Maksim E. Eren

Maksim E. Eren

Scientist

Los Alamos National Laboratory

Biography

Maksim E. Eren is an early career scientist in A-4, Los Alamos National Laboratory (LANL) Advance Research in Cyber Systems division. He graduated Summa Cum Laude with a Computer Science Bachelor’s at University of Maryland Baltimore County (UMBC) in 2020 and Master’s in 2022. He is currently pursuing his Ph.D. at UMBC’s DREAM Lab, and he is a Scholarship for Service CyberCorps alumnus. 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, text mining, and high performance computing. 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, contributed to the design and development of various other tensor decomposition libraries, and developed state-of-the-art text mining tools.

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). One-Shot Federated Group Collaborative Filtering. In IEEE Conference on Machine Learning and Applications (ICMLA 2022), 2022.

Preprint PDF Details

(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

Software

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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.