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
Scheduling 5G Radio Resources for the Transmission of Real-time TSN Flows (Sep 2025) Zenepe Satka, Federico Aromolo , Mohammad Ashjaei, Alessandro Biondi , Daniel Casini , Hossein Fotouhi, Niccolo Borgioli , Masoud Daneshtalab, Mikael Sjödin, Saad Mubeen
Machine Learning-Based Prognostic Approaches for Construction Equipment Powertrain Systems (Jun 2025) Zafer Yigit, Håkan Forsberg, Masoud Daneshtalab 36th IEEE Intelligent Vehicles Symposium (IEEE IV2025)
Experimental Evaluation of a CAN-to-TSN Gateway Implementation (May 2025) Aldin Berisa, Saad Mubeen, Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Benjamin Kraljusic , Nejla Zahirovic 28th International Symposium On Real-Time Distributed Computing (ISORC2025)
Bridging Quantization and Deployment: A Fixed-Point Workflow for FPGA Accelerators (May 2025) Obed Mogaka, Håkan Forsberg, Masoud Daneshtalab 28th IEEE Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS 2025)
FORTUNE: A Negative Memory Overhead Hardware-Agnostic Fault TOleRance TechniqUe in DNNs (Dec 2024) Samira Nazari , Mahdi Taheri , Ali Azarpeyvand , Tara Ghasempouri , Masoud Daneshtalab, Maksim Jenihhin 33rd IEEE Asian Test Symposium (ATS-24)
Enhancing Global Model Performance in Federated Learning with Non-IID Data using a Data-Free Generative Diffusion Model (Oct 2024) MohammadReza Najafi , Masoud Daneshtalab, Jeong-A Lee , Seokjoo Shin Journal of IEEE Access (IEEE-Access)
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)
Zenepe Satka
| Thesis Title | Status |
|---|---|
| OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA | finished |