4 ANU Grand Challenge PhD Scholarships in Computer Science: Our Health in Our Hands
Data from smartphones and new, miniaturised sensors hold the potential for early detection and regular monitoring of disease in an affordable, scalable, noninvasive, patient-friendly manner in the comfort of the patient’s own home. However, human subjects’ privacy-sensitive medical information are a prerequisite for conducting these big-data analytics experiments; without them, developing intelligent, domain-tailored technology solutions and evaluating their quality and practical impacts is impossible.
The advertised PhD projects are part of a larger research challenge that proposes to design, develop, and evaluate innovative solutions to safeguard the patient information associated with a new generation of medical devices. Our aim in the “Our Health in Our Hands” (OHIOH) Grand Challenge is to combine our team’s knowledge of the clinical environment and systems in which emerging mobile diagnostic technologies are utilised, with cyber-physical security expertise, to establish and evaluate integrated security measures, assess these as trustworthy and compliant with the relevant standards and constraints.
These future technologies by OHIOH target diabetes and Multiple Sclerosis (MS) through wearable technologies, portable devices, and their machine learning (ML)-based data analytics will mean patients receiving earlier diagnoses, enhanced disease management, and precision therapy regardless of their geographical location or social circumstances. They hold the potential for early detection and regular monitoring of diseases in a scalable, patient-friendly manner.
Up to four PhD scholarships are available for December 2018-January 2019 start to contribute to software engineering and databases related to machine learning (ML) and cybersecurity as keys to unlock big multi-modal time-series data.
The following topics for PhD research plans are called for:
Topic 1: testing the hypothesis that multimodal time-series data can reveal early indicators of disease, details to differentiate disease subtypes, information on disease progression, and hidden knowledge about prognosis
Goal: Test the hypothesis that multimodal time-series data can reveal early indicators of disease, details to differentiate disease subtypes, information on disease progression, and hidden knowledge about prognosis by focusing on
1) feature engineering, extraction, and selection and making the related ML more and more elegant by simplifying data sources and their feature representations for ML. This field of ML is known as dimensionality reduction and/or
2) designing, developing, and evaluating methods for ML and their evaluation. This field of ML is known as multi-modal pattern analysis.
Topic 2: Linking the rich variety of data sources together to study the contribution of each source to the usefulness (e.g., correctness and cost of data processing) and usability (patients’ perception of trustworthiness and ease of use) of our ML system
Goal: Link the rich variety of data sources together to study the contribution of each source to the usefulness (e.g., correctness and cost of data processing) and usability (patients’ perception of trustworthiness and ease of use) of our ML system with the focus on the intersection of ML, sensor technologies, and/or record linking. We will be using two complementary strategies to solve this record linkage problem with respect to geographic location, time, and person’s identity as follows: First, a novel approach of integrating all sensing into a single device (i.e., the individual’s smartphone with appropriate accessories) that circumvents the need for record linkage. Second, linking these recordings with existing environmental and other databases in a way that is sufficiently accurate, secure, and ethical (e.g., data subject’s informed consent for data gathering and use).
Topic 3: Building cyber-physical security into this system and assessing it as trustworthy and compliant with the relevant standards and constraints
Goal: Build cyber-physical security into this system and assess it as trustworthy and compliant with the relevant standards and constraints by focusing on
1) technical aspects in designing, developing, and evaluating the proposed system and/or
2) socio-technical and information systems management aspects.
The device proposed in this project is, from the information collection and exchange perspective, is a cyber-physical system (CPS). This CPS is composed of both hardware components, such as sensors, actuators, and embedded systems, and software products. Notably, the heterogeneity of hardware and software components have introduced significant difficulties to security and privacy protection of the CPS. With the complex cyber-physical interactions, threats and vulnerabilities becomes difficult to assess, and new security issues arise. Also, it becomes difficult to identify, trace and examine the attacks, which may originate from, move between, and target at multiple CPS components.
Students will need to have strong programming skills, an interest in (and preferably some experience with) physiological data and medical applications, and a strong ability to work in teams, including good communication skills. Data analysis and experimental design skills are also required. A track record of app development and/or technical skills in working with integrated devices would be an advantage. There is the potential for commercial applications arising from this project, so IP agreements will be essential.
To express your interest in these PhD scholarship, please email the following documents to Adj/Prof Hanna Suominen (email@example.com) by 20 November 2018 (extended from 11 November 2018):
• A current CV
• A research proposal (max 2 pages)
• Colour copies of all transcripts and completion certificates of prior study, in original language and official English translations
• Contact details for 3 referees.
Further information about the PhD scholarships
See http://www.anu.edu.au/students/scholarships-fees/scholarships/anu-phd-scholarships for further information about the generous stipend (approx. 27,100 AUD per annum in 2018) and fee-waiver in The ANU.
See https://cecs.anu.edu.au/study/phd-mphil for general information about PhD Scholarships in The ANU in computer science.
Please do not hesitate to contact the following academics for further information:
- Project Leader: Adj/Prof Hanna Suominen (machine learning, health informatics, RSCS, CECS)
- Dr Deborah Apthorp (sensory systems, signal processing, RSCS (adj), CECS),
- Dr Nan Yang (cyber-physical security, signal processing, RSEng, CECS), and
- Dr Uwe Zimmer (signal processing, intelligent robotics, RSCS, CECS).
Background literature about the OHIOH grand challenge
OHIOH grand challenge description: http://www.anu.edu.au/research/our-health-in-our-hands
ANU Grand Challenges Scheme: http://www.anu.edu.au/news/all-news/vcs-update-grand-challenges-scheme-%E2%80%93-winning-team-announced
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Artificial Intelligence, Big Data, Cyber-physical Security, Databases, Data Mining, Diabetes, Machine Learning, Multiple Sclerosis, Signal Processing