I try my best to accommodate different levels, said Shang. Room: https://ucsd.zoom.us/j/632116245. Bio. KDD 2017. View Jingbo Shang's profile on LinkedIn, the world's largest professional community. Bioinformatics 35 (10 . How can we teach them with the least amount of effort? . Although Shang is smart, I can already tell that his lectures are not it. Class Time: Tuesdays and Thursdays, 12:30 to 1:50PM. Decent lectures. Course (s) National Scholarship, Top 0.2% nationwide, China, Program Committee Member: ICML, NeurIPS, AAAI, Teaching: TA for CSE 151A: Introduction to Machine Learning, volunteer teacher for middle school students. I am interested in developing robust and efficient machine learning techniques for time-series analysis, context-aware sensing, spatiotemporal modeling, etc. You can find my schedule here. Class Time: Tuesdays and Thursdays, 9:30AM to 10:50AM. Class Time: Wednesdays, 11 to 11:50 AM Pacific Time. Building Structured Databases of Factual Knowledge from Massive Text Corpora. Piazza: piazza.com/ucsd/fall2020/cse291a00, Class Time: Tuesdays and Thursdays, 9:30AM to 10:50AM. Open Peer Review. Room: EBU3B (CSE) 2154. We use cookies to operate our website. USC Associate Professor on AI/NLP Los Angeles, CA. What are the current state-of-the-art solutions and why do you think you can further improve over them? Jingbo Shang is an Assistant Professor at UCSD jointly appointed by Computer Science and Halcolu Data Science Institute. Room: https://ucsd.zoom.us/j/93540989128. He was using Pascal, a computer language co-developed in the 1970s by former UC San Diego Professor Kenneth Bowles. Room: https://ucsd.zoom.us/j/97017584161. In my humble opinion, cooking, to some extent, should be easier than making complicated deep learning models effective in experiments. Piazza: piazza.com/ucsd/spring2023/cse109. Definitely an expert in ML, unfortunately this hurts his lectures for an intro ML course. He received his B.E. My lab has openings for PhD students and research internship opportunities for master/undergrad students (UCSD or external). Briefly explain your motivations and expectations of working with me. Jingbo has 1 job listed on their profile. I'm an Assistant Professor at UCSD jointly appointed by Computer Science and Halcolu Data Science Institute. I received my B.E. With the pandemic and the riots he cut the class alot of breaks. I am a Ph.D. student at CSE, UC San Diego, advised by Professor Rajesh Gupta and Professor Jingbo Shang.I am interested in developing robust and efficient machine learning techniques for time-series analysis, context-aware sensing, spatiotemporal modeling, etc.My research has practical applications in various domains of our daily life, including healthcare, climate science, smart home . Jingbo Shang is an Assistant Professor in CSE and HDSI at UC San Diego. I received my B.S. Modeling Label Semantics Improves Activity Recognition, Xiyuan Zhang, Ranak Roy Chowdhury, Dezhi Hong, Rajesh Gupta, Jingbo Shang, Navigating Alignment for Non-Identical Client Class Sets: A Label Name-Anchored Federated Learning Framework, Jiayun Zhang, Xiyuan Zhang, Xinyang Zhang, Dezhi Hong, Rajesh Gupta, Jingbo Shang, Towards Diverse and Coherent Augmentation for Time-Series Forecasting, Xiyuan Zhang, Ranak Roy Chowdhury, Jingbo Shang, Rajesh Gupta, Dezhi Hong, Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks, Xiaofan Yu, Ludmila Cherkasova, Harsh Vardhan, Quanling Zhao, Emily Ekaireb, Xiyuan Zhang, Arya Mazumdar, Tajana Simunic Rosing, PrimeNet: Pre-training for Irregular Multivariate Time Series, Ranak Roy Chowdhury, Jiacheng Li, Xiyuan Zhang, Dezhi Hong, Rajesh Gupta, Jingbo Shang, First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting, Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang, NeurIPS 2022 All Things Attention Woskshop, Task-Aware Reconstruction for Time-Series Transformer, Ranak Roy Chowdhury, Xiyuan Zhang, Dezhi Hong, Rajesh Gupta, Jingbo Shang, ESC-GAN: Extending Spatial Coverage of Physical Sensors, Filling Conversation Ellipsis for Better Social Dialog Understanding, Xiyuan Zhang, Chengxi Li, Dian Yu, Samuel Davidson, Zhou Yu, Neural Embeddings for Nearest Neighbor Search Under Edit Distance, CPS Rising Stars, 2023 Class Time: Mondays 11AM to 11:50AM. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in, J Shen, Z Wu, D Lei, J Shang, X Ren, J Han, Machine Learning and Knowledge Discovery in Databases: European Conference, Proceedings of the 58th Annual Meeting of the Association for Computational, M Jiang, J Shang, T Cassidy, X Ren, LM Kaplan, TP Hanratty, J Han, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge, Z Wang, J Shang, L Liu, L Lu, J Liu, J Han, Proceedings of The Web Conference 2020, 1908-1919, J Shen, W Qiu, Y Meng, J Shang, X Ren, J Han, NAAC'21: Proceedings of the 2021 Conference of the North American Chapter of, Proceedings of the 2020 Conference on Empirical Methods in Natural Language, New articles related to this author's research, Abel Bliss Professor of Computer Science, University of Illinois, Viterbi Early Career Chair & Associate Professor, University of Southern California, University of Illinois at Urbana-Champaign, Vice President of JD.COM, Chief Data Scientist of JD Digits, Head of JD Intelligent Cities Research, Brigham and Women's Hospital / Harvard Medical School, Empower sequence labeling with task-aware neural language model, Automated phrase mining from massive text corpora, Inferring gas consumption and pollution emission of vehicles throughout a city, Mining quality phrases from massive text corpora, Cross-type biomedical named entity recognition with deep multi-task learning, Learning named entity tagger using domain-specific dictionary, Meta-path guided embedding for similarity search in large-scale heterogeneous information networks, An attention-based collaboration framework for multi-view network representation learning, Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open, Detecting urban black holes based on human mobility data, Setexpan: Corpus-based set expansion via context feature selection and rank ensemble, Contextualized weak supervision for text classification, Metapad: Meta pattern discovery from massive text corpora, Crossweigh: Training named entity tagger from imperfect annotations, X-class: Text classification with extremely weak supervision, Nettaxo: Automated topic taxonomy construction from text-rich network, Efficient contextualized representation: Language model pruning for sequence labeling, TaxoClass: Hierarchical multi-label text classification using only class names, Empower entity set expansion via language model probing, Meta: Metadata-empowered weak supervision for text classification. Jingbo Shang. Room: https://ucsd.zoom.us/j/91491702947. Class Time: Tuesdays and Thursdays, 9:30AM to 10:50AM. Room: WLH 2204. Jingbo Shang's profile, publications, research topics, and co-authors . PO Box 16122 Collins Street West Victoria, Australia, Phone: + (066) 0760 0260 / + (057) 0760 0560. This year we dominated the Southern California Regional Competition, he said. Github Google Scholar Welcome to Jingbo's Homepage! Talented and also very patient with students. Jiaming Shen, Wenda Qiu, Yu Meng, Jingbo Shang, Xiang Ren, Jiawei Han. Piazza: piazza.com/ucsd/fall2020/dsc180a05. Outstanding Graduate of Zhejiang University Wonderful professor. This class covers lots of useful and interesting topics and was a satisfying experience. I am a Ph.D. student at CSE, UC San Diego, My research has practical applications in various domains of our daily life, including healthcare, climate science, smart home, smart city, e-commerce, and beyond. How do you usually write your name as author of a paper? While artificial intelligence (AI) can make connections within all this data, creating those models can be labor-intensive, particularly manually annotating data to train a model. My research focuses on ways to reduce human effort in this model building process, said Shang. Im mainly working on data, mining, natural language processing and machine learning, said Shang. We sent seven teams and one team got first place and another got third place. Shang and colleagues want to see if they can use existing data to make the process less cumbersome. He obtained his Ph.D. from University of Illinois at Urbana Champaign (UIUC) advised by Prof. Jiawei Han in 2019. Halcolu Data Science Institute (HDSI). He did internships at Google and a hedge fund called Two Sigma. Class Time: Mondays, 12PM to 1PM. This makes lecture extremely boring & causes people who don't get it initially to have to learn a significant portion of the course themselves (or ask questions). Dept of Computer Science and Engineering University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0404 U.S.A. Jingbo Shang's profile, publications, research topics, and co-authors. Room: CSE 2154. Jingbo Shang. This information is crucial for deduplicating users, and ensuring you see your reviewing assignments. For more information about how we use cookies, please see our Website Terms of Use. Has an accent but not a big problem. This website design is based on the template from Martin Saveski. Room: OTRSN 1E107 (1st week over Zoom). Im broadly interested in Data Mining Problems and Applications: Most of my research projects focus on (1) developing principled data-driven approaches with light human effort and (2) building effective and robust machine learning models in an efficient way. If you like it, dont forget to subscribe it! Room: WLH 2204 (1st week over Zoom). Im also a coach of the UCSDs ACM-ICPC team. Piazza: piazza.com/ucsd/winter2020/cse191cap, 2023-Spring-CSE291-DSC253-Advanced Data-Driven Text Mining, 2023-Spring-CSE151A-Introduction to Machine Learning and CSE 251A-ML: Learning Algorithms, 2023-Spring-CSE109-Introduction to Programming Contests, 2023-Winter-DSC148-Introduction to Data Mining, 2023-Winter-MGTA415-Analyzing Unstructured Data, 2022-Fall-DSC180B14-Capstone: Weakly Supervised NLP, 2022-Spring-CSE151A-Introduction to Machine Learning, 2022-Spring-CSE109-Introduction to Programming Contests, 2022-Winter-DSC190-Introduction to Data Mining, 2022-Winter-MGTA415-Analyzing Unstructured Data, 2021-Fall-DSC180A02-Capstone: Text Mining and NLP, 2021-Spring-CSE151A-Introduction to Machine Learning, 2021-Spring-CSE191-Introduction to Programming Contests, 2021-Winter-MGTA415-Working with Unstructured Data, 2021-Winter-DSC190-Introduction to Data Mining, 2021-Winter-DSC180A05-Capstone: Text Mining and NLP, 2020-Fall-DSC180A05-Capstone: Text Mining and NLP, 2020-Fall-CSE291-Advanced Data-Driven Text Mining, 2020-Spring-DSC190-Introduction to Data Mining, 2020-Winter-CSE191-Introduction to Competitive Algorithmic Programming, Scientific Text Mining and Knowledge Graphs, Constructing and Mining Heterogeneous Information Networks from Massive Text, TextCube: Automated Construction and Multidimensional Exploration, Towards Multidimensional Analysis of Text Corpora, Mining Entity-Relation-Attribute Structures from Massive Text Data, Building Structured Databases of Factual Knowledge from Massive Text Corpora, Constructing Structured Information Networks from Massive Text Corpora.