Assistant Professor, Department of Electrical and Computer Engineering
Affiliated Faculty, AI Group in the Department of Computer Science and Engineering
University of California San Diego
I obtained my PhD from the Machine Learning Department, School of Computer Science, Carnegie Mellon University. My research interests mainly lie in machine learning inspired by humans’ learning skills (especially classroom learning skills), such as learning by self explanation, small-group learning, learning by teaching, etc., and their applications in Large Language Models, Foundation Models, and Healthcare. Here is a summary of research outcome.
Email: p1xie@ucsd.edu (My another email p1xie@eng.ucsd.edu will no longer be used)
I am looking for highly-motivated PhD and master students to join my group. I am also looking for research interns for summer.
News
- 2023/9. Two papers are accepted by NeurIPS 2023.
- 2023/6. Course evaluations are released. ECE269-Winter2022, ECE285-Winter2022, ECE285-Spring2023, ECE175B-Spring2023, ECE269-Winter2021, ECE285-Winter2021
- 2023/5. A recent project. ProteinChat: Towards Enabling ChatGPT-Like Capabilities on Protein 3D Structures
- 2023/5. A recent project. DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
- 2023/5. A recent project. XrayChat: Towards Enabling ChatGPT-Like Capabilities on Chest X-ray Images
- 2023/4. Three papers are accepted by ICML 2023.
- 2023/1. Two papers are accepted by ICLR 2023, including one Notable-Top-5% paper.
- 2023/1. I received the Best Graduate Teacher Award (presented by ECE at UCSD).
- 2020/8. My PhD thesis was selected as a finalist (top 5) for the AMIA Doctoral Dissertation Award.
Current Students
PhD Students and Postdocs
- Caitlin Aamodt (PhD from UCLA)
- Han Guo (from CS at Rice U.)
- Ramtin Hosseini (from ME at UC Berkeley)
- Mingjia Huo (from CS at Peking U.)
- Amirhosein Javadi (from EE at Sharif U. of Tech.)
- Youwei Liang (from CS at SCAU)
- Sai Somayajula (from EE at IIT Hyderabad)
- Li Zhang (from CSE at Zhejiang U.)
- Ruiyi Zhang (from CS at Peking U.)
Collaborative PhD Students
- Ding Bai (MBZUAI)
- Sang Choe (CMU)
- Nicholas Ho (CMU)
- Blair Jia (UCSD)
- Yingtao Luo (CMU)
- Sazan Mahbub (CMU)
- Shentong Mo (MBZUAI)
- Duy Nguyen (University of Stuttgart)
- Ning Sun (MBZUAI)
- Jiayou Zhang (MBZUAI)
- Shuxian Zou (MBZUAI)
Master Students
- Hongchao Fang
- Yiheng He
- Onkar Litake
- Preyaa Patel
- Shreya Saha
- Abhishek Singh
- Zeyuan Yin
Undergraduate Students
- Shuhang Lin
- Ritvik Gupta
- Yuchen Shen
- Zihan Wang
Past Students
Undergraduates
- Yifeng Wang (2023 –> PhD student at CMU ECE)
- Wenxiao Cai (2023 –> MS student at Stanford EE)
- Zhihao Zhan (2022 –> PhD student at Mila)
- Ruisi Zhang (2020 –> PhD student at UCSD ECE)
- Matt Hong (2020 –> PhD student at UCSD CSE)
- Jiayuan Huang (2020 –> Master student at CMU CS)
- Jiaqi Zeng (2020 –> Master student at CMU CS)
- Meng Zhou (2020 –> Master student at CMU CS)
- Yuhong Chen (2020 –> Master student at CMU INI)
- Yue Yang (2020 –> Master student at Georgia Tech CS)
Master Students
- Jiachen Li (2020 –> PhD student at UCSB)
- Xuehai He (2020 –> PhD student at UCSC)
Teaching
- ECE285 Deep Generative Models. Course evaluations: ECE285-Winter2022, ECE285-Winter2023, ECE285-Winter2021
- ECE175B Probabilistic Reasoning and Graphical Models. Spring 2023. Course evaluations: ECE175B-Spring2023
- ECE269 Linear Algebra and Applications. Course evaluations: ECE269-Winter2022, ECE269-Winter2021
Recent Work on Large Language Models and Foundation Models Based on Multi-level Optimization
- Ruiyi Zhang, Rushi Qiang, Sai Ashish Somayajula, Pengtao Xie. AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
- Sai Ashish Somayajula, Youwei Liang, Li Zhang, Abhishek Singh, Pengtao Xie. Generalizable and Stable Finetuning of Large Language Models on Low-Resource Texts. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
- Mingjia Huo, Sai Ashish Somayajula, Youwei Liang, Ruisi Zhang, Farinaz Koushanfar, Pengtao Xie. Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models, 2024. arXiv:2402.18059
- Li Zhang, Youwei Liang, Pengtao Xie. BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of the Segment Anything Model, 2024. arXiv:2402.16338
- Han Guo, Ramtin Hosseini, Ruiyi Zhang, Sai Ashish Somayajula, Ranak Roy Chowdhury, Rajesh K. Gupta, Pengtao Xie. Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization, 2024. arXiv:2402.18128
Publications
- Ruiyi Zhang, Rushi Qiang, Sai Ashish Somayajula, Pengtao Xie. AutoLoRA: Automatically Tuning Matrix Ranks in Low-Rank Adaptation Based on Meta Learning. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
- Sai Ashish Somayajula, Youwei Liang, Li Zhang, Abhishek Singh, Pengtao Xie. Generalizable and Stable Finetuning of Large Language Models on Low-Resource Texts. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024.
- Sai Ashish Somayajula, Onkar Litake, Youwei Liang, Ramtin Hosseini, Shamim Nemati, David O. Wilson, Robert N. Weinreb, Atul Malhotra, Pengtao Xie. Improving Long COVID-Related Text Classification Based on End-to-End Domain-Adaptive Paraphrasing. Scientific Reports, Nature Portfolio, 2024.
- Han Guo, Ramtin Hosseini, Sai Ashish Somayajula, Pengtao Xie. Improving Image Classification of Gastrointestinal Endoscopy Using Curriculum Self-Supervised Learning. Scientific Reports, Nature Portfolio, 2024.
- Pengtao Xie, Xingchen Zhao, Xuehai He. Transfer Learning Based on Multi-level Optimization. Transactions of the Association for Computational Linguistics (TACL), 2024.
- Duy Minh Ho Nguyen, Hoang Nguyen, Nghiem Tuong Diep, Tan Ngoc Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Mathias Niepert, Daniel Sonntag. LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching. Conference on Neural Information Processing Systems (NeurIPS), 2023.
- Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric Xing. Making Scalable Meta Learning Practical. Conference on Neural Information Processing Systems (NeurIPS), 2023.
- Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie. Fair and Accurate Decision Making through Group-Aware Learning. International Conference on Machine Learning (ICML), 2023.
- Pengtao Xie. Skillearn: Develop Machine Learning Training Strategies by Drawing Inspirations from Human Learning Skills. International Conference on Machine Learning (ICML), 2023.
- Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian. Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. International Conference on Machine Learning (ICML), 2023.
- Sang Keun Choe, Willie Neiswanger, Pengtao Xie*, Eric Xing*. Betty: An Automatic Differentiation Library for Multilevel Optimization. International Conference on Learning Representations (ICLR), 2023. (Notable-Top-5% Paper, *Co-Corresponding Authors)
- Parth Sheth, Pengtao Xie. Improving Differentiable Neural Architecture Search by Encouraging Transferability. International Conference on Learning Representations (ICLR), 2023.
- Zunming Zhang, Xinyu Chen, Rui Tang, Yuxuan Zhu, Han Guo, Yunjia Qu, Pengtao Xie, Ian Lian, Yingxiao Wang, Yu-Hwa Lo. Interpretable Unsupervised Learning Enables Accurate Clustering with High-Throughput Imaging Flow Cytometry. Scientific Reports, Nature Portfolio, 2023.
- Duy Nguyen, Nguyen Hoang, Truong Mai, Cao Tri, Thanh Nguyen, Nhat Ho, Paul Swoboda, Shadi Albarqouni, Pengtao Xie, and Daniel Sonntag. Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering. AAAI Conference on Artificial Intelligence (AAAI), 2023.
- Pengtao Xie, Xingchen Zhao, Xuehai He. Improve the Performance of CT-Based Pneumonia Classification via Source Data Reweighting. Scientific Report, Nature Portfolio, 2023.
- Qian Li, Jianxin Li, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Lihong Wang, Pengtao Xie. Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems. ACM Transactions on the Web (TWEB), 2023.
- Ramtin Hosseini and Pengtao Xie. Saliency-Aware Neural Architecture Search. Neural Information Processing Systems (NeurIPS), 2022.
- Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, and Pengtao Xie. Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations. International Conference on Learning Representations (ICLR), 2022. (Spotlight Presentation)
- Pengtao Xie and Xuefeng Du. Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
- Shubham Chitnis, Ramtin Hosseini, Pengtao Xie. Brain Tumor Classification Based on Neural Architecture Search. Scientific Reports, Nature Portfolio, 2022.
- Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, Wenwu Zhu. Graph Neural Architecture Search Under Distribution Shifts. International Conference on Machine Learning (ICML), 2022. (Oral Presentation)
- Ramtin Hosseini, Pengtao Xie. Image Understanding by Captioning with Differentiable Architecture Search. ACM International Conference on Multimedia (ACM MM), 2022.
- Hongchao Fang, Pengtao Xie. Contrastive Self-Supervised Learning for Language Understanding. Transactions of the Association for Computational Linguistics (TACL), 2022.
- Abhibha Gupta, Parth Sheth, Pengtao Xie. Neural Architecture Search for Pneumonia Diagnosis from Chest X-Rays. Scientific Reports, Nature Portfolio, 2022.
- Sai Somayajula and Pengtao Xie. A Multi-Level Optimization Framework for End-to-End Text Augmentation. Transactions of the Association for Computational Linguistics (TACL), 2022.
- Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, and Pengtao Xie. MetaWeighting: Learning to Weight Tasks in Multi-Task Text Classification. The 60th Annual Meeting of the Association for Computational Linguistics (ACL), Findings, 2022.
- Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric Xing, and Pengtao Xie. Learning from Mistakes – A Framework for Improving Neural Architecture Search. AAAI Conference on Artificial Intelligence (AAAI), 2022.
- Wenwu Zhu, Xin Wang, Pengtao Xie. Self-directed machine learning. AI Open, 2022.
- Jiayuan Huang, Yangkai Du, Shuting Tao, Kun Xu, and Pengtao Xie. Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion. Transactions of the Association for Computational Linguistics (TACL), 2021.
- Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing and Pengtao Xie. Towards Visual Question Answering on Pathology Images. The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
- Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing and Pengtao Xie. On the Generation of Medical Dialogs for COVID-19. The 59th Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
- Ramtin Hosseini, Xingyi Yang and Pengtao Xie. DSRNA: Differentiable Search of Robust Neural Architectures. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
- Meng Zhou, Zechen Li and Pengtao Xie. Self-supervised Regularization for Text Classification. Transactions of the Association for Computational Linguistics (TACL), 2021.
- Jiaqi Zeng and Pengtao Xie. Contrastive Self-supervised Learning for Graph Representation Learning. AAAI Conference on Artificial Intelligence (AAAI), 2021.
- Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing. Explaining Black-box Models Using A Deep Variational Information Bottleneck Approach. AAAI Conference on Artificial Intelligence (AAAI), 2021.
- Luntian Mou, Chao Zhou, Pengtao Xie, Pengfei Zhao, Ramesh Jain, Wen Gao, and Baocai Yin. Isotropic Self-supervised Learning for Driver Drowsiness Detection with Attention-based Multimodal Fusion. IEEE Transactions on Multimedia (TMM), 2021.
- Jeanne Vu, Ghiam Yamin, Zabrina Reyes, Alex Shin, Alexander Young, Irene Litvan, Pengtao Xie, Sebastian Obrzut. Assessment of Motor Dysfunction with Virtual Reality in Patients Undergoing [123I]FP-CIT SPECT/CT Brain Imaging. Tomography, 2021.
- G. Zeng, W. Yang, Z. Ju, Y. Yang, S. Wang, R. Zhang, M. Zhou, J. Zeng, X. Dong, R. Zhang, H. Fang, P. Zhu, S. Chen and Pengtao Xie. MedDialog: Large-scale Medical Dialogue Datasets. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
- Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing. Generalized Zero-shot ICD Coding. International Joint Conference on Artificial Intelligence (IJCAI 2020).
- Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P Xing. Adversarial Domain Adaptation Being Aware of Class Relationships. European Conference on Artificial Intelligence (ECAI 2020).
- B. Huang, K. Zhang, P. Xie, M. Gong, E. P. Xing. Specific and Shared Causal Relation Modeling and Mechanism-based Clustering. Advances in Neural Information Processing Systems (NeurIPS 2019).
- K. Xu, M. Lam, J. Pang, X. Gao, C. Band, P. Mathur, F. Papay, A. K. Khanna, J. B. Cywinski, K. Maheshwari, P. Xie, E. P. Xing. Multimodal Machine Learning for Automated ICD Coding. Conference on Machine Learning for Healthcare (MLHC 2019).
- Z.Wang, N.Dong, S.Rosario, M.Xu, P.Xie, and E.P.Xing. Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Image with Unsupervised Domain Adaption. The IEEE International Symposium on Biomedical Imaging (ISBI 2019).
- P.Xie, W.Wu, Y.Zhu and E.P.Xing. Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. The 35th International Conference on Machine Learning (ICML 2018) (Long Oral Presentation).
- P.Xie, H.Zhang, Y.Zhu and E.P.Xing. Nonoverlap-Promoting Variable Selection. The 35th International Conference on Machine Learning (ICML 2018) (Short Oral Presentation).
- P.Xie, H.Shi, M.Zhang and E.P.Xing. A Neural Architecture for Automated ICD Coding. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) (Oral Presentation)
- B.Jing, P.Xie and E.P.Xing. On the Automatic Generation of Medical Imaging Reports. The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018).
- P.Xie, J.Kim, Q.Ho, Y.Yu and E.P.Xing. Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. Symposium of Cloud Computing (SoCC 2018).
- D.Sachan, P.Xie and E.P.Xing. Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition. Conference on Machine Learning for Healthcare (MLHC 2018).
- X.Liu, K.Xu, P.Xie and E.P.Xing. Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. NIPS ML for Healthcare Workshop, 2018 (Spotlight Presentation).
- P.Xie, R.Salakhutdinov, L.Mou and E.P.Xing. Deep Conditional Determinantal Point Process for Large-Scale Multi-Label Classification. International Conference on Computer Vision (ICCV 2017).
- P.Xie, B.Poczos and E.P.Xing. Near-Orthogonality Regularization in Kernel Methods. Conference on Uncertainty in Artificial Intelligence (UAI 2017) (Plenary Presentation).
- H.Zhang, Z.Zheng, S.Xu, X.Liang, W.Dai, Q.Ho, Z.Hu, J.Wei, P.Xie, and E.P.Xing. Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. 2017 USENIX Annual Technical Conference (ATC 2017) (Oral Presentation).
- P.Xie, A.Singh and E.P.Xing. Uncorrelation and Evenness: A New Diversity-Promoting Regularizer. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
- P.Xie, Y.Deng, Y.Zhou, A.Kumar, Y.Yu, J.Zou and E.P.Xing. Learning Latent Space Models with Angular Constraints. The 34th International Conference on Machine Learning (ICML 2017) (Oral Presentation).
- H.Zhou, J.Li, P.Xie and Y.Zhang. Improving the Generalization Performance of Multi-class SVM via Angular Regularization. The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017).
- P.Xie and E.P.Xing. A Constituent-Centric Neural Architecture for Reading Comprehension. The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017).
- Y.Zhou, K.Yuan, Y.Yu, X.Ni, P.Xie, E.P.Xing and S.Xu. Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions. Heredity, 2017.
- E.P.Xing, Q.Ho, P.Xie and W.Dai. Strategies and Principles of Distributed Machine Learning on Big Data. Engineering, Transactions of Chinese Academy of Engineering (Engineering 2016).
- P.Xie, J.Kim, Y.Zhou, Q.Ho, A.Kumar, Y.Yu and E.P.Xing. Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. The 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016).
- P.Xie, J.Zhu and E.P.Xing. Diversity-Promoting Bayesian Learning of Latent Variable Models. The 33rd International Conference on Machine Learning (ICML 2016) (Oral Presentation).
- E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data (IEEE BigData 2015).
- P.Xie. Learning Compact and Effective Distance Metrics with Diversity Regularization. European Conference on Machine Learning (ECML 2015) (Oral Presentation).
- P.Xie, Y.Deng and E.P.Xing. Diversifying Restricted Boltzmann Machine for Document Modeling. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
- E.P.Xing, Q.Ho, W.Dai, J.Kim, J.Wei, S.Lee, X.Zheng, P.Xie, A.Kumar and Y.Yu. Petuum: A New Platform for Distributed Machine Learning on Big Data. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015) (Oral Presentation).
- P.Xie, D.Yang and E.P.Xing. Incorporating Word Correlation Knowledge into Topic Modeling. The 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2015).
- P.Xie, Y.Pei, Y.Xie and E.P.Xing. Mining User Interests from Personal Photos. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015).
- P.Xie and E.P.Xing. Integrating Image Clustering and Codebook Learning. The 29th AAAI Conference on Artificial Intelligence (AAAI 2015) (Oral Presentation).
- P.Xie and E.P.Xing. Multi-Modal Distance Metric Learning. The 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013) (Oral Presentation).
- P.Xie and E.P.Xing. Integrating Document Clustering and Topic Modeling. Proceedings of the 29th International Conference on Uncertainty in Artificial Intelligence (UAI 2013).
Professional Activities
Area Chairs for:
- ICML 2021, NeurIPS 2021, CVPR 2021, NAACL 2021, ICCV 2021, AAAI 2021, IJCAI 2021,
Co-organizer for:
- NeurIPS 2020 workshop “Self-Supervised Learning – Theory and Practice”
- AAAI 2021 workshop “Trustworthy AI for Healthcare”
- ICLR 2021 workshop “Machine Learning for Preventing and Combating Pandemics”
Member for:
- Machine Learning for Signal Processing Technical Committee
- ACM, IEEE, AMIA
Program Committee Members or Reviewers for:
- Conferences: ICML (2014, 2018-2019), NIPS (2016, 2018), AISTATS (2017-2019), UAI (2018), ICLR (2019), AAAI (2019), CVPR (2016-2019), ICCV (2015, 2017), ECCV (2016, 2018), ACL (2015-2018), EMNLP (2015), KDD (2015), ECML (2016-2017), ACCV (2016), BMVC (2017)
- Journals: TPAMI (2018), TKDE (2015-2018), TMM (2016-2017), PLOS ONE (2017-2018), TNNLS (2015-2016, 2018), JASA (2015)
Selected Awards and Honors
- Finalist (top 5) for AMIA Doctoral Dissertation Award.
- Amazon AWS Research Award.
- Tencent AI-Lab Faculty Award.
- Tencent Faculty Award.
- Google Cloud Research Credits.
- Top Reviewer for ICML 2020.
- Innovator Award, 2018 (presented by the Pittsburgh Business Times).
- 1st Place (out of 400+ participating teams) in both Defenses and Targeted Attacks, 3rd Place in Untargeted Attacks, in NIPS Adversarial Vision Challenge, 2018.
- Siebel Scholarship, 2014 (85 graduate students from around the world).
Recent Invited Talks
- Machine Learning for Medical Decision Support
- Nov 2019, Department of Biomedical Informatics, University of Pittsburgh
- Oct 2019, AI NEXTCon Developer Conference
- Apr 2019, New York University
- Apr 2019, University of Massachusetts Amherst
- Mar 2019, University of California San Diego
- Feb 2019, Columbia University
- Feb 2019, Johns Hopkins University
- Feb 2019, University of California Los Angeles
- Jan 2019, University of Wisconsin-Madison