Subhadip Mukherjee

Work Experience

Assistant Professor Grade - I (May 2023 - Present): Department of Electronics and Electrical Communication Engineering, IIT Kharagpur, India
Assistant Professor / Lecturer (Sep. 2022 - Apr. 2023): Department of Computer Science, University of Bath, UK
Research Associate (Jun. 2020 - Aug. 2022), Cambridge Image Analysis (CIA) group, Dept. of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, UK (with Carola-Bibiane Schönlieb)
Visiting Researcher (26 Aug. 2019 - 30 Sep. 2019) at the Dept. of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, UK
Postdoc (Aug. 2018 - May 2020), Dept. of Mathematics, Royal Institute of Technology (KTH), Sweden (with Ozan Öktem)

Education

Doctor of Philosophy (Ph.D.): (Aug. 2012 - Jul. 2018) Dept. of Electrical Engineering, Indian Institute of Science (IISc.), Bangalore, India
Master of Engineering (M.E.): (Aug. 2010 - Jul. 2012) Dept. of Electrical Communication Engineering, Indian Institute of Science (IISc.), Bangalore, India
Bachelor of Engineering (B.E.): (Jul. 2006 - Jun. 2010) Dept. of Electrical Engineering, Jadavpur University, Kolkata, India

Awards and Fellowships

SPCOM-2020 Doctoral Dissertation Award (Honorable Mention) for my Ph.D. Thesis on "Sparsity Driven Solutions to Linear and Quadratic Inverse Problems."
Recipient of the Tata Consultancy Services (TCS) Research Fellowship for Ph.D. (August 2013 - May 2018)
Prof. I. S. N. Murthy medal for the best Master's student in the Dept. of Electrical Communication Engineering, IISc., Bangalore (March 2013)
University medal for the 2nd position (in a class of ~100 students) in UG (December 2010).

Research Interests

My research interests primarily include areas at the intersection of inverse problems, statistical signal processing, machine learning, and optimization. More recently, I have been focusing on the applications of deep learning for solving inverse problems that frequently arise in medical imaging applications such as computed tomography, magnetic resonance imaging, etc. I take interest in designing novel deep neural network architectures and learning strategies that are particularly suited for image reconstruction problems. Although I primarily focus on medical imaging applications, the learning paradigms I develop often transcend the application at hand and apply to more general computer vision tasks. I am also keenly interested in developing robust and scalable optimization algorithms with theoretical guarantees for high-dimensional signal estimation problems with structural constraints such as sparsity and low-rank.

Talks/Posters (up to Ph.D.)

April 20, 2018: Presented poster in ICASSP-2018, Calgary, Canada (Poster)
December 14, 2017: Talk delivered in the TCS Innovations Lab, Bangalore (slides)
January 12, 2017: Prof. Seelamantula presented our work on deep sparse coding at the Brain, Computation, and Learning Workshop in IISc. (watch)
September 13, 2017: Ph.D. Thesis Colloquium (slides)
April 7, 2017: EECS-IISc. Divisional Symposium (slides) (poster)

Thesis/Reports

Ph.D. Thesis (pdf)
M.E. Thesis (pdf)