About Me

Who Am I?

I am currently a tenure-track Assistant Professor at Pennsylvania State University since August 2019. I received my PhD degree from the Department of Computer Science and Engineering, the State University of New York at Buffalo in 2019. My advisor was Prof. Jing Gao. Before that, I obtained M.S. and B.S. from Dalian University of Technology in China.

My research interests broadly are data mining and machine learning, including healthcare data mining, deep learning, truth discovery and probabilistic graphical model.

News

  • Jan 2020: Dr. Ma was invited to serve in the program committee of KDD 2020.
  • Jan 2020: One paper on human activity recognition is accepted by IMWUT (UbiComp 2020)!
  • Dec 2019: One paper on rare disease prediction is accepted by SDM 2020!
  • Nov 2019: One paper on weakly-supervised fake news detection is accepted by AAAI 2020!
  • Oct 2019: Two papers are accepted by IEEE Big Data 2019! One is on crowdsourcing aggregation, and the other is on federated multitask learning!
  • Oct 2019: Our tutorial "Learning with small data" is accepted by WSDM 2020!
  • Oct 2019: Honored to receive the 2018/2019 UB CSE PhD Dissertation Competition Runner-up!
  • Oct 2019: One paper on pothole profiling with a reliability-aware vehicular crowdsensing system is accepted by IMWUT (UbiComp 2020)!
  • Sep 2019: One paper on remote and through-wall screen attack via mmWave sensing is accepted by S&P 2020!


Data
Mining

Machine
Learning

Artificial
Intelligence

Healthcare
Informatics

My Focus

Research

My research interests lie in data mining and machine learning using big data, with an emphasis on health-related data. My research focuses on the design, analysis, and application of learning algorithms for both health and medical data. The primary goal of my research is to explore both principled methodologies and innovative applications with highly practical performance that can be used to understand the overwhelmingly large and complex health data collected from our daily life. Exploring health data, including but not limited to, electronic health records (EHR), public health communities, mobile and sensor data, and medical knowledge bases, has clearly shown the potential to significantly improve people's health and provide better healthcare delivery.

Machine Learning for Mining Electronic Health Records

With the immense accumulation of EHR data being available, the analysis of such data enables researchers and healthcare providers to get closer to the goal of personalized medicine. However, it is hard to mine knowledge from raw EHR data because the data usually has high dimensionality, temporality, sparsity, irregularity and bias. These challenges dramatically increase the difficulty of directly applying traditional machine learning or statistical models to predict patients' potential diseases, which is an extremely important task in medical domain. To tackle these challenges, our research mainly focuses on exploring characteristics of EHR data per se [KDD17, SDM20] and incorporating external information [KDD18, CIKM18, BIBM18].

Key References:

  • KDD17: Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
  • KDD18: Risk Prediction on Electronic Health Records with Prior Medical Knowledge
  • CIKM18: KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
  • BIBM18: A General Framework for Diagnosis Prediction via Incorporating Medical Code Descriptions
  • SDM20: Rare Disease Prediction by Generating Quality-Assured Electronic Health Records
  • Reliable Medical Diagnosis from Crowdsourced Data

    Besides EHR data, there are many crowdsourced question answering websites in the application of healthcare. For example, on websites such as healthbords.com, users contribute their answers to medical-related questions. However, the "true" information usually hides in a massive amount of noisy or even conflicting crowdsourced data. To automatically extract medical knowledge (i.e., true facts) from these noisy crowd-provided answers, we first need to address a challenge. That is, different users have different reliability levels, and there is usually neither prior knowledge or training data for the derivation of user reliability. In light of this challenge, we developed unsupervised approaches by jointly estimating user reliability and inferring true facts (i.e. truths) from crowdsourced data without any supervision [KDD15, KDD16, KDD17, KDD18, KDD19].

    Key References:

  • KDD15: FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation
  • KDD16: Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach
  • KDD17: Unsupervised Discovery of Drug Side-Effects From Heterogeneous Data Sources
  • KDD18: TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data
  • KDD19: Optimize the Wisdom of the Crowd: Inference, Learning, and Teaching
  • Medical Knowledge Extraction

    Medical knowledge is valuable and significantly useful for various tasks in medical domain. Medical text data carries invaluable information about the current and previous medical history, current symptoms and severity of condition as well as physicians clinical judgment. How to extract correct medical knowledge from large-scale and unstructured medical text data is a major challenge. Since it is extremely difficult to directly extract medical knowledge from raw data, we focus on the following challenges: medical relation extraction [WWW19], medical fact generation [SML17, US Patent18], and multi-grained named entity recognition [ACL19].

    Key References:

  • WWW19: MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern Generation
  • SML17: Long-Term Memory Networks for Question Answering
  • US Patent18: Long-Term Memory Networks for Knowledge Extraction from Text and Publications
  • ACL19: Multi-grained Named Entity Recognition


  • What I am working on

    Publications

      2020

    1. DeepMV: Multi-View Deep Learning for Device-Free Human Activity Recognition
      Hongfei Xue, Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shiyang Wang, Ye Yuan, Shuochao Yao, Aidong Zhang, and Lu Su.
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2020), September 12-16, 2020, Cancún, México.
    2. Rare Disease Prediction by Generating Quality-Assured Electronic Health Records
      Fenglong Ma*, Yaqing Wang*, Jing Gao, Houping Xiao, and Jing Zhou.
      Proceedings of the SIAM International Conference on Data Mining (SDM 2020), May 7-9, 2020, Cincinnati, Ohio. (Acceptance rate: 75/312=24.0%, * indicates equal contribution)
    3. Weak Supervision for Fake News Detection via Reinforcement Learning
      Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Harry Zhong, Qiang Deng, and Jing Gao.
      Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), February 7-12, 2020, New York, NY.
    4. Learning with Small Data
      Zhenhui Li, Huaxiu Yao and Fenglong Ma.
      Conference Tutorial at the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), February 3-7, 2020, Houston, Texas.
    5. WaveSpy: Remote and Through-wall Screen Attack via mmWave Sensing
      Zhengxiong Li, Fenglong Ma, Aditya Singh Rathore, Zhuolin Yang, Baicheng Chen, Lu Su, and Wenyao Xu.
      Proceedings of the EEE Symposium on Security and Privacy (S&P 2020), May 18-20, 2020, San Francisco, CA.
    6. A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling
      Weida Zhong, Qiuling Suo, Fenglong Ma, Yunfei Hou, Abhishek Gupta, Chunming Qiao, and Lu Su.
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/UbiComp 2020), September 12-16, 2020, Cancún, México.
    7. 2019

    8. MCVAE: Margin-based Conditional Variational Autoencoder for Relation Classification and Pattern Generation
      Fenglong Ma, Yaliang Li, Chenwei Zhang, Jing Gao, Nan Du and Wei Fan.
      Proceedings of the 2019 World Wide Web Conference (WWW 2019), May 13-17, 2019, San Francisco, CA. (Short Paper Acceptance rate: 72/361=20%) [Paper]
    9. Influenza-Like Symptom Prediction by Analyzing Self-Reported Health Status and Human Mobility Behaviors
      Fenglong Ma*, Shiran Zhong*, Jing Gao and Ling Bian.
      Proceedings of the 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB 2019), Sep 7-10, 2019, Niagara Falls, NY. (Acceptance rate: 41/157=26.1%) [Paper] (* indicates equal contribution)
    10. Multi-grained Named Entity Recognition
      Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma and Philip Yu.
      Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), July 28th to August 2nd, 2019, Florence, Italy. [Paper]
    11. Metric Learning on Healthcare Data with Incomplete Modalities
      Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Jing Gao and Aidong Zhang.
      Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), August 10-16 2019, Macao, China [Paper]
    12. Optimize the Wisdom of the Crowd: Inference, Learning, and Teaching
      Yao Zhou, Fenglong Ma, Jing Gao and Jingrui He.
      Conference Tutorial at 2019 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), August 4-8, Anchorage, AK, 2019 [Website]
    13. Minimizing Charging Delay for Directional Charging in Wireless Rechargeable Sensor Networks
      Chi Lin, Yanhong Zhou, Fenglong Ma, Jing Deng, Lei Wang and Guowei Wu.
      Proceedings of the IEEE Conference on Computer Communications (INFOCOM 2019), 29 April - 2 May 2019, Paris, France. (Acceptance rate: 288/1464=19.7%) [Paper]
    14. DeepFusion: A Deep Learning Framework for the Fusion of Heterogeneous Sensory Data
      Hongfei Xue, Wenjun Jiang, Chenglin Miao, Ye Yuan, Fenglong Ma, Xin Ma, Yijiang Wang, Shuochao Yao, Wenyao Xu, Aidong Zhang and Lu Su.
      Proceedings of the Twentieth International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2019), July 2-5, 2019, Catania, Italy. (Acceptance rate: 37/156=23.7%) [Paper]
    15. Deep Hierarchical Knowledge Tracing
      Tianqi Wang, Fenglong Ma and Jing Gao.
      Proceedings of the Twelfth International Conference on Educational Data Mining (EDM 2019), July 2-5, 2019, Montréal, Canada. [Paper]
    16. Online Federated Multitask Learning
      Rui Li, Fenglong Ma, Wenjun Jiang and Jing Gao.
      Proceedings of the 2019 IEEE International Conference on Big Data (IEEE Big Data 2019), December 9-12, 2019, Los Angeles, CA, USA. [Paper]
    17. IProWA: A Novel Probabilistic Graphical Model for Crowdsourcing Aggregation
      Tianqi Wang, Houping Xiao, Fenglong Ma and Jing Gao.
      Proceedings of the 2019 IEEE International Conference on Big Data (IEEE Big Data 2019), December 9-12, 2019, Los Angeles, CA, USA. [Paper]

    18. 2018

    19. Risk Prediction on Electronic Health Records with Prior Medical Knowledge
      Fenglong Ma, Jing Gao, Qiuling Suo, Quanzeng You, Jing Zhou and Aidong Zhang.
      Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018. (Acceptance rate: 107/983=10.9%) [Paper][Code][Poster][Slides]
    20. KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
      Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou and Jing Gao.
      Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), October 22-26, 2018, 'Lingotto', Turin, Italy. (Acceptance rate: 147/862=17%) [Paper][Slides]
    21. A General Framework for Diagnosis Prediction via Incorporating Medical Code Descriptions
      Fenglong Ma, Yaqing Wang, Houping Xiao, Ye Yuan, Radha Chitta, Jing Zhou and Jing Gao.
      Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), Dec 3-6, 2018, Madrid, Spain, accepted. (Acceptance rate: 105/534=19.6%) [Paper]
    22. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
      Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su and Jing Gao.
      Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018. (Acceptance rate: 112/496=22.6%) [Paper][Code]
    23. TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data
      Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, Lu Su.
      Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018. (Acceptance rate: 107/983=10.9%) [Paper]
    24. Multivariate Sleep Stage Classification using Hybrid Self-Attentive Deep Learning Networks
      Ye Yuan, Fenglong Ma, Guangxu Xun, Yaqing Wang, Kebin Jia, Lu Su and Aidong Zhang.
      Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), Dec 3-6, 2018, Madrid, Spain, accepted. (Acceptance rate: 105/534=19.6%) [Paper]
    25. MuVAN: A Multi-view Attention Network for Multivariate Temporal Data
      Ye Yuan, Guangxu Xun, Fenglong Ma, Yaqing Wang, Nan Du, Kebin Jia, Lu Su and Aidong Zhang.
      Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), Singapore, November 17-20, 2018, accepted. (Acceptance rate: 8.86%) [Paper]
    26. Multi-Task Sparse Metric Learning for Monitoring Patient Similarity Progression
      Qiuling Suo, Weida Zhong, Fenglong Ma, Ye Yuan, Mengdi Huai and Aidong Zhang.
      Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), Singapore, November 17-20, 2018, accepted. (Acceptance rate: 8.86%) [Paper]
    27. eOTD: An Efficient Online Tucker Decomposition for Higher Order Tensors
      Houping Xiao, Fei Wang, Fenglong Ma and Jing Gao.
      Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), Singapore, November 17-20, 2018, accepted. (Acceptance rate: 20%) [Paper]
    28. Towards Environment Independent Device Free Human Activity Recognition
      Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Xin Ma, Chen Song, Ye Yuan, Hongfei Xue, Dimitrios Koutsonikolas, Wenyao Xuan and Lu Su.
      Proceedings of the 24th Annual International Conference on Mobile Computing and Networking (MobiCom 2018), New Delhi, India, Oct 29 - Nov 2, 2018. (Acceptance rate: 42/187=22%) [Paper]
    29. Leveraging the Power of Informative Users for Local Event Detection
      Hengtong Zhang, Fenglong Ma, Yaliang Li, Chao Zhang, Tianqi Wang, Yaqing Wang, Jing Gao, Lu Su.
      Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), Barcelona, Spain, August, 2018. (Acceptance rate: 15%) [Paper]
    30. Towards Confidence Interval Estimation in Truth Discovery
      Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng and Aidong Zhang.
      IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018, accepted. [Paper]
    31. Deep Patient Similarity Learning for Personalized Healthcare
      Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, Aidong Zhang.
      IEEE Transactions on NanoBioscience (IEEE T NANOBIOSCI), 2018, accepted. [Paper]
    32. WiAU: An Accurate Device-free Authentication System with ResNet
      Chi Lin, Jiaye Hu, Yu Sun, Fenglong Ma, Lei Wang, Guowei Wu.
      Proceedings of IEEE International Conference on Sensing, Communication and Networking (SECON 2018), Hong Kong, China, June 2018. (Acceptance rate: 49/211=23.2%) [Paper]
    33. Online Truth Discovery on Time Series Data
      Liuyi Yao, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao, Aidong Zhang.
      Proceedings of the SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, USA, May 2018. (Acceptance rate: 86/374=23%) [Paper]
    34. A Novel Channel-aware Attention Framework for Multi-channel EEG Seizure Detection via Multi-view Deep Learning
      Ye Yuan, Guangxu Xun, Fenglong Ma, Qiuling Suo, Hongfei Xue, Kebin Jia, Aidong Zhang.
      Proceedings of the 2018 IEEE International Conference on Biomedical and Health Informatics (BHI 2018), Las Vegas, Nevada, USA, March 2018. [Paper]

    35. 2017

    36. Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks
      Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao.
      Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, NS, Canada, August 2017. (Acceptance rate: 85/396=21.5%) [Paper][Code][Video][Bib]
    37. Unsupervised Discovery of Drug Side-Effects From Heterogeneous Data Sources
      Fenglong Ma, Chuishi Meng, Houping Xiao, Qi Li, Jing Gao, Lu Su, Aidong Zhang.
      Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), Halifax, NS, Canada, August 2017. (Acceptance rate: 131/784=16.7%) [Paper][Video][Bib]
    38. Long-Term Memory Networks for Question Answering
      Fenglong Ma, Radha Chitta, Saurabh Kataria, Jing Zhou, Palghat Ramesh, Tong Sun, Jing Gao.
      Proceedings of IJCAI Workshop on Semantic Machine Learning (SML 2017) (SML 2017), Melbourne, Australia, August 2017. [Paper][Data]
    39. Personalized Disease Prediction Using A CNN-Based Similarity Learning Method
      Qiuling Suo, Fenglong Ma, Ye Yuan, Mengdi Huai, Weida Zhong, Jing Gao, Aidong Zhang.
      Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), Kansas City, MO, USA, November 2017. (Acceptance rate: 79/414=19.0%) [Paper]
    40. Discovering Truths from Distributed Data
      Yaqing Wang, Fenglong Ma, Lu Su, Jing Gao.
      Proceedings of the 17th IEEE International Conference on Data Mining (ICDM 2017), New Orleans, USA, November 2017. (Acceptance rate: 72/778=9.25%) [Paper]
    41. A Multi-Task Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks
      Qiuling Suo, Fenglong Ma, Giovanni Canino, Jing Gao, Aidong Zhang, Pierangelo Veltri, Agostino Gnasso.
      Proceedings of AMIA 2017 Annual Symposium (AMIA 2017), Washington DC, USA, November 2017. [Paper]
    42. Discovering Social Spammers from Multiple Views
      Hua Shen, Fenglong Ma, Xianchao Zhang, Linlin Zong, Xinyue Liu, Wenxin Liang.
      Neurocomputing, 2017, pp. 49-57. [Paper] [Code] [Bib]

    43. 2016

    44. Topic Discovery for Short Texts Using Word Embeddings
      Guangxu Xun, Vishrawas Gopalakrishnan, Fenglong Ma, Yaliang Li, Jing Gao, Aidong Zhang.
      Proceedings of the 16th IEEE International Conference on Data Mining (ICDM 2016), Barcelona, Spain, December 2016. (Acceptance rate: 178/904=19.6%) [Paper] [Bib]
    45. Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach
      Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, Aidong Zhang.
      Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2016), San Francisco, CA, USA, August 2016. (Acceptance rate: 142/784=18.1%) [Paper] [Bib]
    46. Influence-Aware Truth Discovery
      Hengtong Zhang, Qi Li, Fenglong Ma, Houping Xiao, Yaliang Li, Jing Gao, Lu Su.
      Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, USA, October 2016. (Acceptance rate: 165/935=17.6%) [Paper] [Bib]
    47. Crowdsourcing High Quality Labels with a Tight Budget
      Qi Li, Fenglong Ma, Jing Gao, Lu Su, Christopher Quinn.
      Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM 2016), San Francisco, CA, USA, February 2016. (Acceptance rate: 67/368=18.2%) [Paper] [Slides] [Bib]

    48. 2015 and before

    49. FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation
      Fenglong Ma, Yaliang Li, Qi Li, Minghui Qui, Jing Gao, Shi Zhi, Lu Su, Bo Zhao, Heng Ji, Jiawei Han.
      Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015), Sydney, Australia, August 2015. (Acceptance rate: 159/819=19.4%) [Paper] [Poster] [Slides] [Bib]
    50. Probabilistic Models for Fine-Grained Truth Discovery from Crowdsourced Data
      Fenglong Ma, Jing Gao.
      Proceedings of the 15th IEEE International Conference on Data Mining Workshop (ICDMW 2015), Atlantic City, NJ, USA, November 2015. [Paper] [Bib]
    51. Topical Influential User Analysis with Relationship Strength Estimation in Twitter
      Xinyue Liu, Hua Shen, Fenglong Ma, Wenxin Liang.
      Proceedings of the 14th IEEE International Conference on Data Mining Workshop (ICDMW 2014), Shenzhen, China, December 2014. [Paper] [Bib]


    Intelligent People

    Group

    Muchao Ye

    South China Univ. of Tech.

    Junyu Luo

    Sichuan University

    Name

    Where are you from?

    Let others know my work

    Talks

    Tutorials

    Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching [Link]
    The increasing need for labeled data has brought the booming growth of crowdsourcing in a wide range of high-impact real-world applications, such as collaborative knowledge (e.g., data annotations, language translations), collective creativity (e.g., analogy mining, crowdfunding), and reverse Turing test (e.g., CAPTCHA-like systems), etc. In the context of supervised learning, crowdsourcing refers to the annotation procedure where the data items are outsourced and processed by a group of mostly unskilled online workers. Thus, the researchers or the organizations are able to collect large amount of information via the feedback of the crowd in a short time with a low cost.
    Despite the wide adoption of crowdsourcing, several of its fundamental problems remain unsolved especially at the information and cognitive levels with respect to incentive design, information aggregation, and heterogeneous learning. This tutorial aims to: (1) provide a comprehensive review of recent advances in exploring the power of crowdsourcing from the perspective of optimizing the wisdom of the crowd; and (2) identify the open challenges and provide insights to the future trends in the context of human-in- the-loop learning. We believe this is an emerging and potentially high-impact topic in computational data science, which will attract both researchers and practitioners from academia and industry.

    Selected Recent Invited Talks

  • Deep Predictive Models for Mining Electronic Health Records. (1) King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, March 2019; (2) Pennsylvania State University, State College, PA, March 2019; (3) Case Western Reserve University, Cleveland, OH, February 2019; (4) Auburn University, Auburn, Alabama, February 2019; (5) Missouri University of Science and Technology, Rolla, MO, February 2019; (6) Nanyang Technological University, Singapore, January 2019; (7) Temple University, Philadelphia, PA, USA, December 2018.
  • Truth Discovery from Multi-Sourced Data. Dalian University of Technology, Dalian, China, March 2017.
  • Truth Discovery for Crowdsourced Data Aggregation. PARC, a Xerox Company, Rochester, NY, USA, August 2016.
  • Selected Conference Presentations

  • "KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare", CIKM Conference Talk, Turin, Italy, October 2018.
  • "Risk Prediction on Electronic Healthcare Records with Prior Medical Knowledge", KDD Conference Talk, London, United Kingdom, August 2018.
  • "TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data", KDD Conference Talk, London, United Kingdom, August 2018.
  • "Deep Learning for Diagnosis Prediction in Healthcare", UB CSE Graduate Research Conference Poster, Buffalo, NY, USA, September 2017. (Best Poster Award)
  • "Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks", KDD Conference Poster, Halifax, NS, Canada, August 2017.
  • "Unsupervised Discovery of Drug Side-Effects From Heterogeneous Data Sources", KDD Conference Poster, Halifax, NS, Canada, August 2017.
  • Knowing and living in academic research

    Services

    Program Committee Member

  • 2020: KDD, ICML, AAAI
  • 2019: AMIA, WISE, GLOBAL HEALTH
  • 2018: AMIA, GLOBAL HEALTH
  • 2016: DATA ANALYTICS
  • Journal Reviewer

  • IEEE Transactions on Knowledge and Data Engineering
  • IEEE Transactions on Mobile Computing
  • IEEE Transactions on Industrial Informatics
  • IEEE Transactions on Emerging Topics in Computational Intelligence
  • IEEE Transactions on Vehicular Technology
  • IEEE Access
  • ACM Computing Surveys
  • ACM Transactions on Knowledge Discovery from Data
  • Journal of Intelligent Systems
  • Annals of Telecommunications
  • Security and Communication Networks
  • Journal of Computer Science and Technology
  • Journal of Systems and Software
  • Journal of Medical Systems
  • Get in Touch

    Contact

    E304A Westgate Building, University Park, PA 16802