Workshop

Women in Big Data

14–15 June 2018
Renaissance Zurich Tower Hotel

Objectives & scope

The goal of this workshop is to bring together women working on Big Data research in academia and industry, to talk about career challenges and their possible solutions, to discuss how to excel in challenges posed by Big Data research, to exchange research insights across different disciplines, and to further advance careers.

The workshop is organized as part of the National Research Program “Big Data” (NRP 75), administered by the Swiss National Science Foundation (SNSF). The focus of NRP 75 is on Big Data research from multiple disciplinary perspectives, tackling a variety of technical, societal, regulatory, and ethical research issues.

This all-women workshop cuts across disciplines. It brings together international scholars working on Big Data research with NRP 75 women for talks, posters, panel discussions, as well as informal exchanges. The workshop invites women working on Big Data research to connect with each other, to discuss the challenges they face in their careers as well as in research, and to allow for potential exchanges leading to future research collaboration.

Is this workshop
right for you?

In Switzerland, fewer than half of all students in STEM fields — life sciences, technology, engineering, and math — are women. This applies to all levels of university training and is particularly pronounced in computer and information sciences, math, and engineering. Across the globe, women account for less than one-third of those employed in scientific research and development. What’s more, women are less likely to enter and more likely to leave tech-intensive business roles.

We consider it important to have an open and broader discussion on building, advancing, and maintaining careers for women in STEM fields as well as other disciplines based on technological and mathematical skill sets, including the social sciences, law, and philosophy, all of which work with Big Data-related research questions. This workshop is the first organized discussion within the NRP 75 framework.

The workshop is targeted at women in all career stages, from doctoral students to senior scientists, including all women PIs, post-docs, and doctoral students within the NRP 75 framework, who are working on Big Data-related research.

Program

Venue & accommodation

Contact

Connect

Collaborate

Lead

THEME 1

Career

We will discuss how women can build, maintain, and advance careers in Big Data fields

THEME 2

Technical excellence

We will examine how women can excel in the challenges of Big Data

FOCUS 1

Industry and academia

Leaders from both areas will share their insights

FOCUS 2

International collaboration

An opportunity for Swiss researchers to connect with international scholars and leaders from Europe, Asia and the US

Venue & accommodation

Renaissance Zurich Tower Hotel
Turbinenstrasse 20
8005 Zürich

www.marriott.com
Tel. 044 630 30 30
GPS 47.388339, 8.514289

Invited speakers

Dr. Lisa Amini

Dr. Lisa Amini

Director, IBM Research Cambridge, Acting Director, MIT-IBM Watson AI Lab, USA
[ Abstract ]

Why AI Needs Even More Diversity, and Vice Versa

Dr. Lisa Amini
Director, IBM Research Cambridge, Acting Director, MIT-IBM Watson AI Lab, USA

Recent advances in AI and deep learning are capturing headlines, and yet suffer from a variety of short-comings, including catastrophic forgetting, inability to generalize robustly, susceptibility to bias, and inadequate techniques for introspection and explanation. Many of these are challenges where a greater diversity of expertise, cultures, genders, and perspectives could have profound effects. For example, recent studies and related press has shown that machine learning algorithms error rates vary widely for differing races and genders. Awareness of such shortcomings, along with mitigation techniques, has come from a variety of perspectives, including data science, big data, information theory, statistics, and digital activism. In another example, AI has an urgent and critical need for learning causal models, an area requiring a sound grasp of statistical analysis, principles of identification, and other mainstays of data science. Conversely, differentiable (deep learning) techniques for learning causal structure could bring powerful new tools to many fields and communities. This talk will cover these, and other examples of projects we are undertaking in the unique academic+industry approach we are taking in the MIT-IBM Watson AI Lab. I will also weave in my personal journey from distributed systems, to stream data mining systems and algorithms, to Big Data, and now leading a new IBM Research AI Lab.

Dr. Isabelle Collet

Dr. Isabelle Collet

Senior lecturer in gender and education, University of Geneva, Switzerland
[ Abstract ]

Is Affirmative Action for the Inclusion of Women in IT Always a “Good” Practice?

Dr. Isabelle Collet
Senior lecturer in gender and education, University of Geneva, Switzerland

Computers play a growing role in the evolution of our societies, but women have been largely under-represented in these professions for several decades. The figure of the geek is often used as a scapegoat in the sense that it carries the representations that would lead women to avoid the field. In reality, research shows a lifelong phenomenon of social censorship excluding girls and women from science and technology, especially computer science. Thanks to systemic good practices, some universities have obtained long-term success in reducing the gender gap in computer science. The purpose of this intervention is to present two effective but different practices to think of the inclusion of women in the professional world of informatics as a change of culture and practices.

Prof. Jennifer Dy

Prof. Jennifer Dy

Department of Electrical and Computer Engineering, Northeastern University, Boston, USA
[ Abstract ]

Learning from Complex Medical Data, Clustering and Interpretable Models

Prof. Jennifer Dy
Department of Electrical and Computer Engineering, Northeastern University, Boston, USA

Machine learning as a field has become more and more important due to the ubiquity of data collection in various disciplines.  Coupled with this data collection is the hope that new discoveries can be made or knowledge gained.  My research spans both fundamental research in machine learning and its application to biomedical imaging, health, science and engineering.  Multi-disciplinary research is instrumental to the growth of the various areas involved.  In many applications, data is often complex, high-dimensional and multi-faceted, where multiple possible interpretations are inherent in the data. Fortunately, domain scientists often have rich knowledge that can guide data-driven methods.  Thus, it is important to enable incorporation of domain input into the design of algorithms.  Furthermore, for clinicians and domain scientists to trust and use the results of learning algorithms, not only must models be accurate but learning models must also be interpretable.  In this talk, I will highlight these challenges through our experience in a collaborative research to discover disease subtypes. I will then provide examples of how these challenges have led to innovations in machine learning and to new discoveries.

Dr. Raia Hadsell

Dr. Raia Hadsell

Research Scientist, Google DeepMind
[ Abstract ]

Deep reinforcement learning

Dr. Raia Hadsell
Research Scientist, Google DeepMind

Deep reinforcement learning has rapidly grown as a research field with far-reaching potential. As the field matures, we are beginning to look to more sophisticated learning systems in order to solve more complex tasks. I will describe some recent research from DeepMind that allows end-to-end learning in challenging environments with real-world variability and complex task structure.

Prof. Gina Neff

Prof. Gina Neff

Senior Research Fellow and Associate Professor, Oxford Internet Institute & Department of Sociology, University of Oxford, UK
[ Abstract ]

Does AI Have Gender?

Prof. Gina Neff
Senior Research Fellow and Associate Professor, Oxford Internet Institute & Department of Sociology, University of Oxford, UK

Machine learning and artificial intelligence make extraordinary discoveries possible, and autonomous systems are being rolled out in vital business and social settings including healthcare, policing, education, and business decision making. But can their assumed neutrality and objectivity encode serious social and political bias into the results? High-profile examples show how these systems already incorporate into their design human flaws, biases, and assumptions, especially about women and their role in society. In this talk, I will show that explicitly thinking about gender in AI will help designers make AI systems that help humans make better—and fairer—decisions.

Prof. Caroline Sporleder

Prof. Caroline Sporleder

Göttingen Centre for Digital Humanities, University of Göttingen, Germany

[ Abstract ]

Big Data in the Humanities

Prof. Caroline Sporleder
Göttingen Centre for Digital Humanities, University of Göttingen, Germany

While data in the Humanities tends to be “smaller” than data from other disciplines, many of the challenges commonly associated with “big data” nonetheless apply, in particular those associated with “variety” and “veracity”. Obtaining sufficient data and the legal and technical difficulties associated with this is, on the other hand, possibly a more pronounced problem in the Humanities than in many other fields. The same may apply to questions of acceptability and evaluation of results. In this talk, I will discuss the chances and challenges of big data research in the Humanities, finishing with a particular relevant question, namely how to combine highly heterogeneous data, in particular data from different modalities.

Posters

Angela Bonifati, Wim Martens, Thomas Timm,
“Large-Scale SPARQL Query Log Analysis”

Celestine Dünner, Andreea Simona Anghel, Thomas Parnell, Dimitrios Sarigiannis, Nikolas Ioannou, Haris Pozidis,
“Snap Machine Learning: A Hierarchical Software Framework for Machine Learning on Heterogeneous Systems”

Agata Ferretti, Marcello Ienca, Samia Hurst, Effy Vayena,
“Health-related Big Data: Challenges and Implications for Ethics Review Committees”

Catalina Goanta, Stephan Mulders,
“Moving Fast and Breaking Things: Social Media and Unfair Commercial Practices”

O. Kaiser, S. Hien, U. Achatz, I. Horenko,
“Stochastic Subgrid-Scale Parametrization”

Emanuela Keller,
“ICU-Cockpit: IT platform for multimodal patient monitoring and therapy support in intensive care and emergency medicine”

Jasmina Malicevic, Baptiste Lepers, Willy Zwaenepoel,
“Everything You Always Wanted to Know about Multicore Graph Processing But Were Afraid to Ask”

MicroGIS,
“2016 Switzerland’s Purchase Power”

D. Monett,
“Undergrad Projects and Data. Wait, What Data?”

Rahel Schär,
“The Governance of Data in Trade Agreements: Design, Diffusion and Implications”

Elissa M. Redmiles,
“Beyond Discrimination: Incorporating Human Judgements of Fairness in Algorithmic Decision-Making”

Raquel Menezes,
“Big Data in Environmental and Health Sciences. Modelling Challenges for Large Spatio-Temporal Data Structures”

Alina Walch, Roberto Castello, Nahid Mohajeri, Jean-Louis Scartezzini,
“Spatio-Temporal Modelling and Uncertainty Estimation of Solar Irradiance Using Extreme Learning Machine Ensembles”

Iris A.J. Weidmann,
“Legality of Diagnostics and Therapy with ‘Virtual Reality’ — Virtual Child Pornography and Art. 197 SCC”

Lauren Zweifel, Maxim Samarin, Katrin Meusburger, Christine Alewell,
“Spatio-Temporal Analysis of Soil Degradation in Swiss Alpine Grasslands”

Program

Thursday, 14 June 2018

Time Speaker
4:00 pm Registration
5:00 pm Welcome
Lydia Chen, IBM Research – Zurich
Sophie Mützel, University of Lucerne
5:15 pm Keynote
“Does AI Have Gender?”
Gina Neff
Senior Research Fellow and Associate Professor, Oxford Internet Institute & Department of Sociology, University of Oxford, UK
6:15 pm Light dinner buffet (Apéro riche)

Friday, 15 June 2018

Time Speaker
8:30 am Welcome
Béatrice Miller
NRP75
8:40 am Introduction
Lydia Chen, IBM Research – Zurich
Sophie Mützel, University of Lucerne
9:00 am Keynote
“Why AI Needs Even More Diversity, and Vice Versa”
Lisa Amini
Director, IBM Research Cambridge, Acting Director, MIT-IBM Watson AI Lab, USA
10:00 am Madness session
11:00 am Coffee break
11:30 am Invited talk
“Is Affirmative Action for the Inclusion of Women in IT Always a ‘Good’ Practice?”
Isabelle Collet
Senior lecturer in gender and education, University of Geneva, Switzerland
12:00 pm Invited talk
“Deep Reinforcement Learning”
Raia Hadsell
Research Scientist, Google DeepMind, London, UK
12:30 pm Lunch & poster session
1:45 pm Invited talk
“Big Data in the Humanities”
Caroline Sporleder
Director of Göttingen Centre for Digital Humanities and Professor of Digital Humanities, University of Göttingen, Germany                                                  
2:15 pm Keynote
“Learning from Complex Medical Data, Clustering and Interpretable Models”
Jennifer Dy
Department of Electrical and Computer Engineering, Northeastern University, Boston, USA
3:15 pm Coffee break
3:40 pm Panel discussion “Women in Big Data Research”
Lisa Amini
Isabelle Collet
Jennifer Dy
Raia Hadsell
Gina Neff
Caroline Sporleder
Moderator: Olivia Kühni, Journalist
5:00 pm Closing remarks
5:30 pm Networking apéro

Questions?

Ruth Guskar-Osborne

Ruth Guskar-Osborne

Workshop secretariat
IBM Research | Zurich
Lydia Chen

Dr. Lydia Chen

Workshop organizer
IBM Research | Zurich
Sophie Mützel

Prof. Dr. Sophie Mützel

Workshop organizer
University of Lucerne

Sponsors

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IBM Research logo
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