Saikat Chatterjee

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.

Collaborations

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.