Prof. Masoud DANESHTALAB, Mälardalen University, Sweden.
Masoud Daneshtalab, Ph.D., Docent, Full Professor
Masoud Daneshtalab is a globally recognized scholar and Full Professor at Mälardalen University (MDU), Sweden. With over two decades of academic and professional excellence, he has made significant contributions to computer science and engineering, specializing in dependable systems, AI, and hardware/software co-design. A prolific researcher with an H-index of 35 and over 5,100 citations, Dr. Daneshtalab is included in the prestigious World’s Top 2% Scientists Ranking. He serves as the Scientific Director of Fundamental AI at MDU and collaborates internationally, holding adjunct professorships and contributing to cutting-edge research initiatives.
Professional Profile:
Google Scholar
Suitability of Masoud Daneshtalab for the Best Researcher Award
Dr. Masoud Daneshtalab is a highly suitable candidate for the “Research for Best Researcher Award,” based on his exceptional academic achievements and professional contributions. Here are the key reasons
Education
🎓 Academic Journey
- Docent (2018): Qualified in Computer Science and Electronics, Mälardalen University, Sweden.
- Ph.D. (2008–2011): Information and Communication Technology, University of Turku, Finland. Dissertation: Adaptive Implementation of On-Chip Networks under Prof. Hannu Tenhunen.
- M.Sc. (2004–2006): Computer Engineering, University of Tehran, Iran. Thesis: Low Power Methods in Network-on-Chips under Prof. Ali Afzali-Kusha.
- B.Sc. (1998–2002): Computer Engineering, Shahid Bahonar University of Kerman, Iran.
Experience
💼 Professional Contributions
- Scientific Director (2024–Present): Fundamental AI, Mälardalen University, Sweden.
- Full Professor (2020–Present): Innovation, Design & Engineering, MDU.
- Adjunct Professor (2019–Present): Computer Systems, Tallinn University of Technology, Estonia.
- Previous Roles: Associate Professor at MDU (2016–2020), EU Marie Curie Fellow at KTH Royal Institute of Technology (2014–2016), Lecturer at the University of Turku (2011–2014), and Researcher at the University of Tehran (2006–2008).
Research Interests
🔬 Key Areas
- Optimization and robustness in deep learning models.
- HW/SW co-design and heterogeneous computing.
- Dependable systems, memory architectures, and interconnection networks.
- Cutting-edge projects include sustainable AI, federated learning, and reliable autonomous systems.
Awards
🏆 Recognitions
- Best Paper Awards: IEEE ECBS (2019), IEEE MCSoC (2018), and multiple HiPEAC Paper Awards (2013–2017).
- Research Grants: Marie Skłodowska-Curie Fellowship (2014), Nokia Foundation (2009), and others.
- Top Reviewer: IEEE Transactions on Computers (2013).
- Fellowships: GETA, Helsinki University of Technology (2008–2011).
Publications
A review on deep learning methods for ECG arrhythmia classification
CITIED: 490
Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities
CITIED: 147
Routing algorithms in networks-on-chip
CITIED: 136
Smart hill climbing for agile dynamic mapping in many-core systems
CITIED: 125
EDXY–A low cost congestion-aware routing algorithm for network-on-chips
CITIED: 124
Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems
CITIED: 122