We’re lucky to have Lab Alum Arkadiusz ‘Arek’ Stopczynski visiting the lab on January 9th and 10th. On the 9th, he’s busy being examiner at a PhD Defense, but on his second day in Denmark, he’s going to give a talk to tell us about what he’s been up to since starting at Google af couple of years ago.
In addition to working with us at DTU, Arek has also been a postdoc in Sandy Pentland’s lab at MIT’s MediaLab, he was an integral part of building the world’s first mobile brain scanner, and he’s given a great TEDx talk. And did I mention that we’ve just put out yet another paper together.
Below, you can find the talk details:
- Date: January 10th, 2019
- Time: 13:30-14:30
- Place: DTU Building 321, in the first floor lab space
- Title: Data Science: Thinking Industry
- Abstract: The practice of Data Science involves employing different methodologies, techniques, and tools, both deconstructive and constructive. In this talk we will discuss some fundamental differences in how Academia and Industry (exemplified by large tech companies) approach teaching and applying Data Science. These differences have important implications for how we teach students and conduct research.
I hope you can make it. Arek will stick around afterwards if you’d like to chat and hang out.
I had an absolutely wonderful time at the Complex Networks 2018 conference last week in Cambridge, UK. I learned a lot and got caught up a bit with all the amazing work that’s going within complex network analysis and see some of the great new young researchers in the field.
At the community detection sessions, I also saw several talks that drew on our work on Link Clustering, expanding and building on those ideas. Now don’t get me wrong: That work is well cited, so I know people have been reading it. But my sense is that most of the citations are of the type “This is also something one could do” or from people applying the algorithm. Those are both great (and a much better fate than what befalls most of my papers), but it is still extra exciting to see people adopting, refining, and developing the ideas – using them for their own work with community detection methods!
Another exciting development was to see how lots of people are starting to apply machine learning (including embeddings, etc) to networks.
Finally, I also got to give my own keynote about our recent paper on the Chaperone Effect in Scientific Publishing. It was a brand new talk (since the paper just came out 2 days prior), but judging from the Twitter reaction, people liked it 🙂
On October 9th, we are lucky to have Pantelis Pipergias Analytis visiting the group. Pantelis recently moved as an assistant professor at the Danish Institute of Advanced Studies (D-IAS) at the University of Southern Denmark.
Before moving to Denmark, he spent the past two years as a postdoctoral researcher at the Computer and Information Science department at Cornell University. Pantelis got his PhD from the Max Planck Institute for Human Development in Berlin.
Pantelis will give a talk based on his recent Nature Human Behavior paper Social learning strategies for matters of taste
- Date: October 9th
- Time: 13:30
- Place: Technical University of Denmark, Building 321, Room 134
Title: Social learning strategies for matters of taste
Abstract: Most choices people make are about ‘matters of taste’, on which there is no universal, objective truth. Nevertheless, people can learn from the experiences of individuals with similar tastes who have already evaluated the available options—a poten- tial harnessed by recommender systems. We mapped recommender system algorithms to models of human judgement and decision-making about ‘matters of fact’ and recast the latter as social learning strategies for matters of taste. Using computer simulations on a large-scale, empirical dataset, we studied how people could leverage the experiences of others to make better decisions. Our simulations showed that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people; by contrast, inexperienced individuals cannot reliably estimate similarity and are better off picking the main- stream option despite differences in taste. Crucially, the level of experience beyond which people should switch to similarity- heavy strategies varies substantially across individuals and depends on how mainstream (or alternative) an individual’s tastes are and the level of dispersion in taste similarity with the other people in the group.
Our old friend Piotr, current postdoc at Northeastern, and graduate from the group is visiting from his new home beyond the Atlantic. This coming Thursday, Piotr will give a short about his most recent work. Details below.
- Time: Thursday, Sept 6th. 11AM
- Location: Technical University of Denmark.B321, lab-space
- Title: Fairness in ranking
Abstract: Ranked lists of persons and items are a core part of the user experience in many online services, such as search, social media feeds, hiring, and dating sites. Studies have shown disparate amount of attention received by high rank results, potentially leading to loss of opportunity and access to resources among the lower ranked items. In this short talk I will give an overview of the work on individual and group fairness in ranked lists and focus on our work in progress: a novel metric for investigating group unfairness in ranked lists. Our approach relies on estimating the amount of attention given to members of a protected group and comparing it to that group’s representation in a defined population. It offers two major developments compared to the state of the art. First, rather than assuming a logarithmic loss in importance as a function of the rank, we allow for attention distributions that are specific to the audited service and the habits of its users. For example, more items are consumed in a single viewing of a social media feed than as a result of a single query in a web search engine. Second, we allow non-binary protected attributes (gender, race, etc.), both to better reflect the way individuals identify, but also to enable measurements on aggregates of multiple search runs, rather than separately for each result list.We investigate the properties of the metric and compare them to the behavior of other established approaches using synthetic ranked lists. Finally, we showcase the metric through a simulated audit of a number of hiring and dating services.
Later this month we will have legendary researcher Bernardo Huberman visiting. And we’re lucky enough to have him giving a talk on one of the most exciting new developments in Network Science: Applying AI to networking problems.
Bernardo has been a central player throughout the rise of network theory (and mentor for field notables, such as Lada Adamic and Jure Leskovec), but that’s just a fraction of what he’s accomplished. If you care about anything related to information sciences, this is a talk you cannot miss.
Bernardo is a Fellow and vice president of the Core Innovation Team at CableLabs. He is also a Consulting Professor in the Department of Applied Physics and the Symbolic System Program at Stanford University. Previously he was Senior Fellow and Senior Vice President at Hewlett Packard Enterprise Company, and Director of the Mechanisms and Design Lab at Hewlett Packard Labs.
- Date: August 29, 2017.
- Time: 14:00
- Location: Technical University of Denmark, Building 321, 1st floor: Room 134
Title: Artificial Intelligence and the Network
Abstract: Artificial Intelligence is the attempt to make computers emulate human cognition and thought processes. It has existed for a long time and has sprouted a number of subfields, from semantic networks and common sense reasoning to robotics, logic programming and machine learning. In spite of the glacial rate of progress in AI, one subfield, machine learning, has recently taken off like wildfire. What powers this incredible growth is the availability of fast processors that have made possible computations than seemed hard to achieve a few years ago. As a result, we now have powerful systems that can easily recognize myriad images and spoken languages. This talk will describe some of the great successes of machine learning, their limitations, and their application to networking problems which pervade modern communications. I will also present a form of artificial intelligence that is distributed in nature and that mimics the ability of groups of people and social insects to solve extremely hard problems.
We’re lucky to have Max Schich visiting DTU tomorrow. Max is an associate professor for arts and technology at The University of Texas at Dallas and a founding member of the Edith O’Donnell Institute of Art History. His work converges hermeneutics, information visualization, computer science, and physics to understand art, history, and culture. Schich is the first author of “A Network Framework of Cultural History” (Science magazine, 2014) and a lead co-author of the animation “Charting Culture” (Nature video, 2014). He is an editorial advisor at Leonardo Journal, an editorial board member at Palgrave Communications (NPG), and the Journal for Digital Art History. He publishes in multiple disciplines and speaks to translate his ideas to diverse audiences across academia and industry. His work received global press coverage in 28 languages.
- Time: April 17th, 14:00
- Location: DTU, Building 321, first floor lab space
- Title: Towards a Morphology of Durations
ABSTRACT: History has no periodic table of elements and no theory of temporal structure, as George Kubler pointed out in 1962, yet, as he also points out, things occupy time in a bounded number of ways. The obvious question still is: Can we capture the shape of time? – Tackling this challenge, this talk looks at historical time systematically, dealing with more or less exponential growth, the archaeological paradox, global and meso-level patterns, cycles, periodicity, condensation, and a bouquet of oddities.
Here’s a cool video about some of Max’s recent work
Sometime last year I became an adjunct professor at University of Copenhagen’s Department of Sociology. And just to be clear: I’m still primarily the Technical University of Denmark. The adjunct position is more of a way of signaling that I work closely with social science researchers (e.g. through my associate director position at SODAS).
Anyway, the important thing here is that I’m finally giving my inaugural lecture. The lecture is a fun chance for me to reflect on what’s happened up to now. My goal is to make the lecture be fun, entertaining, and personal (in a way that I hope will shed light on the mechanics of the scientific process). I hope you’ll come and see it.
Here are the details:
- Date & Time: Friday April 20th
- Location: Room 35.01.44, University of Copenhagen. [It’s not super easy to find building 35, so here’s special directions: The easiest way is to go to Gammeltoftgade 15, Copenhagen K and enter the brand-new building (Building 35), then head to the basement & follow the signs to 35.01.44]
- Official link.
The amazing Villum Foundation (who first funded the SensibleDTU project back in 2012) wanted to showcase some of the work coming out of their Young Investigator Program. As part of that, they have produced a beautiful video about my research (and also a bit about who I am).
It’s directed by Ole Stenum who shot something like 5 hours of conversation and elegantly captured the essence the whole thing in a 3 minute film. It was fun to try to be part of the whole process.
This Wednesday we will have Benjamin Maier, a PhD student (physics) from Dirk Brockmann’s group speaking at DTU. Ben is based at the Robert Koch-Institut but also affiliated with Humboldt University Berlin’s physics department and IRI Life Sciences. His interest lies in identifying the underlying processes of human contact facilitating the spread of diseases. In particular he is investigating the influence of structural properties of human contact networks or human transport networks, both of static and dynamic nature.
- Title: Flockworks, A class of dynamic network models for face-to-face interactions
- Date: Wednesday, November 29th
- Time: 13:30
- Location: DTU, Building 321, 1st floor lab space
Abstract: Studying the dynamics of face-to-face interaction networks is essential for a better understanding of contact mediated processes, contagion processes, and disease spreading. In many studies regarding social systems, networks are reconstructed using time averages or integrated networks, in which links reflect an interaction likelihood, although frequently this measure is not well defined but serves as a qualitative feature from which network properties are computed. During the last years a significant effort was made to resolve this issue by developing algorithms to analyse dynamic processes on the actual time-dependent contact patterns of social systems. However, there is still a lack of simple dynamic network models generating temporal networks of typical behaviour observed in real systems.
We introduce a class of minimal dynamic network models that naturally yield group formation and are easy to control. In those models, randomly chosen individual nodes cut their existing links, connect to a target node and establish links to the target’s neighbors. We discuss a variety of properties of those models and show how to use it for comparison of epidemic processes on real-world data.