The Machine Learning League (MLL) objective at Moffitt Cancer Center is to advance awareness and application of Machine Learning, Deep Learning, and Artificial Intelligence (AI) across the multiple disciplines in cancer research. This will be achieved by sharing the latest advancements in the field and brainstorming on how they could be applied to solving the cancer problem. In addition, there will be educational sessions on new tools and technologies that are used in machine learning applications.
League events will include guest lectures, presentations, and tutorials, as well as, software tools, packages, and libraries.
The Machine Learning League meetings are held internally typically every other Thursday at noon EST. This is an open invitation to anyone who is interested or wishes to participate in learning and understanding about topics related to machine learning.
|May 13, 2021||Eduardo Carranza and Naveena Gorre||Latest advancements in AI discussed at NVIDIA-GTC||In our meeting, we will be discussing topics from this year’s NVIDIA GPU Technology Conference (GTC). The NVIDIA GTC conference brings together researchers, technologists, and innovators from all over the world to share their AI implementations and learn about NVIDIA’s hardware advances.||View video presentation|
|May 27, 2021||Clark MacDonald (CoreScientific)||PLEXUS Software Stack||We are excited to host Clark MacDonald from Core Scientific. He will be demoing their PLEXUS software stack. PLEXUS provides a software stack that streamlines workflows for Data Scientist and AI /ML operations.||View video presentation|
|June 10, 2021||Phil Szepietowski||Transformers and the Hugging Face Library: Tools for Modern NLP Pipelines||We are excited to be hosting a presentation by Phillip Szepietowski. He will be discussing some basic background on the transformer architecture used in many modern NLP algorithms (BERT, for example). To illustrate their use he will give an overview of the Hugging Face library which has implemented many of these algorithms in a streamlined API. Finally, he will illustrate how some of these tools are being used in current Moffitt use-cases.||
View video presentation
|June 24, 2021||Eduardo Carranza and Naveena Gorre||NVIDIA and BMW's Collaboration for the Factory of the Future||We will present BMW’s implantation of Nvidia Ominverse and Isaac Sim to bring you the factory of the future.||
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|July 8, 2021||Larry Hall, Nathan Beach and Tiffany Ferrer||Generalization Challenges for Deep Learning for Medical Imaging: A Case Study||Please join us as we welcome Kaoutar Ben Ahmed and Lawrence O. Hall from the Department of Computer Science and Engineering at the University of South Florida who will discuss Generalization Challenges for Deep Learning for Medical Imaging: A Case Study. Is it possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images? Tune in and find out.||View video presentation|
|July 22, 2021||Steve Gately||NVIDIA Clara - Software Tools and Frameworks for Data Science, Clara Imaging||Clara Imaging is an application framework that accelerates the development and deployment of AI in medical imaging.||View video presentation|
|August 5, 2021||Kedar Kulkarni (Head of Health Informatics)||MCAP and Advanced Analytics||Through this presentation, we will learn about the Moffitt Cancer Analytics Platform (MCAP). Specifically the reasons for moving to a cloud-based analytics platform. The journey to the cloud so far, where are we and where are we going. And finally, about the Art of the Possible with MCAP.||No recording available|
|August 19, 2021||Steve Gately||NVIDIA Clara - Software Tools and Frameworks for Data Science, Clara NLP||NVIDIA Clara NLP is a collection of models and resources that can support natural language processing and understanding workflows in healthcare and life sciences.||View video presentation|
|August 26, 2021||Jakka Ramesh Sairamesh, MPhil, PhD - CEO and President, CapsicoHealth, Inc||Driving Value-Based Care and Population Health Through AI Platforms and Real-Time Decision Tools||In the era of value-based care and rapidly transforming virtual care, our vision is to bring proven AI-driven real-time analytics for providers and payors to simplify care models and reduce costs. Our world-class AI platform enables rapid intelligence tools and FHIR interoperability on very large data sets (including claims, EMR, environmental and socio-economic factors) to enable value-based care models such as BPCI-A, CJR, OCM and Alternative or custom payment models. Our AI platform identifies opportunities and boosts productivity for financial analysts and clinicians by analyzing billions of data points and accurately identifying cohorts at-risk. CapiscoHealth’s platform drives 99% accuracy in data quality, 15% reduction in operational costs, over 80% accuracy in risk prediction (e.g. hospitalizations, emergency visits and infections), and enables teams to deploy, manage and version pathways and risk models. CapsicoHealth has launched the First Population Health and Value-Based Care Solution on Cloud Based FHIR Interoperability Platforms (such as Google Health Data Engine and HAPI based Solutions), and deployed AI Workbenches to identify high-risk populations, streamline care and help clinicians with decision-making.||View video presentation|
|September 2, 2021||Charles Donly||Making Machine Learning Radically Accessible||Since 2011, we have had a boom in compute power for machine learning: era of discovery. This era has created amazing models that can program themselves and find new discoveries. This era also has two roadblocks. First, programming (Machine learning) costs are skyrocketing (>$Millions) due to data preparation and compute time. Second, it is very difficult to explain the model that is created and thus difficult to implement in the business. Neurologix is developing a software/hardware platform that is simpler and less expensive to build ML (focused UI/UX) and virtualized GPUs.||No recording available|
|September 16, 2021||Steve Gately||NVIDIA Clara - Software Tools and Frameworks for Data Science, Genomics||NVIDIA Clara Parabricks is a computational framework supporting genomics applications from DNA to RNA. It employs NVIDIA’s CUDA, HPC, AI, and data analytics stacks to build GPU accelerated libraries, pipelines, and reference application workflows for primary, secondary, and tertiary analysis.||Awaiting presentation|
|September 30, 2021||Andrew Borkowski||Fastai versus Keras for Metastatic Adenocarcinoma Classification, Which one is Better?||During the presentation, I will go over the high-level APIs for PyTorch and TensorFlow using metastatic adenocarcinoma classification as an example. In addition, I will briefly describe non-coding platforms Apple CreateML and Lobe that one may use for image classification.||Awaiting presentation|
|October 15, 2021||Sudeep Sarkar||Measuring Economy from Space||An interdisciplinary team from the University of South Florida (USF), the University of California, Berkeley, and Maxar developed a new artificial intelligence (AI) imagery analysis tool to derive insight about indicators of human activity to characterize the economic impact of lockdowns and reopening during the COVID-19 pandemic. Such indicators can provide insight into understanding patterns of activities such as shopping center parking lots, airports, medical facilities, schools, recreational places, and religious sites. In this talk, I will demonstrate this idea using two objects of interest – cars and airplanes as indicators of economic activity. I will share the underlying AI algorithms and show results on actual data. Our airplane detection solution won the Rapid Action Coronavirus Earth observation (RACE) upscaling challenge, sponsored by the European Space Agency and the European Commission, and now is integrated into the RACE dashboard, providing real-time information.||Awaiting presentation|
|October 29, 2021||Mehdi Damaghi and Naveena Gorre||Awaiting||Awaiting||Awaiting presentation|
|November 11, 2021||Ian Perera||Machine Learning for Analysis of Behavior-Based and Physiological Measures at IHMC||The Florida Institute for Human and Machine Cognition presents recent work on machine learning and natural language processing methods for physiological analysis, including analysis of speech and breath for detecting hypercapnia and stress, resting-state EEG analysis for prediction of performance in fine-grained motor tasks, and natural language processing for determining stress, memory abilities, and other cognitive skills. We also present work on a novel behavioral assessment and associated data collection that tests and stresses multiple communication and cognitive skills simultaneously while providing data for the described analysis measures.||Awaiting presentation|
|December 2, 2021||Michal Tomaszewski||Novel Radiomic Approaches for Maximized Clinical Impact||Dr. Tomaszewski received his PhD from the University of Cambridge, where he developed a new imaging technique for optoacoustic measurement of tumor vascularization. Following graduation, he joined the laboratory of Dr. Robert Gillies at Moffitt Cancer Center, where he focused on novel methods for radiological image quantification, working on several radiomic projects in MRI, MR Linac and CT. In the summer of 2021 Dr. Tomaszewski joined Merck & Co, where he works on MRI biomarker development and quantitative image analytics. In his talk, Dr. Tomaszewski will discuss the application of radiomics for clinical trial enrollment, and the rapid development of the field in MR guided radiotherapy applications.||Awaiting presentation|
|December 9, 2021||Ming Chao||Cluster Model Incorporating Heterogeneous Dose Distribution of Partial Parotid Gland Irradiation for Radiotherapy Induced Xerostomia Prediction with Machine Learning Methods||Ming Chao, PhD will be presenting their work on modeling of xerostomia induced by radiotherapy of head and neck cancer using a model based on cluster formation (percolation theory) and machine learning. Dr. Chao is currently at Mount Sinai Medical Center in New York and is a faculty member in medical physics.||Awaiting presentation|
|December 23, 2022||Hunter Wanket - SupberbAI||Awaiting||Awaiting||Awaiting presentation|
|January 6, 2022||Yovanna Roa||Awaiting||Awaiting||Awaiting presentation|
|January 20, 2022||Matthew Schabath||Awaiting||Awaiting||Awaiting presentation|
|February 3, 2022||Sandhya Prabhakaran||A Primer to Machine Learning||Ever used Amazon recommendations while shopping or Netflix recommendations for movies? Or used Face ID to open your phone? Do you refer to Google maps or other travel apps to monitor real-time traffic? Do you own a personal assistant such as Siri, Alexa, Google Home or Cortana? Do you rely on a spam filter for your email inbox or Grammarly to spell-check emails? If you said ‘yes’ to any of these, then you have used machine learning! Machine learning (ML) is actually a simple concept, although it sounds like a complicated idea requiring a lot of technical background and software programming. To better understand ML, I shall go over the basics of ML, discuss the core machinery used in ML, and (time-permitting) highlight its usage in healthcare.||Awaiting presentation|
If you are interested in joining the Machine Learning League distribution list or have any other questions, concerns, or even suggestions please contact Machine Learning at MachineLearning@moffitt.org.