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Data Works MD is a monthly gathering of professionals, students, and enthusiasts living and working in the Maryland area that come together to discuss diverse topics related to data science, data analytics, data products, software engineering, machine learning and other data engineering topics.

Each event includes time to network with other members, sponsors and partners.

If you are interested in speaking at a future event, becoming a Data Works MD partner, or have any suggestions or comments, please email info@dataworksmd.org


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Upcoming events (2)

Data Science Student Showcase: Towson Edition

University Union

Please join us in May as we visit Towson University to hear from the next generation of data science superstars. We will be featuring several talks on a range of topics from the students of Towson University. Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Student Showcase 8:45 PM -- Conclusion Location ------------------------------------------------- Towson University Union, Chesapeake Room Cross Campus Dr, Towson, MD 21204 Directions ------------------------------------------------- The event will be in the Chesapeake Room at the Union. More information can be found here: https://www.towson.edu/campus/landmarks/union/ Parking ------------------------------------------------- Parking can be found at the University Union Garage. Please refer to the campus map here: https://www.towson.edu/maps/ Food and Drinks ------------------------------------------------- Complimentary food, such as pizza and chips, and non-alcoholic beverages will be provided. Talks ------------------------------------------------- Talk: Elasticsearch for Business Intelligence and Application Insights Speaker: Sean Donnelly Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights. Talk: Machine Learning for Requirements Engineering Speaker: Jon Patton This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering. Talk: An Asynchronous Distributed Deep Learning Based Intrusion Detection System for IoT Devices Speaker: Pu Tian Intrusion Detection Systems (IDS) in IoT devices are crucial for cybersecurity. Existing models may fail due to increased traffic pattern complexity and data complexity. To address these challenges, we propose an asynchronous distributed deep learning based IDS in which only training weights are shared and devices of heterogeneous computing power can train asynchronously. Empirical results on a large network intrusion dataset show that the system achieves high detection accuracy. Talk: Fortune 500 Company Performance Analysis Using Social Networks Speaker: Yi-Shan Shir This presentation focus on studying the correlation between financial performance and social media relationship and behavior of Fortune 500 companies. The findings from this research can assist in the prediction of Fortune 500 stock performance based on a number of social network analysis metrics.

Hard to Read: Recognizing Text with Neural Networks in Unconstrained Settings

Please join us in June as we learn how neural networks are being used to improve Optical Character Recognition. Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Multilingual Optical Character Recognition (OCR) in Unconstrained Image and Video - David Etter 9:00 PM -- Post event drinks at a nearby bar Location ------------------------------------------------- JHU APL Building[masked] Johns Hopkins Rd Laurel, MD 20723 Directions ------------------------------------------------- Building 200 is on the South campus. Parking ------------------------------------------------- There is ample free parking near the building. Food and Drinks ------------------------------------------------- Complimentary food, such as pizza and chips, and non-alcoholic beverages will be provided. Talks ------------------------------------------------- Multilingual Optical Character Recognition (OCR) in Unconstrained Image and Video Optical Character Recognition (OCR) is the task of detecting and recognizing text in images or video. While current OCR systems perform well on tasks such as scanned books or newspapers these systems begin to break down in unconstrained settings. Unconstrained setting include video or images from maps, forms, web pages, and social media. This challenging setting is often multilingual and can include text over complex backgrounds, multiple fonts, lighting changes, and occlusions. In this talk we will discuss state-of-the-art neural network architectures for training models on the unconstrained OCR problem. We will also discuss approaches for synthetic training data generation that promises unlimited training data at zero annotation cost. The talk will include a detailed code walk-through of a PyTorch neural network solution to train and evaluate a multilingual OCR system. Speakers ------------------------------------------------- David Etter David Etter is a Principal Machine Learning Scientist with over 24 years of experience researching and developing large scale multimedia solutions for government and industry. His research experience includes Computer Vision, Natural Language Processing (NLP), and Large Scale Retrieval. David graduated from George Mason University in 2015, with a PhD in Computer Science, where his dissertation focused on multimedia search and ranking. He is currently researching large scale solutions for Face Recognition and Optical Character Recognition (OCR) in unconstrained video and image. David can be found on LinkedIn at https://www.linkedin.com/in/david-etter-3207665/

Past events (10)

Dark Web Data Science


Photos (35)