Difference between revisions of "CSE 7100 Research Seminar on Machine Learning"

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Presenter: Kathryn Sarullo
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Canceled for Big Ideas event
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Paper: Feature Selection and Dimension Reduction of Social Autism Data, Pacific Symposium on Biocomputing 2020, P. Washington et al. ([https://psb.stanford.edu/psb-online/proceedings/psb20/Washington.pdf])
 
  
 
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Revision as of 19:43, 12 February 2020

This seminar examines machine learning by studying seminal papers and recent research results. Each semester, the seminar emphasizes different themes reflecting the current research interests of the participants. The theme of this semester's seminar is Machine Learning for Health. We will read and discuss papers from recent major conferences and journals on machine learning, data mining, and AI related to healthcare. These conferences and journals include:


---Jan 23 ---

Presenter: Dingwen Li

Paper: DeepAlerts: Deep Learning Based Multi-horizon Alerts for Clinical Deterioration on Oncology Hospital Wards, AAAI-2020, D. Li, P. Lyons, C. Lu, M. Kollef. ([9])

---Jan 30 ---

Presenter: Ruixuan Dai

Paper: Shen, Yichen, et al. "Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019. URL: https://www.kdd.org/kdd2019/accepted-papers/view/ambulatory-atrial-fibrillation-monitoring-using-wearable-photoplethysmograp

---Feb 06 ---

Presenter: Ali Ghubaish

Paper: MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare, NeurIPS 2018, E. Choi, et al. ([10])

---Feb 13 ---

Presenter: Hanyang Liu

Paper: Alaa, Ahmed M., and Mihaela van der Schaar. "Bayesian inference of individualized treatment effects using multi-task gaussian processes." Advances in Neural Information Processing Systems. 2017. URL: https://papers.nips.cc/paper/6934-bayesian-inference-of-individualized-treatment-effects-using-multi-task-gaussian-processes.pdf

---Feb 20 ---

Presenter: Jingwen Zhang

Paper: Song, Huan, Deepta Rajan, Jayaraman J. Thiagarajan, and Andreas Spanias. "Attend and diagnose: Clinical time series analysis using attention models." In Thirty-second AAAI conference on artificial intelligence. 2018. URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16325

---Feb 27 ---

Canceled for Big Ideas event

---Mar 05 ---

Presenter: Joss Wang

Paper: Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, Samy Bengio, Transfusion: Understanding Transfer Learning for Medical Imaging, NeurIPS 2019. URL: https://arxiv.org/pdf/1902.07208.pdf

---Mar 12 ---

Spring break - no seminar

---Mar 19 ---

Presenter: Bing Xue

Paper: Hao Wang, Chengzhi Mao, Hao He, Mingmin Zhao, Tommi S. Jaakkola, and Dina Katabi, Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling, AAAI Conference on Artificial Intelligence (AAAI-19), 2019. URL: https://www.aaai.org/ojs/index.php/AAAI/article/view/3855

---Mar 26 ---

Double Header

Presenters: Hao Liu and Adith Boloor

Paper 1: X. Zhang et al., MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records, KDD 2019. URL: https://dl.acm.org/doi/10.1145/3292500.3330779

Paper 2: A. Rajkomar et al., Scalable and Accurate Deep Learning with Electronic Health Records, npj Digital Medicine (2018) 1:18. URL: https://www.nature.com/articles/s41746-018-0029-1.pdf

---Apr 02 ---

Presenter: Zhuangzhuang Zhang

Paper: Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation, NIPS 2018, Y. Li, et al. ([11])


---Apr 09 ---

Presenter: Liang Tong

Paper: Ziqi Zhang, Chao Yan, Diego A Mesa, Jimeng Sun, Bradley A Malin, Ensuring electronic medical record simulation through better training, modeling, and evaluation, Journal of the American Medical Informatics Association, Volume 27, Issue 1, January 2020, Pages 99–108, https://doi.org/10.1093/jamia/ocz161

---Apr 16 ---

Presenter: Sayantan Kumar

Paper: Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections, IEEE Transactions on Medical Imaging, 2018, J. Marsh, et al. ([12])

---Apr 23 ---

Presenter: Raj Venkatesaramani

Paper: Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao, and Panos Kalnis. 2019. GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19). Association for Computing Machinery, New York, NY, USA, 705–713. URL: https://www.kdd.org/kdd2019/accepted-papers/view/gcn-mf-disease-gene-association-identification-by-graph-convolutional-netwo

Previous Semesters

Previous Lab Meetings

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