Writings and Perambulations¶

New Post! Summer of AI - An AgBiome Perspective (interview)

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 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 precision medicine and medicinal AI. In recent years 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.

Currently

DIGITAL BIOLOGY LEAD (traits)

[Details to come]

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.

Previously

HEAD OF DATA SCIENCE/ML/AI AND PRINCIPAL RESEARCH AND MACHINE LEARNING

Created and maintained the core, patented algorithms behind the Firstleaf wine club.

I built and led a local and distributed team of Data Scientists and Machine Learning engineers (The Research and Machine Learning Team) developing machine learning and AI platforms to drive real time recommendations, inform business strategy, and create/integrate with product design life cycle. This included the end-to-end development of the ML stack using both traditional ML and cutting edge deep learning techniques including computer vision, generative AI, and NLP, along with novel algorithm development.

Team size ranged from 5 to 10, across disciplines of Data and Product.

I was further responsible for the continual growth of the team, maintaining stakeholder communication, driving Data Science strategy across the organization, and directly working with C-level executives to maintain a vision and communicate strategy and execution plans aligned with business executives.

The Research and Machine Learning team was responsible for identifying and developing key Data Science and Machine Learning technologies for Firstleaf. We utilized cutting edge approaches to both empower internal company function as well as customer facing products. The team was also responsible for design and implementation of the patented (developed technologies, co-write and secured patents) algorithms that drove the Firstleaf experience. The team was responsible for initiating, driving, and executing on data science strategies across Marketing, Finance, Business Intelligence, and Wine Making functions – the Research and Machine Learning team was a full spectrum B2B and B2C solution within Firstleaf.

Expertise

Experience

Digital Biology Lead at Syngenta
Head of Data Science/ML/AI and Technical Lead / Strategic Vision at AgBiome
Head of Data Science and Principal, Research and Machine Learning at Penrose Hill/Firstleaf
BS in Biochemistry and Molecular Biology, The Pennsylvania State University
PhD in Biochemistry and Molecular Biology, Thomas Jefferson University
Research Postdoctoral Fellow, Lineberger Cancer Center, University of North Carolina at Chapel Hill
20+ Member teams across disciplines in data and product
21 years of Software Engineering and Data Science experience
17 years of rigorous scientific training in academic programs
8+ years of Data Science and Machine Learning leadership
Industry proven leader in Data Science and Machine Learning Innovation

Patents

Systems and methods for labeling and distributing products having multiple versions with recipient version correlation on a per user basis
Systems and methods for controlling production and distribution of consumable items based on their chemical profiles
[Under review (title withheld)]: A patent application for recommender personalization under certain location-based constraints
[Under review (title withheld)]: A patent application for the utilization of chemical data for certain environmental predictive capabilities

Selected Press

Languages, Tooling, Infrastructure

Selected Publications

Nicholas C. Dove, Laura K. Potter, Matthew K. Martz*, Douglas Lawton*. *These authors contributed equally to the work. Ground Truthed Models to Inform Tangible Guids of Global Microbial Diversity Using Deep Neural Network Computer Vision. In Preparation.
Yong Jun Goh*, Brody J. DeYoung, Nicholas C. Dove, Brant R. Johnson, Matthew K. Martz, Patrick Videau. AgBiome: Harnessing the Microbial World for Human Benefit. Trends in Biotechnology. In press.
Ramona Schrage, …, Matthew Martz, …, Evi Kostenis. The experimental power of FR900359 to study Gq-regulated biological processes. Nature Communications 6, Article number: 10156. 14 December 2015.
Michelle C Helms, Elda Grabocka, Matthew K Martz, Christopher C Fischer, Nobuchika Suzuki, Philip B Wedegaertner. Mitotic-dependent phosphorylation of leukemia-associated RhoGEF (LARG) by Cdk1. Cellular Signalling, Volume 28, Issue 1, January 2016, Pages 43-52.
Martz MK, Grabocka E, Beeharry N, Yen TJ, Wedegaertner PW. Leukemia-Associated RhoGEF (LARG) is a Novel RhoGEF in Cytokinesis and Required for the Proper Completion of Abscission. Mol. Biol. Cell September 15, 2013 vol. 24 no. 18 2785-2794.
Matthew Martz and Philip Wedegaertner: Faculty of 1000 Biology, 23 Jul 2010 F1000Prime.com/4242964#eval4039063
Carkaci-Salli N, Flanagan JM, Martz MK, Salli U, Walther DJ, Bader M, Vrana KE. Functional domains of human tryptophan hydroxylase 2 (hTPH2). J Biol Chem. 2006 Sep 22;281(38):28105-12. Epub 2006 Jul 24.

Mutaku is a collection of writings across the spectrum of biomedical research, software engineering (Python), Machine Learning, and Data Science.

Here is a list of most recent posts:

09 November - Summer of AI - An AgBiome Perspective

This is an interview that served as the starting point for a podcast wherein we discussed Artificial Intelligence with an AgBiome perspective. The podcast went beyond what is below and looked at the broader societal perspective; I will link the podcast shortly.
17 April - Notes on MLOps - One

This is a short piece I wrote while at Firstleaf as a response to a really great article on the state of MLOps. I used several strong points in the article to articulate my thoughts on where we did things well and directions I would like to see us take.
13 November - Python Generators and Comprehension

Digging into generators and comprehension - from basics to to implementation in a comprehensive tutorial. This is a walkthrough for beginners that will build up to real world examples.
13 November - Dictionary Lookup - Exploring the Depths

Exploring methods of performant Python dictionary lookups

About Matthew Martz