What we're about

Data Works MD consists of professionals, students, and enthusiasts living and working in the Maryland area that are interested in topics related to data science, data analytics, data products, software engineering, machine learning, and other data engineering topics.

Our monthly events feature presentations and discussions from local experts.

Our monthly newsletter features links to interesting articles, tutorials, and tools related to data science, analytics, and big data.

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

Resources

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Main website: https://dataworksmd.org

Sign up for the new monthly newsletter at http://news.dataworksmd.org

Join us on Slack at http://slack.dataworksmd.org

Newsletter Archive: http://archive.dataworksmd.org

Event Videos: http://videos.dataworksmd.org

Twitter: http://twitter.dataworksmd.org

Facebook: http://facebook.dataworksmd.org

LinkedIn: http://linkedin.dataworksmd.org

Partners

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Erias Ventures - https://www.eriasventures.com

Varen Technologies - http://www.varentech.com/

ClearEdge - http://clearedgeit.com/

Upcoming events (2)

Online: Using NLP to Find Signals of Mental Health Risk

Online event

Powerful tools such as natural language processing and machine learning are helping to reshape the medical industry including in the area of mental health. Join us in January to learn how technology is helping professionals to save lives.

Agenda
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6:00 PM -- Greetings

6:05 PM -- Using Natural Language Processing to Find Signals of Mental Health Risk -- Philip Resnik

7:30 PM -- Closings

Location
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Zoom and YouTube Streaming
A link will be sent out prior to the event. Please note that Zoom is capped at 100, so if you do not get into Zoom, you will be able to watch via YouTube.

Talks
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Using Natural Language Processing to Find Signals of Mental Health Risk
The toll that mental illness takes worldwide is enormous, and in the U.S. in 2016 suicide became the second leading cause of death among those aged 10-34 and the fourth among those aged 35-54. Compounding these existing problems is an “echo pandemic” in the wake of COVID-19, as people have struggled with isolation, stress, and sustained disruptions of day to day life. This talk will look at opportunities and challenges in applying natural language processing and machine learning approaches to assess suicide risk and in computational research on mental health more generally. This will include advocating for a shift in emphasis from binary classification to prioritization under limited-resource constraints, as well as a discussion of secure data enclaves as a practical way to make technological collaborations possible with sensitive data.

Speakers
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Philip Resnik is Professor at University of Maryland in the Department of Linguistics and Institute for Advanced Computer Studies. He earned his bachelor's in Computer Science at Harvard and his PhD in Computer and Information Science at the University of Pennsylvania, and does research in computational linguistics. Prior to joining UMD, he was an associate scientist at BBN, a graduate summer intern at IBM T.J. Watson Research Center (subsequently awarded an IBM Graduate Fellowship) while at UPenn, and a research scientist at Sun Microsystems Laboratories. Resnik was named a Fellow of the Association for Computational Linguistics in 2020. His most recent research focus has been in computational social science, with an emphasis on connecting the signal available in people's language use with underlying mental state -- this has applications in computational political science, particularly in connection with ideology and framing, and in mental health, focusing on the ways that linguistic behavior may help to identify and monitor depression, suicidality, and schizophrenia. He has also been ramping up a new area of research in the computational cognitive neuroscience of language using brain imaging data, with a focus on the role of contextual prediction in sentence understanding.

Outside his academic research, Resnik has been a technical co-founder of CodeRyte (NLP for electronic health records, acquired by 3M in 2012), and he is an advisor to FiscalNote (machine learning and analytics for government relations), SoloSegment (web site search and content optimization), Converseon (social strategy and analytics), and the Coleridge Initiatlve (nonprofit focused on effective use of data for public decision-making).

DAX 2022 - Data | Analytics | Exploration

Online event

After much discussion, we've decided to postpone DAX until Spring 2022. While registrations were increasing, we still fell short of the needed speakers for the event and had always hoped that the first conference would be in-person. We plan on pushing hard the next few months to make our first event a success. We still need help spreading the word and bringing the community together. If you would like to advocate for the conference, please reach out!

The actual date of the conference is not yet set. A placeholder date was listed for now.

The DAX Conference will focus on data science, analytics, and general data exploration. Engineers, data scientists, analytic developers, system architects, and business leaders are encouraged to share their experiences and present a topic that would be of interest to the local data community. Expected attendees include engineers, thought leaders, business leaders, and professionals from local government, government defense and intelligence agencies, start-up companies, large data analytic and data science companies, and local universities.

More information is available at https://daxconf.org

Registration
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DAX 2022 registration will be free and available soon on https://daxconf.org

Call for Speakers
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We are looking for presentations that cover any of the following topics:

- Use cases from industry and government utilizing machine learning, artificial intelligence, and data science
- Analytic successes demonstrating technologies and innovation
- Tools and techniques used for data pipelines, egress, and storage
- Data exploration with visualization, data journalism, and interactive reporting

Location
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The location has not yet been set.

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