Engineer Data

Ming Hao

adb50n@r.postjobfree.com 650-***-****

Palo Alto, CA

https://www.linkedin.com/in/ming-hao-423611116

SUMMARY

A Master Engineer and Principal Data Scientist, with 10+ years extensive experience in big data, machine learning, and visualization. Led real-time very large scale event-based distributed cloud computing software systems to roadmap, architect, and develop next generation scalable platforms for business intelligence and decision making. A recognized technical contributor in deriving new machine learning algorithms and building models for HP SaaS and security products, and ConDati campaign forecasting; received high honors in technical achievements and over 30 patent awards, IEEE papers. PROFESSIONAL EXPERIENCE

ConDati Inc. Palo Alto, CA (2018-Present) Principal Data Scientist

• Anomaly Detection (TensorFlow deep learning, automated visual search on the web) Created live “Consecutive Anomaly Auto-Detection” dashboard from large revenue data streams on a cloud platform. Built learning models for better forecasting. Customers are able to catch sequence of abnormal events.

• Funnel Process (Statistic models, optimized B2B sales pipeline) Built a real-time “Multi-Stage, Multi-Scale, and Auto-Linking” pipeline to visualize live business deals moving from a “new deal” to a “closed won”. This technique of optimizing B2B sales process time translates directly into more sales and more revenue. HP Inc. Immersive Experience Lab, Palo Alto, CA (2015-2016) Master Engineer

• Smart Office (Hadoop, access millions of IoT sensors across thousands of offices) Invented “Exception Bands” to optimize and predict energy consumption (temperature, lighting, etc.) Created learning models on a large IoT time series. Energy savings over 15%. HP System Analysis Lab, Palo Alto, CA (2002-2015) Senior Scientist

• Auto Security Threat Detection (Streaming millions IPs/sec on a Hadoop/Spark platform) Achieved “automated Threat Detection” to discover millions malicious IPs (with 300+ network features) in HP “Cyber Defense Center”. Multi-Dimensional Scaling and Distance Matrix were used to search known-threats with similar behaviors (Port Scan, C&C servers, Botnets). Hierarchical DBSCAN and Classification Neural Networks were explored to find unknown threats.

• Live Text Data Streaming (Amazon Web Services, ~250k tweets/min, NLP) Employed AWS for reading live data streams from customer reviews (tweets). Individual tweets are combined as topics for real-time detecting critical issues. The result of this text mining was integrated into HP Vertica’s sentiment analysis software product.

• Predictive and Risk Analysis (Achieved ~80% accuracy) Created new prediction algorithm: “Peak Preserving” to optimize SaaS customers (Adidas, Airbus, Goodyear) workflow without exceeding capacity. Also, provided risk analysis for HP Supply Chain to make business decisions.

• Visual Analytics Dashboard (Iterative, Reveal insights of data) Combined statistics, machine learning with visualization and human knowledge to identify patterns and outliers. Customers are: Visa (fraud detection), SouthWeston (oil/gas production), Healthcare (adverse drug findings), and HP Data Centers (resource management). SYSTEMS AND LANGUAGE

• Machine Learning, Forecast, Optimization, Visualization, ETL, RESTful Service, Cloud Platform, Business Intelligence, IOT.

• Python, Julia, Java Script, AngularJS, CSS, D3js, R, HTML5, SQL, NOSQL, Hadoop, Spark. EDUCATION

Master degree in Applied Mathematics, City University of New York. Honors, Awards, & Publications

12 Technical Achievement Awards, 3 Innovation Awards, over 30 IEEE papers and US patents. Selected Published Papers

- [HKDS06] M. C. Hao, Daniel Keim, Umeshwar Dayal, Jorn Schneidewind. “Business Process Impact Visualization and Anomaly Detection” Information Visualization Journal.2006

- [HDKL07] M. C. Hao, Umeshwar Dayal, Daniel Keim, Martha Lyons. “Value-Cell Bar Charts for Visualizing Large Transaction Data Set” IEEE Transaction on Visualization and Graphics Journal. 12/2007.

- [RHUHK10] Christian Rohrdantz, Ming C. Hao, Umeshwar Dayal, Lars-Erik Haug. “Feature-based Visual Sentiment Analysis of Text Document Streams”. ACM Transactions on Intelligent System and Technology Journal, 6/2010.

- [HJMHDMS11] M. C. Hao, H. Janetzko, S. Mittelstädt, W. Hill, U. Dayal, D. A. Keim, M. Manish, and R. K. Sharma. “A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series”. In IEEE EuroVis11. 6/2011 and a special issue of the journal Computer Graphics Forum (CGF).

- [HSDP08] Ming C. Hao, Ratnesh K. Sharma, Umeshwar Dayal, Chandrakant Patel. “Visual Monitoring of Temperature Data in a Smart Data Center”. IEEE Information Visualization Symposium, 10/20/2008. Selected US Patents

- Submitted US Patent Application on “System and Method for Automatic Visual Recognition of Consecutive Anomalies in Large Campaign Forecast Data”. 2019

- US20140267290 - Visual Analytics of Multivariate Session Data Using Concentric Rings 2014

- US8773436 Pixel Charts with Data Dependent Display Spaces 2014

- US8212817 Spatio-Temporal Visual Large Data Analysis 2012 (People-Centered)

- US20110029926 Distance Between Attributes 2011

- US20100103176 Non-Overlapping Scatter Plot 2010

- US5844553 - Mechanism to control and use window events

- US5828866 - Real-time synchronization concurrent views (People-Centered) Examples of my visualization work on the following page.

Contact this candidate