Industrial Problems Seminar
In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to present their work to an audience of IMA postdocs, visitors, and graduate students, offering a first-hand glimpse into industrial research. The seminar series is often useful for initiating contact with industrial scientists. The IMA’s seminar series is the oldest and longest running seminar series in industrial mathematics.
Seminars take place Fridays from 1:25-2:25 p.m. in Lind Hall 325 or virtually by Zoom. Please check the individual seminar page for details.
The seminars are organized by Gilad Lerman, School of Mathematics, University of Minnesota.
Past seminars
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2023–24
2023–24
September 8, 2023
Scalable AI for autonomous driving and robotics
Michael Viscardi (Helm.ai)
September 15, 2023
Computable Phenotypes for Long-COVID in EHR data
Miles Crosskey (CoVar Applied Technologies)
September 22, 2023
Sampling diffusion models in the era of generative AI
Morteza Mardani (NVIDIA Corporation)
September 29, 2023
What makes an algorithm industrial strength?
Thomas Grandine (University of Washington)
October 6, 2023
Navigating Interdisciplinary Research as a Mathematician
Julie Mitchell (Oak Ridge National Laboratory)
October 13, 2023
Distinct spatiotemporal tumor-immune ecologies define therapeutic response in NSCLC patients
Sandhya Prabhakaran (Moffitt Cancer Centre)
October 27, 2023
The Impact of Linear Constraints in Mean-Variance Optimization
Christopher Bemis (X Cubed Capital Management)
November 3, 2023
Autoencoders for time series anomaly detection
Parker Williams (Rivian Automotive)
November 10, 2023
Language and graph foundational models: Distillation and pretraining
Vasileios Ioannidis (Amazon Search AI)
November 17, 2023
The Ever-Evolving Role of Data Science in an Organization
Katy Micek (Paramount)
December 1, 2023
Quantitative Ecology: My career in applied mathematics with the USGS
Richard Erickson (U.S. Geological Survey)
December 8, 2023
Bringing AI to Healthcare – Application of Large Language Models to Interpret Complex Microbiome Data
Leo Grady (Jona)
January 19, 2024
My Early Career as a Data Scientist in Renewable Energy
Sarah Milstein (NextEra Analytics)
January 26, 2024
How much math do you really need to make markets in stock options?
John Dodson (Options Clearing Corporation)
February 2, 2024
Lessons learnt on the path from academia to industry and entrepreneurship
Simon Adar (Code Ocean)
February 16, 2024
Algebras to Insights: my journey from mathematics to data science
Kari Eifler (Microsoft)
February 23, 2024
Applications of Artificial Intelligence in Smart Manufacturing – The Cargill Perspective
Abhishek Roy (Cargill)
March 1, 2024
Data Science Career in Tech Companies
Jeremy Gu (Shipt)
March 15, 2024
Math in Sports, for Fun and Profit
John Sears (Los Angeles Dodgers)
March 22, 2024
Computer vision, mostly without AI
Elena Yudovina (CyberOptics)
March 29, 2024
Viva la Revolución of Open Source Large Language Models: Unleashing the Dark Horse in AI Innovation
Patrick Delaney (BloomBoard)
April 5, 2024
Academia, to Industry, to the NBA – Navigating a Non-Academic Career with a PhD
Daniel Martens (Minnesota Timberwolves)
April 12, 2024
Graph AI: Science and Industrial Applications
Jie Chen (IBM Research)
April 26, 2024
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2022–23
2022–23
April 28, 2023
Some Elementary Economics (& Physics) of the Electricity Grid
Sriharsha (Harsha) Veeramachaneni (WindLogics)
April 21, 2023
Squishy Mathematical Reasoning in a Robotics Start-up
Michelle Snider (Service Robotics & Technologies)
April 14, 2023
A Varied and Winding Math Career in Industry
Laura Lurati (Edward Jones)
March 31, 2023
Working as an Artificial Intelligence Advisor to the US Government
Mitchell Kinney (The MITRE Corporation)
March 24, 2023
Applied Math at Boeing
Brittan Farmer (The Boeing Company)
March 3, 2023
Meta-Analysis of Randomized Experiments: Applications to Heavy-Tailed Response Data
Dominique Perrault-Joncas (Amazon)
February 10, 2023
Math & Money: Career Paths in Financial Services
Margaret Holen (Princeton University)
February 3, 2023
Identifying and achieving career goals
Brittany Baker (The Hartford)
January 27, 2023
An Overview of Open Problems in Autonomous Systems
Natalia Alexandrov (NASA Langley Research Center)
January 20, 2023
Quantitative Careers in the Medical Device Industry
Luke Jacobsen (Medtronic) and Jeff Lande (Medtronic)
December 9, 2022
Data Science to Software Engineering and Back Again
Cora Brown (Bridge Financial Technology)
December 2, 2022
How to optimize a power grid
Austin Tuttle (Open Systems International)
November 18, 2022
Shaping Your Own Career as a Mathematical Biologist
Nessy Tania (Pfizer)
November 11, 2022
Using cloud computing? You might benefit from data science!
Marc Light (Censys.io)
November 4, 2022
Seek Truth, Create Value — An engineer’s perspective on 3M “Science. Applied to Life.”
Fay Salmon (3M)
October 28, 2022
AI Model Inspector: Towards Holistic Adversarial Robustness for Deep Learning
Pin-Yu Chen (IBM)
October 14, 2022
Navigating early career steps in industry
Nicole Bridgland (Fulcrum)
October 7, 2022
Navigating a Career Path, a Case Study
Paula Dassbach (Medtronic)
September 30, 2022
Capacity Planning for the Cloud
Alex Gutierrez (Google Inc.)
September 23, 2022
Research Problems in Quantitative Finance
John Goes (GMO)
September 16, 2022
Simplicity Bias in Deep Learning
Prateek Jain (Google Inc.)
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2021–22
2021–22
April 29, 2022
Simplifying Federated Learning Jobs With Flame
Myungjin Lee (Cisco)
April 08, 2022
Data Science in Business vs. Academia
Philippe Barbe (Paramount)
April 1, 2022
Creating Value in PE Using Advanced Analytics
Erik Einset (Global Infrastructures Partners)
March 25, 2022
Multi-Agent Autonomy and Beyond: A Mathematician’s Life at GDMS
Ben Strasser (General Dynamics Mission Systems)
March 18, 2022
Musings from a Computer Vision Career
Evan Ribnick (Reveal Technology)
February 25, 2022
Data Science @ Meta
Zeinab Takbiri (Facebook)
February 18, 2022
Towards a Better Evaluation of Football Players
Eric Eager (ProFootballFocus (PFF))
February 11, 2022
Best Practices A Data Scientist Should Know
Hande Tuzel (Sabre Corporation)
February 4, 2022
Data-Model Fusion to Predict the Impacts of Climate Change on Mosquito-borne Diseases
Carrie Manore (Los Alamos National Laboratory)
January 28, 2022
Pointers on AI/ML Career Success
Paritosh Desai (Google Inc.)
December 14, 2021
New Methods for Disease Prediction using Imaging and Genomics
Eran Halperin (UnitedHealth Group)
December 10, 2021
From Perception to Understanding: The Third Wave of AI
Tetiana Grinberg (Intel Corporation)
December 3, 2021
Licensed to Analyze? An In-Depth Look at the Data Science Career: Defining Roles, Assessing Skills
Hamit Hamutcu (Initiative for Analytics and Data Science Standards (IADSS))
November 19, 2021
Certified Robustness against Adversarial Attacks in Image Classification
Fatemeh Sheikholeslami (Bosch Center for Artificial Intelligence)
November 12, 2021
Lessons Learned in Deploying AI in Manufacturing
Eric Wespi (Boston Scientific)
November 5, 2021
Data Science @ Instacart
Jeffrey Moulton (Instacart)
October 29, 2021
Challenges in Building Intelligent Search Systems
Jiguang Shen (Microsoft Research)
October 22, 2021
Predicting Tomorrow: Industrial Forecasting at Scale
Jimmy Broomfield (Target Corporation)
October 15, 2021
Data Scientists under attack!! Let's help them together
Sharath Dhamodaran (OptumLabs)
October 8, 2021
Research and Opportunities in the Mathematical Sciences at Oak Ridge National Laboratory
Juan Restrepo (Oregon State University)
October 1, 2021
Long-term Time Series Forecasting and Data Generated by Complex Systems
Kaisa Taipale (CH Robinson)
September 17, 2021
SIAM Internship Panel
Montie Avery (University of Minnesota, Twin Cities)
September 10, 2021
Being Smart and Dumb: Building the Sports Analytics Industry
Dean Oliver (NBA's Washington Wizards)