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
- M.E./M.Tech/MS degrees in Engineering/Technology
- MS (Master of Surgery) / MD / MVSc / MDS / MPTh/MPharm/M.O.Th
- 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)
- 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)
- 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 |