Maksim E. Eren

Maksim E. Eren

Graduate Research Assistant

Los Alamos National Laboratory

Biography

Maksim Eren is a graduate Computer Science student at the University of Maryland Baltimore County (UMBC) and a Scholarship for Service CyberCorps alumnus. He graduated Summa Cum Laude with a Computer Science Bachelor’s at UMBC in 2020 and Master’s in 2022. He has held multiple internships, which have ranged from incident response and software engineering at Montgomery County Government (MCGov), a research assistantship at CyberPacks, to a teaching and research assistantship at UMBC’s DREAM Lab. At MCGov, Maksim investigated and responded to hundreds of cyber incidents, and has developed several dashboards and incident analysis tools for the security operations center. Currently, he is working as a graduate research assistant at Los Alamos National Laboratory (LANL).

His interdisciplinary research interests lie at the intersection of machine learning and cybersecurity, with a concentration in tensor decomposition. His tensor decomposition-based research projects include large-scale malware detection and characterization, cyber anomaly detection, data privacy, and topic modeling and evolution of scientific articles. Maksim has developed and published state-of-the-art solutions in anomaly detection and malware characterization. He has also worked on various other machine learning research projects such as detecting malicious hidden code, adversarial analysis of malware classifiers, and federated learning. At LANL, Maksim was a member of the 2021 R&D 100 winning project SmartTensors, where he has released a fast tensor decomposition and anomaly detection software, and contributed to the design of various other tensor decomposition libraries.

Interests

  • Machine Learning
  • Tensor Decompositions
  • Cybersecurity

Education

  • PhD in Computer Science, Present

    University of Maryland, Baltimore County (UMBC)

  • MS in Computer Science, 2022

    University of Maryland, Baltimore County (UMBC)

  • BS in Computer Science, 2020

    University of Maryland, Baltimore County (UMBC)

  • AA in Computer Science, 2018

    Montgomery College (MC)

Recent Publications

(2022). Distributed Out-of-Memory SVD on CPU/GPU Architectures. arXiv preprint. Accepted at IEEE HPEC Conference 2022 with Outstanding Paper Award.

Preprint URL Preprint PDF Details

(2022). SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection. In ACM Symposium on Document Engineering 2022 (DocEng ’22), 2022.

Preprint PDF Details

(2022). Can Feature Engineering Help Quantum Machine Learning for Malware Detection?. Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022.

Poster Details

(2022). Malware Antivirus Scan Pattern Mining via Tensor Decomposition. Presented at the 13th Annual Malware Technical Exchange Meeting, Online, 2022.

Abstract Poster Details

(2022). FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation. arXiv preprint.

Preprint URL Preprint PDF Details

(2022). Distributed Out-of-Memory NMF of Dense and Sparse Data on CPU/GPU Architectures with Automatic Model Selection for Exascale Data. arXiv preprint.

Preprint URL Preprint PDF Details

(2022). General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization. In ACM Digital Threats Research and Practice (DTRAP) Journal, 2022.

DOI Preprint PDF Code Details

(2021). COVID-19 Multidimensional Kaggle Literature Organization. In ACM Symposium on Document Engineering 2021 (DocEng ’21), 2021.

DOI Preprint URL Preprint PDF Interactive Plot Details

(2021). Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery. Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021.

Preprint URL Preprint PDF Abstract Poster Code Details

(2021). Random Forest of Tensors (RFoT). Presented at the 12th Annual Malware Technical Exchange Meeting, Online, 2021.

Abstract Poster Details

Software

*
pyCP_ALS

pyCP_ALS

pyCP_ALS is the Python implementation of CP-ALS algorithm that was originally introduced in the MATLAB Tensor Toolbox.

RFoT

RFoT

Random Forest of Tensors (RFoT) is a novel ensemble semi-supervised classification algorithm based on tensor decomposition. We show the capabilities of RFoT when classifying Windows Portable Executable (PE) malware and benign-ware.

lanl/pyDNTNK

lanl/pyDNTNK

pyDNTNK is a software package for applying non-negative Hierarchical Tensor decompositions such as Tensor train and Hierarchical Tucker decompositons in a distributed fashion to large datasets. It is built on top of pyDNMFk.

lanl/pyQBTNs

lanl/pyQBTNs

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers.

lanl/pyCP_APR

lanl/pyCP_APR

pyCP_APR is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining SmartTensors project. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs.

lanl/pyDNMFk

lanl/pyDNMFk

pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence).

lanl/pyDRESCALk

lanl/pyDRESCALk

pyDRESCALk is a software package for applying non-negative RESCAL decomposition in a distributed fashion to large datasets. It can be utilized for decomposing relational datasets.