WOMEN In Data Science
GM Multiregional Conference 2022

March 8. 2022
Virtual

About WiDS GM Multiregional

WiDS @ GM is an independent event that is organized by General Motors as part of the annual WiDS Worldwide conference organized by Stanford University and an estimated 150+ locations worldwide, which features outstanding women doing outstanding work in the field of data science. All genders are invited to attend all WiDS Worldwide conference events.

General Motors aspires to be the most inclusive company in the world, and we are proud to support Stanford University in the Women in Data Science (WiDS) initiative. WiDS started as a one-day technical conference at Stanford in 2015 aimed to inspire and educate data scientists. Today, WiDS has grown to become a global movement that includes many worldwide initiatives.

Mark your calendars to join us on International Women’s Day, March 8, 2022, to learn from data science professionals in various fields who will share the latest research and applications of data science in a broad set of domains. Attendees will learn how leading-edge companies are leveraging data science for success.


Registration for this conference is sponsored by GM and is free of charge for all attendees.

Detail

Date & Time

Tuesday , March 8th 2022

9:00 AM – 4:00 PM ET

Location

Virtual – register for details

Key Note Speaker

As Global Managing Director at Accenture, Salema Rice is an advisor and evangelist for all things data, analytics, and Artificial Intelligence. Salema has more than 25+ years of rich experience as a Chief Data Officer directing data management strategies across diverse industry sectors and has led multiple large-scale data, analytics, enterprise information management and AI transformation journeys for Fortune 100 companies.

Featured Speakers

Individual Tech Talk

General Motors

Jiabao Li

UT Austin/Apple

 Data Lodge

Dell Technologies

State of California CDO

Tech Talk: Data Science in NASCAR. Importance, applications, and the future

General Motors

General Motors

General Motors

Tech Talk: Detecting vehicle safety issues using XLNet

General Motors

General Motors

Panel: Demonstrating the Value of the Work

Panel: Career Evolution of the Data Scientist

Monica Eboli

Meta

(formerly Facebook)

General Motors

Panel: Data Trends

Virginia Tech, Transportation Institute

Volunteer Chair
UT Austin

Lightning Tech Talks Panel: University Students

Virginia Tech Transportation Institute – Virginia Tech

Georgia Institute of Technology

Virginia Tech Transportation Institute – Virginia Tech

Thank you to our 2022 WiDS @GM Committee & Volunteers

Sheri Marshall

Executive Chair
General Motors

Mary Oleary Kasales

Speaker Logistics Chair
General Motors

Gabriella Lemeni

Communications Chair
General Motors

Elyse Renouf

Committee, Conference Emcee
General Motors

Jerra Murphy

Content Designer
General Motors

Mathew Alford

Broadcast Production Manager
General Motors

Stacy Pawlovich

Committee Member
General Motors

Brian Kleinfelt

Committee Member
General Motors

Kaser Chawla

Conference Panel Coordination
General Motors

Yue Zhuang

Datathon Lead
General Motors

Kalpana Yanamadala

Datathon Committee Member
General Motors

Jiarong Yang

Web Admin Support
UT Austin
Jilie Zeng

Jilie Zeng

Web Admin Support
UT Austin

Agenda

9:00 am ET

Virtual Conference Lines Open

9:15 am – 9:30 am ET

Welcome & Introduction

9:30 am – 10:00 am ET

Keynote Speaker: Salema Rice, Accenture

10:00 am – 10:30 am ET

Panel: Demonstrating the Value of the Work

Moderator: Sangeeta Relan, General Motors

Caitlin Hudon, OnlineMedEd

Erika McBride, Dow Inc

Carolyn Nguyen, GM

Lisa Purvis, Uber

10:30 – 10:50 am ET

Tech Talk: The Pioneering Women of the Data Literacy Movement

Valerie Logan, The Data Lodge

10:50 – 11:00 am ET

Morning Break

11:00 – 11:20 am ET

Tech Talk: Global Data Science: Structuring an International Analytics Team for Market Expansion

Tracey Jabbour, GM

11:20 – 11:40 am ET

Tech Talk: Experiencing data- pandemic, glaciers, and climate change

Jiabao Li, UT Austin/Apple

11:40 am – 12:10 pm ET

Executive Speaker: The Future of Data Work

Melody Meckfessel, Observable

12:10 – 12:40 pm ET

Lunch Break

12:40 – 1:10 pm ET

Panel: Career Evolution of the Data Scientist

Moderator: Caitie Barber-Owona, Uber

Monica Eboli, Meta (formerly Facebook)

Mubassira Khan, GM AI/ML

Phrances Perez, Wejo

Sara Potgeter, Uber

1:10 – 1:30 pm ET

Tech Talk: When every second matters - Data Science in NASCAR. Importance, applications, and the future

Co-Presented by:

Kornelia Bastin, GM ETS-SIO

Katherine Brinker, GM ETS-SIO

Hannah Milano, GM ETS-SIO

1:30 – 1:50 pm ET

Tech Talk: Data Science Applications in Marketing: Determining Cohorts of High Value Customers

Sneha Rajen, Dell Technologies

1:50 – 2:00 pm ET

Break

2:00 – 2:20 pm ET

Tech Talk: Detecting vehicle safety issues using XLNet

Co-Presented by:

Paromita Banerjee, GM Safety

Maira Shahid, GM Safety

2:20 – 2:50 pm ET

Panel: Data Trends

Moderator: Kendall Pfister, General Motors

Faria Mahnaz, GM AI/ML

Mia Mao Ph.D, Uber

Dr. Charlie Klauer, Virginia Tech, Transportation Institute

Tracy Hewitt, RedHat

Dr. Ying Ding, University of Texas at Austin

2:50 – 3:20 pm ET

Executive Speaker: Introducing Data Science in Legacy Organizations

Joy Bonaguro, State of California CDO

3:20 – 4:00 pm ET

Lightning Tech Talks: University Students

Alexandria Rossi-Alvarez, Virginia Tech Transportation Institute - Virginia Tech

" Impact of Highly Automated Vehicle (HAV) External Communication on Vulnerable Road User (VRU) Understanding of Vehicle Intention "

Eesha Sasumana, Georgia Institute of Technology

" The United States' Transition to New & Renewable Energy: An Analysis of Gaps, Trends & Opportunities"

Jackie Chavez Orellana, Virginia Tech Transportation Institute - Virginia Tech

" An Analysis of 2010-2019 Motor Vehicle Traffic Fatalities by Race and Socioeconomic Factors"

4:00 pm ET

Closing Remarks for 2022 WiDS @GM Conference

Sponsor

Salema Rice

Salema Rice

Accenture

Global Data & Analytics Leader, Global Managing Director of Applied Intelligence

Bio

As Global Managing Director at Accenture, Salema Rice is an advisor and evangelist for all things data, analytics, and Artificial Intelligence. Based out of Columbus Ohio, she is a key member of the global leadership team and is responsible for driving enterprise-wide data strategy and Applied Intelligence.

Salema has more than 25+ years of rich experience as a Chief Data Officer directing data management strategies across diverse industry sectors and has led multiple large-scale data, analytics, enterprise information management and AI transformation journeys for Fortune 100 companies.

She has significant leadership experience in data strategy formulation, innovative business solutioning, digital transformation and data management. Salema’s experience in data and analytics spans across top and bottom-line growth, talent development, human resources, procurement, finance, marketing, sales and supply chain management.

Before joining Accenture, Salema played a pivotal role in building up from scratch an enterprise-wide data and analytics capability globally for Bain Capital’s Geometric Results. Prior to that, she held the Global Chief Data Officer role at the Allegis Group. She has worked across Human Capital, Financial Services, Automotive, Energy, Insurance and Telecommunication industries.

Salema is also involved in driving thought leadership on the topic of analytics and artificial intelligence in various industry platforms such as MIT CDOIQ, EDM Council, Gartner CDO Innercircle, ComSpark and the CDO Magazine. She has also authored many articles and journals in the area of data, analytics and AI. She has also been recognized by SIA, VMSA, DataMasters and DataU as one of the top leaders for her work to help organizations achieve data-driven digital transformation and is noted as one of the 50 most influential analytics leaders globally.

Salema attended Northwestern University where she studied economics and marketing. Salema and her husband are the founders of Compassion’s Way, a 501(c)3 non-profit that provides hope to the homeless and underserved in some of the hardest hit communities.  

Topic

The Evolution of Today’s Data & Analytics Leader​

Abstract

In her 25+ years of experience as a Chief Data Officer, Salema Rice has achieved great success as a leader in Data and Analytics. Salema will share her inspiring path to CDO, including past lessons learned, keys to success, and how she’s preparing for the future. As a champion of AI for Good, Salema will illustrate how this approach influences her perspective and her vision for a future where AI plays a critical role in making the world a better place.

Tracey Jabbour

General Motors

Global Expansion Lead – OnStar Analytics

Bio

Tracey Jabbour is the Global Expansion Lead for OnStar Analytics within the Chief Data & Analytics Organization (CDAO) at General Motors. In this role, Tracey oversees a unique team located in Canada, Mexico, South America, and Dubai that develop descriptive and predictive analytics solutions to the OnStar Sales & Marketing teams. She has previous experience at GM as a data scientist in Global Finance and Tax & Customs. Tracey has a B.S. in Statistics from Michigan State University, and is currently completing her M.S. in Business Intelligence, Data and Analytics from Carnegie Mellon.

Topic

Global Data Science: Structuring an International Analytics Team for Market Expansion

Abstract

With the growth of business opportunities in international markets, demand for dedicated data-driven insights has become requisite to success. Since priorities differ in each market, the importance of structuring a high performing international analytics team and building a customized engagement model is integral to support growth and expansion. This talk will address the structure and benefits of a dedicated data & analytics team, the shift in prioritization of global markets, and overcoming various business challenges on our journey to become the most inclusive company in the world. 

Jiabao Li

Jiabao Li

University of Texas at Austin

Assistant Professor, School of Design and Creative Technology

Bio

Jiabao Li creates new ways for humans to perceive the world. She works across nature, humans’ designed environment, and belief structures and creates works addressing climate change, humane technology, and a just, sustainable future. Her mediums include wearable, robot, AR/VR, projection, performance, software, installation. She is a current member of NEW INC incubator at the New Museum. 
 
In Jiabao’s TED Talk, she uncovered how technology mediates the way we perceive reality. At Apple, she invents and explores new technologies for future products. She has been awarded multiple patents for her inventions. Her talk “Discoverable Design” at Apple Worldwide Developers Conference has inspired widespread discussion. She co-founded Snapi Health and advises several Silicon Valley startups. She serves on the IDSA education counsel. 
 
Jiabao is the recipient of numerous awards, including iF Design Award, AACYF 30 Under 30, Falling Walls, National Endowment for the Arts, STARTS Prize, Fast Company World Changing Ideas Award, Core77, IDSA, A’ Design Award. Her work has been exhibited internationally, at Ars Electronica, SIGGRAPH, Milan and Dubai Design Week, Shanghai Ming Contemperary Museum, ISEA, Anchorage Museum, OCAT Contemporary Art Terminal, CHI, Donghu Shan Art Museum, MOOD Museum of Design, Alaska State Museum. Her work has been featured on Yahoo, TED, TechCrunch, Domus, CCTV, Yanko Design, Fast Company, Harvard Political Review, The National, Business Insider, Bloomberg, Leonardo, and South China Morning Post.
 
Jiabao graduated from Harvard Graduate School of Design with a Master of Design in Technology with Distinction. She received her Bachelor of Electrical Engineering with Distinction Honors from the National University of Singapore. She was a researcher at the MIT Media Lab and Royal College of Art.
 
Jiabao’s lab focuses on the intersection of art, technology, and biology. The interests are broad from multi-species world-building to co-creation with animals, from robots to menstrual blood science. 

Jiabao Li: Art that reveals how technology frames reality | TED Talk

Topic

Experiencing data: pandemic, glaciers, and climate change​

Abstract

As COVID-19 spreads across the globe and the number of deaths continues to be updated, the people we’ve lost and the heartbreaking experience they had have been replaced by the collective mourning. ”The death of one man is a tragedy, the death of millions is a statistic.” So we build an online platform “Unfinished Farewell”, trying to document as many people who have left us because of the pandemic as possible. The website also includes the help-seeking information they posted before they passed away, which is the evidence they left to this era. Hope it could provide a space for family members to release their grief and for the public to mourn. Behind every number is a life.​

Glaciers are sentinels of climate change. They are the most visible evidence of global warming today. This series of works embodies the stunning beauty, rapid change, fragility, destructive power, and magnificence of glaciers. At the same time, they challenge the audience with the dramatic, irreversible ecological damages from climate change. In Glacier’s Lament, we used data from glacier melting in the past 60 years to compose music and dance with local musicians who have witnessed the recession of the Mendenhall glacier over their lifetimes in Juneau, Alaska.

Melody Meckfessel

Melody Meckfessel

Observable

CEO

Bio

Melody is the CEO / Co-founder of Observable, where she is building the future of data collaboration.  She is passionate about helping humans thrive through collaboration, inclusion, and insights. Before Observable, she was a VP of Engineering at Google, leading systems with a team of 1,000+ where she created the DevOps practice for Google’s Cloud platform.  Melody was responsible for large scale systems delivering successful outcomes for millions of users. Melody instills passion around data innovation – improving exploration and insights from data. She is an expert in tools and systems for productive teams to thrive, and that’s exactly what she is bringing to the future of data collaboration on Observable.

Topic

The Future of Data Work

Abstract

In this session, you will learn about the future of data work – collaborative data.  Empowering people of diverse backgrounds to explore and analyze data together is the future. When cross-functional teams of data scientists, data analysts, business and financial analysts, spreadsheet users, developers, and decision-makers work together, organizations accelerate data-driven insights and increase the effectiveness of business decision-making. You will learn about the key behaviors and practices of highly productive data teams and how those teams are redefining data collaboration.  

Valerie Logan

Valerie Logan

The Data Lodge

CEO & Founder

Bio

Founding The Data Lodge in 2019, Valerie is as committed to data literacy as it gets.  With advisory services, train-the-trainer bootcamps, and a peer community, she is certifying the world’s first Data Literacy Program Leads.  Previously, Valerie was a Gartner Research VP in the Chief Data Officer research team where she pioneered the Data Literacy research and was awarded Gartner’s Top Thought Leadership Award in 2018.  Valerie has more than 28 years of experience, including two decades in consulting and five years in telecommunications. Valerie holds a B.S. in Math from SUNY College at Buffalo and an M.S. in Applied Math from New Mexico State.  She is based in the Adirondack Mountains with her husband Brian in Brant Lake, New York.

Topic

The Pioneering Women of the Data Literacy Movement

Abstract

With talent and culture at the forefront of challenges of the CDO agenda, data literacy programs have proven to be one of the keys to cracking the data culture code. Interestingly, women are emerging in high proportions and with great impact- as either executive sponsors or program leads- of data literacy programs across the globe.  This session explores the case for change of data literacy, the key aspects of a data literacy program, and the phenomenon behind several of the pioneering women of the data literacy movement.

Sneha Rajen

Sneha Rajen

Dell Technologies

Data Scientist, Marketing Strategy & Operations

Bio

Sneha Rajen is a Data Scientist for Dell Technologies in Marketing Strategy & Operations. She employs machine learning methods, natural language processing, and automation to deliver actionable insights on customer behavior and marketing analytics. She holds a B.S. in Informatics from the University of Michigan and is currently pursuing graduate studies in Artificial Intelligence at Johns Hopkins University. 

Topic

Data Science Applications in Marketing: Determining Cohorts of High Value Customers

Abstract

Machine learning is transformational to marketing organizations. In today’s world of vast data, companies are adopting data-driven decision-making practices. By integrating unstructured and structured data, customer segmentation is one approach that reveals high value customers. This approach enables us to understand customer’s needs, purchasing behavior, and long-term potential and viability. With customer segmentation we can understand engagement, market performance across key KPIs, and ultimately prescriptively implement changes based on cohort characteristics. This talk will dive deeper into the data science methods and extensions which are used to drive marketing strategies and optimization.

Joy Bonaguro

Joy Bonaguro

State of California

Statewide Chief Data Officer

Bio

Joy Bonaguro has spent her career working at the nexus of data, design, technology, and policy. Joy was appointed as Chief Data Officer of the State of California with an overarching goal to improve government use of data. Prior to her state role, she was responsible for scaling internal systems, data, and information security at Corelight, a high-growth cybersecurity startup funded by Accel and General Catalyst. Prior to Corelight, she served as the first Chief Data Officer for the City and County of San Francisco, where she pioneered multiple initiatives to introduce data science, streamline data access, improve data management, and boost capacity to use data. Before that, Joy developed technology, cyber and privacy policy across the Department of Energy’s National Laboratory System. Her career started in New Orleans with seven years designing and managing the development of information systems to support planning and decision-making for local governments and nonprofits. Joy earned her Master’s degree from UC Berkeley’s Goldman School of Public Policy and her Bachelor’s in Mathematics and Philosophy from Tulane University.

Topic

Introducing Data Science in Legacy Organizations

Abstract

Too many data leaders emphasize the technical needs for data science instead of the organizational and cultural needs. Learn how we helped our business leaders understand and take a bite out of the data science pie.

Katherine Brinker

Katherine Brinker

General Motors

Software Engineer, Strategic Incubation Office - Motorsports

Bio

Katherine Brinker is a Software Engineer with the Strategic Incubation Office at General Motors. She uses open-source software to facilitate data science and data visualization and is focused now on Motorsports. Katherine received her Bachelor of Science in Psychology at the Missouri University of Science and Technology and her Master of Science in Business Analytics at Arizona State University.

Topic

When every second matters – Data Science in NASCAR. Importance, applications and the future

Abstract

In recent years, professional sports have started to invest more in collecting data and integrating it with the decision-making process. Sports professionals can use the data to review performance, identify potential improvements, review competition strategies, or decide on their own future tactics. One sport that benefits from data science applications and is becoming more data-driven is NASCAR. We will discuss why data science and analytics play an important role in racing and how the vast amounts of data being collected can be used to enhance drivers’ performance. Finally, we will give some examples of a current project that we are working on to gain more of an edge.

Hannah Milano

Hannah Milano

General Motors

Software Developer, Strategic Incubation Office – Motorsports

Bio

Hannah Milano is a Software Developer on the Strategic Incubation Office team at General Motors. In this role, Hannah integrates data science and machine learning into the software her team develops. She is currently focused on applying this to motorsports, where she uses machine learning to give NASCAR teams tools to improve their performance during races. Hannah received her Bachelor’s Degree in Computational and Applied Mathematics at Carnegie Mellon University, and her education there has given her a deep understanding of the statistics, math, and programming that make up the foundation of many data science and machine learning concepts.

Topic

When every second matters – Data Science in NASCAR. Importance, applications and the future

Abstract

In recent years, professional sports have started to invest more in collecting data and integrating it with the decision-making process. Sports professionals can use the data to review performance, identify potential improvements, review competition strategies, or decide on their own future tactics. One sport that benefits from data science applications and is becoming more data-driven is NASCAR. We will discuss why data science and analytics play an important role in racing and how the vast amounts of data being collected can be used to enhance drivers’ performance. Finally, we will give some examples of a current project that we are working on to gain more of an edge.

 Kornelia Bastin

 Kornelia Bastin

General Motors

Senior Software Developer, Strategic Incubation Office – Motorsports

Bio

Kornelia Bastin is a Senior Software Developer for the Strategic Incubation Office – Motorsports team at General Motors. In this role, Kornelia leads data science related projects that focus on image recognition, natural language processing, artificial intelligence and machine learning  tasks. In parallel to that she is currently working on finishing her PhD with double major in Operations Research and Computer Science with focus on natural language processing and visualization at NC State University. Prior to joining General Motors Kornelia was working in a number of media, marketing and market research companies across Europe as a data scientist. She holds a MSc in Management Science and Operations Research from Warwick Business School, UK and BEng in Engineering from Coventry University, UK.

Topic

When every second matters – Data Science in NASCAR. Importance, applications and the future

Abstract

 In recent years, professional sports have started to invest more in collecting data and integrating it with the decision-making process. Sports professionals can use the data to review performance, identify potential improvements, review competition strategies, or decide on their own future tactics. One sport that benefits from data science applications and is becoming more data-driven is NASCAR. We will discuss why data science and analytics play an important role in racing and how the vast amounts of data being collected can be used to enhance drivers’ performance. Finally, we will give some examples of a current project that we are working on to gain more of an edge.

Paromita Banerjee

Paromita Banerjee

General Motors

Data Scientist (Vehicle Safety Advanced Analytics)

Bio

Paromita Banerjee is a Data Scientist in the Global Product Safety and Systems division at General Motors. Her role involves building machine learning models to detect product safety hazards. Prior to GM, she worked in healthcare analytics, where she built machine learning models to predict clinical outcomes. She completed her bachelor’s degree in Information Technology from India and graduated with a master’s degree from the University of Washington, Seattle.

Topic

Detecting vehicle safety issues using XLNet.

Abstract

With an increase in unstructured data, unsupervised representation learning has gained traction in the domain of natural language processing. Most of these methods pretrain neural networks on large-scale unlabeled text data to learn representations, and then fine-tune the representations on downstream tasks. We will discuss how at Vehicle Safety Analytics, we are using XLNet, a pretrained deep learning model to identify different vehicle safety issues. We’ll compare the results of XLNet with other machine learning models and discuss our strategies to further boost the model’s performance. 

Maira Shahid

Maira Shahid

General Motors

Data Scientist (Vehicle Safety Advanced Analytics)

Bio

Maira Shahid is a data scientist in vehicle Safety team at General Motors. In this role, Maira is responsible for building machine and deep learning models to classify unstructured data to identifying potential vehicle safety issues. Maira joined General Motors in 2014. She earned her master’s in business administration from Wayne State University’s Mike Ilitch School of Business and her second master’s in data science from Northwestern school of professional studies. She has a bachelor’s in business and Information Technology from Punjab University of Pakistan.

Topic

Detecting vehicle safety issues using XLNet.

Abstract

With an increase in unstructured data, unsupervised representation learning has gained traction in the domain of natural language processing. Most of these methods pretrain neural networks on large-scale unlabeled text data to learn representations, and then fine-tune the representations on downstream tasks. We will discuss how at Vehicle Safety Analytics, we are using XLNet, a pretrained deep learning model to identify different vehicle safety issues. We’ll compare the results of XLNet with other machine learning models and discuss our strategies to further boost the model’s performance. 

Sangeeta Relan

Sangeeta Relan

General Motors, Global Innovation

Data Monetization Manager, Innovation Accelerator

Bio

Sangeeta leads activities related to GM’s licensing of connected vehicle data to third parties, including government organizations, private companies, and channel partners. She oversees due diligence, executive approvals, contract negotiations, permissible fields of use, and data privacy compliance. Prior to joining General Motors, Sangeeta spent over 15 years in management consulting supporting multiple enterprise system implementations for various corporate and government clients. Sangeeta also informed litigation strategy for clients and attorneys as a trial consultant by conducting mock trials and extensive jury research exercises.  She also served as a senior advisor for a U.S. Senate campaign for the state of Illinois.  She holds a J.D. from Depaul University, a M.B.A. from Northeastern University, and a B.A. from Michigan State University.  She also earned her CIPP/US and is a Certified Information Privacy Professional.

Caitlin Hudon

Caitlin Hudon

OnlineMedEd

Principal Data Scientist

Bio

Caitlin Hudon is a data scientist with over a decade of experience helping companies get value out of their data in a variety of areas including IoT, marketing, higher education, non-profits, and start-ups. She identifies as a data science generalist with experience spanning machine learning, NLP, hypothesis-driven data deep dives, and experimentation, who loves solving new problems with data. 

 

Erika McBride

Erika McBride

Dow Inc.

Director, Business Intelligence and Analytics
Business IT Director, Coatings & Performance Monomers

Bio

Erika McBride, CPA, Ed.D., is the Director of Business Intelligence and Analytics at Dow Inc.  At Dow since 2014, Erika leads organizational efforts to accelerate the mission of translating data into profit by bringing together data, context and advice to enable decision makers.  She champions the power and leverage of advanced analytic algorithms, Diamond Systems Reporting capabilities, and data-as-a-service methods, with a focus on Future Dow critical priorities.  Under Erika’s leadership, the capacities that provide insight enable faster and better decisions by embedding analytics into the core business processes and strategic digital transformation practices.  Erika also oversees the relationship with the Coatings and Performance Monomers business, engaging with the leadership team to develop the digital strategy and ensure technology enables the business growth.  In 2021, Erika was also appointed to collaborate with diverse leaders across Dow to develop the data pipelines needed to identify the top levers to report and improve Dow’s ESG goals, particularly in the Climate space.

 

Prior to Dow, Erika spent 17 years at Paychex, Inc., a leading national provider of payroll, human resource, and benefits outsourcing solutions for small to medium sized business.  As manager of the Paychex Analytics team, Erika oversaw a team of predictive modeling experts, producing methods to drive millions of dollars to the bottom line. She also led efforts to develop the Paychex | IHS Markit Small Business Employment Watch, which provides timely and accurate insight into national and regional small business employment trends for national consumption.

Erika is an adjunct professor in business at St. John Fisher College, a five-time presenter and panelist at Treasury and Risk’s Alexander Hamilton best practices summit, a previous presenter at the RIMS Conference and Exhibition, Big Data Innovation Summits, AiChE Symposia, SAS Symposia, and Predictive Analytics World Conferences in New York, Toronto, Dusseldorf, and Berlin.

Erika holds an Ed.D. in Executive Leadership from St. John Fisher College in Rochester, NY, an MBA from Rochester Institute of Technology, and a B.S. in Accounting from SUNY Geneseo. 

Erika lives in Midland, Michigan with her husband and two sons

Carolyn Nguyen

Carolyn Nguyen

General Motors

Data Science Manager, Chief Data & Analytics Office (CDAO) - Advanced Analytics Center of Expertise

Bio

Carolyn is a Data Science Manager in the Advanced Analytics Center of Expertise (CDAO) at General Motors. She oversees teams responsible for developing predictive and prescriptive models for GM Sales & Marketing and Customer Experience. Carolyn also spent 5 years leading the Emerging Issues Analytics team in Global Vehicle Safety. In both roles, her teams have applied various text analytics methods to draw meaning from unstructured data. Prior to joining GM, Carolyn spent 7 years at Dell in Digital Marketing. She earned an MBA from the University of Texas at Austin and Bachelors of Science in Industrial & Operations Engineering from the University of Michigan.

Lisa Purvis

Lisa Purvis

Uber

Senior Data Science Manager

Bio

Lisa Purvis is a Senior Manager of Data Science at Uber, where she leads a team of Data Scientists providing product insights, experiment design and analysis, and behavioral analyses for Rides Marketplace domains such as Matching, Pricing, and Incentives, towards understanding the opportunity spaces and guiding the product roadmap for Uber rides products.  Her background includes experience in Marketing Analytics at PayPal, Product and Science leadership in a tech startup Spigit,  as well as Product Development and R&D at Xerox.  Lisa has her PhD in Computer Science from the University of Connecticut, her MS in Computer Science from Rensselaer Polytechnic Institute, and her BS in Computer Science from Clarkson University.  

Caitie Barber-Owona

Caitie Barber-Owona

Uber

Senior Technical Sourcer

Bio

Caitie Barber-Owona is a Senior Technical Sourcer for Uber, specifically with Uber Delivery across North America. In this role, she is recruiting for Engineering Managers and Senior Software Engineers, while also advocating the efforts for diverse candidates across the Delivery business. She has 5 years experience in technical recruiting, including AI and Data Science recruitment. Her passion lies within providing opportunities for diverse talent and she has been involved with organizing the WiDS conference in Austin for the past 3 years.

Monica Eboli

Monica Eboli

Meta Platforms (former Facebook)

Machine Learning Engineer

Bio

Monica develops machine learning based solutions for harm identification on social platforms. Prior to working at Meta, Monica developed machine learning based hazard detection solutions for General Motors. Monica has been successfully applying NLP (natural language processing) solutions from design to implementation for almost 10 years, in various domains such as marketing, automotive, integrity. She has a Master’s degree in Computer Engineering from University of Michigan and an undergraduate degree as automation engineer from University of São Paulo. 

Mubassira Khan

Mubassira Khan

General Motors

Sr. AI/ML Scientist, ADAS Mapping Software

Bio

Mubassira Khan is a Senior AI/ML Scientist and technical lead with the Advanced Driver-Assistance Systems (ADAS) Mapping Software team at GM. In this role, Mubassira uses data science and advanced analytical techniques to create machine learning models and develop map content for the GM Ultra Cruise program. Her unique combination of experience in travel behavior research and knowledge of data analytics helps her to solve complex business problems involving transportation systems. She joined GM in 2018, and in 2019, was recognized with an Intellectual Property Award. She earned a Bachelor’s Degree from Bangladesh University of Engineering and Technology, a Master’s Degree from the University of Idaho, and a PhD from the University of Texas at Austin in Civil Engineering with a focus in Transportation Engineering.

Phrances Perez

Phrances Perez

Wejo

Senior Data Analytics Engineer

Bio

A senior analytics engineer, who loves applying big data analytics to real world problems. I love applying mathematics, geospatial analysis, and using new technologies to help drive informed decisions, provide objective answers and insights. My aspiration is to bring data to life and help stakeholders understand the value of data.
Sara Potgeter

Sara Potgeter

Uber

Tech Sourcing Manager, Data & Applied Sciences

Bio

Sara Potgeter has been in Talent Acquisition since 2011, largely focused on building Data Science teams. She currently manages the Data & Applied Sciences sourcing team at Uber where she partners with department heads across all lines of business to create consistent, efficient, and inclusive hiring processes. Her passion for improving the diversity & inclusion of tech organizations has been a driving force throughout her career.”

Kendall Pfister

Kendall Pfister

General Motors

Delivery Manager

Bio

Kendall graduated from Texas A&M University and worked in the consulting and non-profit industries before joining General Motors in 2017. Since then, Kendall has worked in various roles in software development and big-data organizations with a focus in customer and vehicle telemetry data and analytics.

Charlie Klauer

Dr. Charlie Klauer

Virginia Tech Transportation Institute (VTTI) - Virginia Tech

Associate Professor, Human Factors Engineering and Ergonomics

Bio

Dr. Sheila “Charlie” Klauer is a research scientist and the leader for the Training Systems Group.  She is also an Associate Professor in the Industrial and Systems Engineering Department at Virginia Tech.  Dr. Klauer has been working in transportation research since 1996, previously at the Battelle Human Factors Research Center in Seattle, WA and currently at the Virginia Tech Transportation Institute.  Since joining VTTI in 1999, she has served as the Principal Investigator for a series of naturalistic driving studies that included three teen naturalistic driving studies and the Canada Naturalistic Driving Study.  Currently, she is the PI on the Driver Adaptation of L2 Technologies and two additional NDS’s focused on adolescents: the first is concerned with adolescents diagnosed with Autism Spectrum Disorder and the second NDS is focused on adolescents diagnosed with Attention Deficit and Hyperactivity Disorder.

Dr. Ying Ding​

University of Texas at Austin

Bill & Lewis Suit Professor​

Bio

Dr. Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. Before that, she was a professor and director of graduate studies for data science program at School of Informatics, Computing, and Engineering at Indiana University. She has led the effort to develop the online data science graduate program for Indiana University. She also worked as a senior researcher at Department of Computer Science, University of Innsburck (Austria) and Free University of Amsterdam (the Netherlands). She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences and serves as the editorial board member for several top journals in Information Science and Semantic Web. She is the co-founder of Data2Discovery company advancing cutting edge AI technologies in drug discovery and healthcare. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies.​​

Faria Mahnaz

Faria Mahnaz

General Motors

Sr. AI/ML Scientist, ADAS Mapping Software

Bio

Faria is involved in developing machine learning algorithms and advanced analytical models for ADAS Mapping Software, supporting GM’s Ultra Cruise program, as a Senior Artificial Intelligence/Machine Learning Scientist. She also serves as a member of the Intellectual Review Board (IRB) for Data Analytics & Business Methods at GM. Prior to joining GM in 2019, she was a Data Scientist at Ford Motor Company, supporting the Global Data Insight and Analytics Team. She holds a B.S. and M.S. in Mathematics from the University of Dhaka, Bangladesh and an M.S. in Computer Science from Wayne State University, where she is currently a Ph.D. candidate in Industrial and Systems Engineering. She has been a member of several research efforts dealing with Deep Learning, Sparse Representation Learning and Computer Vision throughout her graduate programs and professional career.

Tracy Hewitt

Tracy Hewitt

Red Hat

VP Enterprise Data & Analytics, Global​

Bio

Tracy Irwin Hewitt is the VP of Enterprise Data and Analytics at Red Hat.  She is known as a visionary and accomplished professional with expertise in building and leading high performing, cross-functional teams. ​

Her teams have developed award-winning digital products and services that seamlessly deliver insights and business value driven by advanced analytics techniques such as machine learning and network optimization.  ​

Career highlights include:​

  • Conceptualized and designed award-winning programs recognition by the National Science Foundation and Robert Wood Johnson Foundation. ​
  • Played a key role in advising members of Congress and the President’s Cabinet on expediting solutions to social issues via analytics.​
  • Outperformed Uber in national analytics-driven software competition. ​
  • Created business-critical visual data for GM’s CEO to communicate with Congress about 2014 ignition switch recall. ​
  • Invited to speak twice before the National Academy of Sciences to outline performance enhancement of the National Research Initiative and analyze the unintended impacts of industrial agriculture.​

Topic

Experiencing data: pandemic, glaciers, and climate change

Abstract

As COVID-19 spreads across the globe and the number of deaths continues to be updated, the people we’ve lost and the heartbreaking experience they had have been replaced by the collective mourning. ”The death of one man is a tragedy, the death of millions is a statistic.” So we build an online platform “Unfinished Farewell”, trying to document as many people who have left us because of the pandemic as possible. The website also includes the help-seeking information they posted before they passed away, which is the evidence they left to this era. Hope it could provide a space for family members to release their grief and for the public to mourn. Behind every number is a life.

Glaciers are sentinels of climate change. They are the most visible evidence of global warming today. This series of works embodies the stunning beauty, rapid change, fragility, destructive power, and magnificence of glaciers. At the same time, they challenge the audience with the dramatic, irreversible ecological damages from climate change. In Glacier’s Lament, we used data from glacier melting in the past 60 years to compose music and dance with local musicians who have witnessed the recession of the Mendenhall glacier over their lifetimes in Juneau, Alaska.

Mia Mao

Mia Mao, Ph.D

Uber

Head of Data Science in Identity, Account Security and Payment

Bio

Mia Mao has been leading DS efforts in Identity, Account Security, and Payments since she joined Uber in 2019. In this role, Mia oversees a group of DS teams responsible for developing predictive models to identify and verify the real identity of users, ensure good users to onboard Uber platform with frictionless experience, detect fake and duplicate accounts, prevent frauds like Account Take-Over (ATO), and enable dynamic routing as well as smart auto retry to improve success rate in Payments using cutting edge statistical and machine learning techniques. She earned her Ph.D. degree in Information Science from University of Pittsburgh. Mia was a senior IEEE member and part of the organizing/program committee of multiple top ranked international conferences and an invited reviewer for scientific journals. Mia was also a joint lecturer at UCSF.

Alexandria Ida Rossi Alvarez

Virginia Tech Transportation Institute (VTTI) - Virginia Tech

Graduate Research Assistant

Bio

Alexandria Rossi Alverez completed an accelerated five-year course of study in the Department of Human Factors at Embry-Riddle Aeronautical University (ERAU), earning a B.S. in Human Factors Psychology, and an M.S. in Human Factors and Systems. Alexandria went on to work at Lextant from 2014 to 2018 as a Senior Human Centered Design Associate. She then received her M.E. in Human Factors Engineering and Ergonomics at Virginia Tech and is currently a Ph.D. candidate there in the Department of Industrial and Systems Engineering.

Topic

Impact of Highly Automated Vehicle (HAV) External Communication on Vulnerable Road User (VRU) Understanding of Vehicle Intention

Abstract

The advancement of SAE Level 4+ Automated Vehicles (L4+ AVs) has led to the development of external communication systems for these vehicles by numerous stakeholders. Most research on vehicles emulating these displays has been conducted using one vehicle. However, it is vital to understand how communication to vulnerable road users (VRUs) is affected when multiple L4+ vehicles are present. This study examined how L4+ AVs can best communicate their intentions (e.g., driving, yielding, ready) to VRUs and drivers of conventional vehicles. Subjective and objective data was collected to assess road user responses to two vehicles emulating L4+ displays, from both a passenger and pedestrian perspective. Participants experienced three light patterns that provided information regarding L4+ AVs’ intent to slow/stop, begin, and travel with simulated automation active without any prior knowledge. Overall, participants were overwhelmed with multiple vehicles with different light bars in their crossing vicinity and found it difficult to prioritize attention. These results have implications for future design of external communication displays on L4+ AVs. Training may be necessary for road users, given the relatively low percentage of participants who understood the meaning of these displays after multiple exposures and participants’ confusion in where to look and how to interpret the intention of the display when multiple vehicles were present.

Eesha Sasumana

Eesha Sasumana

Georgia Institute of Technology

Data Analyst | Performance Analytics at InterContinental Hotels Group | Graduate Student

Bio

Eesha Sasumana is a Data Analyst on the Global Insights, Analytics and Data Team at InterContinental Hotels Group. She focuses on Performance Analytics and works with business partners to build brand and initiative specific dashboards, relaying industry trends, company performance and key business insights. Her work influences business strategy and informs key brand placement efforts. Eesha earned her B.S. in Biology and B.A. in Health Care Economics from the University of Texas at Austin. In addition to her role at IHG, Eesha is enrolled at The Georgia Institute of Technology and completing her Masters of Science in Analytics.

Topic

The United States’ Transition to New & Renewable Energy: An Analysis of Gaps, Trends & Opportunities

Abstract

The shift to new and renewable energy sources for energy generation, production and consumption is widely supported. However, preliminary data analysis shows efforts across the United States appear to be regionally siloed, suggesting a lack of coordination amongst local governing bodies. This talk will explore the current gaps and trends in the shift to sustainable energy sources and conclude with a discussion of potential opportunities to promote the adoption of alternative fuels.

Jackie Chavez Orellana

Jackie Chavez Orellana

Virginia Tech Transportation Institute (VTTI) – Virginia Tech

Graduate Research Assistant

Bio

Jackie is a first year Industrial and Systems Engineering PhD student and a graduate research assistant within the Division of Data and Analytics at VTTI. She uses statistical methods to analyze crash data and traffic fatalities. Her current research focuses on identifying disparities in transportation safety by examining how race, ethnicity, and socioeconomic factors contribute to motor vehicle crash involvement and outcomes. Prior to join Virginia Tech, Jackie served in Teach for America and was a research assistant at Virginia Commonwealth University, where she also earned her bachelor’s degree in Biomedical Engineering.

Topic

An Analysis of 2010-2019 Motor Vehicle Traffic Fatalities by Race and Socioeconomic Factors

Abstract

There are over 30,000 motor vehicle traffic fatalities each year in the US, which represents a serious public health problem. Numerous studies have examined how Black, Indigenous, and People of Color (BIPOC) are disproportionately represented in the traffic fatalities. Previous work focuses on high-level population summaries but fails to delve into factors contributing differentially to fatalities across racial groups. Our study uses Fatality Analysis Reporting System (FARS) data to evaluate how factors such as population density, alcohol use, restraint use, fatality characteristics, vehicle age, driver age, and EMS transportation differ in fatal crashes across racial groups. While the work is ongoing, initial results suggest some important trends that will be discussed in this presentation. For example, American Indians are more likely to have a lower restraint use, higher alcohol use, and are more likely to die at scene in rural areas in contrast to other racial groups. Ultimately, the goal of this research is to advance current understanding of racial disparities to help address these inequities.