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.
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]