Dr. E. Mocanu | People Pages: Find employees & contact details

News

29 March 2024, I have one PhD and one PostDoc open positions on scalable energy-efficient deep learning (link and link)
29 March 2024, I have one PhD open position on sparse training for deep reinforcement learning (link)
19 January 2024, The MISD project was accepted (link). Happy to be part of a larger effort within UT on sustainable data centers.
6 November 2023, Alexander's work on "Enhancing Learning in Sparse Neural Networks: A Hebbian Learning Approach" was nominated for the Best Student Thesis Abstract Award at BNAIC2024.
22 September, 2023 I gave a keynote talk about "Sparse training of neural networks" at the E-pi: Re-thinking Uncertainty and AI Workshop in TU Delft (link)
6 June, 2023 I am doing a short research visit and hold a seminar about "Sparse Training in Deep Reinforcement Learning" at CaSToRC, the National HPC Competence Center - The Cyprus Institute (link)
24 April, 2023 Our tutorial "Sparse Training for Supervised, Unsupervised, Continual, and Deep Reinforcement Learning with Deep Neural Networks" has been accepted at IJCAI 2023.
11 April, 2023 I gave an invited talk on "Sparsity in neural networks" at the Big Data & AI workshop during the Smart Diaspora 2023 conference in Timisoara, Romania.
30 January, 2023 I gave an invited talk on "Scalable and Efficient Agents using Sparse Neural Networks" at the AI for E&S Think Tank, TU Delft (link)
4 January, 2023 Our paper "Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning" has been accepted at AAMAS 2023 (link)
4 January, 2023 The third edition of the workshop "Sparsity in Neural Networks: On practical limitations and tradeoffs between sustainability and efficiency" - SNN 2023 will be colocated with ICLR 2023 (link)
27 October, 2022 I gave an invited talk at the CoMBE Seminar Series, University of Duisburg-Essen
14 September, 2022 One paper on sparse training and time series classification accepted at NeurIPS 2022 (link)
15 July, 2022 I gave an invited talk during the AI Seminar at the University of Alberta/Alberta Machine Intelligence Institute titled "Sparse training in supervised, unsupervised, and deep reinforcement learning" (link)
13 July, 2022 I was recognised as an Outstanding Reviewer at ICML 2022
13 July, 2022 We are organising the second edition of the "Sparsity in Neural Networks: Advancing Understanding and Practice" Workshop - SNN 2022 (link)
10 June, 2022 I gave an invited talk at Calgary AI, University of Calgary
21 May, 2022 I am doing a research visit to the group of Dr. Matthew Taylor at the University of Alberta
10 May, 2022 Our paper "Dynamic Sparse Training for Deep Reinforcement Learning" received best paper award at ALA 2022, collocated with AAMAS 2022 (link)
25 April, 2022 We had the pleasure of hosting Utku Evci, Research Engineer at Google Brain Montreal, to give a very engaging in-person talk.
20 April, 2022 One paper on sparse training and deep reinforcement learning accepted at IJCAI-ECAI 2022 (link)
15 April, 2022 Our tutorial "Sparse Neural Networks Training" has been accepted at ECMLPKDD 2022 (link)
20 January, 2022 One paper on dynamic sparse training has been accepted at ICLR 2022 (link)

Organisations

Publications

2025

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2025)In The Thirteenth International Conference on Learning Representations. Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., Keulen, M. v., Mocanu, D. C. & Mocanu, E.https://openreview.net/forum?id=daUQ7vmGap

2024

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness (2024)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Wang, S., Strisciuglio, N., Pechenizkiy, M., van Keulen, M., Mocanu, D. C. & Mocanu, E.https://doi.org/10.48550/arXiv.2410.03030Insights into Dynamic Sparse Training: Theory Meets Practice (2024)[Contribution to conference › Poster] European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024. Wu, B., van Keulen, M., Mocanu, D. C. & Mocanu, E.Digital Twin-Empowered Autonomous Driving for E-mobility: Concept, framework, and modeling (2024)IEEE Electrification Magazine, 12(3), 68-77. Li, Y., Xu, J., Li, T., Mocanu, E., Jensen, C. S., Gao, D. W. & Zhang, Y.https://doi.org/10.1109/MELE.2024.3423148E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2024)In 38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024. MLResearchPress. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://openreview.net/forum?id=Xp8qhdmeb4

2023

E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2023)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)In AAMAS '23: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1932-1941) (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2023). ACM Press. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://dl.acm.org./doi/10.5555/3545946.3598862Dynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewDynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewAutomatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)[Working paper › Preprint]. ArXiv.org. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2302.06548

Research profiles

Courses academic year 2025/2026

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

Courses academic year 2024/2025

Courses academic year 2023/2024

In the press

2021, Inovex GmbH, AI blog Pruning and Sparsification of Neural Networks
2021, Towards Data Science, Neural Network Pruning 101
2020, Numenta, The Case For Sparsity in Neural Networks, Part 2: Dynamic Sparsity
2019, TechXplore, A bio-inspired approach to enhance learning in ANNs
2018, Nature Collections, The multidisciplinary nature of machine intelligence
2018, Towards Data Science, The Sparse Future of Deep Learning
2018, Phys.org, New AI method increases the power of artificial neural networks
2018, E&T magazine, Artificial neural networks could run on cheap computers using new method
2018, Technologist, AI method increases the power of artificial neural networks
2018, Elektor magazine, Nieuw algoritme versnelt kunstmatige intelligentie kwadratisch
2018, EMERCE, Nieuwe AI-methode vergroot de kracht van kunstmatige neurale netwerken
2018, deVolkskrant, Ook het stroomnet denkt straks na

Address

University of Twente

Zilverling (building no. 11)
Hallenweg 19
7522 NH Enschede
Netherlands

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