July 24-26, 2018

Boston, USA

Speakers

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Alex Zhavoronkov
CEO
InSilico Medicine

Dr. Zhavoronkov specializes in the development of the next-generation artificial intelligence and Blockchain technologies for drug discovery, biomarker development and aging research. At Insilico he pioneered the applications of generative adversarial networks and reinforcement learning techniques for generating the novel molecular structures with the desired properties and launched multiple research and consumer oriented biomarker systems including the popular iPANDA system and Young.AI. Prior to founding Insilico Medicine, he worked in senior roles at ATI Technologies (acquired by AMD in 2006), NeuroG, the Biogerontology Research Foundation and YLabs.AI and established AgeNet.net competitions and diversity.AI initiative. Since 2012 he published over 80 peer-reviewed research papers and books including “The Ageless Generation: How Biomedical Advances Will Transform the Global Economy”.

Pre Conference Workshop A

Tuesday, 24th July, 2018

10.00am | AI in Discovery to Clinic: A Workshop on Strategies & Applications

Day One

Wednesday, 25th July, 2018

09.00 | Case Study: GANs'n'Roses: GANs Generating De Novo Molecular Structures with Experimental Validation

Joseph Lehar
Executive Director, Computational Biology, MRL
Merck

As the Executive Director, Computational Biology, MRL at Merck, Joseph Helps connect computational teams between organizations, contributing to digital health initiatives, and engaging with external companies on bringing artificial intelligence to Merck. He has over a dozen years experience building computational biology teams for translational drug discovery, and 25 years of research in systems biology, oncology, and astrophysics. Prior to Merck and Google, Joseph worked at Novartis, CombinatoRx, and the Broad Institute (was then Whitehead Inst CGR). Since 2002, through BU, He has been exploring systems biology topics and engaging with students.

Day One

Wednesday, 25th July, 2018

14.00 | Discuss: Real World/Pragmatic Clinical Trials and the Use of AI to Design & Monitor Them:

Haoda Fu
Research Advisor & Enterprise Lead for Machine Learning and Artificial Intelligence Group
Eli Lilly

Dr. Haoda Fu is a research advisor and a stats group leader for Machine Learning, artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 70 publications in the areas, such as Bayesian adaptive design, survival analysis, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and drug safety evaluation for rare events. In recent years, his research area focuses on machine learning and artificial intelligence.

Pre Conference Workshop B

Tuesday, 24th July, 2018

13.30pm | Artificial Intelligence, Machine Learning, and Precision Medicine: Colliding Technology & Science to Improve Personalized Patient Outcomes

Day One

Wednesday, 25th July, 2018

11.30 | Case Study: The Future is Now– The Most Promising Areas of AI/Machine Learning for Pharmaceutical Industry

Jie Cheng
Associate Director, Data & Statistical Sciences
Abbvie

Dr. Jie Cheng received his Ph.D. in Computer Science in 1998 and completed postdoctoral training in AI & Machine Learning in 2000. He has more than 15 years of experience in applying machine learning and predictive modeling to clinical and biomarker data analysis. He has won several data mining competitions including KDD-cup and FDA led MicroArray Quality Control data analysis. Dr. Cheng is an associate director at Abbvie, leading biomarker and exploratory analysis in immunology and neuroscience projects

Day Two

Thursday, 26th July, 2018

14.45 | Discuss: The Application of AI & Machine Learning in Clinical Development Requires Large Amounts of Quality Data: How to Develop the Best Model of Practices in Terms of Data Access and Sharing Amongst the Industry?

09.00 | Case Study: Machine Learning Based Patient Subgroup Identification for Precision Medicine

Ruthie Davi
Director, Data Science
Medidata

Ruthie Davi has worked in the area of pharmaceutical product development, and specifically the FDA, over 20 years. In her current position as a Director in Data Science at Medidata Solutions, she is part of a team focused on providing centralized systematic monitoring and other novel clinical trial tools. Ruthie has held numerous positions within the FDA, including Statistical Reviewer, Team Leader, and Deputy Director, where she implemented innovative statistical methods and made recommendations for clinical trial designs tailored to the regulatory setting. She has conducted the statistical review, represented the Agency at Advisory Committee meetings, and provided recommendations regarding FDA marketing approval for numerous New Drug and Biologic Licensing Applications. With a particular interest in pediatric clinical trials, she was an active member of FDA's Pediatric Review Committee. Ruthie holds a Ph.D. in biostatistics from George Washington University.  

Day One

Wednesday, 25th July, 2018

15.45 | Roundtable Discussions: Our breakout roundtables will allow you to have more intimate discussions with AI and pharma leaders around some of the hottest topics in the field. Discover multiple perspectives on these key issues, so that you can learn from your fellow experts in the audience. Drive your own learning, crowd-source ideas and get inspired. Immerse yourself in the following discussions:

Ray Liu
Senior Director & Head, Advanced Analytics & Statistical Consultation
Takeda

Dr. Ray Liu is the Sr. Director and Head of Statistical Innovation and Consultation group at Takeda Pharmaceutical Company with responsibility to develop novel statistical methodology, and provide statistical consultation and project support to various functional areas in R&D, including Discovery, CMC, Translational Research, and Outcome Research.   Ray is an active contributor in the statistical community. He was the conference chair of Midwest Biopharmaceutical Statistics Workshop for years 2015 and 2016, and has served at the organizing committee and as session chairs for various conferences, including NCB, ICSA and JSM meetings. He is the author of more than 30 statistical and scientific manuscripts and book chapters, and is the member of ASA, PSTC Stats working group, IQ consortium, and NCBLF.  He also edited two Springer books titled “Statistical Applications from Clinical Trials and Personalized Medicine to Finance and Business Analytics” and “Recent Trends in Pharmaceutical Statistics”.  Dr. Ray Liu received his bachelor and master degrees from National Taiwan University and PhD degree from Columbia University. His current research interests are big data, text mining and joint analysis of hi-dimensional data.

Day One

Wednesday, 25th July, 2018

14.00 | Discuss: Real World/Pragmatic Clinical Trials and the Use of AI to Design & Monitor Them:

Arun Asaithambi
CEO
Lantern Pharma

Dr. Arun K Asaithambi is an experienced healthcare & technology entrepreneur, who is successfully building global organizations integrating multiple technology applications in many industries. As a serial entrepreneur, Arun Asaithambi successfully helped build and grow 4 tech companies over the last 8 years and has brought multi-millions in total funding, partnerships, revenues and grants/contracts.  Currently, Arun Asaithambi leads Dallas based Lantern Pharma Inc, a Genomics & AI based biotech company developing precision cancer drugs that he co-founded in 2013. In 2012, he also co-founded Intuition Payment Systems, which is a top 5 AI-based integrated Point of Sales/Care and Payments Systems company in India.

Day One

Wednesday, 25th July, 2018

12.00 | Case Study: Pharma’s Holy Grail: How to Solve the Right Patient Stratification Problem?

Day Two

Thursday, 26th July, 2018

14.00 | Discuss: How AI & Machine Learning Could Present an Opportunity to Lower the Failure Rates of Clinical Trials Compared to the Contemporary Approaches?

Gregory Goldmacher
Executive Director, Translational Biomarkers
Merck

Gregory Goldmacher is Executive Director in the Translational Biomarkers department at the Merck Research Laboratories. He obtained his Bachelor’s degree in biological sciences from the University of Chicago, and his MD and PhD (in neuroscience) at the UT Southwestern Medical Center in Dallas. He did residency training in diagnostic radiology, and was a fellow at the Massachusetts General Hospital and at Thomas Jefferson University. He also obtained an MBA from Temple University, with a finance focus. Prior to joining Merck, he was the head of cancer imaging at ICON. At Merck, he has led imaging across many late and early phase oncology programs. He has also led internal research efforts and external collaborations focused on applying artificial intelligence to the development of novel biomarkers for use in oncology trials and clinical care. He is active in clinical trial research and standardization efforts, with participation and high-level leadership in a variety of national collaborations such as the Quantitative Imaging Biomarkers Alliance and the Clinical Data Interchange Standards Consortium. He spends most of his free time playing with his kids, but also enjoys running and training in the martial arts.

Day Two

Thursday, 26th July, 2018

14.00 | Discuss: How AI & Machine Learning Could Present an Opportunity to Lower the Failure Rates of Clinical Trials Compared to the Contemporary Approaches?

10.00 | Case Study: The Application of Machine Learning to the Development of Novel Imaging Biomarkers

Raj Bandar
Senior Director, Data Sciences Strategy, Translational Informatics
Sanofi

Raj Bandaru is currently the global head of a data sciences and knowledge management practice at Sanofi Pharmaceuticals developing cloud based analytics platforms and data services for quantitative systems pharmacology, drug disease modeling and trial simulations.  Prior to this he ran a clinical and RWE data analytics consulting practice for several years and was also the head of the translational informatics and clinical operations digital innovation at AstraZeneca. Raj did his graduate training in statistics at Rutgers University NJ, as well as a Masters in Population Genetics and more recently an MBA in operations research from Babson college in Boston, MA where he lives and works currently.

Day Two

Thursday, 26th July, 2018

14.45 | Discuss: The Application of AI & Machine Learning in Clinical Development Requires Large Amounts of Quality Data: How to Develop the Best Model of Practices in Terms of Data Access and Sharing Amongst the Industry?

10.30 | Case Study: The Application of Machine Learning Approaches for Enabling Translational Medicine

Shanrong Zhao
Director, Computational Biology- Worldwide Research & Development
Pfizer

More than twenty years of experience in computational biology with strong interdisciplinary skills in computer science, biostatistics, computational biology and chemistry. A recognized pioneer in next generation sequencing, big data analysis, cloud computing and machine learning. Have led multiple cross-functional teams to successfully implement pipelines and informatics systems, including QuickRNASeq, QuickIsoSeq, QuickMIRSeq, Rainbow, Stormbow and ImmunoPortal, for large-scale data analysis to support drug discovery.

Day One

Wednesday, 25th July, 2018

14.00 | Discuss: Real World/Pragmatic Clinical Trials and the Use of AI to Design & Monitor Them:

09.30 | Case Study: Dimensional Reduction of RNA-Seq Data by Deep Learning and Its Application to Explore Cell Transition

Edward Bowen
Head of Data Science & Solutions
GSK

As the Head of Data Science & Solutions at GSK, Edward leads a team of data scientists and solution engineers in leveraging large-scale, distributed systems in both relational and Hadoop environments; building, testing and validating predictive/statistical and machine learning models, leveraging Python, R, SQL, SAS and other analytical tools. He is a R&D leader acquiring and developing the talent and skill of next generation Big Data capabilities to work within GSK’s Data Center of Excellence. As part of his leading role at GSK, he defines strategic priorities and works closely with developers and end users to enable capabilities and data availability in the R&D information platform. He has built a high-performing team of Data Scientists and Solution Engineers to deliver a wide range of solutions supporting advanced analytics needs, including novel applications of machine learning to drug discovery.

Day One

Wednesday, 25th July, 2018

14.00 | Discuss: Real World/Pragmatic Clinical Trials and the Use of AI to Design & Monitor Them:

Leonardo Rodrigues
Senior Director, AI & Machine Learning
Berg

Combining his academic and enterprise experience, Dr. Rodrigues holds more than 15 years of experience in data analysis and R&D. With a solid background in computer science, biology and systems engineering, Dr. Rodrigues is a reference in applying AI and advanced analytical methods to extract actionable insights from clinical and biological data. Dr. Rodrigues was the pioneer data-scientist at BERG, leading the design and development ETL and data-analysis solutions as well as the architectural lead responsible for implementing the current infrastructure used by BERG Analytics. Currently, as Senior Director of AI &Machine Learning, Dr. Rodrigues leads the research and analysis of disruptive projects, as well as the development and deployment of innovative analytics technologies and IT platforms. Dr. Rodrigues holds a B.Tech. in Data Processing, a B.S. in Molecular Sciences, a Ph.D. in Biochemistry and concluded his postdoctoral studies at the Whitehead Institute-MIT.

Day One

Wednesday, 25th July, 2018

14.45 | Case Study: Applying AI-Driven Approaches to Improve Clinical Development Efficiency

Day Two

Thursday, 26th July, 2018

14.45 | Discuss: The Application of AI & Machine Learning in Clinical Development Requires Large Amounts of Quality Data: How to Develop the Best Model of Practices in Terms of Data Access and Sharing Amongst the Industry?

Ronald Dorenbos
Associate Director, Materials & Innovation
Takeda

Ronald Dorenbos joined Takeda mid-2017 as Associate Director of the Materials & Innovation group. Prior to this he operated as management consultant at the Life Science division of PA Consulting Group leading strategy & commercial projects for some of the world’s top 10 pharmaceutical companies. With his company BioFrontline he provides management, strategy and commercial advice to life science companies around the world. Ronald has MAs in Biotechnology and Molecular Biology and after obtaining a PhD in Pharmaceutical Biology spent six years at Harvard studying Parkinson’s, Schizophrenia and the genetics of aggressive behaviour before making the transition to industry. Ronald is a keen follower of AI and deep learning applications in the healthcare sector and founded ‘AI – Artificial Intelligence’, a group on LinkedIn following the latest developments in the field of AI.

Day Two

Thursday, 26th July, 2018

14.00 | Discuss: How AI & Machine Learning Could Present an Opportunity to Lower the Failure Rates of Clinical Trials Compared to the Contemporary Approaches?

09.30 | Case Study: How to Maximise the Benefits of Integrating AI in Clinical Trials to Improve Recruitment Efficiency

Amy Zwanziger
Head, Digital Catalyst
Sanofi

Amy co-leads Sanofi’s efforts in digital clinical. Amy’s Digital Catalyst team in Boston particularly focuses on identifying new potential partners for reducing time and cost to market in our clinical trials, steering new partners through their first pilots with Sanofi, and assessing their results and potential to scale. Prior to joining Sanofi, Amy was a manager at McKinsey & Company.

Day One

Wednesday, 25th July, 2018

12.30 | Case Study: Real talk: 5 Ways to Succeed with Big Pharma

Christos Papadelis
Head, Laboratory of Children’s Brain Dynamics, Boston Children’s Hospital; Assistant Professor of Pediatrics
Harvard Medical School

Christos Papadelis is an Assistant Professor of Pediatrics in Harvard Medical School and Head of the Children’s Brain Dynamics laboratory in the Division of Newborn Medicine at Boston Children’s Hospital. Dr. Papadelis has more than ten years of experience in magnetoencephalography (MEG) and electroencephalography (EEG) technology with both adults and children. His research covers a broad range of studies on neuroscience, clinical neurophysiology, and biomedical engineering. Dr. Papadelis has a demonstrated record of productive research projects leading to >50 peer-reviewed articles and numerous articles in conference proceedings. He has funded projects from the National Institute of Child Health and Human Development, the American Epilepsy Society, the Faculty Development Office of Harvard Medical School, and the Boston Children's Hospital. He is Academic Editor in PLOS ONE, ad-hoc reviewer in >40 journals, as well as guest editor in special issues of his field. Figures of his work have been selected as covers in scientific journals.

Day Two

Thursday, 26th July, 2018

11.30 | Case Study: Innovative Biomarkers in the Presurgical Evaluation of Children with Epilepsy

Sehyo Yune
Director of Research Translation, Laboratory of Medical Imaging & Computation
Massachusetts General Hospital

Dr. Yune is the Research Translation Director of the Laboratory of Medical Imaging and Computation at Massachusetts General Hospital. She manages multiple projects on clinical decision supporting systems and hospital data analytics empowered by artificial intelligence (AI), facilitates translation of research outcomes into clinical tools, and develops AI platforms to be used in under-resourced settings. She is a board-certified physician in internal medicine with six years of experience in academic medical center. Her interest ranges from quality of care to business analytics, and she works at the intersection of digital technology, clinical practice, patient safety, population health, and business development.

Day Two

Thursday, 26th July, 2018

12.30 | Case Study: AI for Connecting Patients, Clinicians, and Pharmaceuticals in Clinical Trials

Sean Grullon
Machine Learning Data Scientist
GSK

Sean Grullon blends over ten years of experience in data analysis and machine learning in a particle physics research setting with hands-on experience in the health care and life science domains. He is a Machine Learning Data Scientist with GSK in the R&D Data Centre of Excellence. Prior to joining GSK, Sean was at IBM Watson Health, where he helped health care and life science organizations develop solutions with machine learning and cognitive computing technologies, including IBM Watson. Prior to working as a data scientist in the health care and life sciences domains, Sean was a particle physics researcher at the University of Pennsylvania and received his Ph.D from the University of Wisconsin – Madison.

Day One

Wednesday, 25th July, 2018

10.00 | Case Study: Using Machine Learning to Analyze Clinical Trials that Fail to Meet Primary Endpoints

Vladimir Morozov
Bioinformatics Solutions Architect
Shire Pharmaceuticals

Ph.D. in Chemical Enzymology from Moscow State University. 20 years of industrial experience in biomedical data analysis

Day Two

Thursday, 26th July, 2018

12.00 | Case Study: Application of Deep Neural Networks for Developing Digital Biomarkers from Speech and Accelerometer Data