Ph.D. Program

Program Overview

Ph.D. Program in Digital Health is a program that provides an opportunity for students to fine-tune their informatics and data analytics skills as well as gain a firm grounding in the area of healthcare and its related domains. It gives students an opportunity to learn about biomedical ethics, to apply domain skills such as AI/ML in the hospital system as well as at the community level for public health informatics and in a broader sense for global health.

PhD topics for TAP seat

PhD topic for RA seat

Proposed Ph.D. topics for Spring 2023 admission


Ph.D. Entrance Sample Paper


IIT Bombay Ph.D. Information brochure

For important guidelines, important dates, eligibility criteria, admission process, fee structure or any other information related to Ph.D. courses at IIT Bombay, refer to IITB Information Brochure for Ph.D. admissions.

For information related to Ph.D. in Digital Health at Koita Centre for Digital Health, refer B27 on Page no. 48 of the brochure.


Apply for Ph.D. in Digital Health

To apply for admission for Ph.D. Program for the Academic year (2022-23) - Autumn Semester visit: https://www.iitb.ac.in/newacadhome/phd.jsp
Selection Process: Written-test and/or Interviews


Eligibility Criteria

As per norms for admission for Ph.D. program at IIT Bombay, students having first Class or 60% marks (55% marks for SC/ST), as specified in the General Eligibility Criterion, in the qualifying degree namely

  1. M.E./M.Tech/MS degrees in Chemical, Computer Sci. Engg., Electrical, Energy, Mechanical, Resources Engg., Material Science, Engineering Physics, Biomedical Engineering (or equivalent disciplines)
  2. MS (Master of Surgery)/MD/MVSc/MDS/MPTh/MPharm/M.O.Th
  3. MBBS, BDS, BPharm, BVSc, BPTh, BOTh (degree Program to be of 4 years or more) with 60 percent aggregate AND qualifying All India level postgraduate entrance examination for corresponding disciplines such as AIIMS/NEET-PG/MCI/JIPMER/ PGI Chandigarh/AFMC­Pune/ for MBBS/BDS or GPAT (for Pharmacy graduates) or a valid CSIR/UGC/DBT/ICMR JRF not linked to ICMR project (for FA any fellowship that will provide scholarship for 5 years
  4. M.Sc./ M.S./ Integrated BS-MS in Physical sciences (Physics, Chemistry or equivalent disciplines), M.A./M.Sc./Integrated BS-MS in Mathematical Sciences (Mathematics, Statistics, Biostatistics, Applied Statistics, Applied/Computational Mathematics or equivalent disciplines). M.Sc./ M.S. / Integrated BS-MS in Life sciences (specifically Biochemistry, Biophysics, Biotechnology, Physiology, Molecular Biology or equivalent disciplines)
  5. B.E./B. Tech/4 year BS (in Chemical, Computer Sci. Engg., Electrical, Mechanical, Engineering Physics, MEMS-Material Science & Metallurgy, Biomedical Engineering, Mathematics, Applied/Computational Mathematics, Economics, Statistics or equivalent disciplines), with eligibility subject to specified admission procedure, would be considered. A valid GATE score and other IITB norms as per IITB Ph.D. brochure will be required.

Additional requirement for C, D & E:
The candidates with qualifying degrees as mentioned in C, D & E’ must fulfil one of the following:

  • A valid GATE score
  • A valid CSIR/UGC/DBT/ICMR JRF not linked to the ICMR project (for FA any fellowship that will provide scholarship for 5 years)
  • Experience as specified in A.5 and A.6 for CT, EX, IS, PS, SF, SW category in the IITB Ph.D. Information Brochure.

Curriculum Structure and Credit Structure

A student undertaking the Ph.D. Program will be exposed to a broad range of theoretical and practical issues related to Healthcare Informatics. While the Program is interdisciplinary, courses and research will be organized around three broad tracks/baskets:

Group 1: Healthcare Standards, Clinical applications & Bio related courses

Course Code Course Name Credits Department/Center
CL 662 Introduction to Computational Biology 6 Chemical Engineering
BB 663 Medical Imaging Physics 3 Biosciences and Bioengineering
BB 627 Medical Imaging Methods 3 Biosciences and Bioengineering
BB 640 Biologics 3 Biosciences and Bioengineering
BB 603 Physiology for Engineers 6 Biosciences and Bioengineering
BB 626 Modeling Biological Systems and Processes 6 Biosciences and Bioengineering
BB 619 Mathematics for Biologists 6 Biosciences and Bioengineering
DH 301 Basic Epidemiology 6 Koita Centre for Digital Health
TD 617 Healthtech Innovation and Design 6 Centre for Technology Alternatives for Rural Areas (CTARA)
BB 607 Proteomics: Principles and Techniques 6 Biosciences and Bioengineering
BB 645 Drug design and development 3 Biosciences and Bioengineering
BB 656 Introduction to Mechanobiology 3 Biosciences and Bioengineering
BB 624 Microfluidics: Physics and Applications 6 Biosciences and Bioengineering
ME 780 Biofluid Mechanics 6 Mechanical Engineering
ME 410/724 Microfluidics 6 Mechanical Engineering
DH 803 Wearable Health Technologies 6 Koita Centre for Digital Health

Group 2: Healthcare informatics & analytics

Course Code Course Name Credits Department/Center
DH 801 Biostatistics in Healthcare 6 Koita Centre for Digital Health
EE 610 Image Processing 6 Electrical Engineering
SI 541 Statistical Epidemiology 6 Mathematics Department
SI 422 Regression Analysis 8 Mathematics Department
CS 736 Medical Image Computing 6 Computer science and Engineering
CS 419 Introduction to Machine Learning 6 Computer science and Engineering Department
EE 769 Introduction to Machine Learning 6 Electrical Engineering
CS 754 Advanced Image Processing 6 Computer Science and Engineering Department
CS 769 Optimization in ML 6 Computer Science and Engineering Department
IE 615 Data Analytics in Operations Research 6 Industrial Engineering and Operational Research
DH 306 Healthcare Performance Metrics 6 Koita Centre for Digital Health
DH308 Clinical Data Management 6 Koita Centre for Digital Health
CS 663 Digital Image Processing 6 Computer Science Engineering Department
EE 610 Image Processing 6 Electrical Engineering
CS 769 Optimization in Machine Learning 6 Computer Science Engineering Department
CS 725 Foundations of Machine Learning 6 Computer Science Engineering Department
CS 726 Advanced Machine Learning 6 Computer Science Engineering Department
ME 781 Statistical Machine Learning and Data Mining 6 Mechanical Engineering
CS 631 Database Management Systems 6 Computer Science Engineering Department
IE 501 Optimization 6 Industrial Engineering and Operations Research
IE 643 Deep Learning 6 Industrial Engineering and Operations Research

Group 3: Health systems & Policy & Ethics

The course structure and credit structure for the Ph.D. Program in Digital Health is given in the following sections.

Students with MS/M.Tech or equivalent (categories A & B in eligibility criteria):

Credit requirements Credit Structure
Minimum 16 credits and Maximum 22 credits required1 Communication skills + Credit seminar + as per recommendation from an adviser(s) - course on Introduction to Public Health informatics

Students with M.Sc. or equivalent (category D in eligibility criteria):

Credit requirements Credit Structure Course Structure
Minimum 34 credits and Maximum 46 credits required2 Communication skills + Credit seminars (up to 2) + as per recommendation from an adviser(s) - course on Introduction to Public Health informatics Students with a bio-background need to take at least one from Group 2. Any non-bio student needs to take at least one from Group 1/Group 3

Students with B.Tech or equivalent (categories C & E in eligibility criteria):

Credit requirements Credit Structure Course Structure
Minimum 44 credits and Maximum 56 credits required3 Communication skills + Credit seminars (up to 2) + as per recommendation from an adviser(s) - course on Introduction to Public Health informatics Students with a bio-background need to take at least one from Group 2. Any non-bio student needs to take at least one from Group 1/Group 3

1 R4.1 of https://www.iitb.ac.in/newacadhome/phdRules.pdf

2 R4.2 of https://www.iitb.ac.in/newacadhome/phdRules.pdf

3 R4.3 of https://www.iitb.ac.in/newacadhome/phdRules.pdf

Apart from the minimum credit requirement, additional credit requirements will be decided by a faculty advisory from the KCDH centre.

The newly proposed “Introduction to Public Health informatics” course will be a core mandatory course for all students who join the Ph.D. program. Those who have already credited an equivalent course in Public Health Informatics will be eligible for a waiver.


Courses offered in Spring 2022-23

Course Code Course Name Instructor Prerequisite
BB 640 Biologics Prof. Ashutosh Kumar NA
SL 422 Regression Analysis (8) Prof. Siuli Mukhopadhyay SI 427 (Exposure) (For students from other departments, instructor’s permission will be required)
HS 638 Financial Econometrics> Prof. Puja Padhi NA
ME6114 Joint Biomechanics Prof. Darshan Shah NA
BB 624 Microfluidics: Physics and Applications Prof. Debjani Paul NA
CS796 Optimization in Machine Learning Prof. Ganesh Ramakrishnan NA
SC 647 Topological Methods in Control and Data Science Prof. Debasish Chatterjee Systems theory at the level of SC625 / SC301 or equivalent is necessary
BB 625 Motor Control in Health and Disease Prof. Neela Kanekar NA
SOM 744 Multivariate "Data Analysis" Prof. Usha Ananthkumar NA
DH 899 Communication Skills> Dr. Kshitij Jadhav NA
DH 307 R&D Project Dr. Kshitij Jadhav NA
DH 308 Clinical Data Management Prof. Nirmal Punjabi NA
DH 304 Economics of Health Care Prof. Souvik Banerjee HS 101