diiP Seminars

The diiP Distinguished Lectures, as well as Seminars + Hands-On Workshops, are organized the 1st Wednesday of each month at 4pm (CET: Paris time). The seminars focus on different topics around data analytics, data science and data intelligence (including data management, machine learning, and deep learning).

In the Distinguished Lectures series, diiP will host invited speakers that are internationally recognized for their research and/or applied work, who will talk about their latest results.

The Seminars + Hands-On Workshops are animated by the diiP associate researchers, as well as by international experts in areas related to diiP. Several of the seminars include hands-on workshops, where participants will have the chance to learn how to use the techniques described in the seminar.

Please see below for the detailed agenda. The material related to the talks will appear below, as well.

Agenda

Please note that due to the covid-19 situation, the seminars will take place on zoom until further notice. If you’re interested in participating at an event, please register (for free) by emailing us at diip[at]math-info.univ-paris5.fr with the date and title of the seminar you are interested in. You may also find below recordings and materials from previous seminars that already took place.

Distinguished Lectures

6 October 2021, Building Data Equity Systems

Building Data Equity Systems

Who: Prof Julia Stoyanovich (New York University)
When: October 6, 4pm (Paris time).
Where: online (zoom)

title:
Building Data Equity Systems

abstract:
Equity as a social concept — treating people differently depending on their endowments and needs to provide equality of outcome rather than equality of treatment — lends a unifying vision for ongoing work to operationalize ethical considerations across technology, law, and society.  In my talk I will present a vision for designing, developing, deploying, and overseeing data-intensive systems that consider equity as an essential requirement.  I will discuss ongoing technical work in scope of the “Data, Responsibly” project, and will place this work into the broader context of policy, education, and public outreach activities.

short bio:
Julia Stoyanovich is an Institute Associate Professor of Computer Science & Engineering at the Tandon School of Engineering, Associate Professor of Data Science at the Center for Data Science, and Director of the Center for Responsible AI at New York University (NYU).  Her research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle.  She established the “Data, Responsibly” consortium and served on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio.  Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic.  In addition to data ethics, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst.  She is a recipient of an NSF CAREER award and a Senior Member of the ACM.

logistics: please send an email to diip[at]math-info.univ-paris5.fr to register to this event.

lecture material will be posted here after the event.

5 May 2021, Deploying a Data-Driven COVID-19 Screening Policy at the Greek Border

Deploying a Data-Driven COVID-19 Screening Policy

Who: Prof Kimon Drakopoulos (University of Southern California)
When: May 5, 4pm (Paris time).
Where: online (zoom)

title:
Deploying a Data-Driven COVID-19 Screening Policy at the Greek Border

abstract:
In collaboration with the Greek government, we designed and deployed a nation-wide COVID-19 screening protocol for travelers to Greece. The goals of the protocol were to combine limited demographic information about arriving travelers with screening results from recently tested travelers to i) judiciously allocate Greece’s limited testing budget to identify asymptomatic, infected travelers and ii) quickly identify hotspots and spikes in other nations to inform immigration/border policies in real-time. This talk details i) the operations of our designed system (including border screening, database management, closed-loop feedback, and liaising with contact-tracing teams), ii) a novel, batched, contextual bandit algorithm tailored to the unique features of this problem and iii) an empirical assessment of the benefits of the deployed system from the summer/fall 2020.

short bio:
Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business, where he researches complex networked systems, control of contagion, information design and information economics. He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT, focusing on the analysis and control of epidemics within networks. His current research revolves around controlling contagion, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation, fake news propagation and belief polarization.

lecture material:
video recording

Seminars + Hands-On Workshops

17 November 2021, Deep Learning for Sequential Data: Models and Applications

Deep Learning for Sequential Data: Models and Applications

Who: Dr Foula Vagena (Université de Paris, diiP)
When: November 17, 4pm (Paris time)
Where: online (zoom)

title:
Deep Learning for Sequential Data: Models and Applications + Hands-on workshop

abstract:
Recurrent neural networks (RNNs) are a family of specialized neural networks for processing sequential data. They can scale to much longer sequences than would be practical for networks without sequence-based specialization and most of them can also process sequences of variable length. In this tutorial we will first describe the high level RNN architecture and outline popular variations of the former. We will then explain the main challenge the handling of data sequentail presents, namely long term dependenceis and summarize the different mechanisms that are employed to tackle it (i.e. gated architectures, attention mechanisms). We will go on to describe applications where RNN have been succesfule employed and we will conclude the tutorial with an illustrative RNN-supported timeseries prediction example.

The Hands-On Workshop will focus on RNN supported timeseries prediction.

short bio:
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université de Paris. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

logistics: Please send an email to diip[at]math-info.univ-paris5.fr with the name and date of the seminar you’re interested in to register and receive the zoom link. 

material will be posted here.

7 July 2021, Convolutional Neural Networks: An Overview and Applications

Convolutional Neural Networks: An Overview and Applications

Who: Dr Foula Vagena (Université de Paris, diiP)
When: July 7, 4pm (Paris time)
Where: online (zoom)

title:
Convolutional Neural Networks: An Overview and Applications + Hands-on workshop

abstract:
Convolutional neural networks (CNNs), are a specialized kind of neural network for processing data that has a known, grid-like topology. Examples include time-series data, which can be thought of as a 1D grid taking samples at regular time intervals, and image data, which can be thought of as a 2D grid of pixels. Such networks have been tremendously successful in practical applications. They employ a mathematical operation called convolution, a specialized kind of linear operation. In this tutorial we will first describe the convolutional operation and explain how this is leveraged to form CNN architectures. We will then describe applications where CNNs have been very succesful and provide a summary of well known CNN architectures. The tutorial will conclude with an illustrative hands-on example of a CNN-supported image classification task.

The Hands-On Workshop will focus on CNN supported image classification.

short bio:
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université de Paris. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

material:

Video recording

presentation slides

source code for examples

2 June 2021, Deep Learning: An overview using Multi Layer Perceptrons (MLPs) + Hands-On Workshop

Deep Learning: An overview using Multi Layer Perceptrons (MLPs) + Hands-On Workshop

Who: Dr Foula Vagena (Université de Paris, diiP)
When: June 2, 4pm (Paris time)
Where: online (zoom)

title:
An overview using Multi Layer Perceptrons (MLPs)

abstract:
Deep Learning (DL for short) is a field of machine learning that is concerned with algorithms based on (artificial) neural networks and representation learning. The quintessential example of a deep learning model is the feedforward deep network or multilayer perceptron (MLP). In this tutorial we will provide an overview DL and present its main components (e.g. tensors, layered composition of models/functions). We will focus on MLPs and using that we will delineate and explain the main steps/concepts for DL-based modeling. The tutorial will conclude with an illustrative hands-on example of MLP-supported regression and classification models.
The hands-on workshop will focus on MLP supported regression + classification examples.

short bio:
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université de Paris. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

seminar material:
video recording

presentation slides

source code for examples

7 April 2021, Data Science: A high level overview + Hands-On Workshop

Data Science: A high level overview + Hands-On Workshop

Who: Dr Foula Vagena (Université de Paris, diiP)
When: April 7, 4pm (Paris time).
Where: online (zoom)

title:
Data Science: A high level overview

abstract:
Data science is the area of study which involves extracting insights from data using various scientific methods, algorithms, and processes. In this tutorial we will explain the need for data science and provide an overview of the field, its main components, the opportunities that it creates as well as its major challenges. We will then describe the main steps of performing data science starting from an analytics problem up to the point of communicating the results. We will then summarize applications where data science has traditionally been employed and provide examples of data science popular tools. The tutorial will conclude with an illustrative hands-on example of the data science process.
The hands-on workshop will focus on Image Analysis + Segmentation using a pre-trained Mask R-CNN model.

short bio:
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université de Paris. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

logistics:
If you want to register for this event, please contact us at diip[at]math-info.univ-paris5.fr. In the object of your email, please specify the date and title of the seminar you’re interested in.

seminar material:
video recording
presentation slides
source code for examples

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