Machine Learning on Great Lakes

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OVERVIEW

This workshop will go over methods and best practices for running machine learning applications on Great Lakes. We will briefly outline machine learning before stepping through a hands-on example problem to load a project and submit a job to the HPC cluster. Participants are expected to be familiar with Python, the command line, and basic Great Lakes functionality (logging in and navigating the directory structure). Participants must create a user account on Great Lakes prior to the workshop and are required to pre-register to gain access to a training account.

INSTRUCTOR:

Meghan Dailey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing

Meghan Dailey is a machine learning specialist in the Advanced Research Computing (ARC) department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 1:00-2:00 PM for computer setup assistance.

Please note, this session will be recorded.  

To register and view more details, please refer to the linked TTC page.

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

New physics-based computation and AI framework to understand the agressive behavior of cancer cells

By | Feature, Research

Cancer is an illness caused by an uncontrolled division of transformed cells, which can originate in almost  any organ of the body.  Cancer is not a single disease, even when it arises in the same site of the body. Tremendous variability exists in progression of disease and response to therapy among different persons with the same general type of cancer, such as breast cancer. Even at the level of a single person, cancer cells show tremendous heterogeneity within a single tumor and among a primary tumor and metastases. This heterogeneity causes drug resistance and fatal disease. The prevailing dogma is that heterogeneity among cancer cells arises randomly, generating greedy individual cancer cells that compete for growth factors and optimal environments. The rare “winners” in this competition survive and metastasize. However, tumors consistently maintain heterogeneous subpopulations of cancer cells, some of which appear less able to grow and spread. This observation prompted Gary and Kathy Luker, cancer cell biologists at the University of Michigan, to hypothesize that cancer cells may actually collaborate under some circumstances to cause disease and not just compete. The idea that single, heterogeneous cancer cells work collectively within a constrained range of variability to drive population-level outputs in tumor progression is a ground-breaking concept that may revolutionize how we approach cancer biology and therapy.

The team is using innovative approaches to extract and merge data streams from models that generate heterogeneous cell behaviors

...cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer.

To understand causes of single-cell heterogeneity in cancer and conditions that motivate cancer cells to collaborate, an interdisciplinary team of scientists at UM formulated an entirely new conceptual approach to this challenging problem. The cancer cell biologists have teamed up with computational scientists and experts in artificial intelligence to focus the power of these fields on understanding and overcoming heterogeneity in cancer. Building on large, single-cell data sets unique to the team, they will combine inverse reinforcement learning, an artificial intelligence method typically applied to discover motivations for human behaviors, with computational models inferred on the basis of the physics and chemistry of cell signaling and migration. They have proposed an entirely new conceptual approach combining single cell data, physics-based modeling and artificial intelligence to single-cell heterogeneity and intercellular interactions. By discovering  testable molecular processes underlying “decision-making” by single cells and their “motivations” for acting competitively or collaboratively, this research blazes a new path to understand and treat cancer. Their high-risk, high-reward approach to understand how each cell in a population processes information and translates that to action driving cancer progression, has attracted an award of $1 million dollars by the Keck Foundation. 

The team includes Gary Luker (Radiology, Microbiology and Immunology; Biomedical Engineering), and Kathryn Luker (Radiology), who are leading the experimental studies of cell signaling and migration; Jennifer Linderman (Chemical Engineering; Biomedical Engineering); and Krishna Garikipati (Mechanical Engineering; Mathematics), who are leading the machine learning and modeling side of the project. Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering) are using artificial intelligence approaches to discover decision-making policies and rewards for cancer cells, working with the rest of the investigators to incorporate experimental data and physics/chemistry-based models into their approaches.

The W. M. Keck Foundation was established in 1954 in Los Angeles by William Myron Keck, founder of The Superior Oil Company. One of the nation’s largest philanthropic organizations, the W. M. Keck Foundation supports outstanding science, engineering and medical research. The Foundation also supports undergraduate education and maintains a program within Southern California to support arts and culture, education, health and community service projects. This project incorporates elements from all the W. M. Keck Foundation’s focus research areas to tackle cancer with a novel, physics-based modeling and AI-centered approach.  The idea for this project originated in the 2020 MICDE faculty workshop in AI for Physically based Bio-medicine Workshop. The workshop brought together an interdisciplinary group of faculty members to discuss ways to advance artificial intelligence and machine learning methods for biomedical problems. After seeding the idea, a subset of these researchers were awarded an MICDE catalyst grant and a MIDAS PODS grant. These funds were used to establish the proof of concept and to generate preliminary results. 

Computational science is becoming increasingly indispensable in many areas of biomedical science. While the current proposal focuses on cancer, this innovative computational framework represents a transformative leap with widespread applications in multiple other biomedical, physical, and social sciences. MICDE supports innovative and interdisciplinary projects aiming to advance the current paradigms.

Portraits of Kathryn Luker, Gary Luker, Krishna Garikipati, Jennifer Linderman, Nikola Banovic and Xun Huan

Project’s principal investigators (left to right): Kathryn Luker (Radiology), Gary Luker (Radiology, Microbiology and Immonology, and Biomedical Engineering), Krishna Garikipati (Mechanical Engineering, and Mathematics), Jennifer Linderman (Chemical Engineering, and Mathematics), Nikola Banovic (Electrical Engineering and Computer Science) and Xun Huan (Mechanical Engineering).

Machine Learning on Great Lakes

By |

OVERVIEW

This workshop will go over methods and best practices for running machine learning applications on Great Lakes. We will briefly outline machine learning before stepping through a hands-on example problem to load a project and submit a job to the HPC cluster. Participants are expected to be familiar with Python, the command line, and basic Great Lakes functionality (logging in and navigating the directory structure). Participants must create a user account on Great Lakes prior to the workshop and are required to pre-register to gain access to a training account.

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing

Meghan Richey is a machine learning specialist in the Advanced Research Computing (ARC) department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 1:00-2:00 PM for computer setup assistance.

Please note, this session will be recorded.  

To register and view more details, please refer to the linked TTC page.

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

XSEDE: Python Tools for Data Science

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OVERVIEW

Python has become a very popular programming language and software ecosystem for work in Data Science, integrating support for data access, data processing, modeling, machine learning, and visualization. In this webinar, we will describe some of the key Python packages that have been developed to support that work, and highlight some of their capabilities. This webinar will also serve as an introduction and overview of topics addressed in two Cornell Virtual Workshop tutorials, available at https://cvw.cac.cornell.edu/pydatasci1 and https://cvw.cac.cornell.edu/pydatasci2 .

See https://portal.xsede.org/course-calendar/-/training-user/class/2467/session/4161 for more information and registration

 

Register via the XSEDE Portal:

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

 

Geostatistics – III

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this third workshop, we will combine the material we covered in the first two workshops and develop the geostatistical modeling approach. This is mainly a lecture style workshop, but will include an example in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – II

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we are covering the basics of Geostatistics. In this second workshop, we will focus on covariance and variogram, and their estimation in the context of geostatistical modeling. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Geostatistics – I

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sparsely sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this first workshop we will understand the idea of stationary random fields, positive definite functions, and the fundamental building blocks of Gaussian random fields. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the foundations of Gaussian Process Regression, a commonly used technique in Machine Learning and AI.

XSEDE HPC HPC Summer Boot Camp

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OVERVIEW

XSEDE, along with the Pittsburgh Supercomputing Center is pleased to present a Hybrid Computing workshop.

This 4 day event will include MPI, OpenMP, GPU programming using OpenACC and accelerators.

This workshop will be remote to desktop only due to the COVID-19 pandemic.  When the registration has filled, there will be no more students added due to our current limits.

The schedule can be found here:  https://www.psc.edu/resources/training/xsede-hpc-workshop-june-8-11-2021-summer-boot-camp/

 

Register via the XSEDE Portal:

https://portal.xsede.org/course-calendar/-/training-user/class/2338/session/4002

If you do not currently have an XSEDE Portal account, you will need to create one:

https://portal.xsede.org/my-xsede?p_p_id=58&p_p_lifecycle=0&p_p_state=maximized&p_p_mode=view&_58_struts_action=%2Flogin%2Fcreate_account

Should you have any problems with that process, please contact help@xsede.org and they will provide assistance.

Questions

Please address any questions to Tom Maiden at tmaiden@psc.edu.

Advanced ML topics: Algorithms, writing ML code, comparing implementations

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OVERVIEW

This workshop is designed as a follow-up to the basic introduction to machine learning earlier in this series. We will cover several examples in Python and compare different implementations. We will also look at advanced topics in machine learning, such as GPU optimization, parallel processing, and deep learning. A basic understanding of Python is required.

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing – Technology Services

Meghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 9-10 AM for computer setup assistance.

Please note, this session will be recorded.  

Register here

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu

Introduction to Machine Learning

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OVERVIEW

Machine learning is becoming an increasingly popular tool in several fields, including data science, medicine, engineering, and business. This workshop will cover basic concepts related to machine learning, including definitions of basic terms, sample applications, and methods for deciding whether your project is a good fit for machine learning. No prior knowledge or coding experience is required

INSTRUCTORS

Meghan Richey
Machine Learning Specialist
Information and Technology Services – Advanced Research Computing – Technology Services

Meghan Richey is a machine learning specialist in the Advanced Research Computing- Technology Services department at the University of Michigan. She consults on several faculty and student machine learning applications and research studies, specializing in natural language processing and convolutional neural networks. Before her position at the university, Ms. Richey worked for a defense contractor as a software engineer to design and implement software solutions for DoD-funded artificial intelligence efforts.

MATERIALS

A Zoom link will be provided to the participants the day before the class. Registration is required.

Instructor will be available at the Zoom link, to be provided, from 9-10 AM for computer setup assistance.

Please note, this session will be recorded.  

Register here

If you have questions about this workshop, please send an email to the instructor at richeym@umich.edu