What we’re about
DataPhilly is a community run group for anyone interested in gaining insights from data. Topics include (but are not limited to) predictive analytics, applied machine learning, big data, data warehousing and data science. We <3 data!
Join our slack to help plan future events! http://bit.ly/DataPhillySlack
Email us at: admin[at]dataphilly.com
Found a space we can use for future meetups? https://goo.gl/Ru0eth
Found a speaker for an upcoming meetup? https://goo.gl/9DJxq0
Found a sponsor for our events? https://goo.gl/JLVfqh
Want to have access to the video recordings and details of our past events? https://dataphilly.github.io
Upcoming events (4+)
See all- DataPhilly Presents: Fast.AI Lesson 8!Link visible for attendees
Hi Data Enthusiasts,
Action Items (For those interested):
- We'll be discussing and going over a different video lecture in the Fast.AI lecture series. Start Watching Lesson 9 NOW!
- Please watch the video lecture mentioned in the title. We'll be helping people set up with Google Colab or Paperspace to run their Jupyter Notebooks if they haven't already and discussing any confusing bits.
- Join the DataPhilly Channel then find the #fastai-study-group
Logistics:
- Virtually: Here is the Meetup link:
https://meet.google.com/juj-swjy-cry - Unfortunately we can no longer do in person for this week.
- DataPhilly Presents: Fast.AI Lesson 8!Link visible for attendees
Hi Data Enthusiasts,
Action Items (For those interested):
- We'll be discussing and going over a different video lecture in the Fast.AI lecture series. Start Watching Lesson 9 NOW!
- Please watch the video lecture mentioned in the title. We'll be helping people set up with Google Colab or Paperspace to run their Jupyter Notebooks if they haven't already and discussing any confusing bits.
- Join the DataPhilly Channel then find the #fastai-study-group
Logistics:
- Virtually: Here is the Meetup link:
https://meet.google.com/juj-swjy-cry - Unfortunately we can no longer do in person for this week.
- May Data Talks: ML applications for scientific research and image recognition555 E North Ln bldg c ste 5050, Conshohocken, PA
Join us for a 2 hour in person event - May Data Talks at ZeroEyes!
📢🗣️Learn about Machine Learning applications: from the lab bench to the Synchrotron; how visual gun detection software helps to stop mass shootings and gun-related violence.
📅 Thursday May 23 at 6:00 - 8:00 pm
What to Expect:
📈 Two 45 min presentations from experts, followed by Q&A
📓 Get practical advice and best practices in Machine Learning
💡 Connect with other professionals, exchange ideas, & networkOur host is ZeroEyes https://zeroeyes.com/ - company that delivers a proactive, human-verified visual gun detection and situational awareness solution that integrates into existing digital security cameras to stop mass shootings and gun-related violence.
Speakers:
Dave Ramsey: Lessons Learned: A journey from jazz trumpet to Data Science without bootcamps or degrees in between. Rethinking how to learn and get work in the current tech industry.Bio:
LinkedIn
Dave is a jazz-trained freestyle rapper and barista who started programming in Python during the March 2020 COVID shutdown. He currently works as an R&D ML Ops Engineer at ZeroEyes, using data science to enhance computer vision models deployed to stop mass shootings. Dave holds a BA in Jazz Trumpet Performance from Temple U.Venkateswaran Shekar : A tale of scales: Machine learning from the lab bench to the Synchrotron
Abstract:
In this presentation, we will explore the transformative role of machine learning in scientific research, tracing its impact from small-scale university laboratories to large-scale national facilities. At Haverford College, under the auspices of a DARPA-funded initiative, we leveraged advanced ML techniques for high-throughput chemistry to accelerate the discovery of new perovskite materials. I will share insights gained from managing complex cheminformatics data but also the results of ML guided materials discovery.
Transitioning to a larger scale, the talk will delve into the challenges and innovations at a synchrotron facility, where the sheer volume of experimental data generated presents both opportunities and obstacles. Here, ML has been pivotal in managing and analyzing vast datasets, significantly boosting the facility’s capacity to derive meaningful insights rapidly. I will share firsthand experiences and developments from myself and my colleagues, highlighting techniques adapted to handle the unique demands of such a large-scale scientific environment.
Through this journey from the microscopic focus of a university lab to the expansive horizons of a synchrotron, we will shed light on how ML continues to be a critical tool in pushing the boundaries of what modern science can achieve.Bio:
LinkedIn
Venkateswaran Shekar is a distinguished Software Engineer and researcher with a rich background in machine learning, data science, and software development across multiple scientific domains. Holding a PhD in Computer Engineering from the University of Massachusetts, Dartmouth, his career is marked by innovative contributions to computational engineering and science.
At the University of Massachusetts, he led the development of an open-source tool for software error estimation, contributing to advances in software reliability. His research has been recognized in numerous peer-reviewed publications, addressing topics from dynamic traffic simulation for transportation network vulnerability to machine learning applications in drug metabolism and pharmacokinetics. At Haverford College in a DARPA-funded project to discover new perovskite materials using high throughput automated chemistry. This project led to the development of ESCALATE, a sophisticated tool for capturing, processing, and reporting chemistry experiments.
Shekar currently works at the Brookhaven National Laboratory, where he focuses on enhancing data acquisition software and developing machine learning models for biological synchrotron beamlines. His work aims to improve experiment coordination and automation, facilitating faster and more efficient scientific discovery. - DataPhilly Presents: Fast.AI Lesson 8!Link visible for attendees
Hi Data Enthusiasts,
Action Items (For those interested):
- We'll be discussing and going over a different video lecture in the Fast.AI lecture series. Start Watching Lesson 9 NOW!
- Please watch the video lecture mentioned in the title. We'll be helping people set up with Google Colab or Paperspace to run their Jupyter Notebooks if they haven't already and discussing any confusing bits.
- Join the DataPhilly Channel then find the #fastai-study-group
Logistics:
- Virtually: Here is the Meetup link:
https://meet.google.com/juj-swjy-cry - Unfortunately we can no longer do in person for this week.