- Build and Lead a High-Performing and Innovating Clinical Trial Systems & Advanced Analytics Team by Fostering a Culture of Empowered Collaboration, Open Communication, Continuous Learning, Challenging the Status Quo and Leading by Example.- Lead the Implementation of a Clinical Trial Data Repository & Advanced Analytics Systems and Processes, Streamlining Data Collection and Analysis for Improved Clinical Trial Efficiency.- Lead Real-Time Clinical Trial Dashboard to Improve Patient Enrollment, Recruitment, Operational Efficiency & Cost Saving and to Enhance Patient Data Analysis, Safety Monitoring, and Decision Making.- Champion Innovation, Optimization and Efficiency by Leading Open-Source Programming, Data Science, and Generative AI Projects

- Problem Solving with Machine Learning- Estimating Probability Distribution- Learning with Linear Classifiers- Decision Trees and Model Selection- Debugging and Improving Machine Learning Models- Learning with Kernel Machines- Deep Learning and Neural Networks

- Introduction of AI- Knowledge-Based AI Technologies- Machine Learning and Data-Based Approaches to AI- Strategic Implementation of AI Systems- Societal Impacts of AI- Future of AI

- Introduction of LLM, Gen AI and ChatGPT- ChatGPT Use Cases - Prompt it, not Google it- Prompt Engineering- Gen AI tools (e.g., ChatGPT, Copilot, Gemini)- Gen AI API in SAS, Python, R- LangChain, RAG- Gen AI (e.g., ChatGPT) Risk and Concerns- Gen AI (e.g., ChatGPT) Implementation Roadmap- Future of Gen AI/ChatGPT

• Introduce Python programming and Jupyter notebook• Teach Python basic data types – String, Number, List, Dictionary, Array and Date Frame.• Teach how to read and write files (txt, csv, xls, SAS, image)• Teach how to wrangle and manipulate data – sorting, merging, filtering and transposing. • Teach feature engineering and visualization • Teach Machine Learning programming in DNN, CNN and RNN using Numpy, Pandas, Keras, Tensorflow, sklearn

- Introduce Oncology Studies and its types - Solid Tumor, Lymphoma, Leukemia, Immunotherapy- Teach concepts and guidelines of Response Criteria (RECIST, irRC, Cheson, IWCLL)- Teach how to collect, measure and determine tumor responses based on Response Criteria. - Teach Ocology-specific CDISC Standards (SDTM, ADaM, CT) - Teach Oncology-specific Analysis (ORR, OS, PFS, Kaplan Meier Curve)- Demonstrate End to End Standards-driven automated Oncology Studies

- Lead Clinical Development Innovation using Cloud Computing, Data Science, Advanced Analytics, Data Warehouse, AI, LLM & Gen AI.- Lead the implementation of a real-time Clinical Operation and Trial data review platform for a 20% reduction in review & analysis time.- Develop and validate a compliant data review and analytics system, ensuring adherence to Regulatory guidelines(ICH, GxP, CFR, GDPR).- Lead an exploratory analysis to reduce sample size using Gen AI.- Develop and implement AI, LLM & Gen AI strategies - Risk Assessment, Use Case exploration, Policy development, education, PoC & ROI justification.- Collaborate with RWD team for patient recruitment, study design and control arm.- Lead OpenAI ChatGPT and MS Copilot implementation for enhanced data analysis and content generation to increase 15% productivity increase. - Lead department infrastructure projects (SOP development, Data Migration, Sample Size Calculation Reduction, System Integration and Validation).

- Deliver Machine Learning (ML) / Data Science products/services. - Transform SAS Programming department (+150 SAS programmers) to Data Science team by integrating Cloud computing environment (AWS Redshift, S3, EC2) and Opensource analytics system (R Studio / Jupyter).- Provide actionable business intelligence with Tableau, R, Python and SQL Open-source programming using RWE data (e.g., Claim, LAAD, Payer, EHR) - Influence Senior Leadership on business opportunities with RWE. - Provide RWE thought leadership by designing analysis design, developing methodology, and leading the technical expertise in Advanced Analytics. - Lead Big Data & Data Science Strategy, Process and Governance for organizational initiatives. - Build and lead Data Science team of data scientists, data engineers and machine learning engineers to build cutting-edge ML and DS technologies.- Develop and lead MLOPs – business requirements, data preparation, ML model training, validation, deployment, & continuous learning. - Mange and monitor AWS environments - S3, EC2 Instances, EBS, EFS, EMR, ECS, RedShift

Teach Machine Learning Course at Pre-conference Tutorial

- Worked with clients to support the drug development life cycles, including CDISC implementation, MDR implementation, SAS programming, statistical analysis, and electronic submission to FDA.- Helped clients on their biometrics department infrastructure, outsourcing strategies, data analysis, regulatory support and training. - Analyzed information (e.g., current standards implementation, drug development process and business workflows) and performed risk assessment and gap analysis for regulatory compliance. - Provide end to end biometric support from Protocol, SAP, EDC, SDTM, ADaM, TFL, SDRG, ADRG, Define.xml, CSR to Submission- Consult with clients to develop and implement innovative technologies such as data-driven process and Machine Learning implementation. - Supported clients on machine learning implementation - data pipelines, feature engineering, model building/training (regression, classification, DNN, CNN, RNN, NLP, Transfer Learning) and deployment. - Located and developed new business by coordinating business requirements, developing and negotiating contracts, and closing contracts. - Responded to RFI and RFP and presented business proposals and solutions to clients.

- Provided a high level of technical expertise (e.g., NoSQL database) and enterprise architecture integrations.- Conducted detailed customer requirement gathering, discovery and analysis.- Worked closely with customers in the architectural design and implementation or integration of data-driven projects.- Proposed and designed technical solutions which included creative prototypes and proof of concept. - Built and gave customer-specific demonstrations leveraging NoSQL enterprise database system. - Acted as an interface between Sales organization and technical staff for production management. - Collaborated effectively with both the Business and Technical team to provide clients with possible solutions to their issues and problems. - Accompanied sales teams on prospect/customer visits.- Responded to functional and technical elements of RFI and RFP.- Represented the products and company to customers at filed events (e.g., conferences).- Conveyed customer requirements and feedbacks to Product Management teams. - Provided technical consulting, strategic post-sales or systems-integration consulting, technical business development and simple programming in XQuery, SPARQL and SOAP/REST API.- Loaded structured, unstructured data (XML and JSON), triples and ontologies (RDF and OWL) on NoSQL database to show how data could be easily integrated and searchable. - Provided technical experiences on search engines, Hadoop integration, NoSQL databases, AWS integration, ETL tools, geospatial systems, semantic technology, knowledge-management systems and content-management systems.

Worked with clients to support the system change, process change and software implementation. Interviewed or facilitated focused group discussion with stakeholders, managers, and other employees for data, information and documents gathering.Checked and analyzed information such as current standards implementation, metadata management, governance, business flows, and other relevant data.Identified issues, formed hypotheses and solutions and presented findings and recommendations to clientsPerformed risk assessment and gap analysis of client’s current state and future, desirable but obtainable state of solutions. Provided solutions and strategies to clients using Software as a Service(SaaS), mainly Metadata Repository(MDR). Helped clients to process the successful software development life cycle – Plan, Analysis(e.g., URS and FRS), Design (e.g., Design Specification), Development, Validation(e.g., UT, IT, and UAT) and Implementation. Helped to build the conceptual data model, logical data model and physical data model for SaaS. Helped software development team to develop the client’s specific customized SaaS.Developed the new technologies(e.g., system integration over web services, semantic technology, metadata-driven business process) and new strategies(e.g., Standards Capability Maturity model) to help the company exposure in industry.Built the integration between SaaS and analytic system (i.e., SAS) using web service technology. Developed the client’s specific metadata repository to help data-driven clinical data development life cycle. Supported business development through the RFP and sales processKept abreast of industry trends in data management, data analysis, data warehousing, and data(e.g., CDISC) standards. Consulted clients on CDISC Strategic Roadmap. Consulted clients on Standard Governance Design (i.e., organization, process and technology).Supported Semantic Technology(e.g., RDF, OWL, RDFS, SPARQL)

Built and led the collaborative partnership with the clients. Led successful CDISC projects fully utilizing offshore programmers/statisticians. Prepared NDA/IND Electronic submission to FDA in CDISC format. Built a successful team and led the team to successful projects.

Led the clinical trial programmer teams in the CDISC project

Managed SAS Department by managing projects, timelines and human resources

Lead the SAS programming side of the clinical trial by providing the SAS programming support to Data Manager, Oracle Clinical Programmer, Statistician, Medical Writer and Quality Assurance.

Provided SAS programming support to clinical trial data managment.

Prepared the Financial reports using SAS, SQL and Oracle Database.