Original Agenda
We are actively working with our speakers to confirm their availability for our new dates. Initial response from our speakers has been very positive, and we are optimistic we will have the new programs ready to share here soon.

Business Value Outcomes

Investment and application of AI in the pharmaceutical industry is rapidly gaining momentum. The AI: Business Value Outcomes track will bring together CEOs, CDOs, CIOs, CTOs, and Global AI, IT, and informatics experts from leading pharmaceutical and technology companies to give strategic talks from a business perspective—allowing you to assess the value of investing in and implementing AI.

Final Agenda

Monday, April 20

9:00 am - 5:00 pm Hackathon*

*Pre-registration required.

Tuesday, April 21

7:30 am Workshop Registration Open and Morning Coffee

8:30 am - 3:30 pm Hackathon*

*Pre-registration required.

8:30 - 11:30 am Recommended Morning Pre-Conference Workshops*

W2. A Crash Course in AI: 0-60 in Three

Gustavo Arango, PhD, Senior Data Scientist - Oncology Bioinformatics, AstraZeneca

Bino John, PhD, Associate Director, Data Science - Clinical Pharmacology & Safety Sciences, AstraZeneca R&D

John Van Hemert, PhD, Research Scientist, Bioinformatics, Corteva Agri Science, A Dow-Dupont Division

12:30 - 3:30 pm Recommended Afternoon Pre-Conference Workshops*

W11. AI-Celerating R&D: Foundational Approaches to How Emerging Technologies Can Generate Value

Brian Martin, Head of AI in R&D Information Research, Senior Principal Data Scientist, AbbVie

*Separate registration required.

2:00 - 6:30 Main Conference Registration Open


4:00 Welcome Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute




4:05 Keynote Introduction

4:15 PLENARY KEYNOTE PRESENTATION: NIH’s Strategic Vision for Data Science

Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health





Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health





Riffyn_new 5:00 - 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing



Wednesday, April 22

7:30 am Registration Open and Morning Coffee


8:00 Welcome Remarks

Allison Proffitt, Editorial Director, Bio-IT World




8:05 Keynote Introduction

8:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert Green, MD, MPH, Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School




8:45 PANEL DISCUSSION: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

Seth CooperSeth Cooper, PhD, Assistant Professor, Khoury College of Computer Sciences, Northeastern University






Lancashire_LeeLee Lancashire, PhD, Chief Information Officer, Cohen Veterans Bioscience






Pietro Michelucci, PhD, Director, Human Computation Institute






Jérôme WaldispühlJérôme Waldispühl, PhD, Associate Professor, School of Computer Science, McGill University






While the precision medicine movement augurs for better outcomes through targeted prevention and intervention, those ambitions entail a bold new set of data challenges. Various panomic and traditional data streams must be integrated if we are to develop a comprehensive basis for individualized care. However, deriving actionable information requires complex predictive models that depend on the acquisition and integration of patient data on a massive scale. This picture is further complicated by new data streams emerging from quantified self-tracking and health social networks, both of which are driven by experimentation-feedback loops. Tackling these issues may seem insurmountable, but recent advancements in human/AI partnerships and crowdsourcing science adds a new set of capabilities to our analytic toolkit. This talk describes recent work in online collective systems that combine human and machine-based information processing to solve biomedical data problems that have been otherwise intractable, and an information processing ecosystem emerging from this work that could transform the landscape of precision medicine for all stakeholders.


9:45 Coffee Break in the Exhibit Hall with Poster Viewing


10:50 Organizer’s Welcome Remarks

Cambridge Healthtech Institute

10:55 Chairperson’s Remarks

11:00 AI in Pharma: Market Size, Investment Opportunities, and Impact of AI

Louie_AlanAlan Louie, PhD, Research Director, Life Sciences, IDC

While we have seen the hype before, IDC believes that the current wave of AI will finally begin to leave a lasting impact on the pharmaceutical industry. As an IT technology innovation, we regularly track AI, both from technology spending and innovation adoption perspectives. We will present detailed insights from our unique viewpoint in the industry.

11:30 KEYNOTE PRESENTATION: Transforming Patient Health: The Power of Data Science in Pharmaceuticals

Archundia_AbelAbel Archundia, Global Head, IT & Digital Transformation, Bayer Pharmaceuticals

Digital technologies like Artificial Intelligence help us to leverage data so we can diagnose patients earlier, and gain a thorough and deep understanding of diseases. This will be key to tailor treatments to the individual needs of a patient and to identify those patients who will benefit the most. Moreover, data-driven digital solutions help to bring new medicines to patients faster than ever before.

ExpertSystemEnterprise 12:00 pm Presentation to be Announced

12:15 Sponsored Presentation (Opportunity Available)

12:40 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:40 Session Break

1:50 Chairperson’s Remarks

Alan Louie, PhD, Research Director, Life Sciences, IDC

1:55 Enterprise-Wide Data Science Innovation and Enabled Business Strategies

Ramakrishnan_SrikanthSrikanth Ramakrishnan, Director, Intelligent Automation/Data Science, Johnson & Johnson Technology

The convergence of several technology trends has accelerated progress in data science. The volume of data continues to double every couple of years. Data scientists now have massive compute power they can tap into, and they are devising ever more novel algorithms. How does an enterprise approach the key technology building blocks to enable scalable and agile applications in data science and machine learning, including deep learning? The talk will describe some of the foundational technologies, structure and processes that could enable an enterprise to put AI to work for transforming their businesses.


2:15 Using AI to Drive Top-Line and Bottom-Line Growth

Cornejo_EduardoEduardo Cornejo, ITS Digital Innovation Market Lead, Sanofi

2:35 How Can AI Impact Key Business Decisions? Delivering Insights across Business Workstreams

Lefebvre_SebastienSebastien Lefebvre, Senior Director, Data Sciences, Genomics and Bioinformatics, Alexion Pharmaceuticals

Data-driven insights are making their way into various biotech workstreams that are tasked with identifying new indications for research strategy, drug repurposing for clinical development, and clinical asset assessment for BD, to name a few. I will cover our approach, data strategy, and analysis workflows to providing insights into key decision packages like CDP and TPP while highlighting the role of AI along the way.

2:55 Sponsored Presentation (Opportunity Available)

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing


4:00 Chairperson’s Remarks

Brandon Allgood, PhD, Vice President, Integral Health; Co-Founder and Vice Chair, Alliance for Artificial Intelligence in Healthcare (AAIH)

4:05 Integrating AI into the Pharma Business Model

Roach_EmirEmir Roach, MD, Global Head, Emerging Technologies, Takeda Pharmaceuticals

Prodigious amounts of created data and the ubiquitous availability of large-scale computing power provide a foundation for artificial intelligence to generate insights, both to improve the discovery of new therapeutics and to make the delivery of current ones more effective. However scalable, cross-cutting implementation of AI capabilities remains a challenge. A closer look into success cases reveals valuable insights on the ideal organizational structures, business processes, cultural transformation requirements and technical infrastructure to enable realizing the full potential of AI at scale.

4:35 PANEL DISCUSSION: Examining Industry Collaborations and Capital Formation for AI Platform Developers in Drug Development


Mudiwa_Mhaka_AnnastasiahAnnastasiah Mudiwa Mhaka, PhD, Co-Founder and Convenor, Alliance for Artificial Intelligence in Healthcare (AAIH)


Alipour_NavidNavid Alipour, Co-Founder and Managing Partner, Analytics Ventures

Jessica Federer, Venture Affiliate Partner, Boston Millennia Partners, Former CDO, Bayer

Harry Glorikian, General Partner, New Ventures Fund

  • Explore trends and lessons learned with regards to how AI platform developers are strategically partnering with BioPharma to accelerate business and asset value
  • Discuss how AI developers are creating value around (validated) AI tools to secure capital, including key challenges and opportunities
  • Examine tangible case studies of what is working and what isn’t within this dynamic of stakeholders.


5:05 Hype, Hope, and Reality of AI in Clinical Trials

Bergau_DennisDennis Bergau, PhD, Senior Research Pharmacologist & Operations Lead Cardiac Safety, AbbVie

Interest in and use of Artificial Intelligence (AI) continues to grow in many industries. This discussion will cover the balance between hype, hope, and reality of AI, and how it might be used to augment clinical pharmaceutical trials as our understanding and acceptance of AI continues to evolve, as well as some of the benefits and challenges we currently face.





5:35 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing






6:45 End of Day

Thursday, April 23

7:30 am Registration Open and Morning Coffee


8:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute





8:05 Awards Program Introduction

8:10 Benjamin Franklin Award and Laureate Presentation

J.W. Bizzaro, Managing Director, Bioinformatics.org





Discngine8:35 Bio-IT World Innovative Practices Awards

Allison Proffitt, Editorial Director, Bio-IT World





9:00 AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur





Penguin_Computing_Tagline 9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced at 10:00




10:30 Organizer’s Remarks

Cambridge Healthtech Institute

10:35 Chairperson’s Remarks

10:40 KEYNOTE PRESENTATION: Organizing AI Capabilities to Deliver Business Value throughout the Drug Development Pipeline

Kamkolkar_MilindMilind Kamkolkar, Chief Data Officer, Cellarity; Former Chief Data Officer, Sanofi

This talk will detail how to integrate AI into the pharmaceutical business model and describe, using practical examples, how AI can digitally transform the industry. Find out how to overcome challenges faced when integrating AI talent, technology, and science within your workforce.

11:00 Establishing Leadership Roles: The Search for Experienced Talent in a Young Industry

Bowen_EdwardEd Bowen, PhD, Vice President, AI & Machine Learning, GSK

Within the last 8 years, the co-emergence of data sources like ImageNet and advances in computational capabilities have led to transformational advances in Deep Learning. This has created a demand for skilled machine learning engineers that is greater than the supply of experienced talent. This session will address strategies for developing talent in support of establishing an AI/ML capability.


11:20 Real-World Data and AI Hype: Stimulating and Supporting Each Other

Bartels_DorotheeDorothee B. Bartels, PhD, Clinical and Real World Data Strategy Lead, X, Alphabet

The real-world data hype caused high expectations, including RCTs only playing a minor role in future drug development, when in fact, they are complementary to RCT data and cannot replace them. Artificial intelligence may change drug development and time to market significantly, but will not replace past knowledge and experience. Real-World Evidence generation can be enhanced by AI and is key for public health.

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

12:20 Luncheon Presentation (Sponsorship Opportunity Available) or Enjoy Lunch on Your Own

1:20 Dessert Refreshment Break in the Exhibit Hall with Last Chance Poster Viewing


1:55 Chairperson’s Remarks

Nick Lynch, PhD, Founder and CTO, Curlew Research; External Liaison and Advisor, Pistoia Alliance

2:00 AI & Strategy Development: Collaboration, Externalisation, Data Sharing, and Integration

Lynch_NickNick Lynch, PhD, Founder and CTO, Curlew Research; External Liaison and Advisor, Pistoia Alliance

AI is at peak hype at present, but its full potential will not be realised unless a clear and incremental strategy is adopted. In this talk, we will discuss the current state of collaboration and pre-competitive activities within AI in Life Sciences R&D, where this is heading, and how we can influence it as a community. We will discuss how data sharing and the use of data standards will need to underpin the future potential of AI and looking ahead to the future approaches.

2:30 Bridging Business and Technical Functions: How to Translate AI between the Two

Allgood_BrandonBrandon Allgood, PhD, Vice President, Head of Technology and Innovation, Integral Health; Co-Founder and Vice Chair, Alliance for Artificial Intelligence in Healthcare (AAIH)

Artificial Intelligence has been studied by computer scientists for more than 70 years. The term ‘Artificial Intelligence’ itself was coined in 1956, but the theory and topics that became known as AI have a much longer history. Even so, it remains one of the most complex and misunderstood topics in computer science because of the vast number of techniques employed and the often nebulous goals being pursued. Add to this the complexities of business and it is no wonder that in most cases, AI is taking a long time to show impact. But, the transformation that AI will bring is worth it. I will discuss how companies can approach and implement AI for success. I will also help to dispel many of the myths and hype surrounding AI, focusing on how companies can quickly get to practical solutions.

3:00 Generating Genomic Variant Data: Solving Both Data Privacy and AI Robustness Problems

neumann_ericEric Neumann, PhD, CEO & Founder, AIDAKA LLC

In Scientific Data Analytics, the utility of data is accompanied by issues around individual privacy guarantees. Many argue that having large sets of detailed data is essential for effective machine learning. Yet the same detail impacts privacy issues via the increased risk of exposure of matched individuals behind such data. We show here that both issues are usually two sides of the same coin and can both be solved together.

3:30 Applications of Groundbreaking AI Technology to Produce Realistic, Fully De-Identified Patient Data to Test and Validate Preclinical Modelling Methods

Robasky_KimberlyKimberly Robasky, PhD, Head, Translational Science, Renaissance Computing Institute (RENCI)

Researchers use biomarker and outcomes data to model and predict adverse events. However, access restrictions to safeguard patient privacy necessarily slow down the rate of discovery and increase research costs via IRB review. For these reasons, synthetic data that preserve patient-variable relationships have been an active area of research. We discuss current advances made by generative models in this area and the breakthrough AI technologies accelerating those advances.

4:00 Close of Conference

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