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Inferring users' context from their smartphone data

Inferring users' context from their smartphone data. Speaker: Preeti Bhargava Host: Lori Pollock CRA-W Undergraduate Town Hall September 28 th , 2017. Logistics. This is the Q&A Box. Logistics. This is the Polling Section. Speaker & Moderator. Preeti Bhargava. Lori Pollock.

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Inferring users' context from their smartphone data

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  1. Inferring users' context from their smartphone data Speaker: Preeti Bhargava Host: Lori Pollock CRA-W Undergraduate Town Hall September 28th, 2017

  2. Logistics This is the Q&A Box

  3. Logistics This is the Polling Section

  4. Speaker & Moderator Preeti Bhargava Lori Pollock Dr. Lori Pollock is a Professor in Computer and Information Sciences at University of Delaware. Her current research focuses on program analysis for building better software maintenance tools, software testing, energy-efficient software and computer science education. Dr. Pollock is an ACM Distinguished Scientist and was awarded the University of Delaware’s Excellence in Teaching Award and the E.A. Trabant Award for Women’s Equity. Preeti Bhargava completed her PhD in Computer Science from University of Maryland, College Park in 2015. Her research interests include pervasive and ubiquitous computing, context-aware computing and systems, mobile systems and applications, user modeling, personalization, recommender systems, and Internet of Things. Preeti is the proud recipient of the UMD Ann G Wylie Dissertation Fellowship, the UMD Dean’s Fellowship, the Palantir Scholarship for Women in Technology and several conference travel grants. Prior to this, Preeti was a Senior Member Technical Staff at Oracle, in the development team for Oracle Web Services Manager. Preeti obtained her Bachelors of Engineering degree in Information Technology from Delhi College of Engineering, Delhi, India in 2007.

  5. About Me‡ Work Experience Education PhD Internships • Current: Senior Research Engineer, Data Science, Lithium Technologies | Klout (2016 – present) • Senior Member Technical Staff, Oracle India (2007 - 2010) • MS (2012) and PhD (2015), UMD, College Park • Advisor: Prof. Ashok Agrawala • BE (2007), Delhi College of Engineering • Xerox PARC (2013) • Samsung Research America (2014, 2015) ‡http://preetibhargava.info pretsbhargava@gmail.com

  6. About Me PhD Research Focus‡ Dissertation: Proactive Context-aware Computing and Systems • Mobile and Ubiquitous Systems • Locus (Mobiquitous’12, JLBS’15) • RoverII (UbiComp’ 12) • SenseMe (Mobiquitous’14, EAI Endorsed Tran. on CASA 2016) • TellMe (Mobiquitous’15) Personalization and Recommender Systems • User Interest Modeling from Facebook (IUI’15) • Multi-dimensional collaborative recommendations (WWW’15) • User Modeling • User Behavior Modeling from smartphone data collection (EAI Endorsed Tran. on CASA 2016) • Internet of Things • ThingTalk Systems Context-aware Systems Mobile Systems Internet of Things HCI UbiComp Context and Activity Recognition AI Machine Learning NLP AI Planning ‡Pertinent papers, posters, talks etc. available at http://preetibhargava.info

  7. Current Research Focus‡ Extracting rich information from noisy user generated text on social media • Densely Annotated Wikipedia Text (WWW 2017 workshop) • Entity Disambiguation and Linking (WWW 2017 workshop) • Lithium NLP (EMNLP 2017 workshop) • Twitter Sentiment analysis (ICDM 2017 workshop) AI Machine Learning NLP Data Science Big Data Big data modeling Big data mining ‡Pertinent papers, posters, talks etc. available at http://preetibhargava.info

  8. Inferring users' context from their smartphone data Preeti Bhargava Senior Research Engineer, Data Science Lithium Technologies | Klout

  9. Context and its dimensions • “Any information that can be used to characterize the situation of an entity.” • Multiple dimensions of user’s context: • Who is the user? What do we know about him? Preferences/Interests/Demographics/Mood • Where is the user? – Location • What is the user doing? - Activity • When? – Time • Who is the user with? - People around him Image source: http://myparadigmshift.org/wp-content/uploads/2013/04/who-what-where-when-why.png

  10. Modeling users' context from their smartphone data Smartphones – ubiquitous and powerful • Multitude of sensors - GPS, accelerometer, WiFi and cellular radio, gyroscope, camera, microphone etc. • Come equipped with an increasing range of computational, storage and communication capabilities • Can be used to: • infer several dimensions of user’s context • deliver information to users • Current talk will focus on 2 dimensions – location and activities

  11. Poll question 1 How many sensors can you count on your smartphone? • 0 – 5 • 5 – 10 • 10 – 15 • 15 – 20 • More than 20

  12. Poll question 1 How many sensors can you count on your smartphone? Image source: http://www.technologyace.com/technology/types-sensors-modern-smartphones/ (2013)

  13. Modeling users' context from their smartphone data (contd.) Where is the user? Location • Outdoor localization – GPS • Indoor localization – Wi-Fi, Bluetooth, RFID, NFC • Alternative technologies exist but still several challenges • Low cost of deployment and maintenance • Accuracy vs Calibration effort tradeoff • Robustness to environmental changes • Multi-story environments - Floor determination

  14. Indoor localization Selected existing approaches and their limitations • Wi-Fi Fingerprinting – RADAR (2000), Horus (2003) • Very accurate but… • Requires Wi-Fi Radio map calibration effort, • Expensive to set up and maintain, • Not robust to environmental changes • Bluetooth bases solutions (iBeacon) • Need proprietary hardware • Some works on Floor determination • User input (Active Campus (2002) , FTrack (2012)) • Low accuracy GSM fingerprinting (Skyloc (2007))

  15. Indoor localization (contd.) My research work - Locus ‡ • Calibration free, minimal set up, robust, room level accuracy • Floor and location determination on the floor in multi-story buildings • Uses knowledge of infrastructure – buildings, AP locations, room boundaries • Deployed and tested on UMD campus (~220 buildings with ~4500 APs) • Designed to enable several LBS such as indoor navigation and tracking in medical emergency scenarios ‡ P. Bhargava, S.Krishnamoorthy, A.K.Nakshathri, M. Mah, A. Agrawala, Locus: An indoor localization, tracking and navigation system for multi-story buildings using heuristics derived from Wi-Fi signal strength,  MobiQuitous 2012 ‡P. Bhargava, S. Krishnamoorthy, A. Shrivastava, A.K. Nakshathri,  M. Mah, A. Agrawala,Locus: Robust and Calibration-free Indoor Localization, Tracking and Navigation for Multi-story Buildings, Journal of Location based Services, 2015

  16. Indoor localization (contd.) Locus System High Level Overview

  17. Indoor localization (contd.) Locus Results and Benefits • Average Floor accuracy (% of correct floor estimations) > 95% • Average Euclidean Location Error < 6.5m (Room level accuracy) • One of the first calibration-free systems for floor and location determination in multi-story buildings • Minimum setup, deployment and maintenance expenses • Readily deployable • Robust to environmental changes • Relies on existing infrastructure and mobile device capabilities • Scalable to buildings with any number of floors • Low software and hardware complexity • Designed to support multiple indoor location based context-aware applications

  18. Indoor localization (contd.) Applications of indoor localization systems • Indoor Navigation • Retail – coupons based on proximity • Health care • Emergency scenarios • Tracking patients in a hospital • Can you think of any?

  19. Poll question 2 Another application of such a localization system would be to share your location with your friends on a social network. Would you use it? • Yes • No • Maybe • Unsure

  20. Modeling users' context from their smartphone data (contd.) What is the user doing? Activity Recognition • In addition to location, context or situation of the user includes several dimensions - activities, environment, people around him • Challenges in multi-dimensional context and activity recognition : • automated - embedded in ubiquitous devices • robust • power efficient • non-invasive manner • accurate • scalable • privacy preserving …

  21. Context and Activity Recognition Selected existing approaches and limitations • Environmental context (Indoor/Outdoor detection) • IODetector • uses light and magnetic field sensors, and cell tower signals • dependency on device manufacturer • sensor output varies with time of the day and weather • Physical Activity Recognition • CenceMe and Jigsaw • Latency and privacy challenges due to backend server • Some calibration required for accelerometer (gait, position, orientation)s

  22. Context and Activity Recognition(contd.) Existing approaches and limitations (Not exhaustive) • Social Context Recognition • SenceMe – bluetooth and location sharing • Privacy invasion • Device Activity Recognition • MFU, MRU apps

  23. Context and Activity Recognition (contd.) My research - SenseMe ‡ • SenseMe – On-device system that recognizes 5 dimensions of user’s context: ‡ P. Bhargava, N. Gramsky, A. Agrawala, SenseMe: A System for Continuous, On-Device, and Multi-dimensional Context and Activity Recognition, MobiQuitous 2014

  24. Context and Activity Recognition (contd.) SenseMe architecture <Indoor; Stationary; Phone Call; A.V. Williams Building, College Park; With 4 people>

  25. Context and Activity Recognition (contd.) SenseMeVis Context-aware location Environmental and social context Device Activity Physical Activity

  26. Context and Activity Recognition (contd.) SenseMe results

  27. Context and Activity Recognition (contd.) SenseMe advantages • Uses power conservation techniques - Suppression, Piggybacking and Adaptation to duty cycle GPS • Calibration-free - Uses techniques that are agnostic to orientation, body position, time, weather etc. , • Scalable – tested with users having varied schedules and mobility patterns • Device independent and universally applicable • Minimum latency and Privacy preserving - All computation and processing carried out on device • Non-invasive - runs in the background to collect and process user’s data without the need for any intervention.

  28. Poll question 3 SenseMe is an on-device system. But if a similar system stored all your data on a server/cloud, would you use it? • Yes • No • Maybe • Unsure

  29. Questions?

  30. Graduate school application and admission process - How to go from CS undergraduate to a PhD program? What does graduate school look like for CS? Speaker: Preeti Bhargava Host: Lori Pollock

  31. Getting involved in undergraduate research Summary: • Excellent UTH on Dec 1st 2016 by Katherine Sittig-Boyd • Apply to CREU, DREU (CRA-W) programs in USA, DAAD in Europe • Email professors • Intern and try to publish your research • Attend conferences – • Grace Hopper Conference research track • Lots of labs/companies in the career fair • Maintain an updated webpage/portfolio

  32. Getting involved in undergraduate research (contd.) Benefits : • You realize whether you like research • Gives you an edge when applying for graduate programs – demonstrates ability to conduct independent research • Publications • Recommendations from professors/supervisors

  33. Poll question 4 Have you undertaken any independent research as an undergraduate? • Yes, as part of an industry internship • Yes, at a research lab at my school • Yes, as part of my undergraduate curriculum • Yes, over a summer through an external program (CREU/DREU/any other) • No

  34. How to go from CS undergraduate to a PhD program? (contd.) Pick universities • USNews is a good source • Overall and Discipline specific rankings – AI, Systems, HCI etc. • Top 20-30 in your field (CS/EE) • Check out specific departments and professors • Shortlist about 10 schools • Distribute MS and PhD applications

  35. How to go from CS undergraduate to a PhD program? (contd.) Application materials* (Covered in detail in a previous UTH on July 14 2016 by Tanya Amert ) • General application • SOP • Recommendations • Transcripts • Test scores – GRE/TOEFL • CV • Fees *Resources for applying to graduate school: http://preetibhargava.info/gradschool

  36. What does CS graduate school look like? General Timeline • Year 1 - 2 : Finish your coursework, find a research topic and an advisor • Year 2 - 4 : Start your research and publish your work • Year 3 : Qualifying exam (some schools require it) • End of year 4 : Propose your thesis • Year 5 - 6: Finish your research • End of year 6 : Defend your thesis • Have a plan (A and B) for these ~6 years! • Disclaimers: • May vary across schools and departments • Very high level overview of milestones

  37. CS PhD - Key milestones Finding an advisor • Guide for the rest of your graduate school journey – choose wisely! • In your broad area of interest - read his/her papers • Conducive working atmosphere and relationship • Size of research group • Funding • Talk to other students

  38. CS PhD - Key milestones (contd.) Finding a research topic/problem • That you like and that you can contribute to • Remember – your thesis should be a novel and significant contribution to CS! • Read recently published papers – discuss with research group and advisor • Take courses relevant to your research • Attend conferences (find the top tier conferences in your area) • Tips: • Many professors and researchers maintain a calendar of upcoming conferences and deadlines • Search for conferences rankings and find the top tier ones

  39. CS PhD - Key milestones (contd.) Funding* • Apply for scholarships or fellowships at your school • Several government and private organizations and companies sponsor awards, scholarships and fellowships – NSF, DOE, Facebook, Microsoft, Google, IBM etc. • Writing grant proposals (with your advisor) – really helps if you want to pursue an academic career • Travel grants for conference attendance *List of scholarships, fellowships and travel grants: http://preetibhargava.info/resources-for-funding-grad-school

  40. CS PhD - Key milestones (contd.) Publishing your work • Write and publish papers - top tier conferences and journals • Professors usually have a minimum requirement for their students • Try to maintain a good cadence (~1-2 papers every year) • Less stressful • Network and collaborate with other researchers in your field • Find them at conferences • Follow their work

  41. CS PhD - Key milestones (contd.) Internships • Industry internships - Extremely useful for a career in industry • Apply to academic labs and schools • Try to find a project close to your PhD research • Publish your work – can be possibly included in dissertation! • 3 papers through PARC and SRA internships • International students in US: Make sure you take care of CPT/OPT requirements at school

  42. CS PhD - Key milestones (contd.) Thesis proposal • Formulate the problem that your research is addressing • Have a story that ties everything together • Propose your thesis – write it up and present to a committee!

  43. CS PhD - Key milestones (contd.) Defend and apply for jobs • Finish your thesis work • Start applying for jobs before you defend • Less stressful • 2-3 months or more on average • Academic – prepare your CV, research statement, go to the universities and present your work • Ask your advisor for guidance on where to apply for Post doc or assistant professor positions • Industry – prepare your CV, apply to the teams and companies that interest you, ask friends to refer you for open positions, use LinkedIn effectively, interview • References from advisor, internship mentors, professors

  44. Questions? Thank you! Email your questions or feedback to me at pretsbhargava@gmail.com

  45. Survey URL: http://bit.ly/2omKWfZ Feedback

  46. Mark Your Calendars! Join us February 8th at 5:00pm ET REGISTER TODAY High Performance Computing at Oak Ridge National Laboratory: Careers, Research Opportunities, and State-of-the-Art Technology Verónica G. Vergara Larrea HPC User Support Specialist/Programmer Verónica G. Vergara Larrea is originally from Quito, Ecuador. Verónica earned a B.A. in Mathematics/Physics at Reed College and a M.S. in Computational Science at Florida State University. Verónica has six years of experience in the high performance computing field and is currently working as an HPC User Support Specialist at the Oak Ridge Leadership Computing Facility. In addition to providing assistance to OLCF users, Verónica is part of the systems testing team and leads acceptance for Summit, ORNL's next generation supercomputer. Her research interests include high performance computing, large-scale system testing, and performance evaluation and optimization of scientific applications. Verónica is a member of both IEEE and ACM and serves in the ACM SIGHPC Executive Committee.

  47. Resources Visit CRA-W.org for more resources for all levels of your career Join our CRA-W mailing list, CRA-W Updates, by going to bit.ly/1McQCDd Follow @CRAWomen to find out about upcoming events or programs Don’t forget to take the feedback survey! PLEASE COMPLETE FEEDBACK SURVEY Survey URL: http://bit.ly/2omKWfZ

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