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.


Important dates related to Ph. D admission

  • Written Test (Online): November 17, 2023
  • 1st round of Interviews (Online): November 24, 2023
  • 2nd round of Interviews (on campus): November 29, 2023

Tentative Topics for PhD admission


Shortlisted candidates list for written test


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 Academic year (2023-24) - Spring semester visit: https://www.iitb.ac.in/newacadhome/phd.jsp


Eligibility Criteria

  1. M.E./M.Tech/MS degrees in Engineering/Technology
  2. MS (Master of Surgery) / MD / MVSc / MDS / MPTh/MPharm/M.O.Th
  3. MBBS, BDS, BPharm, BVSc, BPTh, BOTh (degree programme to be of 4 years or more) with 60 percent aggregate AND qualifying All India level post graduate 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/ (Bachelor’s degree in Engineering/Technology)/ 4 year BS (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 PhD brochure will be required.

The candidates with qualifying degrees as mentioned in ‘C, D & E’ must fulfill one of the following:

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

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
BB 603 Physiology for Engineers 6 Biosciences and Bioengineering
BB 607 Proteomics: Principles and Techniques 6 Biosciences and Bioengineering
BB 619 Mathematics for Biologists 6 Biosciences and Bioengineering
BB 624 Microfluidics: Physics and Applications 6 Biosciences and Bioengineering
BB 626 Modeling Biological Systems and Processes 6 Biosciences and Bioengineering
BB 627 Medical Imaging Methods 3 Biosciences and Bioengineering
BB 633 Movement Neuroscience 6 Biosciences and Bioengineering
BB 640 Biologics 3 Biosciences and Bioengineering
BB 645 Drug design and development 3 Biosciences and Bioengineering
BB 656 Introduction to Mechanobiology 3 Biosciences and Bioengineering
BB 663 Medical Imaging Physics 3 Biosciences and Bioengineering
CL 662 Introduction to Computational Biology 6 Chemical Engineering
DH 301 Basic Epidemiology 6 Koita Centre for Digital Health
DH 803 Wearable Health Technologies 6 Koita Centre for Digital Health
ME 724 Essentials of Turbulence 6 Mechanical Engineering
ME 780 Biofluid Mechanics 6 Mechanical Engineering
TD 617 Healthtech Innovation and Design 6 Centre for Technology Alternatives for Rural Areas (CTARA)

Group 2: Healthcare informatics & analytics

Course Code Course Name Credits Department/Center
CS 419 Introduction to Machine Learning 6 Computer Science & Engineering
CS 631 Implementation Techniques for Relational Database Systems 6 Computer Science & Engineering
CS 663 Digital Image Processing 6 Computer Science & Engineering
CS 725 Foundations of Machine Learning 6 Computer Science & Engineering
CS 726 Advanced Machine Learning 6 Computer Science & Engineering
CS 736 Medical Image Computing 6 Computer Science & Engineering
CS 754 Advanced Image Processing 6 Computer Science & Engineering
CS 769 Optimization in Machine Learning 6 Computer Science & Engineering
DH 306 Healthcare Performance Metrics 6 Koita Centre for Digital Health
DH 308 Clinical Data Management 6 Koita Centre for Digital Health
DH 801 Biostatistics in Healthcare 6 Koita Centre for Digital Health
DS 303 Introduction to Machine Learning 6 Centre for Machine Intelligence and Data Science
EE 610 Image Processing 6 Electrical Engineering
EE 769 Introduction to Machine Learning 6 Electrical Engineering
GNR 652 Machine Learning for Remote Sensing 1 6 Centre of Studies in Resources Engineering
IE 501 Optimization Models 6 Industrial Engineering and Operations Research
IE 615 Data Analytics in Operations Research 6 Industrial Engineering and Operational Research
IE 643 Deep Learning Theory & Practice 6 Industrial Engineering and Operations Research
ME 781 Statistical Machine Learning and Data Mining 6 Mechanical Engineering
SI 422 Regression Analysis 8 Mathematics
SI 541 Statistical Epidemiology 6 Mathematics

Group 3: Health systems & Policy & Ethics

Course Code Course Name Credits Department/Center
DH 304 Economics of Health Care 6 Koita Centre for Digital Health
DH 802 Service Operations and Quality Management in Healthcare 6 Koita Centre for Digital Health
DH 899 Communication Skills 6 Koita Centre for Digital Health
ES 899/CM 899 Communication Skills 6
HS 633 Econometrics of Programme Evaluation 6 Humanities and Social Science
HS 638 Financial Econometrics 6 Humanities and Social Sciences
HS 426 Theory and Policy of Managerial Finance 6 Humanities and Social Sciences
IE 709 IEOR for Health Care 8 Industrial Engineering and Operational Research
PS 619 Health Policy: An Introduction 6 Ashank Desai Centre for Policy Studies
SOM 633 Quality Management 3 SJM School of Management

Group 4: R&D Project

Course Code Course Name Credits Department/Center
DH 307 R&D Project 6 Koita Centre for Digital Health

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.


Course Offered in Autumn 2023 - 24

Course Code Course Name Instructor Prerequisite
BB 663 (FH) Medical Imaging Physics Prof. Debjani Paul NA
BB 627 (SH) Medical Imaging Methods Prof. Hari Varma NA
BB 645 (FH) Drug Discovery and Development Prof. Ashutosh Kumar NA
BB 607 Proteomics: Principles and Techniques Prof. Sanjeeva Srivastava NA
BB 681 Physical Biology at Microscopic scale. Prof. Ambarish Kunwar NA
CS 663 Digital Image Processing Prof. Ajit Rajwade NA
CS 768 Learning with Graphs Prof. Abir De NA
DH 803 Wearable Health Technologies Dr. Nirmal Punjabi NA
DH 301 Basic Epidemiology Prof. Ganesh Ramakrishnan, Dr. Kalyani Addya & Dr. Sandip Mandal NA
DH 302 Introduction to Public Health Informatics Prof. Kshitij Jadhav NA
DH 307 R&D Project Prof. Ganesh Ramakrishnan NA
ES 601 Environmental Health and Safety Harish C. Phuleria NA
ES 899/CM 899 Communication Skills Harish C. Phuleria NA
EE 782 Advanced Machine Learning Prof. Amit Sethi NA
GNR 650 Advanced topics in deep learning for image analysis Prof. Biplap Banerjee NA
Introduction: Yoga and Positive Psychology for Managing career abd life: (NPTEL course) Prof. Ashish Pandey NA