations• Association for Computing Machinery, since 2016

Professional Service• General Co-Chair of SISAP 2020
• Co-Chair of Reproducibility Track at ACM ICMR 2021 and ICMR 2022
• PC Service: SEA 2018, ESA 2018 (Track B), MMM 2019, ACM MM Reproducibility 2019, EDML 2019,
SISAP 2019, LATIN 2022
• Reviewer: WADS 2011, ICALP 2013, ESA 2013, Information Processing Letters, CSR 2016, MFCS
2016, STOC 2017, PODS 2017, Algorithmica 2017, WADS 2017, SODA 2018, ANALCO 2018, Software:
Practice and Experience, PPoPP 2018, Artificial Intelligence, ICALP 2019, ESA 2019, Mathematics
in Computer Science, SODA 2020, Transactions on Knowledge and Data Engineering, Information
Systems 2020, AofA 2020, Euro-Par 2020, PODS 2021, Information Systems 2021, ESA 2021, APPROXRANDOM 2021, IEEE BigData 2021.
• Coordinator: 2017, Dagstuhl Seminar 17181, “Theory and Applications of Hashing”• Organizer: ARCO 2018, IT University of Copenhagen• Co-organizer: 69. Workshop über Algorithmen und Komplexität at TU Ilmenau, 2015
• PhD committees: Nina Mesing Stausholm Nielsen, ITU (2021).
• Local organizer: I help organizing programming events such as “Will Code for Drinks” and “LilleKat”
at ITU (headed by Thore Husfeldt). On a regular basis, I presented my research work and practicalapplications of algorithms at workshops for pupils and at open house days. Moreover, I organized
and fund-raised the “summer festival” of the faculty in Ilmenau in 2015.
• Internal Committee work at ITU: SD 2019/2022 admission committee, CS department computing
resources, CS reference group for relocation, ITU representative at HPC forum Denmark, CS representative SPS students

Martin Aumüller

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Invited workshop participation• Dagstuhl Seminars on “Data Structures and Advanced Models of Computation on Big Data” in 2014
& 2016• Analysis of Algorithms 2015, Strobl (by invitation only in odd years)

Languages• German (native), English (fluent), Danish (conversational)• Basic knowledge in Russian, Swedish, Japanese