Masoud Daneshtalab, Professor

E-mail:
masoud.daneshtalab@mdh.se
Phone:
+4621103111
Division:
Division of Intelligent Future Technologies
Research group:
Heterogeneous systems - hardware software co-design
Web:
Official university homepage
Linkedin page


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 

[Show all publications]

[Google Scholar author page]

Latest publications:

A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks (Jan 2024)
Mohammad Ahmadilivani , Mahdi Taheri , Jaan Raik , Masoud Daneshtalab, Maksim Jenihhin
ACM Computing Surveys (CSUR)

Analysis and Improvement of Resilience for Long Short-Term Memory Neural Networks (Oct 2023)
Mohammad Ahmadilivani , Jaan Raik , Masoud Daneshtalab, Alar Kuusik
36th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT 2023)

Efficient On-device Transfer Learning using Activation Memory Reduction (Sep 2023)
Amin Yoosefi , Seyedhamidreza Mousavi, Masoud Daneshtalab, Mehdi Kargahi
International Conference on Fog and Mobile Edge Computing (FMEC)

DASS: Differentiable Architecture Search for Sparse Neural Networks (Sep 2023)
Seyedhamidreza Mousavi, Mohammad Loni, Mina Alibeigi , Masoud Daneshtalab
ACM Transactions on Embedded Computing Systems (TECS 2024)

DASS: Differentiable Architecture Search for Sparse Neural Networks (Sep 2023)
Seyedhamidreza Mousavi, Mohammad Loni, Mina Alibeigi , Masoud Daneshtalab
EMBEDDED SYSTEMS WEEK (ESWEEK 2023)

Comparative Evaluation of Various Generations of Controller Area Network Based on Timing Analysis (Sep 2023)
Aldin Berisa, Mohammad Ashjaei, Masoud Daneshtalab, Mikael Sjödin, Adis Panjevic , Imran Kovac , Hans Lyngbäck , Saad Mubeen
28th International Conference on Emerging Technologies and Factory Automation (ETFA 2023)

MSc theses supervised (or examined):
Thesis TitleStatus
OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA finished