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):
Interviews (Online) :
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 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
- 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.
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 |