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.


Featured Sponsor: Database, Data Mining, and Bioinformatics Lab – UC Santa Barbara

The Database, Data Mining, and Bioinformatics lab’s (DBL)
research focuses on network science, scalable querying, and mining of graphs, and bioinformatics.

Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical or engineered networks, information networks, biological networks, cognitive and semantic networks, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior.

DBL is developing methodologies, algorithms, and implementations needed for scalable, dynamic, and resilient networks. Specific problems include querying composite networks, modeling dynamic networks, sentiment analysis, analysis of content and user behavior, discovering unusual patterns, and sampling in composite networks.

DBL PeopleDBL will use the data for academic research, studying the dataset from a network-based modeling perspective.

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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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Featured Sponsor: Hiflylabs, Budapest

HiflylabsHiflylabs is a company that provides Business Intelligence, Consulting, and Customer Development services. Based in Budapest, Hungary, they create business value from data. Hifly carries out BI projects in many areas, from data mining and data warehousing to the solution of Big Data problems. To expand their activity, they established a mobile application development department, which has become a reliable element of our skill set. They have extensive experience with the tools of well-known data warehouse and BI technology vendors, and we also use new generation open-source solutions in their projects.

Marton Zimmer
Hiflylabs managing partner, Marton Zimmer.

The core team at Hiflylabs has been working together for 15 years, currently with more than 50 passionate employees: data analysts, data scientists and enthusiastic data ninjas. They have extensive experiences in working and managing multicultural projects and are keen on keeping their exceptional price/performance ratio on all projects.

Hifly will use the data experiment with applying Big Data technology to sensor data, checking performance and usability possibilities.

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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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