Chris Hammerschmidt

Skilled machine learning and data scientist with strong research background and extensive experience in designing and implementing complex ML systems with leadership experience

Research Expertise

machine learning
cyber security
data science
generative models
software engineering

About

Chris Hammerschmidt is a highly skilled computer scientist with a specialization in machine learning. He earned his Ph.D. in Computer Science in 2017 and his Diploma in Computer Science with a minor in Math in 2013. His research focus has been on developing innovative machine learning algorithms on cyber data at the intersection of cyber security and software engineering, and consequently applying them to various industries such as cybersecurity, finance, and the DevOps space.

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Publications

Preprint repository arXiv achieves milestone million uploads
Physics Today
2014
arXiv
100 Years of Math Milestones
2019
BotGM: Unsupervised graph mining to detect botnets in traffic flows
2017 1st Cyber Security in Networking Conference (CSNet)
2017
flexfringe: A Passive Automaton Learning Package
2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)
2017
Learning behavioral fingerprints from Netflows using Timed Automata
2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)
2017
Behavioral clustering of non-stationary IP flow record data
2016 12th International Conference on Network and Service Management (CNSM)
2016
Federated Learning For Cyber Security: SOC Collaboration For Malicious URL Detection
2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
2020
Efficient Learning of Communication Profiles from IP Flow Records
2016 IEEE 41st Conference on Local Computer Networks (LCN)
2016
ArXiv preprint server plans multimillion-dollar overhaul
Nature
2016
Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families
Malware Analysis Using Artificial Intelligence and Deep Learning
2020
The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search
Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security
2020
Reliable Machine Learning for Networking: Key Issues and Approaches
2017 IEEE 42nd Conference on Local Computer Networks (LCN)
2017
Dctgain: Dual Conditional Tabular Generative Adversarial Imputation Network for Missing Data
Unknown Venue
2024
Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models
2021 IEEE Sensors
2021
Auto Semi-supervised Outlier Detection for Malicious Authentication Events
Machine Learning and Knowledge Discovery in Databases
2020
About the social role of child and adolescent psychiatrists in times of epidemic
IACAPAP ArXiv
2020
Improving Real-Time Bidding Using a Constrained Markov Decision Process
Advanced Data Mining and Applications
2017
Life is good in Luxembourg
OECD Economic Surveys: Luxembourg 2017
2017

Education

Ph.D., Computer Science / Machine Learning / October, 2017

Luxembourg

Diploma, Computer Science / Math / June, 2013

Erlangen

Links & Social Media

Research Web Site

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