Publications & More

2024

• Handbook of Trustworthy Federated Learning. My T. Thai, NhatHai Phan, Bhavani Thuraisingham. A first-of-its-kind book, focusing on providing insights into trustworthy federated learning. Part of the book series: Springer Optimization and Its Applications (SOIA, volume 213). [Book]

• User Dynamics and Thematic Exploration in r/Depression during COVID-19: Insights from Overlapping r/SuicideWatch Users. Jianfeng Zhu, Ruoming Jin, Deric Kenne, NhatHai Phan, Wei-Shinn Ku. Journal of Medical Internet Research (JMIR). IF: 7.4.

• Secure Prompt for Secure Code Generation with Large Language Models. Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah, NhatHai Phan. US Provision Patent.

• Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection. Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah, NhatHai Phan, Yao Ma. The 45th IEEE Symposium on Security and Privacy (IEEE S&P 2024). (acceptance rate 17.8%)

• FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps. Xiaopeng Jiang*, Han Hu*, Thinh On, Phung Lai, Vijaya Datta Mayyuri, An Chen, Devu M. Shila, Adriaan Larmuseau, Ruoming Jin, Cristian Borcea, and NhatHai Phan. IEEE Transactions on Mobile Computing (IEEE TMC). (* equal contribution). IF: 7.9.

• Federated Learning Overview and Inspiration for Robotics Research. Rui Liu, Amy Zhang, Sherry Li, Ruoming Jin, NhatHai Phan. IEEE International Conference on Robotics and Automation (ICRA 2024) - WOROBET.

• Achieving Certified Fairness with Differential Privacy. Khang Tran, Ferdinando Fioretto, Issa Khalil, My T. Thai, NhatHai Phan*. AAAI Conference on Artificial Intelligence (AAAI 2024) - Privacy-preserving AI. (* corresponding author)

• ZoneFL: Zone-based Federated Learning at the Edge. Xiaopeng Jiang, Hessamaldin Mohammadi, Cristian Borcea, NhatHai Phan*. Book chapter in the "Trustworthy Federated Machine Learning" book, Springer. To appear, 2024.

2023

• How to Backdoor HyperNetwork in Personalized Federated Learning? Phung Lai, NhatHai Phan*, Abdallah Khreishah, Issa Khalil, and Xintao Wu. NeurIPS 2023 - Backdoor in Deep Learning. (* corresponding author)

• Differential Privacy in HyperNetworks for Personalized Federated Learning. Vaisnavi Nemala*, Phung Lai, and NhatHai Phan. The 32nd ACM International Conference on Information and Knowledge Management (ACM CIKM 2023). (* an honored undergrad)

• Exploring COVID-19’s Impact on Mental Health: A Longitudinal and Thematic Analysis of Reddit Users’ Discourse. Jianfeng Zhu, Ruoming Jin, Neha Yalamanchi, Deric Kenne, NhatHai Phan. Journal of Medical Internet Research (JMIR). IF: 7.4.

• Active Membership Inference Attack under Local Differential Privacy in Federated Learning. Truc Nguyen, Phung Lai, Khang Tran, NhatHai Phan, and My T. Thai. The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023). [code release] (acceptance rate 29%)

• XRand: Differentially Private Defense against Explanation-Guided Attacks. Truc Nguyen*, Phung Lai*, NhatHai Phan, and My T. Thai. AAAI Conference on Artificial Intelligence (AAAI 2023). (acceptance rate 19%: 1,721 / 8,777), (* equal contributions) [AAAI 2023 Distinguished Paper Award]

• Zone-based Federated Learning for Mobile Sensing Data. Xiaopeng Jiang*, Thinh On*, NhatHai Phan, Hessamaldin Mohammadi, Vijaya D Mayyuri, An Chen, Ruoming Jin, and Cristian Borcea. IEEE International Conference on Pervasive Computing and Communications (IEEE PerCom 2023). (acceptance rate 17%), (* equal contributions)

2022

• Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness. Nicholas Furth, Abdallah Khreishah, Guanxiong Liu, NhatHai Phan, Yasser Jararweh. The 9th IEEE International Conference on Software Defined Systems (IEEE SDS-2022). (Best Paper Award)

• User-Entity Differential Privacy in Learning Natural Language Models. Phung Lai, NhatHai Phan*, Tong Sun, Rajiv Jain, Franck Dernoncourt, Jiuxiang Gu, and Nikolaos Barmpalios. IEEE BigData 2022. Regular Paper (acceptance rate: 122/633). (* corresponding author)

• Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks. Khang Tran, Phung Lai, NhatHai Phan*, Issa Khalil, Yao Ma, Abdallah Khreishah, My Thai, and Xintao Wu. IEEE BigData 2022. Short paper. (* corresponding author)

• Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning. Phung Lai, Han Hu, NhatHai Phan*, Ruoming Jin, My Thai, An Chen. The Conference on Lifelong Learning Agents (CoLLAs 2022), Proceedings of Machine Learning Research (PMLR). (* corresponding author)

• Distinguishing the Effect of Time Spent at Home During COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation. Pelin Ayranci, Cesar Bandera, NhatHai Phan, Ruoming Jin, Dong Li, Deric Kenne. International Journal of Environmental Research and Public Health (ISSN 1660-4601).

• OnML: An Ontology-based Approach for Interpretable Machine Learning. Pelin Ayranci*, Phung Lai*, NhatHai Phan, Han Hu, David Newman, Alexander Kalinowski, and Dejing Dou. Journal of Combinatorial Optimization - Springer. (* equal contribution)

2021

• A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples. Guanxiong Liu, Issa Khalil, Abdallah Khreishah, and NhatHai Phan. IEEE International Conference on Big Data (IEEE BigData'21), December 15-18, 2021. [Regular Paper][Acceptance Rate: 97 / 486][Github]

• c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation. Minh Vu, Truc D. Nguyen, NhatHai Phan, Ralucca Gera, My T. Thai. IEEE International Conference on Big Data (IEEE BigData'21), December 15-18, 2021. [Regular Paper][Acceptance Rate: 97/486][Github]

• Continual Learning with Differential Privacy. Pradnya Desai, Phung Lai, NhatHai Phan, and My T. Thai. The 28th International Conference on Neural Information Processing (ICONIP'21), December 8 - 12, 2021. [Oral Presentation] [Github] (Pradnya Desai is an honor undergraduate student)

• Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network. NhatHai Phan, David Kil, Brigitte Piniewski, and Dejing Dou.  The 10th International Conference on Computational Data and Social Networks (CSoNet'21), November 15 - 17, 2021.  (Invited Paper)

2020

• Scalable Differential Privacy with Certified Robustness in Adversarial Learning. NhatHai Phan, My T. Thai, Han Hu, Ruoming Jin, Tong Sun, and Dejing Dou. The 37th International Conference on Machine Learning (ICML'20), July 12 - 18, 2020. [Github]

• Ontology-based Interpretable Machine Learning for Textual Data. Phung Lai, NhatHai Phan*, Han Hu, Anuja Badeti, David Newman, and Dejing Dou. The International Joint Conference on Neural Networks (IJCNN'20), July 19 - 24th, 2020, Glasgow (UK). [Github] [Oral Presentation] 

• Classification of Ecological Data by Deep Learning. Shaobo Liu, Frank Y. Shih, Gareth Russell, Kimberly Russell, and NhatHai Phan.  International Journal of Pattern Recognition and Artificial Intelligence, doi:10.1142/S0218001420520102.

2019

• Differentially Private Lifelong Learning. NhatHai Phan, My T. Thai, Devu Shila, and Ruoming Jin. Privacy in Machine Learning (PriML), NeurIPS'19 Workshop, December 8-14, 2019, Vancouver, Canada. [pdf]

• Ontology-based Interpretable Machine Learning with Learnable Anchors. Phung Lai, NhatHai Phan*, David Newman, Han Hu, Anuja Badeti and Dejing Dou. Knowledge Representation & Reasoning Meets Machine Learning (KR2ML) Workshop at NeurIPS'19, December 8-14, 2019, Vancouver, Canada. [Oral Presentation] [pdf]

• DrugTracker: A Community-focused Drug Abuse Monitoring and Supporting System using Social Media and Geospatial Data. Han Hu, NhatHai Phan*, Xinyue Ye, Ruoming Jin, Kele Ding, Dejing Dou, and Huy T. Vo. International Conference on Advances in Geographic Information Systems 2019 (ACM SIGSPATIAL'19), Nov 5-8, 2019, Chicago. [GitHub]

• Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness. NhatHai Phan, Minh Vu, Yang Liu, Ruoming Jin, Xintao Wu, Dejing Dou, and My T. Thai. The 28th International Joint Conference on Artificial Intelligence (IJCAI'19), August 10-16, 2019, Macao, China. (acceptance rate = 850/4752, 17.9%) [pdf] [Github]

• An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere. Han Hu, NhatHai Phan*, James Geller, Stephen Iezzi, Huy Vo, Soon Ae Chun. The 17th World Congress of Medical and Health Informatics (MedInfo'19), Lyon, France, August 2019. [pdf]

• An Insight Analysis and Detection of Drug Abuse Risk Behavior on Twitter with Self-Taught Deep Learning. Han Hu, NhatHai Phan*, James Geller, Soon Ae Chun, Ruoming Jin, Kele Ding, Deric Kenne, and Dejing Dou. Computation Social Network - Springer, 2019.

• Limiting the Neighborhood: De-Small-World Network for Outbreak Prevention. Ruoming Jin, Yelong Shen, Lin Liu, Xue-Wen Chen, and NhatHai Phan. The 8th International Conference on Computational Data & Social Networks (CSoNet'19), Hochiminh City, Vietnam, November 2019. (Selected as Best Papers)

• Scalable Self-Taught Deep-embedded Learning Framework for Drug Abuse Behaviors Detection with Spatial Effects. Wuji Liu, Xinyue Ye, NhatHai Phan, and Han Hu. The 8th International Conference on Computational Data & Social Networks (CSoNet'19), Hochiminh City, Vietnam, November 2019.

• Extracting API Tips from Developer Question and Answer Websites. Shaohua Wang, NhatHai Phan, Yan Wang, and Yong Zhao.  The 16th International Conference on Mining Software Repositories (MSR'19), Montreal, Canada, May 26-27, 2019.

2018

• Deep Self-Taught Learning for Detecting Drug Abuse Risk Behavior in Tweets. Han Hu, NhatHai Phan, James Geller, Huy Vo, Bhole Manasi, Xueqi Huang, Sophie Di Lorio, Thang Dinh, Soon Ae Chun. Invited to The 7th International Conference on Computational Data & Social Networks (CSoNet'18), Shanghai, China, December 2018. [pdf] (Selected as Best Papers)

• Recursive Structure Similarity: A Novel Algorithm for Graph Clustering. Han Hu, Yixing Fang, Ruoming Jin, Wei Xiong, Xiaoning Qian, Dejing Dou, NhatHai Phan*. 30th IEEE International Conference on Tools with Artificial Intelligence, Velos, Greece, November 2018. [pdf]

• A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction. Zhihang Hu, Turki Turki, NhatHai Phan, Jason Wang. IEEE Access, July 2018. IF = 3.9 [pdf]

• Deep Learning Model for Classifying Drug Abuse Risk Behavior in Tweets. Han Hu, Pravani Moturu, Kannan Neten Dharan, James Geller, Sophie Di Iorio, NhatHai Phan*, Huy Vo, Soon Ae Chun. IEEE ICHI'18, New York City, NY, USA, June 2018. [pdf] [Github] Dataset released: 5,000 drug abuse risk behavior labeled tweets has been released at: https://github.com/hu7han73/DrugAbuseLabeledTweets.

• DPNE: Differentially Private Network Embedding. Depeng Xu, Shuhan Yuan, Xintao Wu, NhatHai Phan. PAKDD'18, Melbourne, Australia, June 2018. [pdf] [Github] (acceptance rate: 105 / 590)

2017

• Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning. NhatHai Phan, Xintao Wu, Han Hu, Dejing Dou. IEEE ICDM'17, New Orleans, USA 18-21 November 2017. [pdf] [Github] (acceptance rate: 9.25% = 72 / 778)

• Importance Sketching of Influence Dynamics in Billion-scale Networks. Hung Nguyen, Tri Nguyen, NhatHai Phan, Thang Dinh. IEEE ICDM'17, New Orleans, USA 18-21 November 2017. [pdf] [Github] (acceptance rate: 9.25% = 72 / 778) (Selected as Best Papers)

• Preserving Differential Privacy in Convolutional Deep Belief Networks. NhatHai Phan, Xintao Wu, Dejing Dou. Machine Learning 2017, ECML-PKDD Journal Track, Skopje, Macedonia 18-22 Sep 2017. IF = 5.6 [pdf] [Github] Arxiv version (acceptance rate: 13.5%)

• Ontology-based Deep Learning for Human Behavior Prediction with Explanations in Health Social Networks. NhatHai Phan, Dejing Dou, Hao Wang, David Kil, Brigitte Piniewski. Information Sciences - Elsevier. IF = 8.1 [pdf] [code]

• Enabling Real-Time Drug Abuse Detection in Tweets. NhatHai Phan, Soon Ae Chun, Manasi Bhole, and James Geller. In the proceedings of the 2017 IEEE 33rd International Conference on Data Engineering (ICDE-17) - 2nd HDMM Workshop (HDMM-17), San Diego, CA, USA, April 2017. [pdf] [Slides] [code]

2016

• Differential Privacy Preservation for Deep Auto-Encoders: an Application of Human Behavior Prediction. NhatHai Phan, Yue Wang, Xintao Wu, and Dejing Dou. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, February 2016. [pdf] [Github] [oral presentation] (acceptance rate: 549/2,132)

• Topic-aware Physical Activity Propagation in a Health Social Network. NhatHai Phan, Javid Ebrahimi, Brigitte Piniewski, David Kil, and Dejing Dou. IEEE Intelligent Systems, 2016. IF = 6.4 [pdf] [Slides] [code] 

• Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network. Yelong Shen*, NhatHai Phan*, Xiao Xiao, Ruoming Jin, Dejing Dou, Junfeng Sun, Brigitte Piniewski, and David Kil. Knowledge and Information Systems (KAIS), 2016. IF = 2.004[pdf] [Slides] [code] (*equal contribution)

• Topic-aware Physical Activity Propagation with Temporal Dynamics in a Health Social Network. NhatHai Phan, Javid Ebrahimi, Dejing Dou, David Kil, and Brigitte Piniewski. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2016. 5-Year IF = 10.47 [pdf] [code]

• SRBM+: Human Behavior Prediction with Explanations in a Health Social Network. NhatHai Phan, Dejing Dou, David Kil, and Brigitte Piniewski. Social Network Analysis and Mining (SNAM), 2016. [pdf] [Slides] [code] (Invited Article)

• Interaction Network Representations for Human Behavior Prediction. Amnay Amimeur, NhatHai Phan, Dejing Dou, Brigitte Piniewski, and David Kil. Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA'16), Anaheim, California, USA, December 18-20, 2016. (acceptance rate: 24%) [pdf] 

• Characterizing Physical Activity in a Health Social Network. Javid Ebrahimi, NhatHai Phan, Dejing Dou, Brigitte Piniewski, and David Kil. Proceedings of the 6th ACM International Conference on Digital Health (ACM DH'16), Montreal, Canada, April 2016. [pdf] [Slides] [code]

• All in one: Mining Multiple Movement Patterns. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. International Journal of Information Technology & Decision Making (IJITDM), 2016. IF = 1.664 [pdf] [demo] [code]

2015

• Ontology-based Deep Learning for Human Behavior Prediction in Health Social Networks. NhatHai Phan, Dejing Dou, Hao Wang, David Kil, and Brigitte Piniewski. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2015), Atlanta, GA, September 2015. (Invited to IEEE J-BHI)  [pdf] [Slides] [code] (Selected as Best Papers)

• Social Restricted Boltzmann Machine: Human Behavior Prediction in Health Social Networks. NhatHai Phan, Dejing Dou, Brigitte Piniewski, and David Kil. Proceedings of the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2015), Paris, France, August 2015. (acceptance rate: 18%) (Invited to SNAM) [pdf] [Slides] [code] (Selected as Best Papers)

• Mining Multi-Relational Gradual Patterns [supplementary]. NhatHai Phan, Dino Ienco, Donato Malerba, Pascal Poncelet, and Maguelonne Teisseire. Proceedings of the SIAM International Conference on Data Mining (SDM 2015), Vancouver, Canada, May 2015. [pdf] [Slides] [code] (acceptance rate: 21.9%)

2014

• Analysis of Physical Activity Propagation in a Health Social Network. NhatHai Phan, Dejing Dou, Xiao Xiao, Brigitte Piniewski, and David Kil. Proceedings of the 23rd ACM Conference on Information and Knowledge Management (CIKM 2014), Shanghai, China, November 2014. [pdf] [Slides] [code] (acceptance rate: 20%)

2013

• Mining Representative Movement Patterns through Compression. NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. [pdf] [demo] [code] (acceptance rate: 11.3%)

• Co2Vis: A Visual Analytics Tool for Mining Co-expressed And Co-regulated Genes Implied in HIV Infections. A.Z.E. Aabidine, A. Sallaberry, S. Bringay, M. Fabregue, C. Lecellier, NhatHai Phan, and P. Poncelet. (IEEE BioVis 2013), Atlanta, GA, USA, 13-14 October 2013. [link] [vimeo]

• An Efficient Spatio-Temporal Mining Approach to Really Know Who Travels with Whom! NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. Ingénierie des Systèmes d'Information (special issue, selected from BDA’12), 2013. [pdf]

2012

• Mining Fuzzy Moving Object Clusters. NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 8th International Conference on Advanced Data Mining and Applications (ADMA 2012), Nanjing, China, December 2012. [pdf] [code] (acceptance rate: 19%)

• Mining Time Relaxed Gradual Moving Object Clusters. NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%)

• How to Extract Relevant Knowledge from Tweets? F. Bouillot, NhatHai Phan, N. Béchet, S. Bringay, D. Ienco, S. Matwin, P. Poncelet, M. Roche, and M. Teisseire. The 7th International Workshop on Information Search, Integration and Personalization (ISIP 2012), Sapporo, Japan, October 2012. [pdf] (Selected as Best papers)

• GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. [pdf] [demo] [code]

• An Efficient Spatio-Temporal Mining Approach to Really Know Who Travels with Whom! NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In 28th Advance in Data Mining (BDA 2012), Clermont Ferrand, France, October 2012. [pdf] [demo] [video] [code] (Selected as Best papers)

• Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System. NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In ECML-PKDD 2012, Demo Paper, Bristol, UK, September 2012. [pdf] [demo] [code]

2011

• Moving Objects: Combining Gradual Rules and Spatio-Temporal Patterns. NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. 2011 International Conference on Spatial Data Mining and Geographical Knowledge Services (IEEE ICSDM 2011), Fuzhou, China, June 2011. [pdf]

2010

• Adaptive Combination of Tag and Link-based User Similarity in Flickr. NhatHai Phan, Hoang Van Duc Thong, and Hyoseop Shin. 2010 ACM Multimedia International Conference (ACM MM 2010), Firenze, Italy, October 2010. [pdf]

• Effective Clustering of Dense and Concentrated Online Communities. NhatHai Phan, and Hyoseop Shin. The 12th International Asia-Pacific Web Conference (APWeb 2010), Busan, Korea, April 2010. [pdf] (acceptance rate: 16%)

• An Empirical Analysis of User Clusters in Online Communities. Van Duc Thong Hoang, NhatHai Phan, and Hyoseop Shin. The 2nd International Conference on Emerging Databases (EDB 2010), Jeju Korea, August 2010. [pdf]

2008

• Towards an Extensible Library System for Data Mining. NhatHai Phan, Hoang Anh Nguyen, Minh Quang Tran, Hoang Hai Ly, and Tran Khanh Dang. Proceedings of International Workshop on Advanced Computing and Applications(ACOMP 2008), Ho Chi Minh City, Vietnam, March 2008. [pdf]

2007

• Basic Algorithms Library System for Data Mining. NhatHai Phan, Hoang Anh Nguyen, Minh Quang Tran, and Hoang Hai Ly. Proceedings of 10th Conference on Science and Technology, HCM University of Technology, Ho Chi Minh city, Vietnam, October 2007.

Awards

• Best papers at CSoNet'18, CSoNet'19

• Best papers at ICDM'17

• Best paper at BDA'12.

• Outstanding Vietnamese Research Award of Montpellier, France, 2012.

• CNRS Research Fellow, 2010-2013.

• Korean Government Research Fellow, BK21, 2008-2010.

• eB Corporation Research Fellow, 2008-2010.

• Hochiminh University of Technology Scholarship for good student, 2006-2007.

• Won the Mathematics Contest of Khanh Hoa Province, 2002-2003. 

• Khanh Hoa Province Scholarship for good student on mathematics, 2002-2003.

PhD Thesis

Oct 2013 - Mining Object Movement Patterns from Trajectory Data - [pdf] [Slides]

Supervisors: Dr. Dino ienco, Prof. Pascal Poncelet, Prof. Maguelonne Teisseire

Thesis Committee:

Prof. Osmar Zaïane -   University of Alberta

Prof. Arno Siebes      -   Utrech University

Dr. Francesco Bonchi - Yahoo! Research Lab

Prof. Bruno Crémilleux - Université de Caen

Recent Talks

• Human Behavior Modeling in Health Social Networks. Invited talk at American Family Insurance, Madison, Wisconsin, USA, July 2015. [Slides]

• Analysis of Physical Activity Propagation in a Health Social Network. CIKM Conference Talk, Shanghai, China, December 2014. [Slides]

• Deep Learning and Its Applications in Health Social Networks. Invited talk at UNC Charlotte, North Carolina, USA, July 2014.

• Tutorial on Deep Learning and Applications. Invited talk at University of Oregon, Eugene, OR, USA, April 2014. [Slides]

• Mining Object Movement Patterns: Challenges and Directions. Invited talk at Yahoo! Research Lab, Barcelona, Spain, May 2013. [Slides]

• Mining Fuzzy Moving Object Clusters. ADMA Conference Talk, Nanjing, China, December 2012. [Slides]

• Mining Time Relaxed Gradual Moving Object Clusters. ACM SIGSpatial GIS Conference Talk, Redondo Beach, Los Angeles, USA, November 2012.

• Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System. ECML-PKDD Conference Talk, Bristol, UK, September 2012.

• GeT_Move: An Efficient and Unifying Movement Pattern Mining Algorithm. BDA Summer School Talk, Aussois, France, August 2012.

• Moving Object: Combining Gradual Rules and Spatio-Temporal Patterns. ICSDM Conference Talk, Fuzhou China, June 2011.

• Effective Clustering of Dense and Concentrated Online Communities. APWeb Conference Talk, Busan, Korea, April 2010.

• Adaptive Combination of Tag and Link-based User Similarity in Flickr. ACM Multimedia Conference Poster Presentation, Firenze, Italy, October 2010.

• Basic Algorithms Library System for Data Mining. ACOMP Conference Talk, Hochiminh, Vietnam, March 2008.

Services

• Organization Committee: IEEE ICMLA'20, DOCTISS'12.

• PC & External Reviewer: ICDM ('12, '13, '14, '15, '17), CIKM'17, ADMA'17, IDA'12, DBKDA ('14, '15), DEXA ('13, '14), NFMCP'13, DS ('13, '14), ADMA'12, ECML-PKDD'14, DASFAA ('10, '14), DSAA ('14, '15), BigSim ('14, '15, '16, '17).

• Journal Reviewer: JIT, PlosOne, TKDE, IEEE Intelligent Systems.

• Social Activities: Committee of Vietnamese Student Association at Montpellier, France ('11, '12, '13)

Experiences

• Research Intern, Yahoo! Research Lab, Barcelona, Spain              05/2013 – 08/2013

With the success of online social networks and microblogs such as Facebook, Flickr and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has recently attracted the interest of computer scientists, information technologists, and marketing specialists. In this work we take a data mining perspective and we discuss what (and how) can be learned from the available traces of past propagations.(with Dr.Francesco Bonchi)

• Data Mining Specialist, R&D Dept, Pyramid Consulting Corp       03/2013 – 05/2013

Spoofing fan detection on social media communities. Indeed, there are many social media communities such as Youtube, Facebook and also Tweets. The issue here is that many artists desire to have many fans following their activities. To speed up the growing of number of fans, some of them buy spoofing fans from fan suppliers. To tackle this issue, we design an effective approach, named “Artist Adaptive Spoofing Fan Detection Algorithm”, to detect spoofing fans.

• Internship, R&D Center, EB Corp, Seoul, South Korea                 03/2008 – 09/2008

Research on designing communication method between traffic card and client server. I also involved in Korean training course held by the EB Corp.

• Software Engineering, FPT Corp, Hochiminh, Vietnam                 01/2008 – 03/2008

Software Bridge Engineering. Analyze, report and deliver Japanese customer requirements to software development team.