Manuel Gomez Rodriguez

Alumni

Empirical Inference

Since November 2014, I am a Tenure Track Faculty at MPI for Software Systems. Before that, I did my Ph.D. jointly at MPI for Intelligent Systems and Stanford University.

My research interests lie in the development of machine learning and large-scale data mining methods for the analysis and modeling of large real-world networks and processes that take place over them. I am particularly interested in problems motivated by the Web and social media. For more information, please visit my personal website.

social networks information networks information diffusion point processes

For more information, please visit my personal website.

To download my resume in pdf, click here.

31 results (View BibTeX file of all listed publications)

2019

Enhancing Human Learning via Spaced Repetition Optimization

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI [BibTex]

2019

Tabibian, B., Upadhyay, U., De, A., Zarezade, A., Schölkopf, B., Gomez Rodriguez, M. Enhancing Human Learning via Spaced Repetition Optimization Proceedings of the National Academy of Sciences, 2019, PNAS published ahead of print January 22, 2019 (article)

DOI [BibTex]

2018

Enhancing the Accuracy and Fairness of Human Decision Making

Valera, I., Singla, A., Gomez Rodriguez, M.

Advances in Neural Information Processing Systems 31, pages: 1774-1783, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

arXiv link (url) [BibTex]

2018

Valera, I., Singla, A., Gomez Rodriguez, M. Enhancing the Accuracy and Fairness of Human Decision Making Advances in Neural Information Processing Systems 31, pages: 1774-1783, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018 (conference)

arXiv link (url) [BibTex]

Bayesian Nonparametric Hawkes Processes

Kapoor, J., Vergari, A., Gomez Rodriguez, M., Valera, I.

Bayesian Nonparametrics workshop at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

Kapoor, J., Vergari, A., Gomez Rodriguez, M., Valera, I. Bayesian Nonparametric Hawkes Processes Bayesian Nonparametrics workshop at the 32nd Conference on Neural Information Processing Systems, December 2018 (conference)

PDF link (url) [BibTex]

Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages: 324-332, (Editors: Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek), ACM, Febuary 2018 (conference)

DOI [BibTex]

Kim, J., Tabibian, B., Oh, A., Schölkopf, B., Gomez Rodriguez, M. Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), pages: 324-332, (Editors: Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek), ACM, Febuary 2018 (conference)

DOI [BibTex]

A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

[BibTex]

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K. A Flexible Approach for Fair Classification Journal of Machine Learning, 2018 (article) Accepted

[BibTex]

2017

From Parity to Preference-based Notions of Fairness in Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K., Weller, A.

Advances in Neural Information Processing Systems 30, pages: 229-239, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

link (url) [BibTex]

2017

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K., Weller, A. From Parity to Preference-based Notions of Fairness in Classification Advances in Neural Information Processing Systems 30, pages: 229-239, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)

link (url) [BibTex]

Distilling Information Reliability and Source Trustworthiness from Digital Traces

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 26th International Conference on World Wide Web (WWW), pages: 847-855, (Editors: Barrett, R., Cummings, R., Agichtein, E. and Gabrilovich, E. ), ACM, April 2017 (conference)

Project DOI [BibTex]

Tabibian, B., Valera, I., Farajtabar, M., Song, L., Schölkopf, B., Gomez Rodriguez, M. Distilling Information Reliability and Source Trustworthiness from Digital Traces Proceedings of the 26th International Conference on World Wide Web (WWW), pages: 847-855, (Editors: Barrett, R., Cummings, R., Agichtein, E. and Gabrilovich, E. ), ACM, April 2017 (conference)

Project DOI [BibTex]

2016

Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm

Gomez Rodriguez, M., Song, L., Daneshmand, H., Schölkopf, B.

Journal of Machine Learning Research, 17(90):1-29, 2016 (article)

link (url) [BibTex]

2016

Gomez Rodriguez, M., Song, L., Daneshmand, H., Schölkopf, B. Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm Journal of Machine Learning Research, 17(90):1-29, 2016 (article)

link (url) [BibTex]

Influence Estimation and Maximization in Continuous-Time Diffusion Networks

Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B.

ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)

DOI [BibTex]

Gomez-Rodriguez, M., Song, L., Du, N., Zha, H., Schölkopf, B. Influence Estimation and Maximization in Continuous-Time Diffusion Networks ACM Transactions on Information Systems, 34(2):9:1-9:33, 2016 (article)

DOI [BibTex]

2014

Quantifying Information Overload in Social Media and its Impact on Social Contagions

Gomez Rodriguez, M., Gummadi, K., Schölkopf, B.

In Proceedings of the Eighth International Conference on Weblogs and Social Media, pages: 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (inproceedings)

Web Project Page [BibTex]

2014

Gomez Rodriguez, M., Gummadi, K., Schölkopf, B. Quantifying Information Overload in Social Media and its Impact on Social Contagions In Proceedings of the Eighth International Conference on Weblogs and Social Media, pages: 170-179, (Editors: E. Adar, P. Resnick, M. De Choudhury, B. Hogan, and A. Oh), AAAI Press, ICWSM, 2014 (inproceedings)

Web Project Page [BibTex]

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Daneshmand, H., Gomez Rodriguez, M., Song, L., Schölkopf, B.

In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 793-801, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF Project Page [BibTex]

Daneshmand, H., Gomez Rodriguez, M., Song, L., Schölkopf, B. Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm In Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), pages: 793-801, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

PDF Project Page [BibTex]

Uncovering the Structure and Temporal Dynamics of Information Propagation

Gomez Rodriguez, M., Leskovec, J., Balduzzi, D., Schölkopf, B.

Network Science, 2(1):26-65, 2014 (article)

Abstract

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

DOI Project Page [BibTex]

Gomez Rodriguez, M., Leskovec, J., Balduzzi, D., Schölkopf, B. Uncovering the Structure and Temporal Dynamics of Information Propagation Network Science, 2(1):26-65, 2014 (article)

DOI Project Page [BibTex]

Shaping Social Activity by Incentivizing Users

Farajtabar, M., Du, N., Gomez Rodriguez, M., Valera, I., Zha, H., Song, L.

In Advances in Neural Information Processing Systems 27, (Editors: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, ND., and Weinberger, KQ.), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) Project Page [BibTex]

Farajtabar, M., Du, N., Gomez Rodriguez, M., Valera, I., Zha, H., Song, L. Shaping Social Activity by Incentivizing Users In Advances in Neural Information Processing Systems 27, (Editors: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, ND., and Weinberger, KQ.), Curran Associates, Inc., 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

Web link (url) Project Page [BibTex]

Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem

Gomez Rodriguez, M., Song, L., Schölkopf, B.

Proceedings of the 27th Conference on Learning Theory, 35, pages: 1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 (conference)

PDF [BibTex]

Gomez Rodriguez, M., Song, L., Schölkopf, B. Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem Proceedings of the 27th Conference on Learning Theory, 35, pages: 1276-1279, (Editors: Balcan, M.-F. and Szepesvári, C.), JMLR.org, COLT, 2014 (conference)

PDF [BibTex]

2013

Structure and Dynamics of Information Pathways in On-line Media

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In 6th ACM International Conference on Web Search and Data Mining (WSDM), pages: 23-32, (Editors: S Leonardi, A Panconesi, P Ferragina, and A Gionis), ACM, WSDM, 2013 (inproceedings)

Web DOI Project Page [BibTex]

2013

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B. Structure and Dynamics of Information Pathways in On-line Media In 6th ACM International Conference on Web Search and Data Mining (WSDM), pages: 23-32, (Editors: S Leonardi, A Panconesi, P Ferragina, and A Gionis), ACM, WSDM, 2013 (inproceedings)

Web DOI Project Page [BibTex]

Modeling Information Propagation with Survival Theory

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.

In Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), pages: 666-674, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

Web Project Page [BibTex]

Gomez Rodriguez, M., Leskovec, J., Schölkopf, B. Modeling Information Propagation with Survival Theory In Proceedings of the 30th International Conference on Machine Learning, JMLR W&CP 28 (3), pages: 666-674, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

Web Project Page [BibTex]

Scalable Influence Estimation in Continuous-Time Diffusion Networks

Du, N., Song, L., Gomez Rodriguez, M., Zha, H.

In Advances in Neural Information Processing Systems 26, pages: 3147-3155, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF Project Page [BibTex]

Du, N., Song, L., Gomez Rodriguez, M., Zha, H. Scalable Influence Estimation in Continuous-Time Diffusion Networks In Advances in Neural Information Processing Systems 26, pages: 3147-3155, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

PDF PDF Project Page [BibTex]

2012

Influence Maximization in Continuous Time Diffusion Networks

Gomez Rodriguez, M., Schölkopf, B.

In Proceedings of the 29th International Conference on Machine Learning, pages: 313-320, (Editors: J, Langford and J, Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Web Project Page [BibTex]

2012

Gomez Rodriguez, M., Schölkopf, B. Influence Maximization in Continuous Time Diffusion Networks In Proceedings of the 29th International Conference on Machine Learning, pages: 313-320, (Editors: J, Langford and J, Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Web Project Page [BibTex]

Submodular Inference of Diffusion Networks from Multiple Trees

Gomez Rodriguez, M., Schölkopf, B.

In Proceedings of the 29th International Conference on Machine Learning , pages: 489-496, (Editors: J Langford, and J Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Web Project Page [BibTex]

Gomez Rodriguez, M., Schölkopf, B. Submodular Inference of Diffusion Networks from Multiple Trees In Proceedings of the 29th International Conference on Machine Learning , pages: 489-496, (Editors: J Langford, and J Pineau), Omnipress, New York, NY, USA, ICML, July 2012 (inproceedings)

Web Project Page [BibTex]

Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

ACM Transactions on Knowledge Discovery from Data, 5(4:21), February 2012 (article)

Abstract

Information diffusion and virus propagation are fundamental processes taking place in networks. While it is often possible to directly observe when nodes become infected with a virus or publish the information, observing individual transmissions (who infects whom, or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks. We demonstrate the effectiveness of our approach by tracing information diffusion in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news for the top 1,000 media sites and blogs tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

Web DOI Project Page [BibTex]

Gomez Rodriguez, M., Leskovec, J., Krause, A. Inferring Networks of Diffusion and Influence ACM Transactions on Knowledge Discovery from Data, 5(4:21), February 2012 (article)

Web DOI Project Page [BibTex]

Structure and Dynamics of Diffusion Networks

Gomez Rodriguez, M.

Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Gomez Rodriguez, M. Structure and Dynamics of Diffusion Networks Department of Electrical Engineering, Stanford University, 2012 (phdthesis)

Web [BibTex]

Bridging Offline and Online Social Graph Dynamics

Gomez Rodriguez, M., Rogati, M.

In 21st ACM Conference on Information and Knowledge Management, pages: 2447-2450, (Editors: Chen, X., Lebanon, G., Wang, H. and Zaki, M. J.), ACM, CIKM, 2012 (inproceedings)

PDF DOI Project Page [BibTex]

Gomez Rodriguez, M., Rogati, M. Bridging Offline and Online Social Graph Dynamics In 21st ACM Conference on Information and Knowledge Management, pages: 2447-2450, (Editors: Chen, X., Lebanon, G., Wang, H. and Zaki, M. J.), ACM, CIKM, 2012 (inproceedings)

PDF DOI Project Page [BibTex]

2011

Towards Brain-Robot Interfaces in Stroke Rehabilitation

Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Gharabaghi, A., Schölkopf, B., Peters, J.

In pages: 6, IEEE, Piscataway, NJ, USA, 12th International Conference on Rehabilitation Robotics (ICORR), July 2011 (inproceedings)

Abstract

A neurorehabilitation approach that combines robot-assisted active physical therapy and Brain-Computer Interfaces (BCIs) may provide an additional mileage with respect to traditional rehabilitation methods for patients with severe motor impairment due to cerebrovascular brain damage (e.g., stroke) and other neurological conditions. In this paper, we describe the design and modes of operation of a robot-based rehabilitation framework that enables artificial support of the sensorimotor feedback loop. The aim is to increase cortical plasticity by means of Hebbian-type learning rules. A BCI-based shared-control strategy is used to drive a Barret WAM 7-degree-of-freedom arm that guides a subject's arm. Experimental validation of our setup is carried out both with healthy subjects and stroke patients. We review the empirical results which we have obtained to date, and argue that they support the feasibility of future rehabilitative treatments employing this novel approach.

PDF Web DOI [BibTex]

2011

Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Gharabaghi, A., Schölkopf, B., Peters, J. Towards Brain-Robot Interfaces in Stroke Rehabilitation In pages: 6, IEEE, Piscataway, NJ, USA, 12th International Conference on Rehabilitation Robotics (ICORR), July 2011 (inproceedings)

PDF Web DOI [BibTex]

Uncovering the Temporal Dynamics of Diffusion Networks

Gomez Rodriguez, M., Balduzzi, D., Schölkopf, B.

In Proceedings of the 28th International Conference on Machine Learning, pages: 561-568, (Editors: L. Getoor and T. Scheffer), Omnipress, Madison, WI, USA, ICML, July 2011 (inproceedings)

Abstract

Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.

PDF Web Project Page [BibTex]

Gomez Rodriguez, M., Balduzzi, D., Schölkopf, B. Uncovering the Temporal Dynamics of Diffusion Networks In Proceedings of the 28th International Conference on Machine Learning, pages: 561-568, (Editors: L. Getoor and T. Scheffer), Omnipress, Madison, WI, USA, ICML, July 2011 (inproceedings)

PDF Web Project Page [BibTex]

Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.

Journal of Neural Engineering, 8(3):1-12, June 2011 (article)

Abstract

The combination of brain–computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.

PDF PDF DOI [BibTex]

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery Journal of Neural Engineering, 8(3):1-12, June 2011 (article)

PDF PDF DOI [BibTex]

2010

Combining Real-Time Brain-Computer Interfacing and Robot Control for Stroke Rehabilitation

Gomez Rodriguez, M., Peters, J., Hill, J., Gharabaghi, A., Schölkopf, B., Grosse-Wentrup, M.

In Proceedings of SIMPAR 2010 Workshops, pages: 59-63, Brain-Computer Interface Workshop at SIMPAR: 2nd International Conference on Simulation, Modeling, and Programming for Autonomous Robots, November 2010 (inproceedings)

Abstract

Brain-Computer Interfaces based on electrocorticography (ECoG) or electroencephalography (EEG), in combination with robot-assisted active physical therapy, may support traditional rehabilitation procedures for patients with severe motor impairment due to cerebrovascular brain damage caused by stroke. In this short report, we briefly review the state-of-the art in this exciting new field, give an overview of the work carried out at the Max Planck Institute for Biological Cybernetics and the University of T{\"u}bingen, and discuss challenges that need to be addressed in order to move from basic research to clinical studies.

PDF Web [BibTex]

2010

Gomez Rodriguez, M., Peters, J., Hill, J., Gharabaghi, A., Schölkopf, B., Grosse-Wentrup, M. Combining Real-Time Brain-Computer Interfacing and Robot Control for Stroke Rehabilitation In Proceedings of SIMPAR 2010 Workshops, pages: 59-63, Brain-Computer Interface Workshop at SIMPAR: 2nd International Conference on Simulation, Modeling, and Programming for Autonomous Robots, November 2010 (inproceedings)

PDF Web [BibTex]

Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M.

In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pages: 121-126, IEEE, Piscataway, NJ, USA, IEEE International Conference on Systems, Man and Cybernetics (SMC), October 2010 (inproceedings)

Abstract

Brain-Computer Interfaces (BCIs) in combination with robot-assisted physical therapy may become a valuable tool for neurorehabilitation of patients with severe hemiparetic syndromes due to cerebrovascular brain damage (stroke) and other neurological conditions. A key aspect of this approach is reestablishing the disrupted sensorimotor feedback loop, i.e., determining the intended movement using a BCI and helping a human with impaired motor function to move the arm using a robot. It has not been studied yet, however, how artificially closing the sensorimotor feedback loop affects the BCI decoding performance. In this article, we investigate this issue in six healthy subjects, and present evidence that haptic feedback facilitates the decoding of arm movement intention. The results provide evidence of the feasibility of future rehabilitative efforts combining robot-assisted physical therapy with BCIs.

PDF Web DOI [BibTex]

Gomez Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., Grosse-Wentrup, M. Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), pages: 121-126, IEEE, Piscataway, NJ, USA, IEEE International Conference on Systems, Man and Cybernetics (SMC), October 2010 (inproceedings)

PDF Web DOI [BibTex]

Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis

Gomez Rodriguez, M., Grosse-Wentrup, M., Peters, J., Naros, G., Hill, J., Schölkopf, B., Gharabaghi, A.

In Proceedings of the 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010), pages: 36-39, (Editors: J. Richiardi and D Van De Ville and C Davatzikos and J Mourao-Miranda), IEEE, Piscataway, NJ, USA, 1st Workshop on Brain Decoding (WBD), August 2010 (inproceedings)

Abstract

Brain-Computer Interfaces (BCI) that rely upon epidural electrocorticographic signals may become a promising tool for neurorehabilitation of patients with severe hemiparatic syndromes due to cerebrovascular, traumatic or tumor-related brain damage. Here, we show in a patient-based feasibility study that online classification of arm movement intention is possible. The intention to move or to rest can be identified with high accuracy (~90 %), which is sufficient for BCI-guided neurorehabilitation. The observed spatial distribution of relevant features on the motor cortex indicates that cortical reorganization has been induced by the brain lesion. Low- and high-frequency components of the electrocorticographic power spectrum provide complementary information towards classification of arm movement intention.

PDF Web DOI [BibTex]

Gomez Rodriguez, M., Grosse-Wentrup, M., Peters, J., Naros, G., Hill, J., Schölkopf, B., Gharabaghi, A. Epidural ECoG Online Decoding of Arm Movement Intention in Hemiparesis In Proceedings of the 1st ICPR Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging (ICPR WBD 2010), pages: 36-39, (Editors: J. Richiardi and D Van De Ville and C Davatzikos and J Mourao-Miranda), IEEE, Piscataway, NJ, USA, 1st Workshop on Brain Decoding (WBD), August 2010 (inproceedings)

PDF Web DOI [BibTex]

Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., Krause, A.

In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 1019-1028, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

Abstract

Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

PDF Web DOI Project Page [BibTex]

Gomez Rodriguez, M., Leskovec, J., Krause, A. Inferring Networks of Diffusion and Influence In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010), pages: 1019-1028, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

PDF Web DOI Project Page [BibTex]

BCI and robotics framework for stroke rehabilitation

Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Peters, J., Schölkopf, B., Gharabaghi, A.

4th International BCI Meeting, June 2010 (talk)

PDF Web [BibTex]

Gomez Rodriguez, M., Grosse-Wentrup, M., Hill, J., Peters, J., Schölkopf, B., Gharabaghi, A. BCI and robotics framework for stroke rehabilitation 4th International BCI Meeting, June 2010 (talk)

PDF Web [BibTex]

2009

Denoising photographs using dark frames optimized by quadratic programming

Gomez Rodriguez, M., Kober, J., Schölkopf, B.

In Proceedings of the First IEEE International Conference on Computational Photography (ICCP 2009), pages: 1-9, IEEE, Piscataway, NJ, USA, First IEEE International Conference on Computational Photography (ICCP), April 2009 (inproceedings)

Abstract

Photographs taken with long exposure or high ISO setting may contain substantial amounts of noise, drastically reducing the Signal-To-Noise Ratio (SNR). This paper presents a novel optimization approach for denoising. It is based on a library of dark frames previously taken under varying conditions of temperature, ISO setting and exposure time, and a quality measure or prior for the class of images to denoise. The method automatically computes a synthetic dark frame that, when subtracted from an image, optimizes the quality measure. For specific choices of the quality measure, the denoising problem reduces to a quadratic programming (QP) problem that can be solved efficiently. We show experimentally that it is sufficient to consider a limited subsample of pixels when evaluating the quality measure in the optimization, in which case the complexity of the procedure does not depend on the size of the images but only on the number of dark frames. We provide quantitative experimental results showing that our method automatically computes dark frames that are competitive with those taken under idealized conditions (controlled temperature, ISO setting, exposure time, and averaging of multiple exposures). We provide application examples in astronomical image denoising. The method is validated on two CMOS SLRs.

PDF Web DOI [BibTex]

2009

Gomez Rodriguez, M., Kober, J., Schölkopf, B. Denoising photographs using dark frames optimized by quadratic programming In Proceedings of the First IEEE International Conference on Computational Photography (ICCP 2009), pages: 1-9, IEEE, Piscataway, NJ, USA, First IEEE International Conference on Computational Photography (ICCP), April 2009 (inproceedings)

PDF Web DOI [BibTex]