About
I am motivated by challenging problems, particularly those that remain unsolved. My career has focused on identifying and addressing these complex issues.
As a research scientist with a strong software engineering background, I take ideas from concept to prototype and deploy production infrastructure relied upon by other teams and autonomous vessels. At NSWC Crane, I lead and mentor a team of eight or more software engineers and research scientists. I also serve as the computer vision subject matter expert for Navy program offices, Technical Warrant Holders, and SBIR initiatives.
I am completing a Ph.D. in Computer Science at Indiana University, focusing on deep neural networks for denoising and developing verifiable models rather than black-box solutions. My published work covers computer vision, representation learning, scalable clustering, and statistical relational AI.
Experience
Research scientist and computer vision lead
- Lead and mentor a team of eight or more software engineers and research scientists building ML Ops and evaluation infrastructure for EO/IR computer vision.
- Architected the full ML Ops pipeline for EO/IR algorithm evaluation: sensor processing, storage and compute, an algorithm SDK, test harness and metadata standards, and a searchable catalog of video, metadata, and historical algorithm performance.
- Fielded the pipeline on autonomous surface vessels for real-time EO/IR perception, integrated with the Unmanned Maritime Autonomy Architecture, and into modeling and simulation software for synthetic testing. Now integrating it into the Navy's Rough Casper electronic-warfare framework for EO/IR awareness.
- Drove adoption across NAVSEA, NAVAIR, and NAVWAR, and contributed to a Navy-wide test harness interoperability working group on test and evaluation standards for AI and autonomous systems.
- Led automated video target labeling research, reaching 95 percent or more fully autonomous labels.
- Researched how image and video compression affects object detection and built hardware-accelerated, high-fidelity compression. Custom lossless encoders cut a raw sensor archive from 2 petabytes to under 800 terabytes.
- Demonstrated a TRL-4 prototype that converts natural-language prompts to segmentation masks with real-time gimbal tracking.
- Draft research portfolios, roadmaps, and technology surveys for program offices, including the Portfolio Acquisition Executive for Robotic and Autonomous Systems, then execute the research. Secured competitive funding for a three-year computer vision roadmap.
- Monitor SBIR execution and author standards and white papers on perception metadata and EO/IR evaluation.
- Advised the University of Notre Dame team that won Overall Grand Champion at the 2025 AI Maritime Maneuver Indiana Collegiate Challenge.
Ph.D. candidate and research assistant
Doctoral research on deep neural networks for denoising single-cell RNA-seq data, and on building verifiable models rather than black-box ones. Thesis proposal completed. Defense scheduled for November 2026.
Adjunct professor, computer science
Developed and taught the undergraduate Introduction to Python course. Mentored five or more students a semester on academic pathways, research opportunities, and careers.
Adjunct professor, introductory programming
Taught introductory programming, building students' foundational coding and problem-solving skills.
Education
Skills
- Computer vision
- Object detection, object tracking, segmentation, vision-language models, EO/IR sensors, video compression, synthetic imagery
- ML infrastructure
- ML Ops pipelines, evaluation harnesses, automated labeling, metadata standards, edge deployment with TensorRT and ONNX, continuous integration
- Autonomy
- Unmanned surface vessels, Unmanned Maritime Autonomy Architecture, modeling and simulation, autonomy test and evaluation, counter-UAS evaluation
- Languages and tools
- Python, C++, C, Java, CUDA, PyTorch, TensorFlow, OpenCV, Docker, Kubernetes, Linux
- Leadership
- Technical leadership, mentoring, science and technology roadmaps, proposal writing, SBIR oversight
Publications
- P. Sharma, M. Malec, et al. "Geometric-k-means: A Bound-Free Approach to Fast and Eco-Friendly k-means." Machine Learning (Springer), 2026.
- M. Malec. "An Interpretable Latent-Supervised Autoencoder for Single-Cell Representation Learning." Bioinformatics, 2026.
- M. Malec et al. "Blurred Lines: Training Object Detection Algorithms with Degraded Synthetic Datasets." MSS Parallel Proceedings, 2026.
- M. Malec et al. "Evaluating Effect of Image/Video Compression on Imagery Utility." MSS Parallel Proceedings, 2025.
- S. Koutsares, M. Malec, et al. "Modeling and Visualization for Emission Signatures (MoVES)." MSS Parallel Proceedings, 2025.
- S. Koutsares, M. Malec, et al. "Synthetic and Hybrid Imagery Products for Standardization (SHIPS)." MSS Parallel Proceedings, 2024.
- M. Malec, H. Kurban, M. Dalkilic. "ccImpute: An Accurate and Scalable Consensus Clustering Based Algorithm to Impute Dropout Events in Single-Cell RNA-seq Data." BMC Bioinformatics, 2022.
- M. Malec et al. "Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks." ILP, 2016.
Honors
- Best Student Paper, International Conference on Inductive Logic Programming, 2017
- Mentor of the University of Notre Dame team, Overall Grand Champion, AI Maritime Maneuver Indiana Collegiate Challenge, 2025
- NSWC Crane: two Time Off Awards, two On the Spot Cash Awards, two Demo Awards
- Outstanding Computer Science Student Award, Gettysburg College, 2013