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.

Emerging AI Technologies

AI technologies are essential to the advancement of precision medicine. There is no shortage of examples of how they can be leveraged from R&D to translational research, clinical trials, and real-world data. However, the current round of solutions can be expensive and complex to deploy and manage. The Emerging AI Technologies track will explore currently deployed use cases of AI technology, what problems have been addressed successfully, where there are some challenges, and what future technology developments to keep an eye on, such as machine vision, emotion gestures/affective computing, NLP, NLG, knowledge graphs, deep learning, reinforcement learning, quantum computing, synthetic data, and more. Hear from pharma industry use cases, as well as technology users and emerging technology solution providers. How can these emerging AI technologies move from correlation to causality analysis?

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

PLENARY KEYNOTE SESSION

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

PLENARY KEYNOTE SESSION

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.

Vast_Data

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

MAKING THE ALEXA OF CHEMISTRY

10:50 Organizer’s Welcome Remarks

Cambridge Healthtech Institute

10:55 Chairperson’s Remarks


11:00 KEYNOTE PRESENTATION: The MPrint-Open Knowledge Network: Creating a Central Portal for Molecular AI Activities

McQuade_TylerTyler McQuade, PhD, Professor of Chemical and Life Sciences Engineering, Virginia Commonwealth University; CTO, Medicines for All Institute

The research objective of MPrint-OKN is to create a valuable collaborative system that develops and distributes the most advanced molecular models, machine learning, data science, and data visualization tools available to the many disciplines requiring molecular systems for product development. We seek to reduce both the cost and time of discovering and developing next-generation molecular-based applications by placing advanced tools in the hands of more researchers. This presentation will cover our development of the portal and a discussion of some of the tools and strategies we are using to centralize molecular AI activities.

AUGMENTED INTELLIGENCE

11:40 CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data

Head_RichardRichard Head, MS, Professor, Genetics and Pathology & Immunology; Director, Genomics, McDonnell Genome Institute Genetics; Genome Technology Access Center at the McDonnell Genome Institute (GTAC@MGI), Washington University School of Medicine

Utilizing a revolutionary combination of contextual language processing and memory generation with components of artificial intelligence, the platform enables rapid human interpretation and hypothesis generation (Augmented Intelligence). Direct assessment of disease processes, target identification, drug mechanism of action, safety concerns, and the identification of translational mechanisms between animal models and human disease can occur in hours or days instead of the weeks to months traditionally required to reach this depth of understanding.

12:00 pm Sponsored Presentation (Opportunity Available)

12:30 Session Break

12:40 LUNCHEON PANEL PRESENTATION: AI Beyond the Hype – Addressing Practical, High-Impact Health Challenges

Panelists:

Esteban Rubens, Healthcare AI Principal, NetApp

David LaBrosse, Global Director - Emerging Technologies, NetApp

George Vacek, PhD, Global Director, Sequencing Strategic Development, NVIDIA

Rahul Sathe, Vice President, Surgical Innovation, Cambridge Consultants

The promise of AI continues to elude those who can benefit most, but new technologies are providing practical pathways to its use. Discuss the common challenges to getting AI projects off the ground, including how to move from PoC, to scale, to AI as an integral contributor to the business or mission. Hear how AI is transforming healthcare in genomics, medical imaging, digital pathology and global health, accelerated by Cambridge Consultants, NVIDIA, and NetApp.

1:10 Luncheon Presentation II (Sponsorship Opportunity Available)

1:40 Session Break

MACHINE LEARNING AND DEEP LEARNING

1:50 Chairperson’s Remarks

Vishakha Sharma, PhD, Principal Data Scientist, Roche

1:55 CO-PRESENTATION: Harnessing Machine Learning to Identify Causal Drivers of Operational Success in Clinical Trials

Marecki_SylviaSylvia Marecki, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany


Nair_OmesanOmesan Nair, PhD, Design Analyst, Operational Design Center (ODC), Global Clinical Operations, EMD Serono, Inc., an affiliate of Merck KGaA, Darmstadt, Germany

The Operational Design Center at EMD Serono has leveraged causal machine learning to analyze hundreds of variables across thousands of clinical trials with the objective of identifying causal drivers of enrollment success. Understandings gained from these efforts allows for creating a predictive algorithm for optimizing study design to conduct faster, less expensive trials. Early insights will be shared.

2:25 Automated Information Extraction from Pathology Reports Using Natural Language Processing

Sharma_VishakhaVishakha Sharma, PhD, Principal Data Scientist, Roche

Many critical facts required by healthcare AI applications are locked in unstructured free-text data. Recent advances in deep learning have raised the bar on achievable accuracy for tasks like named entity recognition, entity resolution, de-identification, and others, using novel healthcare-specific networks and models. Dr. Sharma will discuss how Roche applies the greatest advances in AI for healthcare to extract clinical facts from pathology reports and radiology. Dr. Sharma will then detail the design of the deep learning pipelines used to simplify training, optimization, and inference of such domain-specific models at scale.

DNAnexus 2:55 Presentation to be Announced

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

NOVEL IMAGING TECHNOLOGIES

4:00 Chairperson’s Remarks

Vishakha Sharma, PhD, Principal Data Scientist, Roche

4:05 AI-Celerating Preclinical Research: Machine Learning, Deep Learning and Robotic Process Automation to Scale Crystallography Throughput

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

AbbVie is implementing the AbbVie CHILE (Crystallography with Human-In-Loop Enhancement) platform where machine learning models use human-in-loop feedback to improve classifications at important decision points in the process of structure identification. RPA and other automation technologies provide integration with external and internal systems to handle repetitive and directive tasks. Our work illustrates how combining the power of multiple digital technologies can help transform scientific workflows and deliver operational, analytical, and experiential value to our scientists.

4:35 Clinical Translation of Machine Learning in Medical Imaging

Michalski_MarkMark Michalski, MD, Executive Director, Center for Clinical Data Science, Mass General Hospital and Brigham and Women’s Hospital

Deep learning has transformed image analysis for web search to autonomous vehicles, and is now making impacts in medical imaging. The adoption of these technologies requires the development of new capabilities and infrastructure in the clinical environment. In this talk we identify promising new approaches to the application of deep learning in medical imaging, including clinical evaluation of these technologies, keys to translation, and upcoming challenges/opportunities.

5:05 Sponsored Presentation (Opportunity Available)

 

Stellus_Technologies

 

 

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

 

RedRiver

 

 

 


6:45 End of Day

Thursday, April 23

7:30 am Registration Open and Morning Coffee

PLENARY KEYNOTE SESSION & AWARDS PROGRAM

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

 

 

QUANTUM COMPUTING’S IMPACT ON PHARMA

10:30 Organizer’s Remarks

Cambridge Healthtech Institute

10:35 Chairperson’s Remarks

10:40 PANEL DISCUSSION: Framework and Approach to Unlock the Potential of Quantum Computing in Drug Discovery

Moderator:

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


Panelists:

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


Christian Baber, PhD, Head, Scientific Informatics, Takeda

Xinjun Hou, PhD, Director, Computational Chemistry, Pfizer

In 2019, major life sciences companies mobilized to form a pre-competitive, collaborative quantum computing working group (QuPharm) and delineate a framework and approach to accelerate realizing the potential of quantum acceleration in drug discovery. Learn from industry thought leaders on how to valuate and map problems into quantum algorithms, set up organizations to enable and scale quantum computing pilots and establish effective cross-industry, tech, and start-up collaborations.

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

FUTURE OF AI: DATA SHARING, STANDARDS, SECURITY AND PRIVACY

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 & 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


Platinum Sponsors

accenture

BenchlingNEW

Elsevier-square

L7-informatics

linguamatics

Nutanix

PerkinElmer

Weka_Purple