Who will speak at the Texas AI Summit?

Don't plan on leaving early. Martin Fowler (Refactoring) will be speaking at 7PM!

We have more confirmations to publish. Bookmark this page for updates.

Confirmed Speakers

Opening Keynote
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 will be presenting the keynote session:
Data Driven Natural Language Generation: Linking Humans to the Machine with the Power of Narrative

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

Chris Sachs (SF Bay) @c9r

Chris Sachs is the founder and chief architect at SWIM.AI. He has developed innovative, real-time solutions for some of the most challenging data use cases around the world. Applications that Chris has developed continue to power airports, malls, intelligent traffic systems, and more. Prior to founding SWIM, Chris served as the lead architect for Sensity Systems before its acquisition by Verizon in 2016.
Chris will be presenting the following session: Edge intelligence: Machine learning at the enterprise edge

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) @AlexGDimakis

Alex Dimakis (linkedin) is an Associate Professor at the ECE department, University of Texas at Austin. He received his Ph.D. in 2008 from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. He received an NSF Career award, a Google faculty research award and the Eli Jury dissertation award. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. He is currently serving as an associate editor for the IEEE Transactions on Information Theory. His research interests include information 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.
Alex will present the following session: Deep Generative Models and Inverse Problems

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

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.

Dr. Cheryl Martin (Austin)

Dr. Cheryl Martin (LinkedIn) is the Chief Data Scientist and VP Research at Alegion, Inc., where she works on developing machine learning capabilities and analysis to support Alegion's platform for AI Enablement. Her background includes a mix of software and cognitive science, and she has extensive experience building machine learning models for real-world applications. Prior to Alegion, she worked as a Research Scientist at both the University of Texas and NASA.
Cheryl will be giving the following presentation: Addressing Training Data Bias in Machine Learning.

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.
Jonathan will present the following 90 minute session: Practical Methods for Overcoming the Machine Learning Data Bottleneck

Ethan Rosenthal (New York City ) @eprosenthal

Ethan Rosenthal (LinkedIn) is an independent data science consultant who specializes in building full stack machine learning products and advising startups on data strategy and vision. Prior to consulting, Ethan was the founding member of the Data team at Dia&Co where he led a team of scientists, analysts, and engineers building data applications involving recommendations, logistics, computer vision, and more. Before Dia&Co, Ethan worked as a Data Scientist at Birchbox and earned a PhD in Physics from Columbia University building atomic-resolution microscopes to study superconductors. Outside of his day jobs, Ethan writes a technical data science blog, open sources his code, advises early career data scientists at Insight Data Science, and likes to get away from the computer and be active.
Ethan will present the following session: Empowering the Humans in the Loop by Synthesizing Machine Learning and Optimization

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?

KC tung (New York )

KC Tungjust joined Microsoft as an AI Architect. Formerly, KC was the principal data scientist at AT&T Advertising & Analytics. His technical specialties include algorithms and methods that are important for programmatic advertising ecosystem, such as deep learning, LSTM, neural networks, gradient boosting, random forest, parametric and nonparametric Bayesian methods, stochastic processes, the Dirichlet process mixture multinomial model, and Markov chain Monte Carlo (MCMC). He is a sought-after collaborator and guest speaker at conferences. KC holds a PhD in molecular biophysics from the University of Texas Southwestern Medical Center in Dallas, TX.
KC will present the following session: A novel adoption of LSTM in customer touchpoint prediction problems

Ayin Vala (SF Bay )

Ayin Vala is the founder of DeepMD and cofounder and chief data scientist at the nonprofit organization Foundation for Precision Medicine, where he and his research and development team work on statistical analysis and machine learning, pharmacogenetics, molecular medicine, and sciences relevant to the advancement of medicine and healthcare delivery. Ayin has won several awards and patents in the healthcare, aerospace, energy, and education sectors. Ayin holds master’s degrees in information management systems from Harvard University and mechanical engineering from Georgia Tech.
Ayin will present the following session: Predicting Alzheimer’s: Generating neural networks to detect the neurodegenerative disease

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