Institute for Data Science Unveils 2020-2021 Talks, Many to Discuss COVID-19

Written by: Evan Koblentz
Published: Monday, October 12, 2020
Institute for Data Science will host many more talks than last year

About two dozen experts on data science are giving seminars to the NJIT community this semester and next, with many of them excited to participate virtually from far away with the students and faculty here in New Jersey .

David Bader, director of NJIT's Institute for Data Science, said he is excited to host such prestigious guest speakers representing academia, government and industry. While the Institute hosted several guest speakers in the past year, such as the chief data scientist from The New York Times last spring, the broad scope of this year’s series covers more real-life applications of data science, he noted.

The speakers so far were NJIT's own Senjuti Basu Roy, who spoke on Optimization Opportunities in Human-in-the-Loop Systems; Yifan Hu (Yahoo Research Labs), whose topic was What’s in a Name? Deciphering Names Through Machine Learning; Adam McLaughlin, (D.E. Shaw Research), Accelerating GPU Betweenness Centrality; Michael Mahoney (University of California, Berkeley), Dynamical Systems and Machine Learning: Combining in a Principled Way Data-Driven Models and Domain-Driven Models; Srinivas Aluru (Georgia Institute of Technology), Genomes Galore: Big Data Challenges in Computational Genomics and Systems Biology; and Francine Berman (Rensselaer Polytechnic Institute), The Internet of Things: Utopia or Dystopia?

Following is the rest of the schedule. Additional speakers are being planned.

  • October 14, 2020, Viktor Prasanna, University of Southern California, Accelerating Data Science at the Edge
  • October 21, 2020, Jon Kleinberg, Cornell University, Fairness and Bias in Algorithmic Decision-Making
  • October 28, 2020, Manish Parashar, National Science Foundation and Rutgers University, Transforming Science in the 21st Century: NSF’s Vision for a National Cyberinfrastructure Ecosystem
  • November 4, 2020, Rick Stevens, Argonne National Laboratory, Artificial Intelligence for Science 
  • November 11, 2020, Leman Akoglu, Carnegie Mellon University, Graph-based Anomaly Detection: Problems, Algorithms, and Applications
  • November 18, 2020, Narayan Srinivasa, Intel Labs, Towards building new AI using Neuromorphic Computing Systems​
  • December 2, 2020, Helen Berman, Rutgers University, Building Community Resources for Structural Biology
  • December 9, 2020, Chandra Bajaj, University of Texas at Austin, Learning to Correct Form and Function With Reinforcement
  • January 27, 2021, Steven Skiena, SUNY Stony Brook, Word and Graph Embeddings for Machine Learning
  • February 3, 2021, Tanya Berger-Wolf, The Ohio State University, Trustworthy AI for Wildlife Conservation: AI and Humans Combating Extinction Together
  • February 10, 2021, Jeannette Wing, Columbia University, Data for Good: Ensuring the Responsible Use of Data to Benefit Society
  • February 17, 2021, Prashant Reddy, J.P. Morgan AI Resarch, Diverse Applications of AI in Finance
  • February 24, 2021, Deja Bond, Remitly, Responsible Data Science
  • March 3, 2021, Vipin Kumar, University of Minnesota, Big Data in Climate and Earth Sciences: Challenges and Opportunities for Data Science
  • March 10, 2021, Cynthia Rudin, Duke University, Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
  • March 24, William Reus, Department of Defense, Arkouda: Interactive Supercomputing for Data Science
  • March 31, 2021, Ilkay Altintis, University of California San Diego, Toward a Scalable Computing Ecosystem Advancing Data-Integrated Applications for Science and Society
  • April 7, 2021, Charles Lieserson, Massachusetts Institute of Technology, Software Performance After Moore's Law
  • April 14, 2021, Aydin Buloc, Lawrence Berkeley National Laboratory, Large-scale Graph Representation Learning and Computational Biology through Sparse Matrices
  • April 21, 2021, Tina Eliassi-Rad, Northeastern University, Geometric and Topological Graph Analysis for Machine Learning Applications
  • April 28, Joseph JaJa, University of Maryland, Deep Learning Techniques to Characterize Dynamics in Spatio-Temporal Neuroimaging Data

Some of the speakers are discussing the coronavirus. "Understanding the spread of the virus, rapid detection, vaccine development and treatment for SARS-CoV-2 is a major contribution of the data science community," Bader said. Georgia Tech's Aluru covered how the virus spreads, Argonne’s Stevens will discuss machine learning for massive screening of COVID-19 molecular docking, while Rutgers/NSF's Parashar will address the federal COVID-19 High Performance Computing Consortium. There are sure to be many more examples in the remaining talks because many aspects of modeling and simulation closely align with data science, Bader added.

Bader said the lectures by Rensselaer's Berman and Rutgers/NSF's Parashar are especially exciting. All of the lectures will stream live on YouTube and most will also be archived there.

"NJIT is very fortunate to attract this broad set of speakers and interact with our students," Bader continued. "These speakers represent the highest caliber of data science thought leaders in the nation."

Click here for the full schedule and registration details.

This article was updated on March 18, 2021 to reflect the current schedule.