UMBC Professor Lara Martin is one of the organizers of the Wordplay: When Language Meets Games workshop, which will be held at the annual Association of Computational Linguistics conference in Bangkok, Thailand, on August 16, 2024.

The workshop will focus on exploring the utility of interactive narratives, think everything from classic text-adventures like Zork to modern Twine games, to fill a role as the learning environments of choice for language-based tasks including but not limited to storytelling. Previous iterations of this workshop took place successfully, with hundreds of attendees at NeurIPS 2018, NeurIPS 2020, and NAACL 2022. Since then, the community of people working in this area has rapidly increased. 

This workshop aims to be a centralized place where all researchers involved across many fields can interact and learn from each other. Furthermore, it will act as a showcase to the broader NLP/RL/Game communities on interactive narrative’s place as a learning environment. The program will feature a collection of invited talks in addition to contributed talks and posters from each of these sections of the interactive narrative community and the wider natural language processing and reinforcement learning communities.

The UMBC Cyber Defense Lab presents

Tensor decomposition is a powerful unsupervised machine learning method used to extract hidden patterns from large datasets. This presentation aims to illuminate the extensive applications and capabilities of tensors within the realm of cybersecurity. We offer a comprehensive overview by encapsulating a diverse array of capabilities, showcasing the cutting-edge employment of tensors in the detection of network and power grid anomalies, identification of SPAM e-mails, mitigation of credit card fraud, and detection of malware. Additionally, we delve into the utility of tensors for classifying malware families, pinpointing novel forms of malware, analyzing user behavior, and utilizing tensors for data privacy through federated learning techniques.

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. in the UMBC 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, knowledge graphs, 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. Maksim is currently the lead for the Cyber Science Research Program (CSRP), a cybersecurity research internship at LANL.

 

Support for this event was provided in part by the National Science Foundation under SFS grant DGE-1753681.

 

 

The theme of of the 2024 KIL workshop is on developing and using metrics, methods, and datasets for consistent, reliable, explainable and safe LLMs.

Professor Gaur was also an organizer of the three previous KIL workshops, held at KDD in 2023, the knowledge Graph conference in 2021, and at KDD 2020.