Anirudh Suri

Experience

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  ALPHA BIO CELL

  Mar 2019 - Sept 2020

  Core committee member

  Led operation & general management. Head of project incubation & development, & led external enterprise collaborations.

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  Farcast Biosciences

  Jul 2021 - Jul 2022

  - Developed and executed an experimental workflow to extract and study the biological components (Immune Cells) for further investigation of their quantitative and qualitative properties in the Tumor Micro- Environment at a patient-specific level. - Analyzed large biological datasets providing insights into tumor prognosis. - Multi-omics data analysis and integration to generate complete immune profiles at a patient-specific level to investigate responses to therapeutics and predict the optimum treatment, enhancing personalized medicine. - Reported scientific and technological market trends as inputs to stakeholders for road-map planning. Show less

  • Business Development Associate

    Jan 2022 - Jul 2022

  • Associate Researcher

    Jul 2021 - Jan 2022
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  Emory University

  Aug 2022 - Dec 2023

  Graduate Research Assistant

  Intervention and early detection for clinically ill sepsis patients using CNN to forecast Multiple Organ Dysfunction Syndrome. Clinical informatics researcher working on novel ML models and applications to enrich prognosis in the domains of critical care and neurology using continuous physiologic data streams, such as EKG, along with clinical + hospital patient data.- Machine Learning Engineering for MODS Prediction & Critical Care Support Systems: Designed and implemented a Graphical Convolution Neural Network (CNN) model to predict Multiple Organ Dysfunction Syndrome (MODS) in clinically ill Sepsis patients utilizing a Graph-Based substructure pattern mining (gSpan) algorithm. Model was trained on vast clinical data sourced from 3 leading hospitals.- Clinical Informatics Data Engineering and Analytics: Conducted and contributed to extensive data engineering and processing tasks using tools such as the MIMIC-III & IV Clinical Databases, Google Cloud Platform (GCP), SQL, and Python. This comprehensive approach facilitated deep insights and the construction of accurate predictive models for clinically ill patients.- Clinical Informatics Data Science: Clinical Phenotype analysis on health informatics data to establish metrics such as SOFA scores enabling predictive insights and health outcomes.- High-Dimensional Gene Expression Data Analysis : Engineered and optimized a high-dimensional data analysis pipeline to analyze gene expression data effectively. Leveraged DeSeq2, Bioconductor to identify dysregulated genes from RNASeq data of clinically ill patients. Show less

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  Rewire AI

  Jun 2023 - Oct 2023

  Bioinformatics & Machine Learning Engineer Intern

  - Machine Learning Engineering: MLE for predicting ototoxicity of chemical compounds in collaboration with WSU.- Optimum Drug Prediction: Led a high-profile Contract Research Organisation (CRO) project along with CXOs to build an internal tool that utilizes patient-specific RNA-Seq data to enable precise prediction and dosage optimization of drug/ therapeutics based on dysregulated genes. Recognised for exceptional client interactions, stakeholder engagement and project management.

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  EmTheraPro

  Feb 2024 - now

  Senior Data Scientist- Informatics

  ∙ Building 0 to 1 Data Products. Leveraged statistical software, methods, and techniques to gather, analyze, and interpret research and biological data. Accelerating clinical trials through clinical biomarker analysis and engaging with scientists, clinicians, and executives.∙ Data Wrangling: Streamlined data processing and cleaning workflows/ automations, ensuring high-quality, standardized data ready for analysis, significantly reducing preprocessing time and errors in downstream analyses.∙ Quantitative BioStatistics: Implemented biostatistical techniques such as TAMPOR Normalization, Median Polish, Variance Regression, effectively minimizing inter and intra batch effects and enhancing the reliability of clinical research outcomes. ∙ Longitudinal Data Analysis: Conducted comprehensive longitudinal data analyses to uncover critical insights into treatment conditions and time series, directly supporting improved clinical outcome evaluations and efficacy assessments.∙ Machine Learning: Developed robust ML models (e.g., XGBoost, LightGBM, CatBoost, RF, SVM) to analyze omics and real-world clinical data, successfully predicting patient prognosis and enhancing biomarker discovery, thereby informing targeted therapeutic interventions & health outcomes∙ Explainable Models Analysis: Led the application of SHAPLEY value-based explainable model analysis to elucidate biomarker feature contributions, paving the way for transparent and interpretable clinical decision-making tools. Show less