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Data Science New York

January 23-24, 2019

Download the Agenda
Conference Day 1
23 January
08:00

Registration & Networking in the Exhibition Area

Registration & Networking in the Exhibition Area

January 23 | 08:00 - 08:45

Speaking:

08:45

Chair’s Opening Remarks

Speaking:

Mark Wang

Mark Wang

Chief Data Scientist, Alorica

Chair’s Opening Remarks

January 23 | 08:45 - 09:00

Speaking:

Mark Wang

Mark Wang

Alorica

09:00

Turning Insights into Income - Growth Through Commercial Data Science

  • Using Data Science to effectively examine which pipelines are still profitable and which are not
  • Growing the market share by identifying weakness in both your firm and the competition
  • Looking at alternative data: can you use public databases such as census data to understand where the enterprise should be investing more time and money?

Speaking:

Cary-1

Cary Correia

Chief Commercial Data Scientist, GE

Turning Insights into Income - Growth Through Commercial Data Science

January 23 | 09:00 - 09:30

  • Using Data Science to effectively examine which pipelines are still profitable and which are not
  • Growing the market share by identifying weakness in both your firm and the competition
  • Looking at alternative data: can you use public databases such as census data to understand where the enterprise should be investing more time and money?

Speaking:

Cary-1

Cary Correia

GE

09:30

Why Big Data Efforts Are Failing to Deliver

  • Organizations lack of Big Data Foundations
  • Silos of knowledge
  • Lack of finding people with the right "BIG" skill sets
  • Tool and technology mismatch

Speaking:

Robert-Whetsel-230x230

Robert Whetsel

Chief Data Scientist, U.S. Department of Defense

Why Big Data Efforts Are Failing to Deliver

January 23 | 09:30 - 10:00

  • Organizations lack of Big Data Foundations
  • Silos of knowledge
  • Lack of finding people with the right "BIG" skill sets
  • Tool and technology mismatch

Speaking:

Robert-Whetsel-230x230

Robert Whetsel

U.S. Department of Defense

10:00

Research Paper: Loan Default Prediction in the Peer-to-Peer Market for Better ROI

  • Presentation of a research paper on using AWS SageMaker to predict loan defaults in the peer-to-peer market
  • Improving ROI through improved dataset identification and clasification
  • Working with risk teams and regulators to explain the machine learning model and why credit was denied or approved

Speaking:

roymark-archive-badge-photo-lolores

Mark Roy

Partner Solutions Architect , Amazon Web Service (AWS)

Alberto Artasanchez

Alberto Artasanchez

Artificial Intelligence Lab Director, Knowledgent, a part of Accenture

Research Paper: Loan Default Prediction in the Peer-to-Peer Market for Better ROI

January 23 | 10:00 - 10:30

  • Presentation of a research paper on using AWS SageMaker to predict loan defaults in the peer-to-peer market
  • Improving ROI through improved dataset identification and clasification
  • Working with risk teams and regulators to explain the machine learning model and why credit was denied or approved

Speaking:

Alberto Artasanchez

Alberto Artasanchez

Knowledgent, a part of Accenture

roymark-archive-badge-photo-lolores

Mark Roy

Amazon Web Service (AWS)

10:30

The Secret Sauce Behind Top Performing Data Science Teams

Building and growing a successful data science team is no easy feat.

This presentation will share best practices and examples based on experience of leading and growing an award-winning 40-people data science team from the grounds up, with focus on model building and experimenting.

Topics will include -

• Setting up technical infrastructure for model training

• Facilitating collaboration, knowledge sharing and transparency

• Ways to promote experimentation and ideation

Speaking:

Piotr Niedzwiedz

Piotr Niedzwiedz

Founder & CEO, neptune.ml

The Secret Sauce Behind Top Performing Data Science Teams

January 23 | 10:30 - 10:45

Building and growing a successful data science team is no easy feat.

This presentation will share best practices and examples based on experience of leading and growing an award-winning 40-people data science team from the grounds up, with focus on model building and experimenting.

Topics will include -

• Setting up technical infrastructure for model training

• Facilitating collaboration, knowledge sharing and transparency

• Ways to promote experimentation and ideation

Speaking:

Piotr Niedzwiedz

Piotr Niedzwiedz

neptune.ml

10:45

Coffee & Networking in the Exhibition Area

Coffee & Networking in the Exhibition Area

January 23 | 10:45 - 11:15

Speaking:

11:15

Panel Discussion: Customer Personalization - Leveraging Data Science and Technology to Deliver an Optimised Customer Journey

  • Customers demand a personalized experience that delivers them value - organizations need to deliver this while optimizing their financial business metrics
  • With productized Data Science (data, analytics, technology and product integration), this can be done at scale and at speed in many shapes and forms
  • The perception of value of 1-1 personalization can be affected by individual customer's perception of a company's leveraging "their data" in ways they find infringing on their privacy.
  • With GDPR, a customer can do more than just choose not to be a customer, they can demand you act – working with the customer is key

Speaking:

Mark Wang

Mark Wang

Chief Data Scientist, Alorica

Carl Gold Zuora

Carl Gold

Chief Data Scientist, Zuora

Alberto Artasanchez

Alberto Artasanchez

Artificial Intelligence Lab Director, Knowledgent, a part of Accenture

Panel Discussion: Customer Personalization - Leveraging Data Science and Technology to Deliver an Optimised Customer Journey

January 23 | 11:15 - 11:45

  • Customers demand a personalized experience that delivers them value - organizations need to deliver this while optimizing their financial business metrics
  • With productized Data Science (data, analytics, technology and product integration), this can be done at scale and at speed in many shapes and forms
  • The perception of value of 1-1 personalization can be affected by individual customer's perception of a company's leveraging "their data" in ways they find infringing on their privacy.
  • With GDPR, a customer can do more than just choose not to be a customer, they can demand you act – working with the customer is key

Speaking:

Mark Wang

Mark Wang

Alorica

Carl Gold Zuora

Carl Gold

Zuora

Alberto Artasanchez

Alberto Artasanchez

Knowledgent, a part of Accenture

11:45

Cast Study: How Condé Nast are Boosting Both Subscription and Advertising Revenue Using Data Science Products

Speaking:

Cast Study: How Condé Nast are Boosting Both Subscription and Advertising Revenue Using Data Science Products

January 23 | 11:45 - 12:15

Speaking:

12:15

How to Drive Institutional Change to Increase Profit & Productivity as a Data Science Leader

  • We all know the value of data science to an organization, but leading the drive for real institutional change is not as easy as it seems
  • This session will examine the techniques and pitfalls when using the data as a tool for widespread change
  • It will use real world examples and case studies to provide you with the tools you need to be the force for change at your company

Speaking:

Dan Bio

Daniel Costanza

Chief Data Scientist, Citi

How to Drive Institutional Change to Increase Profit & Productivity as a Data Science Leader

January 23 | 12:15 - 12:45

  • We all know the value of data science to an organization, but leading the drive for real institutional change is not as easy as it seems
  • This session will examine the techniques and pitfalls when using the data as a tool for widespread change
  • It will use real world examples and case studies to provide you with the tools you need to be the force for change at your company

Speaking:

Dan Bio

Daniel Costanza

Citi

12:45

Panel Discussion: How to Make Sure Your Synthetic Dataset Produces Usable Results

  • Privacy, data ownership, and data-hungry algorithms – synthetic datasets are becoming a major asset to any Chief Data Scientist
  • Synthetic datasets take time to build and are difficult to get right – what models are working in the real world?
  • Research and the future: will we ever see perfect synthetic datasets?

Speaking:

Sanji Fernando - UnitedHealthGroup

Sanji Fernando

Vice President, Center for Applied Data Science, OptumLabs

Nathan Sutton

Nathan Sutton

Data Scientist, Vituity

Panel Discussion: How to Make Sure Your Synthetic Dataset Produces Usable Results

January 23 | 12:45 - 13:15

  • Privacy, data ownership, and data-hungry algorithms – synthetic datasets are becoming a major asset to any Chief Data Scientist
  • Synthetic datasets take time to build and are difficult to get right – what models are working in the real world?
  • Research and the future: will we ever see perfect synthetic datasets?

Speaking:

Sanji Fernando - UnitedHealthGroup

Sanji Fernando

OptumLabs

Nathan Sutton

Nathan Sutton

Vituity

1:15

Lunch & Networking in the Exhibition Area

Lunch & Networking in the Exhibition Area

January 23 | 13:15 - 14:15

Speaking:

Track A | People

2:15

Discussion Group 1A: Hiring the Unicorn: What Makes a Great Data Scientist?

Speaking:

Deepna#

Deepna Devkar

VP, Head of Data Science, Dotdash

Haftan-Eckholdt-Plated-230x230

Haftan Eckholdt

Chief Data Officer & Chief Data Science Officer, Understood.org

Discussion Group 1A: Hiring the Unicorn: What Makes a Great Data Scientist?

January 23 | 14:15 - 15:00

Speaking:

Deepna#

Deepna Devkar

Dotdash

Haftan-Eckholdt-Plated-230x230

Haftan Eckholdt

Understood.org

Track B | Data Science Transformation

2:15

Discussion Group 1B: Data Science Advocacy: Educating and Working with the Business Stakeholders

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

Global Chief Pricing Officer, McDonald's

mike berger

Mike Berger

VP, Chief Data & Analytics Officer, Mount Sinai Health System

Discussion Group 1B: Data Science Advocacy: Educating and Working with the Business Stakeholders

January 23 | 14:15 - 15:00

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

McDonald's

mike berger

Mike Berger

Mount Sinai Health System

Track C | Law and Ethics

2:15

Discussion Group 1C: Discrimination and Privacy - What are the Key Legal and Ethical Considerations for the Chief Data Scientist?

Speaking:

Edmund-Jackson-HCA-230x230

Edmund Jackson

VP & Chief Data Scientist, Hospital Coporation of America (HCA)

Discussion Group 1C: Discrimination and Privacy - What are the Key Legal and Ethical Considerations for the Chief Data Scientist?

January 23 | 14:15 - 15:00

Speaking:

Edmund-Jackson-HCA-230x230

Edmund Jackson

Hospital Coporation of America (HCA)

Track A | People

3:00

Discussion Group 2A: Structuring Data Science Teams for Maximum Efficiency

Speaking:

Siva

Siva Kumpatla

Global Leader, Data Science, Corteva Agriscience, Agriculture division of DowDuPont

Discussion Group 2A: Structuring Data Science Teams for Maximum Efficiency

January 23 | 15:00 - 15:45

Speaking:

Siva

Siva Kumpatla

Corteva Agriscience, Agriculture division of DowDuPont

Track B | Data Science Transformation

3:00

Discussion Group 2B: Transforming Traditional Industries into Data Science Champions

Speaking:

Cary-1

Cary Correia

Chief Commercial Data Scientist, GE

Glenn Grossman

Glenn Grossman

Global Director of Forecasting, Insights and Analytics, Sanofi

Discussion Group 2B: Transforming Traditional Industries into Data Science Champions

January 23 | 15:00 - 15:45

Speaking:

Cary-1

Cary Correia

GE

Glenn Grossman

Glenn Grossman

Sanofi

Track C | Law and Ethics

3:00

Discussion Group 2C: Data Science’s Impact on the Workforce - Job Loss, Creation and Evolution

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

Global Chief Pricing Officer, McDonald's

Discussion Group 2C: Data Science’s Impact on the Workforce - Job Loss, Creation and Evolution

January 23 | 15:00 - 15:45

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

McDonald's

3:45

Coffee & Networking in the Exhibition Area

Coffee & Networking in the Exhibition Area

January 23 | 15:45 - 16:15

Speaking:

4:00

Building the Homo Deus – How Augmenting Workers with AI Will Lead to a More Productive Workforce

  • The enormous effort devoted to developing “artificial intelligence” that mimics human decision-making may be misplaced and even counterproductive for many organizations.
  • For knowledge work, in particular, we should not strive for AI systems to replace humans, since we run the risk of reifying the cognitive and organizational biases that already exist.
  • Instead, we should harness the power of computers to perform tasks that human experts are not very good at accomplishing (such as situational awareness), leaving more time in the workday for human experts to do what they are good at (thinking creatively and identifying causal relationships).
  • By building joint human-machine platforms, we can efficiently share knowledge across an organization and prioritize what additional information to collect.
  • This approach can also ease the transition from today’s workplace to a future one, by starting with expertise-driven causal explanations and by requiring AI methods to be understandable.

In addition to describing a future workflow in which we leverage the best of human and machine intelligence, this presentation also points out the challenges in moving to this future. Specifically, the presenter will discuss key technical and cultural gaps that will need to be overcome if we are to get greater value out of the data we possess and collect and to enable data-driven decision-making at scale. These gaps represent priority areas for research funding, which business leaders should support.

Speaking:

Michael Simon

Michael Simon

Chief of Analytics, Central Intelligence Agency (CIA)

Building the Homo Deus – How Augmenting Workers with AI Will Lead to a More Productive Workforce

January 23 | 16:00 - 16:30

  • The enormous effort devoted to developing “artificial intelligence” that mimics human decision-making may be misplaced and even counterproductive for many organizations.
  • For knowledge work, in particular, we should not strive for AI systems to replace humans, since we run the risk of reifying the cognitive and organizational biases that already exist.
  • Instead, we should harness the power of computers to perform tasks that human experts are not very good at accomplishing (such as situational awareness), leaving more time in the workday for human experts to do what they are good at (thinking creatively and identifying causal relationships).
  • By building joint human-machine platforms, we can efficiently share knowledge across an organization and prioritize what additional information to collect.
  • This approach can also ease the transition from today’s workplace to a future one, by starting with expertise-driven causal explanations and by requiring AI methods to be understandable.

In addition to describing a future workflow in which we leverage the best of human and machine intelligence, this presentation also points out the challenges in moving to this future. Specifically, the presenter will discuss key technical and cultural gaps that will need to be overcome if we are to get greater value out of the data we possess and collect and to enable data-driven decision-making at scale. These gaps represent priority areas for research funding, which business leaders should support.

Speaking:

Michael Simon

Michael Simon

Central Intelligence Agency (CIA)

4:45

Panel Discussion: Writing the Trust Contract – How Do You Help Foster Trust in the Data Science Process and its Results?

  • While most enterprise leaders view data science as an integral part of the modern business, forming a trust contract is still an incredibly difficult task
  • How do we overcome the mistrust in both the complex process and the fact data science is often iterative?
  • What can we do to make sure business leader’s understand data science’s value beyond the “magic wand” projects?

Speaking:

Piotr Niedzwiedz

Piotr Niedzwiedz

Founder & CEO, neptune.ml

0-230x230

Tom Hamilton

VP, Head of Analytics Center of Excellence, Voya Financial

Subhashini-Tripuraneni-7eleven-230x230

Subhashini Tripuraneni

Head of Data Science & Head of Artificial Intelligence, 7-Eleven

Chris Hutchins-1

Chris Hutchins

AVP, Healthcare Analytics, Northwell Health

Panel Discussion: Writing the Trust Contract – How Do You Help Foster Trust in the Data Science Process and its Results?

January 23 | 16:45 - 17:15

  • While most enterprise leaders view data science as an integral part of the modern business, forming a trust contract is still an incredibly difficult task
  • How do we overcome the mistrust in both the complex process and the fact data science is often iterative?
  • What can we do to make sure business leader’s understand data science’s value beyond the “magic wand” projects?

Speaking:

Subhashini-Tripuraneni-7eleven-230x230

Subhashini Tripuraneni

7-Eleven

Chris Hutchins-1

Chris Hutchins

Northwell Health

0-230x230

Tom Hamilton

Voya Financial

Piotr Niedzwiedz

Piotr Niedzwiedz

neptune.ml

5:15

Chair’s Closing Remarks

Speaking:

Mark Wang

Mark Wang

Chief Data Scientist, Alorica

Chair’s Closing Remarks

January 23 | 17:15 - 17:30

Speaking:

Mark Wang

Mark Wang

Alorica

5:30

Drinks Reception & Networking in the Exhibition Area

Drinks Reception & Networking in the Exhibition Area

January 23 | 17:30 - 18:30

Speaking:

Conference Day 2
24 January
08:15

Registration, Coffee & Networking in the Exhibition Area

Registration, Coffee & Networking in the Exhibition Area

January 24 | 08:15 - 08:45

Speaking:

08:45

Chair’s Opening Remarks

Speaking:

Mark Wang

Mark Wang

Chief Data Scientist, Alorica

Chair’s Opening Remarks

January 24 | 08:45 - 09:00

Speaking:

Mark Wang

Mark Wang

Alorica

09:00

Interactive Spatio Temporal Intelligence - a new Frontier for Analytics and Data Science

Speaking:

Venkat Krishnamurthy

Venkat Krishnamurthy

VP, Product Management, OmniSci

Interactive Spatio Temporal Intelligence - a new Frontier for Analytics and Data Science

January 24 | 09:00 - 09:30

Speaking:

Venkat Krishnamurthy

Venkat Krishnamurthy

OmniSci

09:30

Utilizing Data Science Techniques to Improve Enterprise Risk Management Models

Speaking:

Aziz Lookman

Aziz Lookman

Chief Analytics Officer, RationalAi

Mihaela Nistor

Mihaela Nistor

Head of Enterprise Risk Management, Bloomberg LP

Utilizing Data Science Techniques to Improve Enterprise Risk Management Models

January 24 | 09:30 - 10:00

Speaking:

Mihaela Nistor

Mihaela Nistor

Bloomberg LP

Aziz Lookman

Aziz Lookman

RationalAi

10:00

Panel Discussion: Shrinking the Haystack - Combatting Fraud and Insider Threat with Advanced Monitoring Solutions for High Fidelity Alerts

  • Insider threat is a very small proportion of employee behaviour, so how can we help our models to differentiate between legitimate access and illegal activity?
  • Can cognitive computing shrink the haystack to identify insider threat and fraud in real time?
  • Different techniques used to improve the detection rates and filter out the noise of false positives at scale

Speaking:

Joel Amick-TIAA-1

Joel Amick

Director, Cyber Analytics and Data Science, TIAA

Aziz Lookman

Aziz Lookman

Chief Analytics Officer, RationalAi

Alberto Artasanchez

Alberto Artasanchez

Artificial Intelligence Lab Director, Knowledgent, a part of Accenture

roymark-archive-badge-photo-lolores

Mark Roy

Partner Solutions Architect , Amazon Web Service (AWS)

Panel Discussion: Shrinking the Haystack - Combatting Fraud and Insider Threat with Advanced Monitoring Solutions for High Fidelity Alerts

January 24 | 10:00 - 10:30

  • Insider threat is a very small proportion of employee behaviour, so how can we help our models to differentiate between legitimate access and illegal activity?
  • Can cognitive computing shrink the haystack to identify insider threat and fraud in real time?
  • Different techniques used to improve the detection rates and filter out the noise of false positives at scale

Speaking:

Joel Amick-TIAA-1

Joel Amick

TIAA

Aziz Lookman

Aziz Lookman

RationalAi

Alberto Artasanchez

Alberto Artasanchez

Knowledgent, a part of Accenture

roymark-archive-badge-photo-lolores

Mark Roy

Amazon Web Service (AWS)

10:30

Coffee & Networking in the Exhibition Area

Coffee & Networking in the Exhibition Area

January 24 | 10:30 - 11:00

Speaking:

Track A | Data

11:00

Discussion Group 3A: What Are The Most Effective Strategies For Scaling Your Data Science Infrastructure?

Speaking:

Subhashini-Tripuraneni-7eleven-230x230

Subhashini Tripuraneni

Head of Data Science & Head of Artificial Intelligence, 7-Eleven

jeff_bx_headshot-230x230

Jeffrey Sternberg

SVP, Data Science Engineering, The Blackstone Group

Discussion Group 3A: What Are The Most Effective Strategies For Scaling Your Data Science Infrastructure?

January 24 | 11:00 - 11:45

Speaking:

Subhashini-Tripuraneni-7eleven-230x230

Subhashini Tripuraneni

7-Eleven

jeff_bx_headshot-230x230

Jeffrey Sternberg

The Blackstone Group

Track B | Methodologies & Creativity

11:00

Discussion Group 3B: How Do You Prevent Group Think in a Data Science Team?

Speaking:

Carl Gold Zuora

Carl Gold

Chief Data Scientist, Zuora

Discussion Group 3B: How Do You Prevent Group Think in a Data Science Team?

January 24 | 11:00 - 11:45

Speaking:

Carl Gold Zuora

Carl Gold

Zuora

Track A | Data

11:45

Discussion Group 4A: Legacy Data, Legacy Systems, And Legacy Processes – How Can We Minimize Time Spent On Data Governance?

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

Global Chief Pricing Officer, McDonald's

Discussion Group 4A: Legacy Data, Legacy Systems, And Legacy Processes – How Can We Minimize Time Spent On Data Governance?

January 24 | 11:45 - 12:30

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

McDonald's

Track B | Methodologies & Creativity

11:45

Discussion Group 4B: Deep Learning ROI – Where are the Benefits and Draw Backs Compared to Traditional ML Techniques?

Speaking:

Venkat Krishnamurthy

Venkat Krishnamurthy

VP, Product Management, OmniSci

Discussion Group 4B: Deep Learning ROI – Where are the Benefits and Draw Backs Compared to Traditional ML Techniques?

January 24 | 11:45 - 12:30

Speaking:

Venkat Krishnamurthy

Venkat Krishnamurthy

OmniSci

12:30

Lunch & Networking in the Exhibition Area

Lunch & Networking in the Exhibition Area

January 24 | 12:30 - 13:30

Speaking:

Track A | Data

1:30

Discussion Group 5A: How Can We Limit Bias in Our Data?

Speaking:

Alaa Moussawi

Alaa Moussawi

Chief Data Scientist, New York City Council

Haftan-Eckholdt-Plated-230x230

Haftan Eckholdt

Chief Data Officer & Chief Data Science Officer, Understood.org

Discussion Group 5A: How Can We Limit Bias in Our Data?

January 24 | 13:30 - 14:15

Speaking:

Alaa Moussawi

Alaa Moussawi

New York City Council

Haftan-Eckholdt-Plated-230x230

Haftan Eckholdt

Understood.org

Track B | Building the Data Science Team

1:30

Discussion Group 5B: What Does the Future Hold for the Chief Data Scientist Role?

Speaking:

jeff_bx_headshot-230x230

Jeffrey Sternberg

SVP, Data Science Engineering, The Blackstone Group

Edmund-Jackson-HCA-230x230

Edmund Jackson

VP & Chief Data Scientist, Hospital Coporation of America (HCA)

Discussion Group 5B: What Does the Future Hold for the Chief Data Scientist Role?

January 24 | 13:30 - 14:15

Speaking:

Edmund-Jackson-HCA-230x230

Edmund Jackson

Hospital Coporation of America (HCA)

jeff_bx_headshot-230x230

Jeffrey Sternberg

The Blackstone Group

Track A | Data

2:15

Discussion Group 6A: Outside the Box - How Do You Encourage Creative and Unusual Use of Data?

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

Global Chief Pricing Officer, McDonald's

Discussion Group 6A: Outside the Box - How Do You Encourage Creative and Unusual Use of Data?

January 24 | 14:15 - 15:00

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

McDonald's

Track B | Building the Data Science Team

2:15

Discussion Group 6B: Leading Global Data Science Teams

Speaking:

Siva

Siva Kumpatla

Global Leader, Data Science, Corteva Agriscience, Agriculture division of DowDuPont

Discussion Group 6B: Leading Global Data Science Teams

January 24 | 14:15 - 15:00

Speaking:

Siva

Siva Kumpatla

Corteva Agriscience, Agriculture division of DowDuPont

3:00

Coffee & Networking in the Exhibition Area

Coffee & Networking in the Exhibition Area

January 24 | 15:00 - 15:30

Speaking:

3:30

The Bionic Newsroom: AI-powered journalism

Speaking:

Salah Zalatimo

Salah Zalatimo

Chief Data Officer, Forbes

The Bionic Newsroom: AI-powered journalism

January 24 | 15:30 - 16:00

Speaking:

Salah Zalatimo

Salah Zalatimo

Forbes

4:00

Q&A with Advisory Board

  • Open Q&A with the advisory board

Speaking:

Siva

Siva Kumpatla

Global Leader, Data Science, Corteva Agriscience, Agriculture division of DowDuPont

Rajeeve Kaul-1

Rajeeve Kaul

Global Chief Pricing Officer, McDonald's

Joel Amick-TIAA-1

Joel Amick

Director, Cyber Analytics and Data Science, TIAA

Q&A with Advisory Board

January 24 | 16:00 - 16:30

  • Open Q&A with the advisory board

Speaking:

Rajeeve Kaul-1

Rajeeve Kaul

McDonald's

Siva

Siva Kumpatla

Corteva Agriscience, Agriculture division of DowDuPont

Joel Amick-TIAA-1

Joel Amick

TIAA

4:30

Chair's Closing Remarks

Speaking:

Mark Wang

Mark Wang

Chief Data Scientist, Alorica

Chair's Closing Remarks

January 24 | 16:30 - 16:45

Speaking:

Mark Wang

Mark Wang

Alorica

4:45

End of Main Conference

End of Main Conference

January 24 | 16:45

Speaking: