Who will speak at the Texas AI Summit?

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We are just beginning to announce the first round of speakers for Texas AI Summit. Expect to see frequent updates.

Confirmed Speakers

Paul Azunre (Austin)

Dr. Paul Azunre is the Director of AI Research at New Knowledge, with a focus on natural language processing, parallel computing, and graph analysis. Paul holds a PhD from MIT, where his research focused on cutting-edge optimization algorithms for improving the efficiency of solar panels.
Paul will be co-presenting the following session: Character-Level Convolutional Neural Networks for Semantic Classification

Lukas Biewald (San Francisco) @l2k

Lukas Biewald (Wikipedia / LinkedIn / GitHub) is co-founder and CEO of Weights & BIases. He was previously a co-founder for Figure-Eight (formerly Crowdflower), a human in the loop ai training platform. Prior to that, Lukas was a Senior Scientist and Manager within the Ranking and Management Team at Powerset, Inc., a natural language search technology company later acquired by Microsoft. He also led the Search Relevance Team for Yahoo, Japan! Lukas combines his passion for AI, Robotics and Computer Science in his robotics and AI lab in the basement of his outer mission house. On any given day, you can see Lukas tinkering with his inventions, programing facial recognition drones and recording youtube how to videos on how to build robots and build ai algorithms. He graduated from Stanford University with a BS in Mathematics and an MS in Computer Science. Lukas is also an expert-level Go player.
Check out Lukas' recent interview with Ben Lorica for the O'Reilly Data Show
Lukas gave the highly rated keynote at Data Day Texas 2018 : Deep Learning in the Real World. We're happy that he can return for the inaugural Texas AI Summit.

Numa Dhamani (Austin)

Numa Dhamani is a Machine Learning Engineer at New Knowledge, where she focuses on neural networks, natural language processing, and computer vision. Previously, Numa worked in the AI & Machine Learning team at Accenture’s Innovation Hub in Houston on short-term global client engagements to prototype new functionality and technologies across various industries. She holds degrees in Physics and Chemistry from UT Austin.
Numa will be co-presenting the following session: Character-Level Convolutional Neural Networks for Semantic Classification

Alex Dimakis (Austin) @alexb80

Alex Dimakis (linkedin) is an Associate Professor in the Dept. of Electrical and Computer Engineering at the University of Texas. He is also a member of the Wireless Networking and Communications Group and the Computer Science Graduate Studies Committee. Alex's interests include information theory, coding theory, and machine learning.
Alex's recent publications include: Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs and Batch Codes through Dense Graphs with High Girth. For a list of publications, view Alex's homepage at UT.

Graham Ganssle (Austin) @grahamganssle

Graham Ganssle (LinkedIn / GitHub) loves data. As Head of Data Science at Expero, his favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.
Graham Ganssle’s physics Ph.D. focused on digital signal processing, specifically on a (then) new optimization method which used naturally coupled wavefields to stabilize convergence. He also holds a masters degree in applied physics and a professional geoscientist license. Graham worked in the oil and gas vertical for ten years, performing data science and quantitative geophysics for clients around the world. He has numerous publications on a variety of scientific topics and has been awarded both scientific and business achievement awards.
Off the clock, Graham’s an inept aspiring rock climber and a triathlete. He’s constantly imploring his bride (and, more successfully, his puppy) to get muddy with him on the trail. Most Saturday mornings you can find Graham clacking away at his keyboard on his newest experiment or craziest inspiration.
Graham will present the following sessions:
Interpretability of ML Systems: Can Physical Models Learn from Deep Learning?
Using Deep Learning to Measure Objects in 3DImages

Kristian Hammond (Chicago) @kj_hammond

Kristian Hammond (LinkedIn) is chief scientist at Narrative Science and professor of computer science and journalism at Northwestern University. Previously, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.
Kristian gave the highly rated Data Day Texas 2018 presentation: Here and now: Bringing AI into the enterprise.

Mayank Kejriwal (Los Angeles) @kejriwal_mayank

Mayank Kejriwal is a research scientist and lecturer at the University of Southern California's Information Sciences Institute (ISI). He received his Ph.D. from the University of Texas at Austin under Daniel P. Miranker. His dissertation involved Web-scale data linking, and in addition to being published as a book, was recently recognized with an international Best Dissertation award in his field. Some of his projects at ISI, all funded by either DARPA or IARPA, include: automatically extracting information from large Web corpora and building search engines over them (the topic of his talk); 'automating' a data scientist with advanced meta-learning techniques; representing, and reasoning over, terabyte-scale knowledge graphs; combining structured and unstructured data for causal inference; constructing, embedding and analyzing networks over billion-tweet scale social media; and building a platform that makes research easy for geopolitical forecasters. His research sits at the intersection of knowledge graphs, social networks, Web semantics, network science, data integration and AI for social good. He is currently co-authoring a textbook on knowledge graphs (MIT Press, 2018), and has delivered tutorials and demonstrations at numerous conferences and venues, including KDD, AAAI, ISWC and WWW.
Mayank will be giving the following presentation: Fighting human trafficking with AI.

Alex Korbonits (Seattle) @korbonits

Alex Korbonits (Linkedin / GitHub) is a machine learning engineer at augmented writing platform Textio, where he ships machine learning models that drive the platform's predictions and guidance. Formerly, Alex was Remitly's first data scientist where he worked extensively on feature extraction and shipping machine learning models. Outside of work, he is an avid sailor and is working on a writing project. Alex is a graduate of the University of Chicago with degrees in Mathematics and Economics.
Alex gave one of the most highly rated talks at Data Day Texas 2017 - Distilling Dark Knowledge from Neural Networks. We're happy to have him back for the inaugural Texas AI Summit.
Alex will present the following session: Embeddings all the way down

Jonathan Mugan (Austin) @jmugan

Jonathan Mugan (Linkedin) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day Texas conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.

Robert Munro (San Francisco ) @WWRob

Robert Munro (LinkedIn) is Chief Technology Officer at Figure-Eight (formerly known as Crowdflower). Robert is an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to Text, Speech, Image and Video Processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He most recently ran Product for AWS's first Natural Language Processing services in the Deep Learning team at Amazon AI. He is currently the VP of Machine Learning at Crowdflower.
Robert has published more than 50 papers and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University.

Peter Skomoroch (San Francisco ) @peteskomoroch

Peter Skomoroch (LinkedIn) is a data scientist and entrepreneur focused on building intelligent systems to collect information and enable better decisions. Most recently, Peter was cofounder and CEO of SkipFlag (recently acquired by Workday). Pete specializes in solving hard algorithmic problems, leading cross-functional teams, and developing engaging products powered by data and machine learning. Previously, he applied his skills to the consumer internet space at LinkedIn, the world’s largest professional network, where he was an early member of the data science team. As principal data scientist, he led data science teams focused on reputation, search, inferred identity, and building data products. He was also the creator of LinkedIn Skills and LinkedIn Endorsements.

Chris Thomas (New York )

Chris Thomas is a Senior Cyber Security Technology Specialist for Darktrace Industrial, based out of the company’s New York office. Chris has comprehensive technological experience with Darktrace’s Enterprise Immune System, the only AI technology capable of detecting and autonomously responding to early-stage cyber-threats. He advises Darktrace’s strategic Fortune 500 customers in North America on advanced threat detection, machine learning, and automated response. Chris holds a Bachelor’s Degree from The University of North Carolina – Chapel Hill.
Chris will present the following session: AI-based Autonomous Response: Are Humans Ready?

Weifeng Zhong (Washington D.C )

Weifeng Zhong is a research fellow in economic policy studies at the American Enterprise Institute, where his research focuses on Chinese economic issues and political economy. His recent work has been on the application of text-analytic and machine-learning techniques to political economy issues such as the US presidential election, income inequality, and predicting policy changes in China. He has been published in a variety of scholarly journals, including the Journal of Institutional and Theoretical Economics. In the popular press, his writings have appeared in the Financial Times, Foreign Affairs, The National Interest, and Real Clear Politics, among others. He has a Ph.D. and an M.Sc. in managerial economics and strategy from Northwestern University. He also holds M.Econ. and M.Phil. degrees in economics from the University of Hong Kong and a B.A. in business administration from Shantou University in China.
Weifeng will present the following session: Reading China: Predicting Policy Change with Machine Learning