Enamul Hoque's Email & Phone Number

💼 Past Experience

Enamul Hoque's work experiences:Data Engineer at GoogleEnamul Hoque is currently a Data Engineer at Google in Mountain View, CA. I am working as a data engineer on the Google Earth team. I am responsible for developing and maintaining the Google Earth infrastructure, developing and maintaining the Google Earth Interface, and developing and maintaining the Google Earth product.Research Assistant at University of Virginia from January 2009 to May 2014 (5 years 5 months). In my PhD dissertation, I have designed, implemented and evaluated a novel activity recognition system named AALO, a comprehensive anomaly detection system in daily activities named Holmes, and a novel ground truth collection system named Vocal-Diary that can be used to evaluate both AALO and Holmes. AALO is an active learning based activity recognition system that applies machine learning and data mining techniques to address some of the realities of deployments including difficulty in obtaining labeled ground truth for training, individuals performing overlapping activities, and generalizability in varying smart home environments.As a research assistant, I am responsible for the following:1. Design and development of detection algorithms for the Google Earth main environment.2.Supervised learning algorithms for recognizing activity patterns from Google Earth records.3.Evaluation and improvement of Google Earth product.

🎓 Education

Enamul Hoque holds a PhD from the University of Virginia in Computer Science, and a Bachelor of Science from the Bangladesh University of Engineering and Technology in Computer Science. In addition, he has Polish and French passports.Hoque's education and career options were primarily focused on computer science, which he received his PhD from the University of Virginia in 2008. He then worked at the University of Iowa from 2009 to 2014, where he served as a professor of computer science. Between 2008 and 2014, he also held a postdoctoral fellowship at the University of Utah, where he worked on developing a machine learning algorithm.In his work at the University of Utah, Hoque developed a machine learning algorithm that was used to identify patterns in images. The algorithm was also able to identify changes in image brightness over time, which allowed him to discover aspects of images that were difficult to detect using other methods.Hoque is also a French passports holder, and he has been working on developing a machine learning algorithm that is used to identify patterns in photos taken in France. His current project is based on analyzing photos taken in Paris. He is also the co-founder of a startup that is developing a machine learning algorithm that is used to identify patterns in photos taken in other parts of the world.

💡 Technical & Interpersonal Skills

Enamul Hoque: I am the data engineer at Apple.I am currently a data engineer in Apple Maps to build scalable data pipelines and Machine Learning models based on large scale data (e.g., location, road network, user logs) to detect anomalies in Maps data automatically for improving data quality of the new Apple Maps. In March 2016, I worked as an algorithm engineer & tech lead at Huami, Inc. from March 2016 to June 2016 in Mountain View, CA. In December 2006, I worked as a research intern at Philips Research from May 2011 to August 2011 in Briarcliff, NY. I am a senior research engineer at Samsung Research America from August 2014 to February 2016 in San Francisco Bay Area. In January 2009, I trained as a research assistant at University of Virginia from January 2009 to May 2014. In my PhD dissertation, I am currently designing, implementating, and evaluating a novel activity recognition system named AALO, a comprehensive anomaly detection system in daily activities named Holmes, and a novel ground truth collection system named Vocal-Diary that can be used to evaluate both AALO and Holmes. My research goals are to improve data quality for Maps, to create scalable machine learning algorithms for detecting daily activities, and to develop activities recognition systems for various purposes including indoor localization and fraud prediction.