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.


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

Group 2: Healthcare informatics & analytics

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.

Group 1: Healthcare Standards, Clinical Applications & Healthcare Foundation
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 6 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.
Course Code Course Name Credits Department/Center
PS 619 Health Policy: An Introduction 6 Ashank Desai Centre for Policy Studies
IE 709 IEOR for Health Care 8 Industrial Engineering and Operational Research
HS 633 Econometrics of Programme Evaluation 6 Humanities and Social Science
DH 304 Economics of Health Care 6 Koita Centre for Digital Health
DH 302 Introduction to Public Health Informatics 6 Koita Centre for Digital Health
DH 899 Communication Skills 6 Koita Centre for Digital Health
DH 802 Service Operations and Quality Management in Healthcare 6 Koita Centre for Digital Health
ES 899/CM 899 Communication Skills 6
HS 633 Econometrics of Programme Evaluation 6 Humanities and Social Sciences
SOM 633 Quality Management 3 SJM School of Management
HS 638 Financial Econometrics 6 Humanities and Social Sciences
HS 426 Theory and Policy of Managerial Finance 6 Humanities and Social Sciences

Courses offered in Autumn 2022-23

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 603 Physiology for engineers Prof. V.P.Soni* + Prof. Nivethida T. NA
BB 619 Mathematics for Biologists Prof. Sushil Kumar NA
BB 645 (FH) Drug Discovery and Development Prof. Ashutosh Kumar NA
EE 610 Image processing Prof. Amit Sethi NA
SI 541 Statistical Epidemiology Prof. Siuli Mukhopadhyay NA
DH 803 Wearable Health Technologies Dr. Nirmal Punjabi NA
DH 302 Introduction to Public Health Informatics Prof. Ganesh Ramakrishnan NA
BB 640 Biologics Prof. Ashutosh Kumar NA
SI 422 Regression Analysis Prof. Siuli Mukhopadhyay SI 427 (Exposure) (For students from other departments, instructor’s permission will be required)
ES 899/CM 899: Communication Skills Harish C. Phuleria NA
HS 633 Econometrics of Programme Evaluation Prof. Rama Pal Background in statistic/ econometrics
SOM 633 Quality Management Prof. Indrajit Mukherjee Basic Statistics
HS 638 Financial Econometrics Prof. Puja Padhi NA
HS 426 Theory and Policy of Managerial Finance Prof. Puja Padhi Finance and Econometrics knowledge is necessary
CS 631 Database Management Systems Prof. Sudarshan Must have done a basic course in databases either as part of curriculum or online, which covers  relational databases, SQL, functional dependencies and normalization at a minimum, and read up on other material on your own.
IE501 Optimization Prof. KS Mallikarjuna Rao NA
MNT 802 Statistics for Management Research Prof. Indrajeet Mukharjee NA

The Course List

Will be updated soon