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Deep Learning to Accelerate Drug Development: An NYC Symposium

Deep Learning to Accelerate Drug Development: An NYC Symposium

Tuesday, November 13, 2018

The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York

Presented By

Roche

The New York Academy of Sciences

 

Technological advances in artificial intelligence, especially in the field of deep learning, hold the potential to transform modern drug discovery and development. In recent years, the accumulation of multi-faceted big data in medicine offers exciting new opportunities to apply state-of-the-art deep learning advances to pressing global health challenges. However, significant ground-work in applying this research is still critically needed before real breakthroughs can happen.

In this symposium, we will bring together the best minds from the biopharmaceutical industry and academic deep learning communities to create focused dialogues and research collaborations in New York City. The meeting will feature exciting new opportunities on topics including mobile health and sensors, medical and pathology imaging, genomics, computer vision and robotics for lab automation, and electronic health records (EHRs), and beyond.

Call for Abstracts

Abstract submissions are invited for a poster session. For complete submission instructions, please visit our online portal. The deadline for abstract submission is Friday, September 28, 2018.

Students and postdocs whose abstracts are accepted for poster presentation will receive complimentary meeting registration.

Registration

Member
$60
Nonmember Academia, Faculty, etc.
$105
Nonmember Corporate, Other
$160
Nonmember Not for Profit
$105
Nonmember Student, Undergrad, Grad, Fellow
$70
Member Student, Post-Doc, Fellow
$25
Deadline:
7
days
left

Scientific Organizing Committee

Deep Learning to Accelerate Drug Development: An NYC Symposium
James Cai, PhD,
Roche
Deep Learning to Accelerate Drug Development: An NYC Symposium
Wei-Yi Cheng, PhD,
Roche
Deep Learning to Accelerate Drug Development: An NYC Symposium
Nicholas Tatonetti, PhD,
Columbia University
Deep Learning to Accelerate Drug Development: An NYC Symposium
Sara Donnelly, PhD,
The New York Academy of Sciences
Deep Learning to Accelerate Drug Development: An NYC Symposium
Sonya Dougal, PhD,
The New York Academy of Sciences

Keynote Speaker

joel-dudley
Joel Dudley, PhD,
Icahn School of Medicine at Mount Sinai

Speakers

Deep Learning to Accelerate Drug Development: An NYC Symposium
Wei-Yi Cheng, PhD,
Roche
Deep Learning to Accelerate Drug Development: An NYC Symposium
Noémie Elhadad, PhD,
Columbia University
Deep Learning to Accelerate Drug Development: An NYC Symposium
Thomas Fuchs, PhD,
Memorial Sloan Kettering Cancer Center
Casey Greene
Casey S. Greene, PhD
Perelman School of Medicine, University of Pennsylvania
Deep Learning to Accelerate Drug Development: An NYC Symposium
Nima Mesgarani, PhD,
Columbia University
Deep Learning to Accelerate Drug Development: An NYC Symposium
Narges Razavian, PhD,
NYU School of Medicine
Deep Learning to Accelerate Drug Development: An NYC Symposium
Fabian Schmich,
PhD, Roche
Deep Learning to Accelerate Drug Development: An NYC Symposium
Heidi Schambra, MD,
NYU School of Medicine
Deep Learning to Accelerate Drug Development: An NYC Symposium
Nicholas Tatonetti, PhD,
Columbia University
Deep Learning to Accelerate Drug Development: An NYC Symposium
Olga Troyanskaya, PhD,
Princeton University

Presenting Partner

Tuesday

November 13, 2018

8:30 AM

Continental Breakfast and Registration

9:00 AM

Introduction and Welcome Remarks

Speakers

Sonya Dougal, PhD
The New York Academy of Sciences
James Cai, PhD
Roche Innovation Center
9:15 AM

KEYNOTE LECTURE: A Deep Learning Approach for Integrative Modeling of Molecular and Imaging Phenotypes of Drug Effects

Speaker

Joel Dudley, PhD
Icahn School of Medicine at Mount Sinai
10:00 AM

Networking Coffee Break

Session 1: Applying Deep Learning to develop Digital Biomarkers and Health Sensors

10:30 AM

Counting Reps: Quantitating Rehabilitation Dose After Stroke

Speaker

Heidi Schambra, MD
NYU Langone
11:00 AM

Deep Learning-Enabled Remote Gait and Mobility Monitoring Using Mobile Phones Sensor Data

Speaker

Wei-Yi Cheng, PhD
Roche
11:30 AM

Speech Processing In The Human Brain Meets Deep Learning

Speaker

Nima Mesgarani, PhD
Columbia University
12:00 PM

Networking Lunch

Session 2: Deciphering Real World Data with Deep Learning Methods

1:00 PM

Integration of High-throughput Data Capture Technologies with EHR

Speaker

Nick Tatonetti, PhD
Columbia University Irving Medical Center
1:30 PM

Phenotyping Endometriosis through Modeling of Self-Tracking Data

Speaker

Noemie Elhadad, PhD
Columbia University
2:00 PM

Deep Learning on Medical Records for Disease Prediction and Biomarker Discovery

Speaker

Narges Razavian, PhD
NYU School of Medicine
2:30 PM

Networking Coffee Break

Session 3: Advanced Image Analysis using Deep Learning

3:00 PM

Digital Pathology

Speaker

Thomas Fuchs, PhD
Memorial Sloan Kettering Cancer Center
3:30 PM

Image Processing

Speaker

Fabian Schmich, PhD
Roche

Session 4: Deep Learning Approaches for Genomics

4:00 PM

Opportunities and Obstacles for Deep Learning in Drug Development

Speaker

Casey S. Greene, PhD
Perelman School of Medicine, University of Pennsylvania
4:30 PM

Dynamic Predictive Models to Study the Effects of Genetic and Environmental Perturbations (TBC)

Speaker

Olga Troyanskaya, PhD
Princeton University
5:00 PM

Networking Reception and Poster Viewing

6:00 PM

Closing Remarks and F1000 Poster Prize Presentation

Speaker

James Cai, PhD
Roche
6:15 PM

Adjourn