.. _about: About Matthew Martz ============================ My expertise is in building and leading teams of Data Scientists, Machine Learning Engineers, Data Engineers, and Data Product experts of diverse experience levels, distributed across cultural and knowledge bases, to drive innovation, product development, and cutting edge, actionable research. I have a proven track record of mentoring, growing, executing, and building strategy around ambitious goals across the Data Science technical platform to provide business value. My current research focuses on modernizing predictive pipelines and modeling capabilities using knowledge graphs, agentic AI, and analytical AI, with a strong emphasis on clinical implementation. I specialize in multimodal models, ranging from naive builds to fine-tuning foundation models and GraphRAG, aiming to provide clinicians with predictive assessment data, therapeutic planning strategies, and risk identification to improve patient outcomes and reduce fatigue. I am also developing an algorithm and model platform to identify and quantify cognitive complexity in patient interactions and clinicians' daily workloads.My collective experience in advanced AI, knowledge graph development, and multimodal modeling is strategically directed towards pioneering the creation of comprehensive patient digital twins for transformative impact in both clinical practice and research.My core research endeavors are dedicated to revolutionizing predictive healthcare through the strategic integration of cutting-edge AI methodologies. Specifically, I focus on modernizing predictive pipelines and enhancing modeling capabilities by leveraging the synergistic power of knowledge graphs, agentic AI, and analytical AI. A paramount aspect of this work is its strong emphasis on clinical implementation, ensuring that theoretical advancements translate into tangible improvements in patient care. My specialization lies in the domain of multimodal models. This encompasses a broad spectrum of approaches, ranging from the development of foundational, "naive" builds to the sophisticated fine-tuning of pre-trained foundation models. A particular area of expertise is GraphRAG, which combines the strengths of knowledge graphs with retrieval-augmented generation to enhance the accuracy and interpretability of predictive insights. The overarching goal of these efforts is to equip clinicians with robust predictive assessment data, actionable therapeutic planning strategies, and early risk identification capabilities. By doing so, we aim to significantly improve patient outcomes, optimize care pathways, and reduce the burden of clinician fatigue, thereby fostering a more efficient and effective healthcare system. Beyond predictive modeling, I am also actively engaged in the development of an innovative algorithm and model platform designed to objectively identify and quantify cognitive complexity within patient interactions and the daily workloads of clinicians. This initiative seeks to provide a granular understanding of the demands placed on healthcare providers, paving the way for optimized workflows and improved clinician well-being. Collectively, my extensive experience in advanced AI, sophisticated knowledge graph development, and nuanced multimodal modeling is strategically directed towards a pioneering vision: the creation of comprehensive patient digital twins. These digital twins are envisioned as dynamic, continuously evolving representations of individual patients, amalgamating diverse data streams to provide a holistic and real-time understanding of their health status. The ultimate aim of this transformative endeavor is to profoundly impact both clinical practice and medical research, ushering in an era of personalized, predictive, and proactive healthcare. My background is in Quantitative Cell Biology and I have a PhD in Biochemistry and Molecular Biology. I spent much of my career researching novel cancer therapeutics through live cell imaging and machine learning. You can now find me developing and implementing machine learning platforms and algorithms for everything from optimization problems to personalized medicine and medicinal AI. I have researched and built novel recommenders, 24/7 production machine learning solutions, and received patents for personalization solutions in the consumer sales space and chemical profile recommenders. Most recently, I have been working in the Biotechnology space where I have developed the long-term strategy and vision for Data Science, led a growing team of Data Scientists to develop novel algorithms in the predictive modeling and Bioinformatics spaces, and accelerated biological discovery. Moreover, I designed and implemented novel algorithms to detect differential modes of action in both natural products and whole microbe systems, and engineered a proprietary online learning platform to serve as an artificial intelligence decision guidance system for our extensive microbial collection and proprietary product discovery platform (Genesis), a digital twin. I rebuilt global research and enablement programs across domains for knowledge generation and product creation and delivery using cutting edge deep learning techniques on unstructured records, genomics, omics, and image data as the global lead for digital science discovery and delivery programs. Established, built, and implemented data governance and provenance at both the discovery and enterprise levels. Built multimodal knowledge graphs using LLM guidance to develop GraphRAG technologies in-house for production use in the global pipeline. Find more about me here: :ref:`about` .. panels:: :column: col-lg-12 p-0 I am now pursuing my passion work in improving patient outcomes utilizing cutting edge, clinical AI. The company is in early inception phases, but we are already building out the next generation of clinical AI technology. I would love to discuss with you the exciting work we are doing, so please reach out! .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Currently ^^^^^^^^^^^^^^ Mayo Clinic (Rochester, MN) - Multiple roles spanning teams Additional research focus is on modernizing predictive pipelines and modeling capabilities through the utilization of knowledge graphs, agentic agents, analytical AI, focusing on clinical implementation. I work primarily with Multimodal models and solution from naive builds to fine tuning foundation models and GraphRAG. My goal is to provide clinicians with predictive assesment data, therapeutic planning strategies, and risk identification and scoring to lower clinician fatigue and improve patient outcomes. As part of this work, I am currently developing an algorithm and model platform to identify cognitive complexity for each patient touchpoint and the summation to the clinician's day load. Please reach out to chat about my research, or any of the work below. Owner/Creator and Team Lead - Knowledge Factory AI Platform - Rochester, MN As leader of a highly skilled team, I'm developing a cutting-edge, robust data platform. Engineered for multi-modal data, it integrates seamlessly with diverse databases and data lakes, facilitating real-time ingestion into a sophisticated knowledge graph. Our core mission is to transform complex, disparate data into actionable insights, driving advancements across various domains. We have achieved significant breakthroughs in developing proprietary algorithms and sophisticated workflows that adeptly manage multi-modal data, extracting value from both structured and unstructured sources. A cornerstone of our work lies in the creation, structuring, and iterative refinement of knowledge graphs, which are directly informed and enhanced by the outputs of our advanced AI-driven data processing. Our focus on optimizing data access and retrieval is crucial for both intricate agentic AI workflows, where autonomous systems require immediate access to information, and for advanced graph machine learning applications, including the development of highly accurate predictive models, sophisticated forecasting capabilities, and clinical digital twins in research and practice. Our team has successfully engineered and deployed intelligent agents and powerful toolchains to streamline data retrieval for complex question-answering flows. We've also developed methodologies for neighborhood and shortest path summarization, invaluable for comprehensive documentation and establishing a single source of truth (SSOT). Currently, we're deeply immersed in developing a robust execution graph for advanced agentic capabilities. This groundbreaking system will revolutionize operations and decision-making, from optimizing patient care to accelerating scientific research. It will enable our AI agents to perform complex, multi-step tasks, interact intelligently with various systems, and drive significant improvements in effectiveness and outcomes across critical Mayo Clinic domains. Technical Lead and Lead Solution Architect (Transformation Hub) - Rochester, MN Leading technical development of the Transformation Hub products and core infrastructure assets and partnering with product to realize needs through solutioning and execution Collaborating tightly with product management to create a cohesive execution strategy and ways of work Serving as the technical liaison between the Transformation Hub and other Mayo teams and initiatives Designed and executing the overarching Transformation Hub technical and product architecture that focuses on future state technologies and capabilities to bring the highest value to CDH and the Mayo Clinic through innovation acceleration, knowledge generation, analytics engines, artificial intelligence, data and model assets, and modular for efficiency and scalability Developing a Knowledge Hub utilizing data scraping and aggregation to build an advanced knowledge graph using an LLM and exposing these data through APIs, chat, document generation, and product integration through GraphRAG approaches - LLM-created multimodal knowledge graph supporting GraphRAG, Analytical Agents, and direct queries for institutional knowledge understanding, resource planning, and enablement opportunities Developing an Intake and Triage web application that will utilize the Knowledge Hub and CDH Capabilities models through artificial intelligence to guide innovators and CDH customers/stakeholders to the quickest realization of CDH service - this system aims to further provide CDH with internal insights around gaps, opportunities, staffing, and the ability to build capacity balancing into the general Intake and Triage engine Senior Analytics Architect (Solution Enablement) - Rochester, MN Serving as a team leader for a group of analytics architects to provide solutioning to Center for Digital Health (CDH) and Enterprise needs Developing an artificial intelligence system to provide self service analytic, dashboard, and insights capabilities from Mayo Clinic data encompassing practice information to capabilities writ large More about my work to come. Reach out to me through email or LinkedIn to chat. .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Previously ^^^^^^^^^^^^^^ GLOBAL TRAITS DIGITAL SCIENCE LEAD, GLOBAL TRAITS DISCOVERY AND DELIVERY PROGRAM LEAD BENCHLING RESEARCH PIPELINE TECHNICAL LEAD Led Trait (gene, phenotype) Discovery and Delivery efforts within RD and IT Digital Science across the entire Syngenta global space Created, built, and led Generative AI program to automate and optimize the delivery process for gene delivery processes to target specific traits (phenotypes) or gene expression objectives Created and led the program to use multimodal modeling with structured and instructed data, and foundation LLMs for knowledge generation, product discovery and delivery pipeline performance guidance, regulatory audits, and prompt-based experimental design Owned stack from data model, application landscape, and AI research initiatives from gene discovery to trait (phenotype) introgression including building of API integrations, MLOps, DevOPs workflows Team comprised business (Analysts, Architects, Delivery Managers, Scrum Masters), IT (Developers, QA, MLOps, DevOPs), and science (SMEs, Product Owners, Wet Lab Researchers) domains - both domestic and distributed teams Mentored junior team members with a focus on professional development and upskilling opportunities Led Data Science efforts to research and deploy foundational Large Language Models (LLMs) for protein design to predict expression levels of various molecular biology constructs as a software workbench for bench researchers - work emphasized multimodal data and contextualization/fine tuning of foundation models Built strategy and vision across discovery and delivery to streamline scientific application portfolio, create single source of truth data streams, and initiated foundational work to bring cutting edge ML/AI technologies to the scientific pipeline Communicated strategy and work initiatives to secure funding and oversee resourcing of execution Collaborated with stakeholders across research and business domains to ensure cooperative acceleration and growth .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Previously ^^^^^^^^^^^^^^ DIRECTOR OF DATA SCIENCE/ML/AI AND TECHNICAL LEAD/STRATEGIC VISION Served as Director of Data Science/Machine Learning/Artificial Intelligence strategy and innovation initiatives. Led a growing team of Data Scientists working to drive biological research and accelerate product development. Team size ranged from 7 to over 20, across disciplines of Data and Product. Developed and drove 4 year strategy and vision for Data Science, Data Engineering, Bioinformatics, and Data Product for entire organization. Advocate and conduct learning around Data Science/ML/AI products and platforms both to internal teams, executive team and board members, and external partners and collaborators (customers). Directly contributed to high impact publications and invited for speaking engagements. Served as Data Science liaison across the company, communicating strategy, accomplishments, initiatives, and best practices. Developed the long-term Data Science strategy and vision, hiring plan, technological innovation pipeline, and overall project management for the department. Developed a proprietary predictive artificial intelligence decision guidance system to serve as a core to our proprietary natural and whole microbe product discovery platform - GENESIS, a digital twin technology. This digital twin technology has already generated a large corpus of actionable research, product leads, and accelerated screening paradigms to put the best leads in the field. Excitingly, my team was able to harness GENESIS to generate cross-indication predictive models to drive and accelerate lead identification across the research platform. Led Data Science contribution to external manuscripts and internal white papers for both research, Data Science, and SOPs. Sat on a team of technical leaders that drove research initiatives across the company and served as a hive mind to solve challenges across domains. Identified and addressed gaps in research and Data Science. Developed a novel algorithm to use interpretable machine learning model ensembles to traverse genomic annotations and drive mode of action discovery from small datasets. Designed and developed a predictive modeling platform to accelerate product discovery in indication screening. Designed and developed a cross-indication prediction platform to enable multi-target identification. Two publications and multiple patentable IP technologies developed within first year. .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Previously ^^^^^^^^^^^^^^ DIRECTOR OF DATA SCIENCE/ML/AI AND PRINCIPAL, RESEARCH AND MACHINE LEARNING Served as director of Data Science/Machine Learning/Artificial Intelligence strategy and innovation initiatives. Created, built, deployed, then led the genomic AI program for disease prediction, novel mode of action identification, biomarker discovery, and understanding population variation from DNA to signaling pathways/omics. Served as technical lead for the development and utilization of mixed effects models to explain the impact of environmental factors on phenotypic outcomes in the background of genomic models. Led a growing team of Data Scientists working to drive biological research and accelerate product development. Team size ranged from 7 to over 20, across disciplines of Data and Product. Developed and drove 4 year strategy and vision for Data Science, Data Engineering, Bioinformatics, and Data Product for entire organization. Onboarded artificial intelligence methodologies like Generative AI and Large Language Models for genomic information and protein variant creation. Owned stack from data model, application landscape, and AI research initiatives across all of ML/AI and Data Science including building of API integrations, MLOps, DevOPs workflows Advocate and conduct learning around Data Science/ML/AI products and platforms both to internal teams, executive team and board members, and external partners and collaborators (customers). Directly contributed to high impact publications and invited for speaking engagements. Served as Data Science liaison across the company, communicating strategy, accomplishments, initiatives, and best practices. Developed the long-term Data Science strategy and vision, hiring plan, technological innovation pipeline, and overall project management for the department. Developed a proprietary predictive artificial intelligence decision guidance system to serve as a core to our proprietary natural and whole microbe product discovery platform - GENESIS, a digital twin technology in part combining genomics and geospatial data modeling. This digital twin technology has already generated a large corpus of actionable research, product leads, and accelerated screening paradigms to put the best leads in the field. Excitingly, my team was able to harness GENESIS to generate cross-indication predictive models to drive and accelerate lead identification across the research platform. Led Data Science contribution to external manuscripts and internal white papers for both research, Data Science, and SOPs. Sat on a team of technical leaders that drove research initiatives across the company and served as a hive mind to solve challenges across domains. Identified and addressed gaps in research and Data Science. Developed a novel algorithm to use interpretable machine learning model ensembles to traverse genomic annotations and drive mode of action discovery from small datasets. Designed and developed a predictive modeling platform to accelerate product discovery in indication screening. Designed and developed a cross-indication prediction platform to enable multi-target identification. Two publications and multiple patentable IP technologies developed within first year. .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Previously ^^^^^^^^^^^^^^ POSTDOCTORAL RESEARCH FELLOW (Biological Machine Learning and AI) - University of North Carolina, Lineberger Cancer Center American Heart Association funded research fellow in AI-driven precision medicine Identifying, modeling, and understanding noise in single cell signaling during stress responses focusing on utilization of live cell imaging and machine learning on big data Built and ran Data Science and Data Engineering capabilities in the areas of computer vision, natural language processing, artificial intelligence, infrastructure, high performance computing, predictive modeling, and mathematical modeling Built and maintained live cell imaging infrastructure, developed a suite of machine learning algorithms to understand noise in single cell signaling, wrote and secured two fellowships, directly contributed to high impact publications and invited for speaking engagements, and mentored several graduate students and postdoctoral fellows. Extensive experience working with time series data from signal data from engineering of pipelines and early data processing to complex machine learning algorithm development and implementation for decision-making and novel hypothesis generation .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Expertise ^^^^^^^^^^^^^^ Predictive Modeling | Medicinal AI | Machine Learning | Strategy and Vision Building | Resource Allocation Logistics | Algorithm Development | Technical Writing | Leadership | Mentorship | Team Building | Cross-departmental Collaboration | Data Science | Artificial Intelligence | Generative AI | Python/Software Engineering | Cell Biology | Biophysics | Microscopy | Cancer Therapeutics | E-commerce | Manuscript and Grant Preparation | Patent Development | Bioinformatics | Microbiology | Crop Protection | AgTech | Digital Twinning | Biotechnology | Precision Medicine | Multimodal Modeling | Generative AI | Fine-Tuning .. panels:: :column: col-lg-12 p-0 :header: text-secondary font-weight-bold Experience ^^^^^^^^^^^^^^ GLOBAL TRAITS DIGITAL SCIENCE LEAD, GLOBAL TRAITS DISCOVERY AND DELIVERY PROGRAM LEAD - Syngenta 8+ years of Data Science and Machine Learning Engineering Leadership and Strategy Development at the Director or VP level 11+ years solutioning ML/AI architecture 5 patents awarded or in final status for the development and application of novel applications of ML/AI 25+ Member teams across disciplines in data and product 21 years of production Software Engineering experience 17 years of production Data Science/ML/AI experience and leadership 17 years of rigorous scientific training in academic programs - healthcare and precision medicine Industry proven leader in Data Science and Machine Learning Innovation across the AI landscape Mutaku is a collection of writings across the spectrum of biomedical research, software engineering (Python), Machine Learning, and Data Science.