I am an associate professor in Dept of Information Science and Engineering, School of Electrical Engg and Computer Science, KTH. I am also a Fellow of Digital Futures.
Signal procesing and machine learning - these two vast fields are highly interconnected. My research mainly is in these two fields. In few words, the research interests are processing and analysis of signals and/or data for inferences such as detection, estimation, classification, prediction, learning, and finally decision. Please note that I get (easily) excited by the potential and/or prospects of algorithm design, analysis and applications. Therfore, please moderate your expectations. To mention some topics:
- Signal modeling,such as sparsity, compressive sensing, dynamical systems.
- Statistical signal processing, statistical machine learning, deep learning.
- Speech,audio, image processing.
- Medical data analytics.
- Perception for autonomous vehicles.
- Explainable machine learning.
Explainable machine learning is close to my heart having a signal processing background. Medical data analysis is slowly becoming a passion due to inherent challenges and societal importance.
Present group members (PhDs and Postdocs)
I am currently supervisor / co-supervisor of the following scholars.
- Anubhab Ghosh (PhD; my role: main supervisor, co-supervisor: Mikael Skoglund)
- Antoine Honore (PhD; my role: main supervisor, co-supervisors: Mikael Skoglund and Eric Herlenius @ Karolinska)
- Sandipan Das (PhD; my role: main supervisor, co-supervisors: Magnus Jansson and Maurice Fallon @ Oxford)
- Mostafa Shayan (Postdoc; my role: co-supervisor, main supervisor: Sara Garcia Ptacek @ Karolinska)
Past group members
I was supervisor/ co-supervisor of the following PhD scholars who graduated.
- Alireza M. Javid (PhD; my role: main supervisor, co-supervisor: Mikael Skoglund); graduated in 2021. Thesis title: "Neural Network Architecture Design: Towards Low-complexity and Scalable Solutions"
- Xinyue Liang (PhD; my role: main supervisor, co-supervisor: Mikael Skoglund); graduated in 2021. Thesis title: "Decentralized Learning of Randomization-based Neural Networks"
- Ahmed Zaki (PhD; my role: co-supervisor, main supervisor: Lars K. Rasmussen); gradutaed in 2018. Thesis title: "Cooperative compressive sampling".
- Arun Venkitaraman (PhD; my role: co-supervisor, main supervisor: Peter Händel); graduated in 2018. Thesis title: "Graph signal processing meets machine learning".
- Dave Zachariah (PhD; my role: co-supervisor, main supervisor: Magnus Jansson); graduated 2013. Thesis title: "Estimation for Sensor Fusion and Sparse Signal Processing".
- Amirpasha Shirazinia (PhD; my role: co-supervisor, main supervisor: Mikael Skoglund); graduated 2014. Thesis title: "Source and Channel Coding for Compressed Sensing and Control".
- Dennis Sundman (PhD; my role: co-supervisor, main supervisor: Mikael Skoglund); graduated 2014. Thesis title: "Greedy Algorithms for Distributed Compressed Sensing".
Master Thesis Opportunity
I am willing to supervise interesting master thesis projects, preferably within the above mentioned research topics, but not limited to them. Interested candidates may contact either by a mail or to drop in my office for an informal discussion.
I am open to collaborations. You are welcome to approach me. Few points below.
- KTH has an outstanding enviroment and many of my colleagues are experts, with whom I collaborate in a regular basis. You may directly approach them, or approach me to bring them on board.
- I have established collaborations with several institutions across the world.
- Fortunately I enjoy close cooperations with several companies and medical institutions, where I try to place my students for thesis and jobs.
- From this year 2022, supported by a generous grant from SSF, I spend time in Karolinska University Hospital and Karolinska Institutet for medical data analysis.
- I also like to provide consultancy in data analysis (or data science). Please note that the consultacy will be only given if KTH has no competing interest.
What I do wish or like
Teaching and research both I like. I love algorithm design and analysis. While I like various application scenarios, medical data analysis is slowly becoming an objective, perhaps because Covid has taught us a meaning of life. A new wish - research for defence of Sweden, Nordics or EU, given the sudden war situations. While I get (easily) excited by machine learning and sigal processing, I try to avoid 'extraordinary wishful claims', but search for theoretical motivations and analytical foundations. Naturally,explainable machine learning is an way forward.
Pattern Recognition, Machine Learning and Data Analysis (FEO3274), examiner, course responsible, teacher | Course web