Data Science Lunch Seminar Series

Important announcement: The Lunch Seminar is back to being held in an in-person format. All seminars will take place at 12:30 on Wednesdays in the CDS open space. Talks will be recorded and shared here when possible.

The Data Science Lunch Seminar Series is an informal weekly gathering of NYU Data Science affiliated persons to discuss data science related topics. The series meets Wednesdays from 12:30-2:00 PM at the Center for Data Science.

The lunch seminar is organized by CDS Faculty Fellows Marco Morucci and Ravid Shwartz-Ziv. Please contact Marco or Ravid if you are interested in giving a talk.

UPCOMING SEMINARS

2022-2023 Series

SPRING 2023

  • February 2
    • Maximilian Dax, Max Planck Institute for Intelligent Systems
  • February 8
    • Yang Feng, NYU Biostatistics & CD
  • February 15
    • Andrew Caplin, NYU
  • March 1
    • Swapneel Mehta, CDS
  • March 8
    • Yoav Wald
  • March 22
    • Ofir Press
  • March 29
    • Tom Leavitt
  • April 12
    • Mohsen Mosleh

FALL 2022

  • September 14
    • Sophie Hao, New York University
  • September 21
  • September 28
  • October 5
    • Jason Altschuler, New York University
  • October 12
  • October 19
    • Cancelled
  • October 26
  • November 2
    • Grace Lindsay, New York University
  • November 9
    • Tim G. J. Rudner, New York University
  • November 16
    • Elisha Cohen, New York University

2021-2022 Series

Spring 2022

  • January 26
    Nora Webb Williams, University of Illinois at Urbana-Champaign. “Images as data in social science research: Where we are and where we’re headed”
    View Recording
  • February 23
    Alex Williams, NYU
    “Statistical methods to characterize variability and individuality in neural recordings”
    **In person**
  • March 2
    Dani S. Bassett, University of Pennsylvania
    “The curious human”
    View Recording
  • March 9
    Francesco Locatello, Amazon AWS
    “From Distributed Representations to Causal Representation Learning”
  • March 23
    Duygu Ataman, NYU
    “Quantifying Synthesis and Fusion and their Impact on Machine Translation”
    Recording available upon request
  • March 30
    Alex Hanna, DAIR
    “Beyond Bias: Algorithmic Unfairness, Infrastructure, and Genealogies of Data”
    Recording available upon request
  • April 6
    Manish Raghavan, MIT
    “The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization”
    View Recording
  • April 13
    Naomi Saphra, NYU, IN-PERSON
    “How Text Models Acquire Syntax”
  • April 20
    Maimuna (Maia) Majumder, Harvard Medical School/Boston Children’s Hospital
    “Machine Learning Research Applications During the First Year of the COVID-19 Pandemic”
    View Recording
  • April 27
    Yasaman Bahri, Google
    “Neural Scaling Laws: Theory and Practice”
    View Recording
  • May 4
    Ashia Wilson, MIT
    “Socially-aware ML via Influence Functions”
    View Recording

Fall 2021

  • September 15
    Lihua Lei, Stanford Statistics, “Conformal Inference of Counterfactuals and Individual Treatment Effects”
    View recording

  • September 22
    Ravid Shwartz-Ziv, NYU Center for Data Science, “Compression in deep learning – an information theory perspective”
    View recording

  • September 29
    Najoung Kim, NYU Center for Data Science, “Compositional Linguistic Generalization in Sequence-to-Sequence Learners”

  • October 6
    Vincent Divol, NYU Center for Data Science/Courant, “Quantifying the topology of datasets using Topological Data Analysis”
    View recording

  • October 13
    Marco Morucci, NYU Center for Data Science, “Matched Machine Learning: A Generalized Matching Framework for Treatment Effect Inference With Learned Metrics”
    View recording

  • October 20
    Jacopo Cirrone, NYU Center for Data Science, “OutPredict: multiple datasets can improve prediction of expression and inference of causality”
    View recording

  • October 27
    Ana Isabel Bento, Indiana University, “The role of data in a world reshaped by COVID-19”
    View recording

  • November 3
    Andy Halterman, NYU Center for Data Science, “Extracting Political Events from Text Using Syntax and Semantics”
    View recording

  • November 10
    Danica J. Sutherland, University of British Columbia, “Can Uniform Convergence Explain Interpolation Learning?”
    View recording

  • November 17
    Ceren Budak, University of Michigan, “Quantifying Political Phenomena on Social Media: Challenges in Data Collection, Labeling, and Classification”
    View recording

  • November 24 — No seminar (Thanksgiving)

  • December 1
    Maithra Raghu, Google, “How Neural Networks See, Learn and Forget”
    View recording

  • December 8
    Casey Fiesler    , University of Colorado Boulder, “Data Is People: Unintended Consequences in AI and Data Science”
    View recording
[SUSPENDED IN-PERSON UNTIL FURTHER NOTICE] This event is open to public. If you are not an NYU affiliate, however, we recommend that you arrive no earlier than 15 minutes prior to the talk to ensure your access to the 7th floor. Should you have any issues getting access, please call 212-998-3307 or email cds@nyu.edu.

data science lunch seminar

PAST SEMINARS

2020-2021 Series

2019-2020 Series

  • September 18, 2019
    Andrew Wilson, New York University, “How do we build models that learn and generalize?”
  • September 25, 2019
    He He, New York University
  • October 2, 2019
    Leo Miolane, CDS Fellow
  • October 9, 2019
    Elena Sizikova, MSDSE/CDS Fellow
  • October 16, 2019
    Ilias Zadik, MSDSE/CDS Fellow
  • October 23, 2019
    Wai Keen Vong, CDS Postdoc
  • October 30, 2019
    Anton Strezhnev, MSDSE/CDS Fellow
  • November 6, 2019
    ***No talk, MSDSE Annual Summit***
  • November 13, 2019
    Gus Xia, NYU Shanghai
  • November 20, 2019
    Clara Vania, CDS Postdoc
  • November 27, 2019
    ***No talk, Thanksgiving***
  • December 4, 2019
    Brandon Stewart, Princeton University
  • December 11, 2019
    Yiqiu Shen, New York University
  • January 29, 2020
    Aaron Kaufman, New York University Abu Dhabi /  Live Stream Recording
  • February 5, 2020
    Matthew Trager, New York University / Live Stream Recording
  • February 12, 2020
    Simon DeDeo, Carnegie Mellon University | Live Stream Recording
  • February 19, 2020
    Michael Picheny, New York University | Live Stream Recording
  • February 26, 2020
    Wesley Maddox, New York University, “Bayesian neural nets” | Live Stream Recording
  • March 4, 2020
    David Uminsky, University of San Francisco | Live Stream Recording
  • March 11, 2020
    ***Cancelled***
  • March 18, 2020
    ***No seminar (Spring Break)***
  • March 25, 2020
    ***Cancelled***
  • April 1, 2020
    David Frohardt-Lane, Sports analytics | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • April 8, 2020
    Clark Bernier, NYU Center for Data Science | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • April 15, 2020
    Richard Anderson, Fubar Labs | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • April 22, 2020
    Tamas Rudas, Eotvos Lorand University | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • April 29, 2020
    Anastasios Noulas, NYU Center for Data Science | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • May 6, 2020
    Clark Bernier, NYU Center for Data Science | Live Stream via Zoom (see CDS community email for details, or contact organizers)
  • May 13, 2020
    Luigi Acerbi, Université de Genève | Live Stream     via Zoom (see CDS community email for details, or contact organizers)

2018-2019 Series

  • September 19, 2018
    Roberto Di Cosmo, IRILL, “Software Heritage: The universal software source code archive”
  • September 26, 2018
    Karen E Adolph, NYU Databrary Project, “Video as Data and Documentation Will Transform Behavioral Science”
  • October 3, 2018
    Matthew Rocklin, Continuum Analytics, “Scalable Analysis in Python with Dask”
  • October 10, 2018
    ***No seminar (MSDSE Summit)***
  • October 17, 2018
    Benny Nissan, Federal Reserve Bank of New York, “Solving Business Challenges with Data Science at the Federal Reserve of New York”
  • October 24, 2018
    William Falcon, New York University, “Life outside the box – Scaling and tuning huge neural networks across
    multiple GPUs”
  • October 31, 2018
    Stefan Karpinski, MSDSE Research Engineer, “An Intro to Julia for Data Science”
  • November 7, 2018
    Timo Ropinski, University of Ulm, “Interactive Visualization Techniques for Molecular Structures”
  • November 14, 2018
    Sam McKenzie, Buzsaki lab, “Extracting synaptic properties from the spiking patterns of neural ensembles”
  • November 21, 2018
    ***No seminar (Thanksgiving)***
  • November 28, 2018
    Joseph Bullock, Global Pulse / United Nation, “Satellite image analysis for humanitarian relief”
  • December 5, 2018
    Elizabeth Bruch, University of Michigan, “How (and Why) Online Dating Experiences Differ across American Cities”
  • December 12, 2018
    Sebastian Schelter, Moore-Sloan Data Science Fellow, “Data Management for Machine Learning (and a little bit of Computational Social Science)”
  • January 30, 2019
    Narges Razavian, NYU Medical School + NYU CILVR lab
    “Machine Learning in Medicine: Disease Prediction and Biomarker Discovery”
  • February 6, 2019
    Mark Cartwright, NYU CUSP
    “Building a machine-listening system to monitor, analyze, and mitigate urban noise pollution”
  • February 13, 2019
    Qing Qu, MSDSE Fellow
    “Nonconvex Recovery of Low-Complexity Models in Data Science”
  • February 20, 2019
    Will Geary, CitySwifter
    “Space-Time Modeling and Visualization for Mobility”
  • February 27, 2019
    Soledad Villar, MSDSE/CDS Fellow
    “Classification-aware dimensionality reduction and genetic marker selection”
  • March 6, 2019
    Matthew Daniels, The Pudding
    Data-driven Storytelling at The Pudding
  • March 13, 2019
    Thomas Laetsch, MSDSE Postdoc
    “A drunkard’s walk into Sociology”
  • March 20, 2019
    ***No talk, NYU Spring Break***
  • March 27, 2019
    Igor Labutov, LAER.AI
    “Teaching Machines like we Teach People”
  • April 3, 2019
    Phu-Mon Htut, CDS PhD Candidate
    “Learning Latent Syntactic Structure of Language through Neural Machine Translation”
  • April 10, 2019
    Guilio Biroli, Ecole Normale Superieure in Paris
  • April 17, 2019
    Martina Balestra, NYU Tandon School of Engineering
  • April 24, 2019
    Krzysztof Geras, NYU School of Medicine & CDS
  • May 1, 2019
    Richard Shen/Carolin Fleissner, Wayve
  • May 8, 2019
    Emin Orhan, CDS Postdoc
  • May 15, 2019
    Petter Kolm, NYU Mathematics
  • May 29, 2019 *Special lunch seminar!*
    Julia Lane, NYU Wagner
    “Making Data Great Again”

2017-2018 Series

  • September 13, 2017
    Tassos Noulas, Moore-Sloan Fellow
    “OpenStreetCab: Insights and Challenges when Deploying a Taxi Price Comparison App in the Wild”
  • September 20, 2017
    Djellel Difallah, Moore-Sloan Fellow
    “The Dynamics of Crowdsourcing Platforms: The case of Amazon Mechanical Turk and Wikidata”
  • September 27, 2017
    Mirco Musolesi, UCL / Alan Turing Institute
    “Modelling Human Behaviour using Mobile Data for Urban Analytics”
  • October 4, 2017
    Carlos Fontaine, Center for Genomics & Systems Biology, NYU
    “Using high-throughput live microscopy and image analysis to study cell cooperation in cancer”
  • October 11, 2017
    Enrique Cruz, Foursquare
    “Foursquare Location Intelligence”
  • October 18, 2017
    Paula Parpart, Brainpool.ai/University College London
  • October 25, 2017
    Chaya D Stern, Memorial Sloan Kettering Cancer Center and Tri-Institutional Chemical Biology Program
    “Is the force with us? Quantifying model uncertainty in molecular simulations”
  • November 1, 2017
    Kyle Cranmer, NYU
  • November 8, 2017
    ***No seminar (MSDSE Summit)***
  • November 15, 2017
    Benjamin Cook, NASA Goddard Institute for Space Studies
    “Monitoring and Modeling Drought and Climate Change from the Past to the Future”
  • November 22, 2017
    ***No seminar (Thanksgiving)***
  • November 29, 2017
    Marc Gourevitch
  • December 6, 2017
    Ward Wheeler, American Museum of Natural History
  • December 13, 2017
    Yelena Mejova, Qatar Computing Research Institute
    “Capturing Social Media Signals for Lifestyle Health”
  • January 24, 2018
    Andreu Casas, MSDSE Postdoc in the Center for Data Science
    “More Effective Than We Thought: Accounting for Legislative Hitchhikers Reveals a More Inclusive and Productive Lawmaking Process”
  • January 31, 2018
    Harish Doraiswamy, MSDSE Research Engineer in the Center for Data Science
    “Speeding up queries over large spatio-temporal data sets”
  • February 7, 2018
    Soledad Villar, MSDSE Fellow in the Center for Data Science
    “Gerrymandering and Math”
  • February 14, 2018
    Vladimir Gligorijevic, New York University
    “Deep Multi-network Embedding for Protein Function Prediction”
  • February 21, 2018
    Richard Galvez, MSDSE Postdoc in the Center for Data Science
    “The First Three Minutes: an exposition on how nuclear elements formed in the early universe and an unsolved mystery involving dark matter and Beryllium-7”
  • February 28, 2018
    Leto Peel, University of Louvain / Namur
  • March 7, 2018
    Esteban Moro, Universidad Carlos III de Madrid / MIT
  • March 14, 2018
    ***No seminar (Spring Break)***
  • March 21, 2018
    Leila Mays, NASA Goddard Space Flight Center, Community Coordinated Modeling Center
  • March 28, 2018
    Neil Bramley, MSDSE Postdoc in the Center for Data Science
  • April 4, 2018
    AI Now Institute
  • April 11, 2018
    Adam Summers, Friday Harbor Laboratory, University of Washington
  • April 18, 2018
    Geoff Boeing, UC Berkeley
  • April 25, 2018
    Iddo Drori, NYU
  • May 2, 2018
    Katharina Kann
  • May 9, 2018
    Julia Stoyanovich, Drexel University
    “Algorithms and Systems for Responsible Data Science”
  • May 16, 2018
    Laura Noren
    “Three Ethical Challenges for Data Science”

2016-2017 Series

  • September 14, 2016
    Juliana Freire, Executive Director of Moore-Sloan Data Science Environment
  • September 21, 2016
    Eleni Zacharatou, “Indexing the Brain”
  • September 28, 2016
    Michael Z. Gill, “The Economic Benefits of Conflict? Estimating Defense Firm Responses to Major Events in U.S. Foreign Policy”
  • October 5, 2016
    Ravi Shroff
  • October 12, 2016 (special time 1:00-2:00pm)
  • October 19, 2016
    Anil Kocak
  • October 26, 2016
    ***No lunch, MSDSE 2016 Data Science Summit***
  • November 4, 2016 (special day, Friday)
    Shankar Iyer, Quora
  • November 9, 2016
    danah boyd, Microsoft Research
  • November 16, 2016
    Max Sklar, Foursquare
  • November 21, 2016 (special day, Monday)
    Mike Williams, Fast Forward Labs ***CANCELED***
  • November 30, 2016
    Manuel Garcia-Herranz, UNICEF Innovation
  • December 7, 2016
    Jason Anastasopoulos
  • December 14, 2016
    Rossano Schifanella, University of Torino
  • February 1, 2017
    Justin Salamon, “Audio Source Identification in Urban/Bio-Acoustic Environments”.
  • February 8, 2017
    Alexander Bock, “OpenSpace — Moving from What to How in Science Communication”
  • February 15, 2017
    Nicola Barbieri, Tumblr
  • February 22, 2017
    Yannis Liodakis, “Revealing the intrinsic timescale distribution in the most active of galaxies”
  • March 1, 2017
    Piotr Brodka, Wroclaw University of Science and Technology (Poland), “Current Challenges in the Area of Spreading Processes in Multilayer Networks”
  • March 8, 2017
    Jaime Earnest, DoD, “Future State: Big Data and Analytics for Better Governance”
  • March 15, 2017
    Spring Break – No lunch seminar
  • March 23, 2017, 1:30-2:30
    Noemi Derzsy, RPI, NASA DataNauts, “Open NASA Data: From API to Data Analysis”
  • March 29, 2017
    Partha Mithra
  • April 5, 2017
    TBD
  • April 12, 2017
    Yana Volkovich, AppNexus
  • April 19, 2017
    Hannah Wallach, Data and Society
  • April 26, 2017
    Ciro Cattuto, Fondazione ISI
  • May 3, 2017
    Mor Naman, “Patterns of Large-Scale Attention”
  • MAY 10, 2017
    Enrico Bertini

2015-2016 Series

2014-2015 Series

  • October 28th
    Ken Benoit, Visiting Professor from the London School of Economics
    Topic: Quantitative Text Analysis for the Social Sciences (Using R): Natural language processing and quantitative analysis of social and political texts, using an R package called quanteda (http://github.com/kbenoit/quanteda) that he is developing.
  • November 5th
    Jonathan Goodman, Professor of Mathematics
    Topic: The hermeneutics of MCMC sampling: points of view about samplers, some of which don’t depend on your point of view.
  • November 13th
    Greg Dobler, Research Scientist at CUSP and a Research Assistant Professor of Physics at NYU
    Topic:     Better Cities through Imaging: I will describe how persistent, synoptic imaging of the NYC skyline can be used to better understand the city (in analogy to how persistent, synoptic imaging of the sky can be used to better understand the heavens), giving specific examples related to energy consumption, public health, and air quality which can lead to improved city functioning and quality of life.
  • December 1st
    Pablo Barbera, PhD Candidate in Politics
    Topic: How Social Media Reduces Mass Political Polarization:     Using a new method to estimate the ideological positions of social media users and their communication networks over time, I provide evidence that exposure to dissonant political messages on social media induces political moderation. For the paper, click here.
  • December 9th
    Mike O’Neil, Assistant Professor, School of Engineering and Courant Institute
    Topic: Fast Algorithms for Gaussian Processes
  • February 5th
    Ying Lu, Assistant Professor of Applied Statistics at NYU and
    Dr. Preeti Raghavan, Assistant Professor at NYU Medical Center
    Topic: Quantifying Feedforward Control: A Linear Scaling Model for Fingertip Forces and Object Weight
    In this paper, we fit a linear growth curve to the biomechanical data of grasping  in order to understand the relationship between fingertip forces and object weight among healthy subjects. Our results show evidence of feedforward control during the grasping task when healthy subject grasp/lift an object with familiar weights.
  • February 23rd
    Daniela Huppenkothen, Moore Sloan Data Science Fellow at NYU
    Topic: Exploring the Violent Universe: Astrophysical Data (Analysis) at High Energies
    This talk will give an introduction into astronomy at very short wavelengths, where we see the universe’s most extreme phenomena, and showcase typical data analysis problems—some of which we are working on at the CDS—which we have to solve in order to understand the underlying physical processes.
  • March 3rd
    Aaditya Rangan, Associate Professor of Mathematics at NYU
    Topic: Efficient Methods for Detecting Low Rank Structure
    Discussion of some methods for finding low-rank submatrices within gene-expression data; It will be necessary to correct for case-control status as well as covariates.
  • March 11th
    Tae Hong Park, Associate Professor of Composition and Music Technology at NYU
    Topic: CityGram: Sensing Urban Soundscapes
    Dynamic mapping of non-ocular spatio-acoustic energies.
  • March 27th
    Lakshminarayanan Subramanian, Associate Professor of Computer Science at NYU
    Topic: Societal Network Science
    Predicting Socio-economic indicators from real-time news streams
  • April 8th
    Patrick Wolfe, Professor of Statistics and Honorary Professor of Computer Science at University College London
    Topic: Network Analysis and Nonparametric Statistics
    Networks are ubiquitous in today’s world.  Any time we make observations about people, places, or things and the interactions between them, we have a network.  Yet a quantitative understanding of real-world networks is in its infancy, and must be based on strong theoretical and methodological foundations.  The goal of this talk is to provide some insight into these foundations from the perspective of nonparametric statistics, in particular how trade-offs between model complexity and parsimony can be balanced to yield practical algorithms with provable properties.
  • April 13th
    Karen Adolph, Professor of Psychology and Neural Science at NYU
    Topic: Databrary and Datavyu: Coding, Sharing, and Repurposing Video
    A description of the promises and challenges of coding, sharing, and repurposing video data
  • April 23rd
    Yoni Halpern, Graduate Student in the Computer Science Department at NYU
    Topic: Provable Methods for Discrete Factor Analysis
    This talk will cover methods for structure and parameter learning in latent variable models and discuss an application for learning diagnosis models from unstructured data in electronic medical records.
  • April 29th
    Joshua Tucker, Professor of Politics at NYU
    Topic: Using Social Media to Study Politics
    How can we use social media to study the political behavior of masses and elites?  What we can do, what we can sort of do, and what we’d like to be able to figure out how to do…
  • May 5th
    Dan Brown, Associate Professor of Computer Science at the University of Waterloo
    Topic: Do better lyrics make a hit?  Complexity of rhymes in music lyrics

2013-2014 Series

  • September 14th
    Jean-Daniel Fekete, Senior Research Scientist, INRIA Saclay
    Topic: Progressive Analytics: A New Programming Paradigm for Large-Scale Data Exploration
  • September 21st
    Kyunghyun Cho, Assistant Professor, Computer Science and Data Science
    Topic: Lost in Interpretability
    I hear from non-ML as well as ML researchers once a while that they find it difficult to work with neural networks due to the lack of interpretability compared to other simpler models which are in many cases some variants of linear models. This has always made me wonder if there is a natural tradeoff between interpretability and modelling accuracy. In this talk, however, I argue that no such tradeoff exists, which has been argued already more than a decade ago by Breiman (2001). Furthermore, I present some recent results from deep learning showing that seemingly complex models, such as deep neural nets and recurrent neural nets, are in fact interpretable, if we try hard enough.
  • September 28th
    Arthur Spirling, Associate Professor, Politics and Data Science
  • October 5th
    No meeting
  • October 12th
    Kenneth J. Kurtz, Associate Professor, Psychology at SUNY Binghamton
  • October 19th
    No meeting, see special time Thursday of that week
  • October 22nd (Thursday, special time)
    Bingni Brunton, Assistant Professor, Biology and Data Science at University of Washington
  • October 26th
    Rachid Ounit, Ph.D Candidate, Computer Science, UC Riverside
    Topic: Classification of metagenomic sequences: how to make it faster and more accurate
  • November 2nd
    Dan Cervone, Data Science Fellow, Center for Data Science