Over the years I have given a lot of talks and the following are slides from many of these talks. Feel free to reuse the slides with the appropriate attribution.
-
Managing the Machine Learning Lifecycle Invited Talk at the DataEngConf. 2018 [pdf, pptx]
-
Rise to the Challenges of AI Systems. Invited Talk at the AAAI Systems Workshop. 2017 [pdf, pptx]
-
Data Science Activities at Berkeley. Invited Talk at the Student run Berkeley Data Science Society. 2017 [pdf, pptx]
-
Ongoing Research Overview. Invited Talk Berkeley AI Research Seminar Series. 2016 [pdf, pptx]
-
Machine Learning Research Challenges in the RISE Lab. RISE Lab Early RISEr seminar series. 2016. [pdf, pptx]
-
Prediction Serving: What happens after learning? Invited Talk in the Learning Systems Workshop at the International Conference on Machine Learning (ICML). 2016. [pdf, pptx]
-
Intelligent Services: Serving Machine Learning Invited Talk in the Learning Systems Workshop at the conference on Neural Information Processing Systems (NIPS). 2015. [pdf, pptx]
-
Optimistic Concurrency Control in the Design and Analysis of Parallel Learning Algorithms. Invited talk at the Information Theory and Applications (ITA) Workshop. 2015. [pdf, pptx]
-
The Missing Piece in Complex Analytics: Scalable, Low Latency Model Serving and Management with Velox. Contributed talk at the Conference on Innovative Database Research (CIDR’15). 2015. [pdf, keynote]
-
GraphX: Graph Processing in a Distributed Dataflow Framework. Contributed talk in the Proceedings of Operating Systems Design and Implementation (OSDI’14) 2014. [pdf, pptx]
-
Concurrency Control For Scalable Bayesian Inference. Invited talk at Annual meeting of the International Society for Bayesian Analysis (ISBA) 2014. [pdf, pptx]
-
Emerging Systems for Large-Scale Machine Learning. Invited tutorial at International Conference for Machine Learning (ICML) 2014. [pdf, pptx]
-
GraphX: Unifying Table and Graph Analytics. Invited talk at the session on Graph Algorithms Building Blocks at the International Parallel and Distributed Processing Systems (IPDPS). 2014. [pdf, pptx]
-
From Graphs to Tables: The Design of Scalable Systems for Graph Analytics. Keynote speaker at Workshop on Big Graph Mining at the International World Wide Web Conference (WWW). 2014. [pdf, pptx]
-
Linear Regression and the Bias Variance Trade-off. Guest lecture in Berkeley class on Statistical Learning Theory. 2014. [pdf, pptx]
-
Parallel and Distributed Systems for Probabilistic Reasoning. Thesis defense talk. 2012. [pdf, pptx]
-
Big Learning with Graphs. Guest lecture in the Berkeley class Analyzing Big Data with Twitter. 2012. [pdf, pptx]
-
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs. Contributed talk in the Proceedings of Operating Systems Design and Implementation (OSDI’12) 2012. [pdf, pptx, extended pptx]
-
Splash Gibbs Sampling. Contributed talk at Artificial Intelligence and Statistics (AISTATS’11) 2011. [pdf, pptx]
-
Parallel Belief Propagation. CMU Machine Learning Seminar Series 2009. [pdf, pptx]
-
Splash Belief Propagation. Contributed talk at Uncertainty in Artificial Intelligence 2009. [pdf, pptx]