NORTHWESTERN INSTITUTEON COMPLEX SYSTEMS

Speaker: Matt Groh, Assistant Professor, Kellogg School of Management

"Deepfake Detection by Humans across Contexts Ranging from Political Speeches to Human Photography to Podcasts"

Talk Abstract and Agenda

Speaker: Stefan Pate, PhD student, Interdisciplinary Biological Sciences Program, Northwestern University

"Tapping Underground Enzymatic Functions to Understand and Direct Metabolic Evolution"

Talk Abstract and Agenda

Data Science Nights returned on Tuesday, September 24, 2024 with a kick off event. The organizing committee gave lightning talks and moderated discussions during this hour.

Speaker: Ryan McKeown, PhD Student, Interdisciplinary Biological Sciences Graduate Program (IBiS), Northwestern University "Decreasing False-discovery Rates in Genome-wide Association Studies with Iterative Principal Component Analysis (PCA)"

Speaker: Jenny Ding, PhD Student, Feinberg School of Medicine, Department of Preventive Medicine "SOAR elucidates disease mechanisms and empowers drug discovery through spatial transcriptomics"

Speaker: Eliza Duvall, PhD Candidate, Interdisciplinary Biological Sciences Graduate Program (IBiS), Northwestern University "Co-expression Network Sparsification via Effective Resistance"

The November 2023 Data Science Night took featured a Career Panel with four Data Scientists.

Speaker: Yingdan Lu, Assistant Professor at the School of Communication, Northwestern University "Understanding the Evolution of Digital Propaganda Through Video-as-Data Methods".

Speaker: Imran Sultan, PhD Student at the Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) at Northwestern, "Simulating Everything Everywhere All at Once: Modeling Galaxy Formation"

Speaker: Mathew Yang, Senior Bioinformatician at NorthShore, “Personalized Medicine and Healthcare at NorthShore University HealthSystem”

Speaker: Kevin Gallagher, Scientist at NeoGenomics Laboratories, “Spatially-resolved cell phenotyping using MultiOmyx technology”

Speaker: Hengyang Li, PhD Candidate, Mechanical Engineering, Northwestern University, "Mechanistic Data Science for Mechanical Simulation and Design"

Speakers:

Spencer Hong, 3rd-year PhD Student, Chemical and Biological Engineering, McCormick School of Engineering "The history of genomics told through machine learning"

Yingkang Xie, 5th-year PhD Student, Marketing, Kellogg School of Management "Platform Leakage: Incentive Conflicts in Two-Sided Markets"

Speakers: Emma Krummenacher, Caitlin Casar, and Kitra Razin of 84.51° "Data Science at 84.51°: Building an Automated Dashboard with Power BI and Databricks "

Speaker: Wesley Orth, Ph.D. Candidate, Northwestern University Department of Linguistics "The Maze Task: Designing tasks for data quality and web-based social science research"

Speakers:

• Yanrong (Jerry) Ji, PhD in Biomedical Informatics, Senior Data Scientist at CVS Health
• Sahil Shah, Data Scientist at Shelf Engine
• Nish Trivedi, MS in Biotechnology, Decision Analytics Associate at ZS Associates
• Nicolas Wagner, PhD in Material Science and Engineering, Research Staff, Institute for Defense Analyses (IDA)

Speaker: Emily Webber, Senior Machine Learning Specialist Solutions Architect, Amazon Web Services: "A day in the life of the Amazon SageMaker (machine learning) team"

Speaker: Qian Cao, Visiting Scientist, U.S. Food and Drug Administration (FDA).

Speaker: Petter Holme, Professor, Institute of Innovative Research, Tokyo Institute of Technology, Japan "Games on Complex Networks"

Speaker: Omkar Ranadive, Applied Scientist at Alchera Labs "Using machine learning to detect wildfires"

From Astrobiology to Retail Data Science at 84.51°

Speakers:

• Caitlin Casar - Data Science Development Program, 84.51°. 2021 PhD, Earth and Planetary Sciences, Northwestern University. NASA Earth and Space Sciences Fellowship
• Alexandra Saldan - Senior Data Scientist, 84.51°. 2016 WCAS graduate, Mathematical Methods in the Social Sciences and Linguistics

Speaker: Juandalyn Burke, Ph.D. Candidate, Biomedical Informatics and Medical Education Department, University of Washington: "Using an Ecological Inference Software Tool to Detect Vote Dilution".

Speaker: Brian Martin, Research Fellow, Head of AI in R&D Information Research, Abbvie "Knowledge is Power, Data is Oil, Convergence is an Engine".

Speaker: Subhashini Tripuraneni, Executive Director at JPMorgan Chase, leader of the Machine Learning initiatives "AI and Machine Learning to Drive Customer Experience".

Speaker: Matt Satusky, Postdoctoral Fellow, Renaissance Computing Institute, University of North Carolina at Chapel Hill "Leveraging the NHLBI BioData Catalyst Ecosystem to Accelerate Heart, Lung, and Blood Research".

Speaker: Aviv Landau, Postdoctoral Research Scientist, Data Science Institute, Columbia University "Artificial Intelligence-Assisted Identification of Child Abuse and Neglect in Hospital Settings with Implications for Bias Reduction and Future Interventions".

Speaker: Bryan Pardo, Associate Professor, McCormick School of Engineering, Northwestern University. "New directions in deep audio source separation: training without ground truth and automatic model selection"

Speaker: William Miller, Professor of Chemical and Biological Engineering at Northwestern University, title: "Distributed High-Frequency Sensing for Assessing Urban Green Infrastructure and Predicting Flooding".

Speaker: Yian Yin, PhD candidate at the McCormick School of Engineering, titled: "Quantifying the dynamics of failure across science, startups and security" (Nature, 2019). 

Speaker: Joe Germuska, Executive Director of the Knight Lab at Northwestern University, titled: "Understanding and Using Census Data". This was followed by a hacking session, including working groups and an introduction to pandas and matplotlib in Python for newcomers by Nicolay Markov.

Speaker: Daniel W. Linna Jr., Director of Law and Technology Initiatives at the Northwestern Pritzker School of Law, titled: "Artificial Intelligence and Law: Creating our Augmented, Automated, Audacious Future".

Speaker: Carlos Gallo from the Feinberg School of Medicine, titled: "Improving suicide prevention services via online chat: a text mining approach".

The 2019 fall kickoff of Data Science Nights took place on Wednesday, September 25, 2019. This month featured a talk by IBM Developer Advocate Svetlana Levitan, titled: "Open standards for machine learning model deployment", plus a discussion about topics and working groups for future hacking night sessions.

The July Data Science Night took place on July 25, 2019.

The June Data Science Night took place on June 27, 2019 centered around the theme: "Deep-learning with Kera".

The May Data Science Night took place on May 30, 2019. "Combining R with Python" was the theme for this meeting.

The April 2019 Data Science Night featured a talk titled "Bring Your Dirty Data to Work Day" by the Data Science Night organizers. Attendees learned best practices for handling real world data to save time and reduce headaches.

Speaker: Michael Lucas, Lead Data Scientist at Tempus, and a PhD graduate from the EECS department with a specialization in machine learning and natural language processing.

The first Data Science Night of the new year kicked off on February 6, 2019 and hosted members of the Sears Deep Learning Center in partnership with Northwestern's Retail Analytics Council.

The December 2018 Data Science Night featured a talk titled "Personality Assessment in the Age of Big Data and Machine Learning" by Andrew Hall, a PhD candidate in Northwestern's Department of Psychology, plus project and learning groups.

The November 2018 Data Science Night featured a talk titled "Introduction to Machine Learning" by Nicholas Wagner and Abhijith Gopakumar from the Department of Materials Science and Engineering at Northwestern University, plus project and learning groups.

The October 2018 kickoff meeting included an introduction to data science and an overview of Data Science Nights, plus project and learning groups.

The February 2018 meeting had a distinct format compared to previous meetings, with a stronger focus on the breakout sessions. These included: multi-week learning groups, single-evening tutorials, multi-week projects, single-evening projects according to preference of attendees, and the ability to bring your own project or have a code-clinic.

The January 2018 meeting featured a talk by Joe Germuska of the Northwestern University Knight Lab: "Data Journalism: Big and Small".

The first Data Science Night featured a talk by Adam Miller (LSSTC Data Science Fellow): "Scikit-learn Soup to Nuts: Developing a Machine-Learning Workflow"