Past events
Last day of instruction
Wednesday, Dec. 15, 2021, Midnight through Wednesday, Dec. 15, 2021, 11:59 p.m.
University of Minnesota
The last day of instruction for the fall 2021 semester is Wednesday, December 15.
View the full academic schedule on One Stop.
IMA Data Science Seminar: New Methods for Disease Prediction using Imaging and Genomics
Tuesday, Dec. 14, 2021, 1:25 p.m. through Tuesday, Dec. 14, 2021, 2:25 p.m.
Walter Library 402
The Institute for Mathematics and Its Applications (IMA) Data Science Seminars are a forum for data scientists of IMA academic and industrial partners to discuss and learn about recent developments in the broad area of data science. The seminars take place on Tuesdays from 1:25 p.m. - 2:25 p.m
This week's speaker, Eran Halperin (UnitedHealth Group), will be giving a talk titled "New Methods for Disease Prediction using Imaging and Genomics."
You may attend the talk either in person in Walter 402 or registering via Zoom.
Abstract
Diagnosis and prediction of health outcomes using machine learning has shown major advances over the last few years. Some of the major challenges remaining in the field include the sparsity of electronic health records data, and the scarcity of high-quality labeled data. In this talk, I will present a couple of examples where we partially address these challenges. Specifically, I will provide an overview of a new neural network architecture for the analysis of three-dimensional medical imaging data (optical coherence tomography) under scarce labeled data and demonstrate applications in age-related macular degeneration. Then, I will describe in more detail a new Bayesian framework for the imputation of electronic health records (addressing sparsity) using DNA methylation data. Our framework involves a tensor deconvolution of bulk DNA methylation to obtain cell-type-specific methylation from bulk data, which we demonstrate is predictive of many clinical outcomes.
Biography
Dr. Eran Halperin is the SVP of AI and Machine Learning in Optum Labs (United Health Group), and a professor in the departments of Computer Science, Computational Medicine, Anesthesiology, and Human Genetics at UCLA. Prior to his current position, he held research and postdoctoral positions at the University of California, Berkeley, the International Computer Science Institute in Berkeley, Princeton University, and Tel-Aviv University. Dr. Halperin’s lab developed computational and machine learning methods for a variety of health-related applications, including different genomic applications (genetics, methylation, microbiome, single-cell RNA), and medical applications (medical imaging, physiological waveforms, and electronic medical records). He published more than 150 peer-reviewed publications, and he received various honors for academic achievements, including the Rothschild Fellowship, the Technion-Juludan prize for technological contribution to medicine, the Krill prize, and he was elected as an International Society of Computational Biology (ISCB) fellow.
Industrial Problems Seminar: From Perception to Understanding: The Third Wave of AI
Friday, Dec. 10, 2021, 1:25 p.m. through Friday, Dec. 10, 2021, 2:25 p.m.
Online
In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.
This week's speaker, Tetiana Grinberg (Intel Corporation), will be giving a talk titled "From Perception to Understanding: The Third Wave of AI."
Registration is required to access the Zoom webinar.
Abstract
What is the next big thing in AI? What does one need to know and prepare for to remain relevant as the industry undergoes transformation? Why is this industry transformation a necessity? In this talk, we will discuss the strengths and weaknesses of traditional Deep Learning approaches to knowledge-centric tasks and look at a blueprint hybrid architecture that could offer solutions to the problems of scalability, reliability and explainability faced by large Deep Learning models of today. Finally, we will discuss the relevant skills that are needed for one to participate at the forefront of this research.
Biography
Tanya Grinberg is a Machine Learning Data Scientist with the Emergent AI lab at Intel Labs. Previously, she co-founded Symbiokinetics, a startup focused on developing AI-assisted robotic interfaces for medical applications like neurological rehab. Her research interests include embodiment, concept formation, and human-compatible value system design.
Graduate Programs Online Information Session
Tuesday, Dec. 7, 2021, 9:30 a.m. through Tuesday, Dec. 7, 2021, 10:30 a.m.
Online - link provided after registration
Prospective students can RSVP for an information session to learn about the following graduate programs:
- Computer Science M.S.
- Computer Science MCS
- Computer Science Ph.D.
- Data Science M.S.
- Data Science Post-Baccalaureate Certificate
During the information session, we will go over the following:
- Requirements (general)
- Applying
- Prerequisite requirements
- What makes a strong applicant
- Funding
- Resources
- Common questions
- Questions from attendees
Industrial Problems Seminar: Licensed to Analyze? An In-Depth Look at the Data Science Career: Defining Roles, Assessing Skills
Friday, Dec. 3, 2021, 1:25 p.m. through Friday, Dec. 3, 2021, 2:25 p.m.
Online
In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.
This week's speaker, Hamit Hamutcu (Initiative for Analytics and Data Science Standards), will be giving a talk titled "Licensed to Analyze? An In-Depth Look at the Data Science Career: Defining Roles, Assessing Skills."
Registration is required to access the Zoom webinar.
Abstract
As the role of data and analytics is expanding very rapidly in creating new business models or changing existing ones, demand for data science and analytics professionals is growing at an increasing rate. However, almost every company in the industry has a unique way of defining roles and assigning titles in data related positions. For any given role or title, such as ‘Data Scientist’ or ‘Data Analyst’, we see a big variety of role definitions and expected knowledge and skills. This creates inefficiencies and makes it difficult for companies to find the right match for a given position, leverage analytics skills effectively and retain talent. It also makes it hard for professionals to understand what a certain position requires and develop their own development plans. This has resulted in a chaotic market that is confusing to employers, academic and training institutions, and candidates.
In order to address this, Initiative for Analytics and Data Science Standards (IADSS) has been launched and has kicked-off a global scale research and thought leadership effort. Our goal is to gain insight into the data profession in the industry and help support the development of standards regarding role definitions, required skills and, career advancement paths.
In this presentation we will share our research findings and recommendations on skill requirements for a variety of data science roles, career paths in the industry, and latest practices of organizations for recruiting, training, and managing data science resources.
Hamit brings 25 years of industry and consulting experience in the areas of analytics and data-driven strategy.
Biography
In his current role as Senior Advisor for The Institute for Experiential AI at Northeastern University, as part of the leadership team, he focuses on strategy and organizational development to launch programs that contribute to and work with the global AI ecosystem.
Hamit is the co-founder of the Initiative for Analytics and Data Science Standards. IADSS aims to develop industry standards for the knowledge and skills required in data science roles and is a best-practice and research hub for the data science profession. The Initiative is also working on an innovative framework for data literacy through its Data Citizen program.
He is also co-founder of Analytics Center, a leading platform in EMEA that provides training, advisory services and organizes strategic events on big data, advanced analytics, disruptive technologies, and new business models.
Previously, Hamit was a partner for Peppers & Rogers Group in Stamford, Connecticut, where he headed the Global Analytics Group and oversaw the growth of the analytics practice. He helped his clients develop best-practice analytics organizations, build data infrastructure, and deploy models to support business goals. He was a founding partner for the Europe, Middle East, and Africa offices and grew the firm in the region. He delivered projects and managed teams across the globe in industries such as logistics, financial services, telecom, and government.
Earlier in his career, Hamit held several marketing analytics and technology positions at FedEx in Memphis, Tennessee, where he led IT and business teams to leverage enormous amounts of company data generated to serve its customers better.
Hamit is a frequent speaker, writer, and board member at various startups and nonprofit organizations. Hamit also volunteers as a mentor with Endeavor to support entrepreneurs and innovation by mentoring startups and acting as a jury member. He earned his Bachelor of Science degree in electronics engineering at Bogazici University in Istanbul. He received his MBA from the University of Florida.
Data Science Poster Fair - Fall 2021
Friday, Dec. 3, 2021, 11:30 a.m. through Friday, Dec. 3, 2021, 1:30 p.m.
GatherTown - (no longer accessible as the event has past)
We invite you to attend the Data Science Poster Fair! This semester's event will be held virtually via GatherTown on Friday, December 3 from 11:30 a.m. - 1:30 p.m.
Every year, data science M.S. students present their capstone projects during this event. The poster fair is open to the public and all interested undergraduate and graduate students, alumni, staff, faculty, and industry professionals are encouraged to attend. Attendees will have the ability to move between poster presentations as they please.
Schedule
Session | Time | Details |
---|---|---|
Introduction | 11:30 a.m. - 11:35 a.m. | Data Science Director of Graduate Studies, Professor Daniel Boley |
Session 1 | 11:35 a.m. - 12:05 p.m. | Poster 1: Won Joon Choi Advisor: Daniel Boley Capstone title: Patterns in Genomic Variation of SARS-CoV2 Poster 2: Ramanish Singh Advisor: Ilja Siepmann Capstone title: Prediction of Unary Adsorption Isotherms in Zeolites Using Neural Networks |
Transition time | 12:05 p.m. - 12:10 p.m. | |
Session 2 | 12:10 p.m. - 12:40 p.m. | Poster 4: Abby Slater Advisor: Daniel Boley Capstone title: Grain Quality Predictive Modeling Poster 5: Miao Yang Advisor: Arindam Banerjee Capstone title: Machine Learning and Equity Investment |
Open discussion | 12:40 p.m. - 1:30 p.m. | Time for attendees and/or presenters who want to connect more |
UMN Machine Learning Seminar: First-order methods for nonlinear-constrained optimization
Thursday, Dec. 2, 2021, Noon through Thursday, Dec. 2, 2021, 1 p.m.
The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.
This week's speaker, Yangyang Xu (Rensselaer Polytechnic Institute), will be giving a talk titled "First-order methods for nonlinear-constrained optimization."
Abstract
First-order methods (FOMs) have recently been applied and analyzed for solving problems with complicated functional constraints. Existing works show that FOMs for functional constrained problems have lower-order convergence rates than those for unconstrained problems. In particular, an FOM for a smooth strongly-convex problem can have linear convergence, while it can only converge sublinearly for a constrained problem if the projection onto the constraint set is prohibited. In this talk, I will first give a lower-bound result of FOM for solving affine-constrained problems. Then I will show that the slower convergence is caused by the large number of functional constraints but not the constraints themselves. When there are only O(1) functional constraints, I will show that an FOM can have almost the same convergence rate as that for solving an unconstrained problem, even without the projection onto the feasible set. Finally, I will give an adaptive primal-dual method for problems with many constraints. Experimental results on quadratically-constrained quadratic programs will be shown to demonstrate the theory.
Biography
Yangyang Xu is now a tenure-track assistant professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He received his B.S. in Computational Mathematics from Nanjing University in 2007, M.S. in Operations Research from the Chinese Academy of Sciences in 2010, and Ph.D. from the Department of Computational and Applied Mathematics at Rice University in 2014. His research interests are mainly in optimization theory and methods and their applications, such as in machine learning, statistics, and signal processing. His research has been supported by NSF and IBM. He was awarded the gold medal in the 2017 International Consortium of Chinese Mathematicians (ICCM).
University closed
Friday, Nov. 26, 2021, Midnight through Friday, Nov. 26, 2021, 11:59 p.m.
University of Minnesota
The University of Minnesota will be closed (floating holiday).
University closed
Thursday, Nov. 25, 2021, Midnight through Thursday, Nov. 25, 2021, 11:59 p.m.
University of Minnesota
The University of Minnesota will be closed in observance of Thanksgiving Day.
IMA Data Science Seminar: The Scattering Transform for Texture Synthesis and Molecular Generation
Tuesday, Nov. 23, 2021, 1:25 p.m. through Tuesday, Nov. 23, 2021, 2:25 p.m.
Walter Library 402
The Institute for Mathematics and Its Applications (IMA) Data Science Seminars are a forum for data scientists of IMA academic and industrial partners to discuss and learn about recent developments in the broad area of data science. The seminars take place on Tuesdays from 1:25 p.m. - 2:25 p.m
This week's speaker, Michael Perlmutter (University of California, Los Angeles), will be giving a talk titled "The Scattering Transform for Texture Synthesis and Molecular Generation."
You may attend the talk either in person in Walter 402 or registering via Zoom.
Abstract
The scattering transform is a wavelet-based feed-forward network originally introduced by S. Mallat to improve our theoretical understanding of convolutional neural networks (CNNs). Like the front end of a CNN, it produces a latent representation of input signal through an alternating sequence of convolutions and non-linearities. Following Mallat's original paper, subsequent work has shown that this latent representation can be used to synthesize new input signals such as textures. In a somewhat orthogonal extension, there has also been a number of papers which have shown how to adapt the scattering transform to graph-structured data.
In my talk, I will present a new network which combines these two ideas and uses the graph scattering transform to generate new molecules with the intended application being drug discovery. In order to ensure that the molecules produced by our network satisfy the laws of chemistry and resemble actual drugs, we use a regularized autoencoder to learn a compressed representation of the scattering coefficients of each graph and a generative adversarial network (GAN) to produce new molecules directly from this compressed representation.
Biography
Michael Perlmutter is a Hedrick Assistant Adjunct Professor in the department of mathematics at the University of California, Los Angeles. Previously he has held postdoctoral positions in the department of Statistics and Operations Research at the University of North Carolina at Chapel Hill and in the department of Computational Math., Science and Engineering at Michigan State University. He earned his PHD in Mathematics from Purdue University.