I know 2020 has just started... BUT, we thought we would continue our Women in AI lists with a Texas edition. Included in the below list are those women that we believe have spearheaded or taken part in some great research both at the end of 2019 and start of 2020. It was near impossible to only pick 30 women, but we hope you can find some inspirational women in STEM to follow!
Tian is an experienced Data Scientist, currently working as Director of ML at Walmart in Plano, Texas. Having previously worked as both a Senior Data Scientist & Head of AI/ML at 7-Eleven, Tian has been heavily focussed on personalization and delivery for customers in the CPG market. Dr Tian holds considerable experience and skill in Advanced Analytics, Data Mining, Statistical Modeling, Machine Learning, Databases and Artificial Intelligence. She also boasts a strong research background with a Ph.D. from Yale University and Master’s Degree focused on Computer Science from Georgia Institute of Technology.
Kristen is a Professor in the Department of Computer Science at the University of Texas and a Research Scientist at Facebook AI Research (FAIR). Prior to joining UT-Austin, Kristen received her Ph.D. from MIT and was later recognised as an AAAI Fellow, Sloan Fellow, a Microsoft Research New Faculty Fellow with a broad range of accolades to her name which can be seen here. Kristen boasts over twenty-thousand citations on authored and co-authored papers, with five published this year alone. Kristen's current research interests include computer vision and machine learning with the goal of developing the algorithms and representations that will allow a computer to autonomously analyze visual information.
Kay Firth-Butterfield LL.M., currently heads the Artificial Intelligence and Machine Learning division of the World Economic Forum in San Francisco, working out of Austin, Texas. Alongside her current work at the WEC, Kay co-founded the Consortium for Law and Policy of Artificial Intelligence and Robotics at the Robert E. Strauss Center, whilst also working as an expert advisor for the All Party Parliamentary Group on AI. Kay is also one of the leading consultants on the impact of AI with an emphasis on Law, Ethics and International Relations for various companies and publications. You can read further musings from Kay on her social media here.
Currently working at State Farm, Priya's recent work has been focussed on both identifying data science solutions and leading data scientist teams with the target of aiding businesses claims area achieve value through measurable and sustainable ROI. Following her Bachelor's Degree in Computer Science, Priya continued her studies at Northwestern University, with particular focus on Predictive Analysis. Prior to her work at State Farm, Priya worked as a Data Science Consultant whilst developing Umachi books, a company dedicated to Indian heritage and mythology to young children. You can see further information on Priya's work and featured keynote presentation videos here.
Subhashini's business background in both public & private sectors (including banking), alongside extensive experience in Artificial Intelligence, Data Science, Software Development, & Development Operations has seen her hold the roles of Head of AI & Director of Machine Learning at 7Eleven and JPMorgan Chase respectively. Currently playing the role of play the role of “IT strategist”, Priya's role mainly focuses on the implementation of customer-centric & automation via Machine Learning use. Alongisde her current role ay JPM, Subhashini also consults as an Analytics advisory board member of Southern Methodist University, and has also found time to author a book via the Packt platform. Keep up with Subhashini's work going forward here.
Jostine Ho is a Senior Data Scientist at SailPoint. With a master’s degree in petroleum engineering and background in computational fluid dynamics, she has a passion for optimizing quantitative models and automating data-driven processes. Realizing that she could combine her deep interest in math and graphics, she transitioned to Data Science to be on the forefront of next-generation AI. Jostine joined SailPoint in the early stages of IdentityAI, a SaaS identity analytics solution, and has been focusing on applying machine learning to solve emerging challenges and refine existing processes in the field of IGA. She has successfully brought several innovations from concept to working prototypes for use-cases in peer group analysis and role mining.
Noa holds a Ph.D. in Artificial Intelligence and Machine-Learning, specializing in anomaly detection and predictive analytics of time series. Roa's current company, Grid4C, was founded in 2013 with the vision of using Artificial Intelligence in order to extract maximum value out of smart meter and IoT data, for the purpose of optimizing the energy supply chain. Alongside running one of the industries leading AI solutions providers, Noa has also published patents in the field as well as publishing various prestigious papers on Fractal Geometry whilst also working as a research member for LAMBDA in Tel Aviv, a city recognised as one of our AI Cities to watch in 2020!
Having held managerial positions in engineering at Santander, Bank of America and Allstate, Narmada has significant experience in the financial industry leading teams of data technologists toward the next generation of 'big data'. Prior to working in industry, Narmada received her Bachelor of Engineering in Computer Software Engineering from Madurai Kamaraj University. With interests in Data Engineering, Data Science, Artificial Intelligence and building Self-service Analytics Platforms, Narmada is someone we believe you should be following if interested in the engineering side of Fintech!
Natalie received her PhD in Computer Science from Rice University in 2013 before embarking on a a career in AI, first working at ExxonMobil in the developer team as an intern before holding Data Science positions at Chevron, Anadarko & Bill.com. Whilst most of Natalie's early research focussed on supporting the medical field, Natalie later moved toward the Oil & Gas industry, which she had previously suggested to be a natural transition to another empirical field (oil & gas) in need of computational analysis and modeling in a previous interview. Interested to hear more? Some of Natalie's previous summit presentations are available to watch on YouTube and many research journals can also be found on ResearchGate here.
With one of the more interesting job titles on the list, Anisha currently works in the Machine Learning department of Schlumberger, the leading provider of technology and services to the energy industry across the world. As with many on this list, Anisha finds herself in the thriving Oil & Gas industry, having also previously worked at the Society of Petroleum Engineers as Vice Chair. Anisha's current role mainly focusses on building Machine Learning models and harness the big data infrastructure to solve problems.
With over 13 years industry experience, Priya works predominantly in information security analytics, developing insights on consumer and market trends through data-driven intelligence. Currently working as a AI and IoT Technical Strategy Leader at Microsoft, Priya leads the global Data Engineering, AI and IoT technical strategy and the technical specialists in each region of the world from both the product and commercial standpoint remotely from Houston. This role gains the experience of Priya's previous role at IBM as DevOps and Analytics Technical Product Manager. Priya's technical expertise is hard to ignore when her key skills include proficiency in Jenkins, JIRA, Ant, Grunt, Maven, ELK stack, Docker containers, Cloud Foundry Containers, TFS, Git and more, meaning that we couldn't ignore Priya's technical expertise.
Founder and CEO of VineSleuth, Amy Gross created a platform which uses sensory science, cognitive computing and excruciatingly accurate wine data to assist wine retailers in growing basket size and selling more wine to the right shoppers. The platform offers truly personalized shopping experiences in store and online. From sheer passion alone, Amy sourced a team that were able to turn a passion for customer service and a good bottle of red into a platform which saw a new use for AI! You can see Amy discuss her business model and the development of Vinesleuth here.
Having worked at Raytheon for over 25 years, Kelly has undertaken roles varying from Rapid Equipment Deployment Manager to Chief Engineer for Advanced Technology. Having completed her BSc at the University of Rochester in Electrical Engineering, Kelly went straight to work for the cybersecurity company. The variation of roles has seen Kelly spend her fair share of time in the classroom, transitioning from spending the first 10 years of her career in the lab, to later heading up the problem solving management role she currently finds herself in.
The current Autonomous Systems Technology Discipline Lead at NASA has held a variety of roles in her 11 years working there. Having received a Bachelor's of Science in Mechanical Engineering from Perdue University and PhD in the same subject from CalTech, Julia joined NASA, beginning her career at the Johnson Space Center. With roles from Robonaut Project Manager to Autonomous Systems Lead, Julia manages a diverse portfolio of projects, including flight projects, research and development, and spacecraft program systems engineering. Julia's career at NASA has gone from strength to strength, stemming from a mere interest in problem solving to programming the first humanoid robot in space.
With a PhD in Computational Material Science and Engineering, Jean-leah studied combined physics, chemistry, and mathematics prior to becoming a Data Science Fellow at Insight Data Science in New York. It wasn't until 2018 that Jean-leah moved to Texas to join Dell EMC as a Senior Data Scientist, during which time she has utilised and built on skills in various programmes including Python, R, SQL and a variety of Machine learning techniques. Jean-leah is a firm believer in improving the world through science & documents many of her thoughts on her blog.
Graduating with a Degree in Computer Science from the University of Pennsylvania, Nanthini started her career teaching Data Science, leading both Biological Data Sciences and Big Data Analytics units at the UPenn. During this time, Nanthini was also the Vice-President of the Penn Data Science Group, which focused on conducting workshops and talks to encourage the interest in Data Science. Now at NVIDIA, Nanthini is part of the RAPIDS team accelerating Machine Learning algorithms on GPU to develop the Data Science process. Current works including the acceleration of ML algorithms on GPU, profiling existing workflows and identifying areas of improvements in cuDF take up much of her time as well as being involved in the bench-marking process to spot improvements/reduction in performance.
Equipped with a passion for data and analytics, Catalina completed her MSEE in Advanced Electronic Engineering at Texas University, going on to begin her career as Product Engineer at Texas Instruments. In her entire career, Catalina has been exposed to state-of-the-art technology solutions across multiple industry verticals, becoming a Product Evangelist & Data Scientist in the SaaS industry. With both educational and technical roles to her name, Catalina now works full-time as a Senior Data Scientist at TIBCO, the Connected Intelligence Cloud service provider.
The former Teacher & Physicist currently holds a Senior Data Science position at USAA, the company responsible for providing insurance, banking, investments, retirement products and advice to more than 12 million current and former members of the U.S. military and their families. Since achieving her Ph.D. in Electrical and Computer Engineering, Sylvia has gone on develop industry experience in Machine Learning (clustering, classification, regression, neural network, ensemble methods, feature engineering), Python (scikit-learn, numpy, scipy, pandas), statistical analysis, data visualization. This development had been facilitated by previous research experience gained at the University of Arizona & TOTAl including Raman spectroscopy,SERS, optical imaging, Raman sensor, optical sensor, image processing.
Formerly a teaching assistant at the University of Texas, Rita re-joined Visa as Staff Data Scientist at the end of last year having previously interned in mid-2018. The Petrophysicist has a passion for developing problem-solving skills for real-world problems & holds proficiency in a range of programming languages (scikit-learn, Pandas, Numpy,Scipy, Seaborn, MATLAB, R, Scala. Rita also has a strong statistics background, experienced with Hypothesis Testing, T-tests, F-tests, ANOVA, Chi-square test. Alongside her technical work, Rita has co-authored a variety of papers on range of topics from clustering algorithms applied to fluid characterization to detection for digital images using machine learning algorithms and image processing.
With extensive modeling and data manipulation experience in Python and R, and hands-on training experience in Spark and Scala. SAS and SQL, Shuling is passionate in seeking insights the right way - by leveraging the statistical inference and machine learning algorithms. Shuling considers herself to be curious and innovative, with the goal of never stopping learning. This passion has seen Shuling take both Senior Data Scientist & Data Scientist positions at Capital One and Apple Respectively. With proficiency in Gradient Boosting, Survival Data Analysis, Regularized Logistic Regression as well as Text mining, Experiment Design and sampling, Principle Component Analysis (PCA) and Clustering, Shuling's work is definitely something to keep up with.
Having completed her Ph.D. in Electrical Engineering, Amy transitioned between Research and Engineering positions before moving into a Data Scientist role at USAA back in 2017. Since then, Amy has taken on three roles, primarily as Founder and CEO of Lucea AI, but also organising the Women in Machine Learning and Data Science collective of San Antonio & acting as chair for the IEEE Engineering Medicine and Biology Society. Using her experience in Predictive analysis, applied Mathematics, Machine Learning and Engineering, Amy aims to make healthcare more human, empowering healthcare providers and organizations to make better life-changing decisions through AI.
23. Anna Chaney, Engineering Director - Machine Learning, Resideo
After received her BS in Mathematics and a MS in Computer Science from the University of Colorado & University of Arizona respectively, Anna pursued a career in engineering which began at the National Optical Astronomy Observatory. In the following ten years, Anna held Engineering and Data Science positions at Raytheon, ARL:UT and IBM, where she took her breadth of machine learning knowledge in applied research to the IBM Watson group. After just over five years at IBM, Anna moved to Resideo at the end of 2019 where she currently works as the Engineering lead for software development of the Buoy Whole Home Water Controller.
Having interned at Insite360 as Bi Data Science Python Developer following her graduation from the University of Houston, Huihui moved to Shell, initially as a Data Analyst, later switching over to Shell's Data Scientist team having honed her skills in Drilling data analyzing, cleaning and processing (with numpy, pandas, matplotlib, etc.). Alongside her current work, Huihui has found time to co-author four papers in the last two years, covering CNNs, Quasi-sychronization, Mobile Sensing and more.
Research internships at the University of Sherbrooke, Telefonica, Apple and Intel Corporation aided Michele in developing skills learnt during her Electrical and Computer Engineering PhD. Data Scientist/Machine Learning Researcher positions followed at CoginitiveScale & Adobe respectively, seeing her move between the US and UK before finally settling in Austin once more. Michele's lists her main areas of focus as Machine learning, Data mining, Computer Vision, Perceptual algorithms and Image and video quality assessment, on which she has collaborated on over 40 articles, all of which are available here.
The MS Computer Science graduate from University of Texas has held lead/senior positions in Software & DevOps Engineering at Texas Instruments, Cisco & Shell. To date, Smita has managed and executed the testing of Cisco’s automation software initiatives such as Cisco Intelligent Automation for the cloud, designed and implemented the deployment infrastructure for various teams within Cisco cable, video and mobility, actively participated in the complete QA life cycle from Conception to Release of the Desktop Software using Agile development methodology and more. You can keep up with Smita and her work here.
The Duke University graduate, Qiuhua, has held various industry positions including as a Researcher, Machine Learning Scientist & Senior AI Engineer. Her current role at Schlumberger has seen projects spanning Drilling Automation, Deep Neural Network, Applied Signal Processing Algorithm and more. During this time Qiuhua developed her varied skillset which includes Deep Reinforcement Learning, Computer Vision, semi-supervised learning, Support Vector Machines, Bayesian Networks, Kalman/Particle Filter, Neural Networks. Qiuhua also boasts a plethora of programming languages knowledge, including, but not limited to Python, MATLAB, PDDL, C/C++, SAS, R.
The current DevOps Engineer has also held Senior Test Engineer and Software QA Engineer positions in Texas and had also held a Staff Software Engineer position at IBM for nine years covering Functional Verification, System Verification and Software testing. Coraly's current role at Blackboard, the original player in the higher education software space with many innovations for storage, communication and coursework planning, aims to enhance user experience through data. Keep up with Corlay's work here.
The current AI/ML lead at General Motors has previously held positions as a Lecturer, Research Assistant and Project Engineer having graduated from the University of Texas with a Ph.D. in Transportation Engineering. Mubassira has also been publishing papers since 2010, most recently covering the topic of tour chaining patterns of urban commercial vehicles. Other papers of note include Application of choice modeling methods to describe commercial vehicle travel behavior in urban areas & Potential Crash Reduction Benefits of Shoulder Rumble Strips on Rural Highways in Idaho.
Having previously held Data Scientist positions at Schlumberger, Accenture & Apex Systems, Tianxia currently works as Machine Learning Lead at Shell, utilising her skills in Data Analytics, Machine Learning, Deep Learning, Big Data, Petroleum, Antennas, Data Analysis, Multiphysics Modeling, ML, numerical analysis, Physics Modeling, PHM and more. Publication wise, Tianxia has papers on topics from electromagnetic propagation measurements to the Effects of Manufacturing Artifacts on Infrared Filter Performance. You can keep up with Tianxia's work here.
Who did we miss? Which city should we do next? Let me know on [email protected]
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