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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)

Detecting Noise: Using Weakly Supervised Algorithms with EEG Data

Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- Two Algorithms for Weakly Supervised Denoising of EEG Data - Tim Oates Location ------------------------------------------------- UMBC Seminar Room UMBC Technology Center, 1450 S Rolling Road, Halethorpe, MD 21227 https://www.google.com/maps/place/UMBC+Technology+Center/@39.232245,-76.716379,16z/data=!4m5!3m4!1s0x0:0xaab221e555b29819!8m2!3d39.2345392!4d-76.714056 Parking ------------------------------------------------- Once on UMBC's South Campus, continue to the building complex on the top of the hill. Turn left into the Visitor Parking lot just past the main entrance. The main building is across the street from the parking lot. Enter at the revolving glass doors at the main entrance. Talks ------------------------------------------------- Two Algorithms for Weakly Supervised Denoising of EEG Data Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However, these approaches are artifact specific. In this talk, I will discuss two algorithms for solving this problem that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. In the first algorithm, the EEG data is decomposed into independent components using Independent Components Analysis, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. The second algorithm is a novel Generative Adversarial Network (GAN) formulation. I’ll present empirical results on EEG data gathered by the Army Research Lab, and discuss pros and cons of both algorithms. Speakers ------------------------------------------------- Dr. Tim Oates is an Oros Family Professor in the Computer Science Department at the University of Maryland, Baltimore County. His Ph.D. from the University of Massachusetts Amherst was in the areas of artificial intelligence and machine learning with a focus on situated language learning. After working as a postdoctoral researcher in the MIT Artificial Intelligence Lab, he joined UMBC where he has taught extensively in core areas of Computer Science, including data structures, discrete math, compiler design, artificial intelligence, machine learning, and robotics. Dr. Oates has published more than 150 peer-reviewed papers in areas such as time series analysis, natural language processing, relational learning, and social media analysis. He has developed systems to determine operating room state from video streams, predict the need for blood transfusions and emergency surgery for traumatic brain injury patients based on vital signs data, detect seizures from scalp EEG, and find story chains (causal connections) joining news articles, among many others. Recently Dr. Oates served as the Chief Scientist of a Virgina-based startup where he developed architectures and algorithms for managing contact data, including entity linking, fuzzy record matching, and connected components on billion node graphs stored in a columnar database. He has extensive knowledge of machine learning algorithms, implementations, and usage. Dr. Oates can be contacted at [masked] Company ------------------------------------------------- Dr. Oates is the co-founder and Chief Data Scientist at synaptiq.ai, a data science consulting firm. https://www.synaptiq.ai

Accelerated Data Science: Analytic Pipelines with GPUs

The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. Join us in March to learn how you can utilize RAPIDS to accelerate your data science! Agenda ------------------------------------------------- 6:30 PM -- Networking & Food 7:00 PM -- Greetings 7:05 PM -- RAPIDS – Open GPU-accelerated Data Science - Corey J. Nolet & Adam Thompson Location ------------------------------------------------- Loyola University Room 210/[masked] McGaw Rd Columbia, MD 21045 Please proceed to the second floor once you enter the building. Parking ------------------------------------------------- There is ample free parking surrounding the building. Talks ------------------------------------------------- RAPIDS – Open GPU-accelerated Data Science RAPIDS is an initiative driven by NVIDIA to accelerate the complete end-to-end data science ecosystem with GPUs. It consists of several open source projects that expose familiar interfaces making it easy to accelerate the entire data science pipeline- from the ETL and data wrangling to feature engineering, statistical modeling, machine learning, and graph analysis. Speakers ------------------------------------------------- Corey J. Nolet Corey has a passion for understanding the world through the analysis of data. He is a developer on the RAPIDS open source project focused on accelerating machine learning algorithms with GPUs. Adam Thompson Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University. Projects ------------------------------------------------- More information about RAPIDS can be found at https://rapids.ai/ and https://developer.nvidia.com/rapids

Past events (6)

Machine Learning Live!

Praxis Engineering Technologies Inc

Photos (15)