Curriculum vitae
Jeremy F. Jenrette, PhD
Data scientist and machine-learning engineer for non-invasive marine monitoring.
Bio
Jeremy Jenrette is a marine and data scientist who develops artificial intelligence, molecular, and statistical tools to monitor marine fauna with non-invasive, cost-efficient methods. His work integrates computer vision, environmental DNA, remote sensing, and spatial modeling to estimate species abundance and distribution, especially for data-deficient and endangered taxa.
At NOAA's Northeast Fisheries Science Center (through Saltwater Inc.), he leads the migration of the center's computer vision infrastructure to Google Cloud and trains object-detection models on Habitat Camera (HabCam) survey imagery to detect, track, and measure sea scallops, crabs, roundfish, and elasmobranchs. He also developed bias-correction methods that turn imperfect model predictions into reliable abundance estimates for stock assessments.
Jeremy earned his PhD in Fish and Wildlife Conservation from Virginia Tech in 2025, where he built the Shark Detector and helped build sharkPulse, a global platform that mines social media and citizen science for shark observations. He has led eDNA expeditions for Mediterranean white sharks with The SeaKeepers Society, studied giant deep-sea isopods in Japan with Beneath The Waves, and collaborated with National Geographic, Ocean Exploration Trust, and the University of Oxford on AI for shark conservation.
Experience
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Jan 2026 – Present
Contract Data Scientist & ML Engineer
Saltwater Inc. · NOAA NMFS Northeast Fisheries Science Center · Woods Hole, MA
- Architected and led the migration of NEFSC marine computer vision and data science infrastructure to Google Cloud; lead administrator for Compute Engine, BigQuery and Vertex AI workflows under federal IT governance.
- Design, train and operationalize detection, tracking and measurement models on Habitat Camera (HabCam) survey media for sea scallops, roundfish, crabs, elasmobranchs and plankton.
- Developed statistical bias-correction for automated misclassification and integrated corrected predictions into spatial GAMs to produce abundance estimates for stock assessments.
- Co-mentored a NOAA Hollings Scholar on deep-learning detection of Jonah crab in survey imagery (May – Aug 2026).
- Operate HabCam systems, maintain at-sea servers and deploy edge-AI workflows on offshore research cruises.
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May 2026 – Present
Visiting Research Scientist
Broad Institute of MIT and Harvard · Karlsson Lab · Cambridge, MA / remote
- Engineer pipelines for genomic sequencing data and integrative bioinformatic AI frameworks with the Vertebrate Genomics Group; consult on multi-species comparative genomics.
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Aug 2024 – Jan 2026
Research Scientist
Beneath The Waves · Remote · Bahamas · Japan
- Built object-detection and tracking pipelines to count sharks in aerial drone video.
- Structured large open-biodiversity datasets to resolve distribution and taxonomy of deep-sea fauna.
- Ran de novo whole-genome and RNA-seq workflows on giant deep-sea isopods.
- Led field expeditions in Japan and the Bahamas deploying underwater video/AI tools and tagging sharks and ocean sunfish.
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May 2024 – Dec 2025
Scientific Leader
The SeaKeepers Society · Remote · Mediterranean Sea
- Directed eDNA collection, bioinformatic processing and spatial analysis to detect endangered sharks across the Mediterranean.
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Aug 2020 – Dec 2025
Graduate Research & Teaching Assistant
Virginia Tech · Dept. of Fish and Wildlife Conservation · Blacksburg, VA
- Built R and Python pipelines for large-scale marine biodiversity and eDNA datasets.
- Administered Linux servers hosting PostgreSQL/MySQL databases, full-stack apps and analytical hubs (sharkPulse).
- Applied GLMs, point-process and Bayesian models and neural networks (NLP and image classifiers) to marine biodiversity data.
- Taught Advanced Fisheries Management (FIW 5714G): stock assessment, mark-recapture and management strategy evaluation.
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May 2022 – Sep 2022
Computer Engineer
National Geographic · University of Oxford · Ocean Exploration Trust · Remote
- Developed image-recognition software and automated video pipelines for elasmobranch species classification.
Education
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2020 – 2025
Ph.D., Fish and Wildlife Conservation
Virginia Tech
Machine learning, statistical ecology, eDNA and marine biodiversity analytics
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2014 – 2018
B.S., Biochemistry
Virginia Tech
Undergraduate research on the Aedes albopictus reference genome
Skills
- Languages
- PythonRSQLBashMATLABC++JavaScriptLaTeX
- ML & computer vision
- PyTorchTensorFlowYOLOOpenCVVIAMEObject detection & trackingEdge AI
- Cloud & data
- Google Cloud (Vertex AI, BigQuery, Compute Engine)DockerCI/CDPostgreSQLMySQLLinux administration
- Statistics & spatial
- Spatial GAMsGLMsBayesian modelsPoint-process modelsArcGISGoogle Earth Engine
- Molecular
- eDNA assays & bioinformaticsGenome assembly & annotationRNA-seqPopulation genetics
- Field
- FAA Part 107 drone pilotPADI Open WaterSmall-boat operatorHabCam operationsAnimal tagging
Mentorship & teaching
- 2026NOAA Hollings Scholar co-mentorDeep-learning detection of Jonah crab in HabCam imagery
- 2023 –International genetics workshop instructorShark eDNA, population genetics and data science for University of Gabès master's students, Sousse, Tunisia
- 2025 –Master's co-supervisorDeep-sea video and eDNA workflows in Suruga Bay, Japan (University of Amsterdam)
- 2020 –Graduate & undergraduate mentor10+ Virginia Tech students in computer science, biology and fisheries on ML and data science
Selected talks
- Aug 2026 Scaling automated optical survey analytics for stock assessments NMFS Optical Strategic Initiative Workshop, Newport, OR
- May 2026 Leveraging social networks and open data for supplementing population assessments of sharks Sharks International, Colombo, Sri Lanka
- Oct 2025 SharkByte: a tool for accelerating shark video surveys with AI MTS TechSurge, Narragansett, RI (virtual)
- Feb 2024 Citizen science and cost efficiency for monitoring shark populations Frugal Engineers Group, Virginia Tech
- Nov 2023 Detecting white sharks in the Mediterranean with eDNA and particle-distribution hindcasting White Shark Global, Port Lincoln, Australia
- Oct 2022 Artificial intelligence for shark conservation! E/V Nautilus with Ocean Exploration Trust & National Geographic (virtual)
- Oct 2022 Shark detection and classification with machine learning Sharks International, Valencia, Spain (virtual)
- Jul 2021 Data mining Instagram as a tool for tracking global shark populations Fisheries Society of the British Isles Symposium (virtual)
Funding & awards
- 2024 – 2026The SeaKeepers Society research funding$9,500
- 2023Robert D. Ross Graduate Scholarship, Virginia Chapter AFS$750
- 2022Burd Sheldon McGinnes Fellowship$2,500
- 2020Best Poster, 1st place, AFS poster reveal (Virginia Tech)
- 2018Certificate of Achievement for Excellence in Undergraduate Research, Virginia Tech
Peer reviewer
Environmental Science & Technology · Fish Biology · Frontiers in Marine Science · Methods in Ecology and Evolution · Ecological Informatics