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Oct 27, 2022

9:00 AM - 1:00 PM, PT

Hear from the best minds in the Biopharma space who will be sharing their know-how and best practices on data FAIRification for digital transformation in life sciences R&D and applying data-centric approaches to machine learning.
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Agenda

Keynote Sessions:

Abhishek Jha - CEO & Co-Founder, Elucidata

9:00 AM - 9:25 AM (PT)

From paper to excel sheets, electronic lab notebooks, and finally to the cloud, biomedical data has been going places in the last few decades! As we transition into a post-cloud world and all your data is on the cloud, any AI initiative will be at risk unless the data is clean and linked to each other. In this session, Abhishek Jha elaborates on how Elucidata is doubling and tripling down on a data-centric approach to accelerate biological discovery.

Chandni Valiathan - Director Modeling and Decision Sciences, Johnson & Johnson

9:25 AM to 9:50 AM (PT)

Published clinical trial data holds rich information on treatments and clinical endpoints that can be useful for future trial design and clinical decisions. This data is usually locked in text form, and not easily accessible. Making such published clinical trial data FAIR is challenging. Even with such data being accessible there remain added complexities in using the data for mathematical models. This talk will explore the various stages of compiling useful datasets from published data all the way to leveraging this clinical knowledge to generate insight and enable data-informed decisions. Challenges and opportunities in this data journey will be discussed.

Reenu Pandey - Associate Director- Informatics, Beam Tx

Manimala Sen - Sr. Product Manager at Elucidata

9:50 AM to 10:15 AM (PT)

Navneet Srivastava - Principal HCLS Data Lake and Analytics Specialist, AWS

10:15 AM to 10:40 AM (PT)

Track 1: Data-Centric AI

Danny Wells - Chief Technical Officer, Santa Ana Bio

Ashutosh M, Assistant Professor- CSE Department, IIT-Kanpur

Shashank Jatav, Director- Data Products, Elucidata

As the world is moving at breakneck speed toward larger models to improve accuracy, we take a step back from the rat race to take a more efficient and realistic look at the technology and explore how it can be improved in a more optimized and well-rounded manner. Join the exciting conversation between ML/NLP expert Dr. Ashutosh Modi and our BioNLP Director, Shashank Jatav, to learn more.

Aneesh Karve - CTO, Quilt Data

As modeling projects grow, so grow the costs of debugging, scaling, and modifying the model pipeline. One method to minimize the costs of model maintenance is to train models in reproducible iterations. In the context of machine learning, we define a reproducible model iteration as the output of an executable script that is a pure function of three variables: code, environment, and data. Reproducible models are not an end, but a means to faster, more correct iterations. A reproducible model history implies that developers can confidently reconstruct any past model iteration. As a result, reproducibility makes it easier for developers to experiment with modifications, isolate bugs, and revert to known good iterations when problems arise.

Rangaprasad Sarangarajan - Chief Scientific Officer, Metabolon

The presentation will use metabolomics as a data construct to highlight the importance of standardization of both protocols and data to enable data-driven analytics for the creation of actionable solutions. A case study will be presented on the use of metabolomics data for target identification and validation of its potential clinical utility as a pathway for drug discovery.

Mya Steadman - Bioinformatics Scientist, Elucidata

Predictive models used in drug discovery require a viable level of data quality. A faulty model can lead to completely off-the-mark predictions and sunk project costs.In sharp contrast, much of the available biomedical data is unstructured and prone to errors due to varying experimental protocols (incomplete metadata information, missing annotations, inconsistent file formats). To ensure their datasets are ML-Ready, R&D teams must set up a system that continuously assesses and iterates on the data and metadata quality. This session will demonstrate Elucidata’s data quality assessment approach, which ensures an input dataset is standardized and has accurate, complete, and a breadth of metadata information before it is considered model quality.

Jainik Dedhia - Senior Product Manager, Elucidata

Neychelle Fernandes - Director Solutions & Technical Sales, Elucidata

Today, biomolecular research produces a large amount of data. This data provides important clues for discovery, translational research, and personalized medicine. At Elucidata, our aim is to build a platform that puts high-quality data central to ML workflows in pre-clinical R&D.  In this session, our speakers shed light on the Do-it-yourself (DIY) data journey on Polly, starting from ingesting raw data on Polly's cloud, analyzing and visualizing processed data and everything in between. Join Neychelle, Director - Solutions and Technical Sales, and Jainik Dedhia, Senior product manager, as they give us insights into what’s coming up next quarter on Polly.

Track 2: FAIR Transformation

Giovanni Nisato - Consultant, Project Manager FAIR Implementation, Pistoia Alliance

Although data standards (e.g., CDISC) are mandatory for clinical trial submission to FDA, the data is often un-FAIR, limiting future reuse. The Pistoia Alliance FAIR4Clin guide explores the state of FAIR in the clinical space. In this session, Giovanni Nisato from the FAIR implementation Project emphasizes how this guide adds value to clinical data, accelerating research for data practitioners like clinical data managers or analysts. His session includes topics like metadata concepts and standards, clinical trial registries, clinical data quality, and governance.

Swetabh Pathak - CTO & Co-Founder, Elucidata

Krutika Gaonkar - Senior Solutions Architect, Elucidata

The challenge of storing and retrieving data exists because various stakeholders need to interface with the data before it is analyzed. Setting up a system that tracks data lineage, quality & versioning is often an afterthought. Here, our CTO & Co-founder, Swetabh Pathak & Senior Solution Architect, Krutika Gaonkar, define a data management strategy, that allows R&D teams to ensure the journey for data generated from instruments/ CROs to insight derivation is seamless. They will also how pre-clinical data can remain clean, linked & thus FAIR at every stage.

Hugh Salamon - Director of Bioinformatics Scientific Strategy, Exelixis

Gautham Sridharan - Principal Scientist, Alnylam Pharmaceuticals

Kishore Nandyala - Executive Director of Digital Transformation, Exelixis

There is constant growth, complexity, and creation speed of data. Therefore, a FAIR approach to data management is paramount. Having led transformative FAIRification efforts within their enterprises, panelists in this talk will discuss what the infrastructure to handle biomedical big data should look like, the challenges they faced while adopting FAIR approaches to data management, and some wins on this journey to FAIR transformation

Jainik Dedhia - Senior Product Manager, Elucidata

Neychelle Fernandes - Director Solutions & Technical Sales, Elucidata

Today, biomolecular research produces a large amount of data. This data provides important clues for discovery, translational research, and personalized medicine. At Elucidata, our aim is to build a platform that puts high-quality data central to ML workflows in pre-clinical R&D.  In this session, our speakers shed light on the Do-it-yourself (DIY) data journey on Polly, starting from ingesting raw data on Polly's cloud, analyzing and visualizing processed data and everything in between. Join Neychelle, Director - Solutions and Technical Sales, and Jainik Dedhia, Senior product manager, as they give us insights into what’s coming up next quarter on Polly.

All Speakers

Chandni Valiathan
Johnson and Johnson
Director Modeling and Decision Sciences
Reenu Pandey
Beam Tx
Associate Director- Informatics
Giovanni Nisato
Pistoia Alliance
Consultant, Project Manager FAIR implementation
Hugh Salamon
Exelixis
Director of Bioinformatics Scientific Strategy
Aneesh Karve
Quilt Data, Inc.
CTO
Gautham Sridharan
Alnylam Pharmaceuticals
Principal Scientist
Rangaprasad Sarangarajan
Metabolon
Chief Scientific Officer
Danny Wells
Santa Ana Bio
Chief Technical Officer
Ashutosh M.
IIT-Kanpur
Assistant Professor- CSE Department
John F. Conway
Chief Visioneer Officer
20/15 Visioneers
Kishore Nandyala
Exelixis
Executive Director
Navneet Srivastava
Amazon Web Services
Principal HCLS Data Lake and Analytics Specialist
Abhishek Jha
Elucidata
CEO & Co-Founder
Swetabh Pathak
Elucidata
CTO & Co-Founder Elucidata
Jainik Dedhia
Elucidata
Senior Product Manager
Shashank Jatav
Elucidata
Director- Data Products
Mya Steadman
Elucidata
Bioinformatics Scientist
Neychelle Fernandes
Elucidata
Director Solutions & Technical Sales
Krutika Gaonkar
Elucidata
Senior Solutions Architect
Manimala Sen
Elucidata
Senior Product Manager

Why Attend DataFAIR 2022?

The spirit behind DataFAIR is to bring to light the recent advancements in AI/ML, and its expanding role in accelerating biological discovery. The biopharma industry is transforming at an unprecedented scale by emerging technologies that unlock data-rich workflows and AI is playing a pivotal role in solving complex problems involving multi-dimensional biological data. However, with big data come bigger problems. Troves of heterogeneous data sourced from different systems and stored in silos impede discovery. Register for DataFAIR 2022 to hear from experts who have successfully transformed data practices within their organizations to de-risk AI/ML initiatives with FAIRification and a data-centric approach.
Explore
ways to implement AI/ML in a reproducible manner.
Deep-Dive
into the data, infrastructure, and culture-related problems hindering successful AI/ML adoption.
Learn
the best practices and know-how to successfully implement AI/ML initiatives in your organizations.

Highlights from 2021

Decision Makers from Premier Biopharma Companies and Academic Institutions Participated in the Event
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