Backer Profile: Nima Nikzad, Viterbi Fellow at Scripps Translational Science Institute

Nima NikzadNima is currently the Viterbi Fellow of Digital Medicine at the Scripps Translational Science Institute, where he is kick-starting new research and engineering efforts related to the use of wearable and passive sensing devices in healthcare. As the first engineer to be working on mHealth related efforts at STSI, he works closely with a team of physicians and statisticians across STSI and the Scripps Health hospital system to develop new technologies that address the needs of patients and physicians. He believes that patient collected data, from outside the clinical setting, will play a major role in ensuring that the future of medical care is both scalable and affordable. Reaching that point will require advances in sensing technologies, user-context recognition, energy-management, and machine learning. His research focuses on the development of new sensing devices (particularly those related to air quality and respiratory health monitoring) and analytical techniques for turning the data collected by phones, wearables, and in-home devices into actionable information for physicians and researchers.

There is a great deal of variance in how individuals interact with technology and the world around them, making the evaluation of context-recognition systems, energy-saving optimizations, and other systems research extremely challenging. The large and diverse dataset, collected by real users in the field, will help us develop more accurate models of how people interact with technology, one another, and the world around them. In particular, the data will be extremely valuable in our dynamic energy-management work, which adjusts sensor and application behavior based on user-context, interaction, and device state.

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Featured Sponsor: TwoSense – Empowering Businesses and Individuals with Their Own Data

TwoSenseTwoSense was founded in 2013 by Dawud and Ulf, two researchers in the field of personal data analytics. They saw an imbalance in the quality and quantity of data that was available to large corporations, and the utility that was being offered to users from the majority of businesses they interact with.

They set out to close this gap by creating technology to give the user a data set of their own real-world and digital behavior, and the ability to get value and utility from it.

TwoSense’s mission is give users their personal data, help them get value and utility from it. They give users the tools they need to track themselves effortlessly, and the ability to share what they want with the businesses they interact with in perfect clarity. The result is better data for businesses to deliver value and utility to the user, and more control and transparency for the user.

TwoSense Features is a mobile data analytics company. Their unique experience and expertise allow them to develop and use embedded, hyper-efficient machine learning algorithms for data collection and fusion that run on the device. This approach allows them to reduce power consumption and network usage to a minimum, reducing the cost of ownership to the user. It also provides data availability on mobile in near real-time by cutting out the need for network API turnaround.

They are also experts in processing large data sets using cutting edge analytics algorithms and technologies. Their cloud engines employ deep convolutional networks combined with probabilistic models and methods to combine data across users and applications and create uniquely accurate and effective insight.

By leveraging their expertise and distributing the data processing pipeline across mobile and cloud, TwoSense creates a unique stack for highly-efficient data collection, processing, analysis and insight delivery in real time for our users and customer.


Backer Profile: Thomas Ploetz, Reader at Newcastle University

Thomas PloetzThomas Ploetz is a Reader at the Open Lab at Newcastle University. His research is focused on computational behaviour analysis, that is building and deploying (statistical) models that capture behavior and enable quantitative assessments of it. Behavior is thereby captured opportunistically, e.g., through wearable or ubiquitous sensing. He considers himself working as an applied machine learning researcher.

The central theme of Thomas’ research is to develop techniques and systems that actually have an impact on people’s life. Therefore, his research is almost always connected to some practical application (in contrast to purely theoretical work) and he is keen on deploying systems he develops in the “wild”, i.e., in real-world settings. The most prominent domain for this kind of work is health where he is working on computational assessments of behavioral phenotypes of, for example, Parkinson’s, Dementia, or Autism. Within the Digital Interaction group at Newcastle University he is involved in a number of projects that address these research themes from different angles.

Open Lab Newcastle University

Thomas intends to use mainly for unsupervised learning of behavior representations.

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Backer Profile: Rijurekha Sen, Post-doc At Max Planck Institute for Software

Rijurekha SenRijurekha Sen is a post-doctoral researcher at Max Planck Institute for Software Systems, Germany. She generally works with mobiles and sensors to build applications related to road traffic monitoring, energy measurement and targeted advertising in the retail sector. The technical skills involved in her kind of work are smartphone programming and embedded systems design, sensor data processing using applied machine learning, wireless networking to connect sensors and backend servers and data visualizations on static and interactive maps. There are some logistic skills involved in deploying the systems that she implements, in collaboration with government organizations or startups. Recently, she is working on mobile data privacy to see if cool mobile applications can be supported, even after ensuring data privacy! She is also interested in auditing mobile services and apps created by third parties to measure their functionality and privacy properties as an independent researcher.


She will explore the data primarily in the context of mobile privacy related research. Whether interesting mobile apps can be supported using encrypted data, needs real datasets to be evaluated. She is hoping that the dataset will fill that gap of easily available mobile and sensor data, which can be used to test such research hypotheses or prototype systems.

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Backer Profile: Takeshi Okadome, Professor at Kwansei Gakuin University

Takeshi OkadomeTakeshi Okadome is a Professor in the Department of Informatics at Kwansei Gakuin University. His group works on environmental media design, using various sensors to design a new media that represents a physical or “mind” state of a physical object in an environment, and using the media, they create various contents, in particular, web contents together with each person’s favorite objects.

They use sensor network, physical computing, and machine learning technologies to attempt to: (1) to establish a method for interpreting sensor data and verbalizing or visualizing them, (2) to interpolate human actions or real-world events that cannot be directly inferred from sensor data by mining the information on the web, (3) to design “environment media” that represent the verbalized or visualized data, and (4) to create web contents using the environment media. The group also works on recognition of daily living activities from sensor data using machine learning techniques.

Kwansei Gakuin University


Prof. Okadome will use the data to construct better recognition methods for daily living activities using Data and also test the methods using them.

Backer Profile: Jonathan Rubin, Research Scientist at PARC

Jonathan Rubin Jonathan is a Research Scientist at PARC, where his research focuses on the use of machine learning and deep learning in biomedical and physiological data. He is interested in the analysis of data from mobile and wearable devices for pervasive and ubiquitous health management purposes. He has investigated how mobile and wearable technology can be used within health condition management systems for disorders such as panic disorder, post-traumatic stress disorder and depressive disorder. He is particularly interested in the analysis of physiological data in order to infer information about psychological state. His research interests include affective computing, ubiquitous computing, artificial intelligence, machine learning, deep learning, and physiological data analysis. Jonathan holds a Ph.D. from the University of Auckland where his research focused on the use of artificial intelligence for strategy generation in computer games.

Jonathan will use the data that provides to improve and broaden the types of activity recognition that currently exist. In addition, he will use physiological data from wearable devices for affective computing purposes.

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On GitHub: