Celestine Dünner

Celestine Mendler-Dünner

I am a postdoctoral researcher at UC Berkeley interested in algorithmic aspects of machine learning and artificial intelligence. I am holding an SNF Early Postdoc.Mobility fellowship and I am hosted by Prof. Moritz Hardt. Prior to that I worked as a research scientist at IBM Research Zurich and contributed to the development of the Snap ML library. I have obtained my PhD from ETH Zurich where I was affiliated with the Data Analytics Laboratory and supervised by Prof. Thomas Hofmann.

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I recieved the ETH Medal for my dissertation
I won the Fritz Kutter Award for the high industrial impact of my dissertation
Our latest paper was accepted at NeurIPS 2019 as a spotlight presentation
I was awarded the SNF Early Postdoc.Mobility fellowship and will join UC Berkeley in Summer 2019


Performative Prediction
Juan C. Perdomo*, Tijana Zrnic*, Celestine Mendler-Dünner, Moritz Hardt
ArXiv preprint
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
N.Ioannou*, C.Mendler-Dünner*, T.Parnell
Advances in Neural Information Processing Systems (NeurIPS - Spotlight)
On Linear Learning with Manycore Processors
E.Wszola, C.Mendler-Dünner, M.Jaggi, M.Püschel
IEEE International Conference on High Performance Computing (HiPC -- best paper finalist)
Sampling Acquisition Functions for Batch Bayesian Optimization
A. De Palma, C.Mendler-Dünner, T.Parnell, A. Anghel, H. Pozidis
ArXiv preprint
System-Aware Algorithms for Machine Learning
ETH Research Collection (PhD Thesis)
Snap ML: A Hierarchical Framework for Machine Learning
C.Dünner*, T.Parnell*, D.Sarigiannis, N.Ioannou, A.Anghel, G.Ravi, M.Kandasamy and H.Pozidis
Advances in Neural Information Processing Systems (NeurIPS)
A Distributed Second-Order Algorithm You Can Trust
C.Dünner, M. Gargiani, A. Lucchi, A. Bian, T. Hofmann and M. Jaggi
International Conference on Machine Learning (ICML)
Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems
M. Vlachos*, C.Dünner*, R.Heckel, V.Vassiliaadis, T.Parnell and K.Atasu
IEEE Transactions on Knowledge and Data Engineering (TKDE)
Tera-Scale Coordinate Descent on GPUs
T.Parnell, C.Dünner, K.Atasu, M.Sifalakis and H.Pozidis
Journal of Future Generation Computer Systems (FGCS)
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
C.Dünner, T.Parnell, M.Jaggi
Advances in Neural Information Processing Systems (NIPS)
Understanding and Optimizing the Performance of Distributed Machine Learning Applications on Apache Spark
C.Dünner, T.Parnell, K.Atasu, M.Sifalakis and H.Pozidis
IEEE International Conference on Big Data (IEEE Big Data)
High-Performance Recommender System Training Using Co-Clustering on CPU/GPU Clusters
K.Atasu, T.Parnell, C.Dünner, M.Vlachos and H.Pozidis
International Conference on Parallel Processing (ICPP)
Large-Scale Stochastic Learning using GPUs
T.Parnell, C.Dünner, K.Atasu, M.Sifalakis and H.Pozidis
IEEE International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning)
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering
R.Heckel, M.Vlachos, T.Parnell and C.Dünner
IEEE International Conference on Data Engineering (ICDE)
Primal-Dual Rates and Certificates
C.Dünner, S.Forte, M.Takac and M.Jaggi
International Conference on Machine Learning (ICML)
*equal contribution

Invited Talks

Zürich Women in Machine Learning and Data Science Meetup -- schedule
AMLD workshop -- Advances in ML: Theory meets practice -- slides schedule
EcoCloud Annual Event in Lausanne -- slides
Zurich ML meetup -- abstract