Social & Information Systems

Junior Research Group leader positions at MPI-SWS

April 2019
The Max Planck Institute for Software Systems (MPI-SWS) is inviting applications for Junior Research Group leader positions in systems and machine learning (SysML), human-oriented machine learning (fairness, accountability, transparency, and ethical aspects of AI), adversarial ML, reinforcement learning,  human-computer interaction with ML/social aspects, natural language processing, and learning & cognitive sciences.

Our Junior Research Group program offers young scientists the opportunity to develop their own research program. The position is funded for 5 years with the possibility of a 2-year extension. Applicants must have completed a doctoral degree in computer science or related areas and must have demonstrated outstanding research vision and potential to successfully lead a research group. Successful candidates are expected to build a highly visible research agenda, to mentor junior scientists, and to participate in collaborative projects.

The Max Planck Institute for Software Systems is located in Saarbruecken and Kaiserslautern in Germany. We maintain an open, international, and diverse work environment and seek applications from outstanding researchers regardless of national origin. Our working language is English. We collaborate with several major research institutions worldwide and have high international visibility. There is generous travel, administrative, and technical support available for all group members.

Please apply at under ``Research Group Leader''. You need to upload your CV, a research plan, an optional teaching statement, and 3-5 references. Reviewing of applications will commence on 15 May 2019 and will continue until the positions are filled. The expecting starting date for the position is Fall 2019. Informal inquiries can be addressed to

The Max Planck Society is committed to employing more individuals with disabilities and expressly welcomes them to apply. The Max Planck Society seeks to increase the percentage of women in the areas where they are underrepresented and expressly welcomes them to apply.

MPI-SWS article published in the Proceedings of Academy of Sciences (PNAS)

January 2019
The article "Enhancing Human Learning via spaced repetition optimization", coauthored by MPI-SWS and MPI-IS researchers, has been published in the Proceedings of the National Academy of Sciences (PNAS), a highly prestigious journal.

The (open-access) article can be found here:

Krishna Gummadi and Alan Mislove awarded a Facebook "Secure the Internet" grant

October 2018
MPI-SWS faculty member Krishna Gummadi and MPI-SWS alumnus Alan Mislove have been awarded a "Secure the Internet" grant by Facebook. Their proposal, “Towards privacy-protecting aggregate statistics in PII-based targeted advertising,” has been awarded $60,000 to develop techniques for revealing advertising statistics that provide hard guarantees of user privacy, based on a (principles-first) approach. Their goal is to develop a differential privacy-like approach that can be applied to existing advertising systems.

The Facebook "Secure the Internet" grant program is designed to improve the security, privacy, and safety of internet users. Gummadi and Mislove's proposal was one of only 10 winning proposals, which were together awarded more than $800,000 by Facebook.

Five MPI-SWS papers accepted at NIPS 2018

October 2018
The following five MPI-SWS papers have been accepted to NIPS 2018, the flagship conference in machine learning:

  • Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

  • Teaching Inverse Reinforcement Learners via Features and Demonstrations

  • Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners

  • Deep Reinforcement Learning of Marked Temporal Point Processes

  • Enhancing the Accuracy and Fairness of Human Decision Making


Four MPI-SWS papers accepted at AAAI 2018

February 2018
Four papers from MPI-SWS have been accepted to AAAI 2018:
  • Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning
  • Learning to Interact with Learning Agents
  • Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
  • Learning User Preferences to Incentivize Exploration in the Sharing Economy

Three MPI-SWS papers accepted at WWW 2018

February 2018
Three papers from MPI-SWS have been accepted to the 2018 Web Conference:

  • Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

  • On the Causal Effect of Badges

  • Fake News Detection in Social Networks via Crowd Signals

Research Spotlight: Teaching machine learning algorithms to be fair

December 2017
Machine learning algorithms are increasingly being used to automate decision making in several domains such as hiring, lending and crime-risk prediction. These algorithms have shown significant promise in leveraging large or “big” training datasets to achieve high prediction accuracy, sometimes surpassing even human accuracy.

Unfortunately, some recent investigations have shown that machine learning algorithms can also lead to unfair outcomes. For example, a recent ProPublica study found that COMPAS, a tool used in US courtrooms for assisting judges with crime risk prediction, was unfair towards black defendants. In fact, several studies from governments, regulatory authorities, researchers as well as civil rights groups have raised concerns about machine learning potentially acting as a tool for perpetuating existing unfair practices in society, and worse, introducing new kinds of unfairness in prediction tasks. As a consequence, a flurry of recent research has focused on defining and implementing appropriate computational notions of fairness for machine learning algorithms.

Parity-based fairness

Existing computational notions of fairness in the machine learning literature are largely inspired by the concept of discrimination in social sciences and law. These notions require the decision outcomes to ensure parity (i.e. equality) in treatment and in impact.

Notions based on parity in treatment require that the decision algorithm should not take into account the sensitive feature information (e.g., gender, race) of a user. Notions based on parity in impact require that the decision algorithm should give beneficial decision outcomes (e.g., granting a loan) to similar percentages of people from all sensitive feature groups (e.g., men, women).

However, in many cases, these existing notions are too stringent and can lead to unexpected side effects. For example, ensuring parity has been shown to lead to significant reductions in prediction accuracy. Parity may also lead to scenarios where none of the groups involved in decision making (e.g., neither men nor women) get beneficial outcomes. In other words, these scenarios might be preferred neither by the decision maker using the algorithm (due to diminished accuracy), nor by the groups involved (due to very little benefits).

User preferences and fairness

In recent work, to appear at NIPS 2017, researchers at MPI-SWS have introduced two new computational notions of algorithmic fairness: preferred treatment and preferred impact. These notions are inspired by ideas related to envy-freeness and bargaining problem in economics and game theory. Preferred treatment and preferred impact leverage these ideas to build more accurate solutions that are preferable for both the decision maker and the user groups.

The new notion of preferred treatment allows basing the decisions on sensitive feature information (thereby relaxing the parity treatment criterion) as long as the decision outcomes do not lead to envy. That is, each group of users prefers their own group membership over other groups and does not feel that presenting itself to the algorithm as another group would have led to better outcomes for the group.

The new notion of preferred impact allows differences in beneficial outcome rates for different groups (thereby relaxing the parity impact criterion) as long as all the groups get more beneficial outcomes than what they would have received under the parity impact criterion.

In their work, MPI-SWS researchers have developed a technique to ensure machine learning algorithms satisfy preferred treatment and / or preferred impact. They also tested their technique by designing crime-predicting machine-learning algorithms that satisfy the above-mentioned notions. In their experiments, they show that preference-based fairness notions can provide significant gains in overall decision-making accuracy as compared to parity-based fairness, while simultaneously increasing the beneficial outcomes for the groups involved.

This work is one of the most recent additions to an expanding set of techniques developed by MPI-SWS researchers to enable fairness, accountability and interpretability of machine learning algorithms.


Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna Gummadi and Adrian Weller. From Parity to Preference: Learning with Cost-effective Notions of Fairness. Neural Information Processing Systems (NIPS), Long Beach (CA, USA), December 2017

MPI-SWS paper accepted into WSDM '18

November 2017
The paper "Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation " by MPI-SWS researchers, in collaboration with researchers at KAIST and MPI-IS, has been accepted to WSDM 2018, one of the flagship conferences in data mining.

WSDM will take place in Los Angeles (CA, USA) in February 2018.

MPI-SWS paper accepted into NIPS '17

September 2017
The paper "From Parity to Preference: Learning with Cost-effective Notions of Fairness" by MPI-SWS researchers, in collaboration with researchers at the University of Cambridge and MPI-IS, has been accepted to NIPS 2017, the flagship conference in machine learning.

NIPS will take place in Long Beach (CA, USA) in December 2017.

Krishna Gummadi and Peter Druschel win ACM SIGCOMM test-of-time award

July 2017
MPI-SWS researchers—faculty members Krishna Gummadi and Peter Druschel and former SWS doctoral students Alan Mislove and Massimiliano Marcon—have received the ACM SIGCOMM Test of Time Award for their IMC 2007 paper on "Measurement and Analysis of Online Social Networks." The work was done in collaboration with Bobby Bhattacharjee of the University of Maryland.

The award citation reads as follows: "This is one of the first papers that examine multiple online social networks at scale. By introducing novel measurement techniques, the paper has had an enduring influence on the analysis, modeling and design of modern social media and social networking services."
The ACM SIGCOMM Test of Time Award is a retrospective award. It recognizes papers published 10 to 12 years in the past in Computer Communication Review or any SIGCOMM sponsored or co-sponsored conference that is deemed to be an outstanding paper whose contents are still a vibrant and useful contribution today.

Adish Singla to join MPI-SWS as tenure-track faculty

June 2017

Adish Singla is joining us from ETH Zurich, where he has completed his Ph.D. in computer science. His research focuses on designing new machine learning frameworks and developing algorithmic techniques, particularly for situations where people are an integral part of computational systems. Adish joins the institute as a tenure-track faculty member, effective Oct 1, 2017.

Before starting his Ph.D., he worked as a Senior Development Lead in Bing Search for over three years. Adish received his Bachelor's degree from IIT Delhi and his Master's degree from EPFL. He is a recipient of the Facebook Fellowship in the area of Machine Learning, the Microsoft Research Tech Transfer Award, and the Microsoft Gold Star Award.

Best Paper Award Honorable Mention at WWW '17

April 2017
The MPI-SWS paper "Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate" has received a Best Paper Award Honorable Mention at WWW 2017.

The 26th International World Wide Web Conference (WWW) took place in Perth (Australia) in April 2017.

Five MPI-SWS papers accepted at WWW '17

December 2016
Five papers from MPI-SWS have been accepted to WWW 2017:

  • Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

  • Modeling the Dynamics of Online Learning Activity

  • Distilling Information Reliability and Source Trustworthiness from Digital Traces

  • Optimizing the Recency-Relevancy Trade-off in Online News Recommendations

  • Predicting the Success of Online Petitions Leveraging Multi-dimensional Time-Series

The 26th International World Wide Web Conference (WWW) will take place in Perth, Australia in April 2017.

Two MPI-SWS papers accepted at WSDM'17

October 2016
Two papers from MPI-SWS were accepted to ACM WSDM 2017:

  • RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks

  • Uncovering the Dynamics of Crowdlearning and the Value of Knowledge

Isabel Valera and Rijurekha Sen win Humboldt fellowships

March 2015
MPI-SWS postdoctoral fellows Isabel Valera and Rijurekha Sen have each won a two-year Humboldt postdoctoral fellowship. The fellowship enables highly-qualified scientists from abroad to spend extended periods of research in Germany. Dr. Valera recently joined the newly created Learning in Networks research group and Dr. Sen collaborates with both the MPI-SWS Distributed Systems and Social Computing research groups.

Rijurekha Sen wins ACM-India Doctoral Dissertation Award

February 2015
MPI-SWS postdoctoral fellow Rijurekha won the 2014 Best Doctoral Dissertation Award by ACM-India for her thesis titled "Different Sensing Modalities for Traffic Monitoring in Developing Regions" Dr. Sen recently joined the MPI-SWS Distributed Systems and Social Computing research groups.

Saptarshi Ghosh wins Humboldt fellowship

October 2014
MPI-SWS postdoctoral fellow Saptarshi Ghosh has won a one-year Humboldt postdoctoral fellowship. The fellowship enables highly-qualified scientists from abroad to spend extended periods of research in Germany. Dr. Ghosh will be spending his fellowship year with the MPI-SWS Social Computing research group.

MPI-SWS research in the New York Times

September 2014
MPI-SWS faculty Cristian Danescu-Niculescu-Mizil's work on linguistic change was mentioned in The New York Times. This is joint work with Robert West, Dan Jurafsky, Jure Leskovec, Christopher Potts.

MPI-SWS researchers win SOUPS distinguished paper award

July 2014
Krishna Gummadi, Mainack Mondal and Bimal Viswanath, along with Yabing Liu and MPI-SWS alumni Alan Mislove, have received a distinguished paper award at SOUPS 2014, for their paper "Understanding and Specifying Social Access Control Lists."

MPI-SWS research in the New York Times

July 2014
MPI-SWS faculty Cristian Danescu-Niculescu-Mizil has had his work on conversational threads in social media mentioned in The New York Times. This is joint work with Lars Backstrom, Jon Kleinberg and Lillian Lee.

MPI-SWS research in the news

May 2014
MPI-SWS faculty member Cristian Danescu-Niculescu-Mizil has had his work on how to ask for a favor featured on various media outlets including the Huffington Post, Gizmodo, Lifehacker, Slate's Future Tense blog, ABC News and Süddeutsche Zeitung. Links to all the articles can be found here. This is joint work with Tim Althoff and Dan Jurafsky.

Cristian Danescu-Niculescu-Mizil quoted by ABC News

August 2013
MPI-SWS faculty member Cristian Danescu-Niculescu-Mizil was quoted in a recent ABC News article about social bias effects in social media.

MPI-SWS student receives Google Scholarship

May 2013
MPI-SWS PhD student Juhi Kulshrestha was awarded a Google Anita Borg Scholarship. She joins Ezgi Cicek, who received an Anita Borg Scholarship in 2012. Juhi previously received a 2011 Google Fellowship for her work in social networking.

Cristian Danescu-Niculescu-Mizil wins WWW best paper award

May 2013
MPI-SWS faculty member Cristian Danescu-Niculescu-Mizil, along with his co-authors, has won the 2013 WWW Best Paper Award for his paper "No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities."

MPI-SWS research in the New York Times

February 2013
MPI-SWS faculty member Cristian Danescu-Niculescu-Mizil's work on the memorability of language was recently covered in a New York Times article about the computational analysis of cultural texts. This work was conducted at Cornell University with Justin Cheng, Jon Kleinberg and Lillian Lee.

Two new faculty to join MPI-SWS

June 2012
We are pleased to announce that two new faculty will join MPI-SWS.


Allen Clement obtained his Ph.D. at the University of Texas at Austin in 2011. Allen's research aims at designing and building systems that continue to work despite the myriad of things that go 'wrong' in deployed systems, including broken components, malicious adversaries, and benign race conditions. His research builds on techniques from distributed systems, security, fault tolerance, and game theory.


Cristian Danescu-Niculescu-Mizil is joining us from Cornell University, where he obtained his PhD in computer science. Cristian's research aims at developing computational frameworks that can lead to a better understanding of human social behavior, by unlocking the unprecedented potential of the large amounts of natural language data generated online. His work tackles problems related to conversational behavior, opinion mining, computational semantics and computational advertising.

MPI-SWS researchers win ICWSM best paper award

June 2012
Krishna Gummadi and Farshad Kooti, along with Winter Mason and previous MPI-SWS postdoctoral fellow Meeyoung Cha, have received a best paper award at ICWSM 2012, for their paper "The Emergence of Conventions in Online Social Networks."

MPI-SWS research in the news

April 2012

A recent WWW 2012 paper by Krishna Gummadi, Bimal Viswanath, and their coauthors was covered by GigaOM, a popular technology news blog, in an article titled Who's to blame for Twitter spam? Obama, Gaga, and you.

Steven le Blond's work on security flaws in Skype and other peer-to-peer applications has been receiving global media attention: WSJ, Le Monde (French), die Zeit (German), Daily Mail, New Scientist, Slashdot, Wired, and the New Scientist "One Percent" blog.

MPI-SWS study exposing Facebook privacy leak attracts global media attention

October 2010
A study by MPI-SWS researchers Saikat Guha (now at Microsoft Research), Bin Cheng, and Paul Francis has been highlighted on CNN, NPR, The Washington Post, Fox News, and other major media outlets.

The study, which will be presented at the ACM Internet Measurement Conference (IMC) in November, looks at the targeting behavior of Google and Facebook. While the goal of the study was to understand targeting in general, the researchers discovered that gay Facebook users can unknowingly reveal to advertisers that they are gay simply by clicking on an ad targeted to gay men. The ads appear innocuous in that they make no mention of targeting gay users (for instance, an ad for a nursing degree). A user's sexual orientation can be leaked even if the user made his sexual orientation private using Facebook's privacy settings.

This study was done as part of a broader research project to design techniques for making advertising more private.

MPI-SWS researchers win ICWSM best paper award

May 2009
Congratulations to Meeyoung Cha, Juan Antonio Navarro Perez, and Hamed Haddadi. Their paper "Flash Floods and Ripples: The Spread of Media Content through the Blogosphere" was selected as the ICWSM'09 best paper using the Spinn3r dataset. The winning paper was selected out of all papers in the main conference and the data challenge workshop that used the 2009 Spinn3r blog dataset.