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)

News

Not too big - Machine learning tames huge datasets

Using the Summit supercomputer, Los Alamos algorithm breaks the exabyte barrier
Last updated on Sep 27, 2023
Not too big - Machine learning tames huge datasets

Our paper that sets a new world record

A new world record by simultaneously classifying an unprecedented number of malware families under extreme class imbalance, surpassing prior work by a factor of 29
Last updated on Sep 27, 2023
Our paper that sets a new world record

R&D 100 winner of the day - SmartTensors AI Platform

The SmartTensors AI Platform, developed at Los Alamos National Laboratory, is a scalable, unsupervised machine-learning software suite capable of identifying, extracting essential hidden features, and efficiently compressing information in massive datasets.
Last updated on Sep 28, 2023
R&D 100 winner of the day - SmartTensors AI Platform

Computer scientists build new tool to fight coronavirus

There are over 57,000 research papers that could help fight COVID-19. A text mining tool is helping scientists navigate the data.
Last updated on Sep 28, 2023
Computer scientists build new tool to fight coronavirus

Recent Publications

Quickly discover relevant content by filtering publications.
Alexander B. Most, Maksim E. Eren, Boian S. Alexandrov, Nigel Lawrence (2023). Electrical Grid Anomaly Detection via Tensor Decomposition. In IEEE Military Communications Conference, Articial Intelligence for Cyber Workshop (MILCOM), 2023.

Preprint PDF Details

Nick Solovyev, Ryan Barron, Manish Bhattarai, Maksim E. Eren, Kim O. Rasmussen, Boian S. Alexandrov (2023). Interactive Distillation of Large Single-Topic Corpora of Scientific Papers. In IEEE Conference on Machine Learning and Applications (ICMLA 2023), 2023.

Preprint PDF Details

Ismael Boureima, Manish Bhattarai, Maksim E. Eren, Erik Skau, Philip Romero, Stephan Eidenbenz, Boian S. Alexandrov (2023). Distributed out-of-memory NMF on CPU/GPU architectures. In The Journal of Supercomputing, 2023.

DOI Details

Maksim E. Eren, Manish Bhattarai, Robert J. Joyce, Edward Raff, Charles Nicholas, Boian S. Alexandrov (2023). Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection. In ACM Transactions on Privacy and Security (TOPS) journal, 2023.

Preprint PDF DOI Details

Maksim E. Eren, Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas (2023). MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware. In IEEE International Conference on Intelligence and Security Informatics (ISI), 2023.

Preprint PDF Details

Maksim E. Eren, Kim O. Rasmussen, Charles Nicholas, Boian S. Alexandrov (2023). Malware-DNA: Machine Learning for Malware Analysis that Treats Malware as Mutations in the Software Genome. Presented at the Malware Technical Exchange Meeting (MTEM), Lawrence Livermore National Laboratory, California. July 25-27, 2023.

Poster Details

Maksim E. Eren, Kim O. Rasmussen, Charles Nicholas, Boian S. Alexandrov (2023). Malware-DNA: Machine Learning for Malware Analysis that Treats Malwares as Mutations in the Genome of the Software. Presented at the Conference on Data Analysis 2023 (CoDA 23), Santa Fe, New Mexico. March 7-9, 2023.

Poster Details

Maksim E. Eren, Nicholas Solovyev, Ryan Barron, Manish Bhattarai, Ismael D. Boureima, Kim O. Rasmussen, Boian S. Alexandrov (2023). Sub-topic and Semantic Sub-structure Extraction via SPLIT: Joint Nonnegative Matrix Factorization (NMF) with Automatic Model Selection. Presented at the Conference on Data Analysis 2023 (CoDA 23’), Santa Fe, New Mexico. March 7-9, 2023.

Poster Details

Maksim E. Eren, Manish Bhattarai, Nick Solovyev, Luke E. Richards, Roberto Yus, Charles Nicholas, Boian S. Alexandrov (2022). One-Shot Federated Group Collaborative Filtering. In IEEE Conference on Machine Learning and Applications (ICMLA 2022), 2022. Awarded Best M.S. Research at 2023 UMBC CSEE Research Day..

Preprint PDF DOI Details

Ismael Boureima, Manish Bhattarai, Maksim E. Eren, Nick Solovyev, Hristo Djidjev, Boian Alexandrov (2022). Distributed Out-of-Memory SVD on CPU/GPU Architectures. arXiv preprint. Accepted at IEEE HPEC Conference 2022 with Outstanding Paper Award.

DOI Preprint URL Preprint PDF Details

See all publications

Recent Posts

Maksim E. Eren
Last updated on Feb 1, 2023 4 min read

Becoming the Ultimate Teacher

This is a story of a professor, a teacher, the best one. Here I shout out to the techniques of one of the best professors, Dr. Ben Johnson of UMBC, whom I have had the privilege to take classes from. We need more of Dr. Johnsons in academia! So, why not make a tutorial on becoming the ultimate teacher, becoming Dr. Ben Johnson?

Maksim E. Eren
Last updated on Dec 20, 2021 5 min read

Anaconda and Jupyter Setup for Research

Using Anaconda and Jupyter for research has become a daily routine for me. Here I list some of the most frequent commands I utilize when setting up conda environments with Jupyter for my research proejcts.

Maksim E. Eren
Last updated on Dec 29, 2021 8 min read

Python Documentation with Sphinx

Documenting your code is an important part of any project from developing a library to research code. In this blog post, I will give a brief tutorial on how to utilize Sphinx for documenting Python code. Sphinx is a tool utilized by several popular libraries. It turns the in-code comments into a user-friendly and modern documentation website.

Maksim E. Eren
Last updated on Dec 20, 2021 3 min read

screen + Jupyter - A way to execute long running Jupyter notebooks headless mode

Jupyter notebooks is a great way to work on research code. But notebooks used to analyse large datasets take long time to execute. And if our local or remote terminal session dies, our notebook dies too, resulting in waste of time. Here I summarize how we can run a notebook in headless mode in a screen session which allows us to run Jupyter notebook indipendent from the terminal session.

Software

*
All Tensor Decomposition Matrix Decomposition Cybersecurity Distributed Computation
lanl/T-ELF

lanl/T-ELF

Tensor Extraction of Latent Features (T-ELF) is one of the machine learning software packages developed as part of the R&D 100 winning SmartTensors AI project at Los Alamos National Laboratory (LANL). T-ELF presents an array of customizable software solutions crafted for analysis of datasets.

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.

Recent Talks

Anomalous Event Detection using Non-Negative Poisson Tensor Factorization

Network intrusion detection systems that are based on statistical User Behaviour Analytics play a fundamental role in the identification of anomalous agents such as malicious insiders, misused accounts, and users with compromised credentials. To this extent, there have been significant results in detecting anomalies from learned user behavior models via non-negative Poisson matrix factorization. We expand upon previous work in this project by exploiting the higher dimensional and sparse problems created by the user authentication data. An integrated multidimensional anomaly scoring method based on tensors and Poisson recommender systems is proposed. In our experiments, we build a higher-order model that can detect the accounts compromised by red-team during penetration testing activities at a large organization.
Aug 10, 2020 9:00 AM — 1:00 PM Virtual Event
Maksim E. Eren, Juston S. Moore, Boian S. Alexandrov
Presentation
Anomalous Event Detection using Non-Negative Poisson Tensor Factorization

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