Past Seminars

• Rajdeep Chatterjee, School of Physics and Astronomy, University of Minnesota, "Applications of Machine Learning Methods by CMS Experiment at the CERN Large Hadron Collider." Thursday, April 1, 10–11:00 a.m.
  More information - https://cse.umn.edu/dtc/events/applications-machine-learning-methods-cms-experiment-cern-large-hadron-collider
   
• Shashi Shekhar, Computer Science and Engineering, University of Minnesota, "What is special about spatial and spatio-temporal data science?" Thursday, March 11, 10–11:00 a.m.
  More information - https://cse.umn.edu/dtc/events/solving-non-clarke-regular-optimization-problems-statistical-and-machine-learning
   
• Ying Cui, Department of Industrial and Systems Engineering, University of Minnesota, "Solving Non-(Clarke)-Regular Optimization Problems in Statistical and Machine Learning. Thursday,  March 4, 10–11:00 a.m.
  View recording here
  View presentation slides here
  More information - https://cse.umn.edu/dtc/events/solving-non-clarke-regular-optimization-problems-statistical-and-machine-learning
   
• Jie Ding, School of Statistics, College of Liberal Arts, University of Minnesota, "New Directions in Privacy-Preserving Machine Learning." Thursday, January 21, 2021, 10:00–11:00 am.
  More information https://cse.umn.edu/dtc/events/new-directions-privacy-preserving-machine-learning
   
• Stefano Martiniani, Department of Chemical Engineering and Materials Science, University of Minnesota, "Design and Discovery in the Protein Fitness Landscape." Wednesday, December 16, 2020, 3:30–4:30 p.m. 
  View recording here
  More information https://cse.umn.edu/dtc/mlseminar20201216
   
• Qi Zhang, Chemical Engineering and Materials Science, University of Minnesota, "Data-driven inverse linear optimization with noisy observations." Wednesday, December 9, 2020, 3:30–4:30 pm. Online via zoom.
  View recording here
  More information https://cse.umn.edu/dtc/mlseminar20201209
   

• Vuk Mandic, School of Physics and Astronomy, University of Minnesota, "Sensing and Machine Learning." Wednesday, December 2, 2020, 3:30–4:30 pm. Online via zoom.
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• Jason Goodpaster, Department of Chemistry, College of Science & Engineering, University of Minnesota, "Transfer learning of neural network potentials for reactive chemistry." Wednesday, November 18, 2020, 3:30–4:30 pm.
  View recording here
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• Edward McFowland III, Information & Decision Sciences, Carlson School of Management, University of Minnesota, "Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Wednesday, November 11, 2020, 3:30–4:30 pm
  View recording here
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• Raphael Stern, Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, "Impacts of automated vehicles: a traffic flow perspective." Wednesday, November 4, 2020, 3:30–4:30 pm.
  View recording here 
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• Sandra Safo, Biostatistics, School of Public Health, University of Minnesota, "Joint association and classification of multi-view structured data." Wednesday, October 28, 2020, 3:30–4:30 pm. Machine Learning Seminar. 
  View recording here
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• Xiaoou Li, School of Statistics, University of Minnesota, "Compound sequential change detection in multiple data streams." Wednesday, October 21, 3:30-4:30 p.m.
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• Yangzesheng Sun, Department of Chemistry and Department of Computer Science & Engineering, University of Minnesota, "Physics-informed machine learning for molecular simulations in nanoporous materials discovery." Wednesday, October 14, 2020, 3:30–4:30 pm.
  View recording here
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• Webinar and Council Meeting, October 20, 2020, 10:30 a.m. – 12:15 p.m., Participation from Zhi-Li Zhang, DTC Assistant Director for research.
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• Jeff Calder, School of Mathematics University of Minnesota, "Poisson learning: Graph-based semi-supervised learning at very low label rates. Wednesday, September 30, 2020, 3:30–4:30 pm. Online via Zoom.
  Download the presentation slides (pdf 12.5 MB)
  View recording here
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• Xuan Bi, Information & Decision Sciences, Carlson School of Management, University of Minnesota, "Sales Forecasting Accuracy: A tensor factorization approach with demand awareness." Wednesday, September 23, 2020, 3:30–4:30 pm
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• Harini Veeraraghavan, Memorial Sloan Kettering Cancer Center, "Cross-domain deep distillation learning for image-guided therapies." Wednesday, September 9, 2020, 3:30–4:30 pm.
  More information
  View recording here

Past seminars archive