Shameer Khader, Ph.D, Mph Email & Phone Number

Paris, France, Fr

Global Head - Cluster leader of multiple teams: Genetics and Genomics, Data Science and Artificial Intelligence, Data Engineering, Translational Bioinformatics and Computational Biology to accelerate drug / target discovery, development and repositioning. Leveraging big data, advanced analytics and machine learning for AI-driven drug discovery initiatives. Managing one of the largest human disease-centric data ecosystem in the pharmaceutical industry.

Cambridge, Cambridgeshire, Gb

Head of a global Data Science and Artificial Intelligence and Machine Learning Research team with a focus on AI-driven drug discovery, development, indication expansion, optimizing clinical trials, and accelerating discoveries. Contributions to various early and late drug/large molecule and vaccine development programs across key therapeutic areas including Oncology, Cardiovascular Renal and Metabolic Diseases, Vaccines and Immune Therapies and Neuroscience.

Lake Success, Ny, Us

• Managing three core work streams (health data science, bioinformatics, and precision medicine) to integrate bioinformatics, data science, and precision medicine capabilities for one of the largest health system in the United States (11B in revenue; 60K employees) • Reports to the Chief Research Informatics Officer on operations and growth strategy to build bioinformatics program and enhancing research informatics capabilities including high-performance computing infrastructure, budgeting, recruitment, organization of bioinformatics and data science core services and educational and training activities to democratize technological and computing assets for all researchers• Reports to the C-suite for delivery of health data science program: directly report to Chief Medical Informatics Officer and Associate Chief Medical Informatics Officer and work collaboratively with Heads of Quality and Data Strategy. Manage, several high impact projects that leverage big data, analytics and machine learning to help the organization to optimize healthcare delivery, reduce cost and improve outcome using data from 22 hospitals and 550 outpatient practicesProjects include: - Precise acuity prediction of health system-scale patient population - Integrating external data sources like PresGaney surveys with provider data to develop provider engagement score - Data-driven improvement of patient satisfaction- Machine learning approaches to predict, plan and prioritize discharge readiness - Machine intelligence approaches to understand the driver and passenger features implicated in advanced illness and in need of paliative support care.

Amsterdam, Noord-Holland, Nl

As the Director of Bioinformatics, Data Science and Precision Medicine, under the Division of Healthcare Transformation Services and Healthcare Integration I manage three core theme by integrating bioinformatics, data science, and precision medicine capabilities: - Genomic Medicine: Leading multiple Genomic Medicine projects leveraging risk prediction, patient stratification, pharmacogenomics and personalized therapies to develop products in the area of various clinical domains including cardiology. - Pharma Informatics: Delivering right drug at the right time to right patient need right informatics infrastructure. With the pharma-informatics projects, we aim to integrate biomedical and healthcare big data to improve the therapeutic stratification and building scalable informatics solutions around it. Further, developing real-world evidence (RWE) based population health analytics services to predict and prioritize pharma verticals. - Precision Medicine: By integrating big data, pan-omics systems medicine approaches and pharma-informatics, we are developing solutions that precision healthcare providers can use at the point of care in a seamless fashion. With a patient-centred approach, we leverage healthcare technology integration, big data analytics, predictive modeling and machine learning as the core competencies of our precision medicine solutions.

New York, Ny, Us

- Design, development, and deployment of health informatics applications - Analyses of biomedical and big data healthcare data to improve healthcare delivery, reduce readmission rates, lower hospital acquired conditions and infectious disease risk mitigation - Leveraging machine learning, deep learning, and predictive modeling to improve healthcare - Oversee data governance, data access, and big data policy development to improve access to biomedical and healthcare information in the hospital setting - Mentoring scientists and researchers from medicine and biomedical science in data science, machine learning, and quantitative healthcare analytics

New York, Ny, Us

Biomedical Data Scientist at Dudley Laboratory in the broad area of translational bioinformatics with emphasis on population-scale and personalized clinical, healthcare and wellness data analytics. Leading research projects in therapeutic stratification, drug repositioning, clinical genomics, biomedical informatics and precision medicine. I am also working on projects that enable better design and smarter implementations of electronic medical records that can bring multi-omics discoveries from bench to bed-side. My therapeutic domains of interest are in cardiovascular diseases, pharmacomicrobiome and multi-scale model based drug development.

New York, Ny, Us

I designed, developed and deployed predictive and prescriptive models and machine learning models using biomedical and healthcare data to improve healthcare delivery. I play a critical role at the interface of implementing healthcare information technology; clinical informatics and biomedical big data analytics to provide compelling insights to enable high-quality, data-driven, precision care for patients.Contribution as technical lead of multiple genomic medicine and genomics-aided drug discovery projects• Involved in data analytics for numerous collaborative projects with global pharma companies including Pfizer, Janssen, Eli Lilly, and Leo Pharma• Lead the Hospital Big Data Research Initiative to leverage big data and machine learning to improve patient care• Technical and strategic leadership including data management and analytics for health system-wide Patient Safety Initiative• Leveraged healthcare big data to reduce readmission, infection control, falls during hospitalization and risk assessment for sepsis• Employed modern Natural Language Processing and Text Mining solutions for deep phenotyping of patient records• Directly interacted and reported to the leadership of the health system and research departments• Lead multiple projects as the team leader of biomedical and healthcare big-data projects• Developed technical solutions, management strategies, maintenance approaches, andexecution workflow to leverage artificial intelligence, deep learning, and predictive analytics on Mount Sinai Data Warehouse with 3.5 million patients and 30 million patient records in various projects

Rochester, Minnesota, Us

Biomedical Informatics, Cardiology, Cardiovascular diseases, Computational Genomics, Next-Generation Sequence (NGS) analysis and Biomedical informatics• Designed, developed and deployed genetics-informed risk prediction algorithms for ascertaining cardiovascular risk• Lead analyst of biomedical and healthcare big data mining projects• Data governance, stewardship and development of clinical registries for multiple clinical trials• Developed and deployed pharmacogenomics tool to match patients with ideal drug dosagesand deployed assessment framework in EHR (100, 00 patients)• Development of clinical decision systems for shared decision making and genomic medicine

Rochester, Minnesota, Us

Computational Biology / Biomedical Informatics Research Fellow in the Division of Cardiovascular Disease, Mayo Clinic, Rochester, MN. My current position includes developing bioinformatics approaches for the analysis and interpretation of disease, pharmacogenomics and quantitative genetic variants from population genomics and next-generation genomics studies. I have developed multiple high-throughput phenotyping algorithms for large-scale genomics studies and knowledge-based, intelligent electronic medical record systems to rapidly prototype personal genomics and individualized medicine in clinical practice.

Bangalore, Karnataka, In

Senior Research Fellowship supported by Department of Biotechnology, India. Participated in a wide array of bioinformatics / computational biology research projects in Prof. R. Sowdhamini's Computational Biology Lab. I have applied data mining, data integration and machine learning approaches for the identification and analysis of domains in proteins.3D domain swapping is a protein structural mechanism underlying a class of diseases called as "deposition diseases" or "proteinopathies" or "conformational diseases". As a graduate student in Prof. R. Sowdhamini's lab, I developed a curated knowledge base of proteins involved in 3D domain swapping. These datasets were utilized to develop first-ever machine learning algorithms to predict 3D domain swapping from protein structure or sequence data. A meta-analysis of swapped proteins encoded in human proteome was performed to decipher functional repertoire, pathways and diseases associated with 3D swapping in the human proteome.

Bangalore, Karnataka, In

My scientific experience begins with the JRF position supported by a Wellcome Trust grant in Prof. R. Sowdhamini's Lab. During this period, I was part of the following projects: I developed an algorithm for protein structure validation from macromolecular data using propensity and substitution scores derived from homologs. This algorithm and the server implementation are published in Nucleic Acids Research. Developed a data-integration-based pipeline to analyze unassigned regions in protein sequences by utilizing homology, intermediate sequence search, HMM of protein domains, and various sequence features like secondary structure, disorder regions, etc. The method was employed to recognize unassigned areas of different target proteins with therapeutic value. A new algorithm STIF is developed to predict stress-responsive transcription factor binding sites in the upstream of genes expressed during abiotic stress in Arabidopsis thaliana. Gene expression data is explored to identify the candidate genes, and the algorithm is validated using statistical methods.

Bangalore, Karnataka, In

Visiting Student in Dr. B. Gopal's Lab, Department of Molecular Biophysics. During this collaboration I have developed a new integrated bioinformatics approach called "PeptideMine".

Master Of Public Health - Mph, Public Health

Accelerate Cambridge Entrepreneurship Weekend, Accelerate Cambridge Is A Start-Up “Accelerator” Established To Enable And Nurture Venture Creation

Machine Learning For Big Data And Text Processing At Mit Professional Education, Big Data, Natural Language Processing, Streaming Analytics, Machine Learning And Deep Learning

Health Information Technology And Management, Public Health, Big Data In Healthcare, Analytics, Project Management

Phd, Computational Biology

Certificate, Clinical Data Management

Msc, Bioinformatics

Bsc, Physics