Masoud Daneshtalab, Professor
I am a Professor at Mälardalen University (MDH) and leading the Heterogeneous System research group (www.es.mdh.se/hero/). I joined KTH as European Marie Curie Fellow in 2014. Before that, I was a university lecturer and group leader at the University of Turku in Finland from 2012-2014.
My research focuses on the theoretical foundations of centralized and distributed AI and deep learning algorithms, their practical applications in resource management, computer vision, biomedical fields, and algorithm-hardware co-design. My research vision encompasses four key areas (KA):
- KA1: Robustness, Reliability, Fairness, and Security in AI
- KA2: Generative AI and synthetic data
- KA3: AI acceleration / AI algorithm-hardware co-design
- KA4: Federated learning
My group has a track record of developing methods and tools for optimizing AI/DL models using multi-objective neural architecture search (NAS), specialized pruning and quantization techniques and designing specialized AI/DL hardware accelerators. List of the open-source tools: https://www.es.mdh.se/hero/tools/
Summary of my leadership qualifications:
- Have (co-)led many research projects including: AutoFL, GreenDL, FASTER-AI, SafeAI, SafeDeep, AutoDeep, DeepMaker, DESTINE, PROVIDENT, HERO, AGENT, CUBRIC, ERoT, and µBrain with a total estimation of 160 MSEK; currently leading 6 AI projects (as PI).
- Have over 15+ years of teaching experience in computer science and AI in four countries: Uni. Tehran, Uni. Turku, Taltech, KTH and MDU (Sweden, Finland, Estonia, and Iran), and have developed more than 10 advanced- and 3 basic- courses in multiple countries and different programs (e.g. robotics, dependable systems, and applied AI).
- Have contributed to 2 international books, 8 book chapters, over 46 journal papers (10+ ACM/IEEE transaction journals) and over 200 reviewed international conference papers.
- Supervisor of over 9 passed PhDs and postdocs since 2011.
- Co-leading the heterogenous system research group (HERO) with 10+ PhD students and 3+ postdoc since 2018.
- Associate editors of journals of Elsevier MICPRO & MDPI Imaging
- Technical program committee of 20+ major conferences in AI and design automation
- General chairman, vice-chairman, and steering committee member of multiple conferences.
- Have been on the Euromicro board of directors and a member of the HiPEAC network since 2016.
- Have collaborated with more than 50 international institutes and co-authors with more than 221 scientists (according to DBLP record)
- 5 grant awards for excellence in research from the Nokia Foundation, Kaute Foundation, Ulla Tuominen Foundation, Nanotechnology Initiative Council, and Telecommunication Research Center.
- Multiple evaluation committees: Belgian Research Council, Irish Research Council, European Horizon, Austrian Science Fund, Natural Sciences and Engineering Research Council of Canada.
- Focusing on core AI principles via pioneering novel theoretical algorithms to address performance, reliability, security, robustness, and fairness concerns in AI models.
- AI algorithm-hardware co-design / AI accelerator
Closing the loop in scene reconstruction: Automated evaluation and guided data collection (Sep 2026) Joakim Lindén, Ludwig Karlsson , Håkan Forsberg, Masoud Daneshtalab 30th International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2026) (DS-RT 2026)
Learning in modal space: Physics-guided diffusion for multi-scale time-series generation (Sep 2026) Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab 31st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2026)
Generative Digital Twin Framework for Reliable and Robust AI-Powered Prognostic Systems (Jun 2026) Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab 30th Ada-Europe International Conference on Reliable Software Technologies (AEiC 2026)
Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling (Apr 2026) Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab 34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2026)
Training-Free Quantum Architecture Search Under Realistic Noise via Expressibility-Guided Evolution (Mar 2026) Seyedali Mousavi, Seyedhamidreza Mousavi, Paul Pettersson, Masoud Daneshtalab
FedLoRASwitch: Efficient Federated Learning via LoRA Expert Hotswapping and Routing (Oct 2025) Joakim Flink, Bostan Khan, Masoud Daneshtalab The 3rd IEEE International Conference on Federated Learning Technologies and Applications (FLTA25)
Ali Zoljodi
Bostan Khan
Joakim Lindén
Mohammad Riazati (former)
Seyedhamidreza Mousavi
Yana Tejasukmana (former)
Zafer Yigit
Aldin Berisa
Amin Majd (former)
Bahar Houtan (former)
Mahdi Taheri
Mohammad Ahmadilivani
Mohammad Loni (former)
| Thesis Title | Status |
|---|---|
| OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA | finished |