Driven by a passion to translate complex data into actionable insights, I leverage my academic background in machine learning, statistics, and network analysis.

My expertise allows me to tackle challenging data-driven problems, from building robust machine learning models to uncovering hidden patterns in real-world datasets. I’m eager to continuously learning and make a real-world impact.

About

Since September 2024, I am a Data Scientist at Deus Ex Machina, subsidiary of PIPA AI, specializing in applications within the life sciences sector. My role focuses on leveraging advanced data analytics and machine learning methodologies to address critical challenges in healthcare and biotechnology, with the goal of driving innovation and delivering data-driven solutions.

During 2022-2023, I was a Postdoctoral Fellow in the Department of Applied Mathematics & Statistics (AMS) at the Whiting School of Engineering, Johns Hopkins University. My faculty sponsor was Professor Carey Priebe. My research areas of interest included Multiscale Statistical Network Inference, Multiple Graph Matching and Time Series of Networks.

During Summer 2022, I was a Joint E+D & MSR research intern at Microsoft. My supervisors were Dr. Anna Bertinger (E+D team) and Dr. Jonathan Larson (Microsoft Research team).

I received my PhD diploma from the Department of Mathematics & Statistics, University of Maryland, College Park on May 2022 under the supervision of Associate Professor Vince Lyzinski.

During Summer 2021, I was a research intern at the Computational and Information Sciences Directorate (CISD), DEVCOM Army Research Laboratory supervised by Dr. Jade Freeman.

Since 2019, I am interested in the statistical analysis of joint spectral embedding methods for multiple networks. From 2017 to 2019, I was a PhD student at UMASS and worked in graph matching problems with my PhD advisor Vince Lyzinski.

I received a B.Sc. in Applied Mathematics from the National & Kapodistrian University of Athens, Greece.

For more information, please check out my industry CV and/or my academic CV. Also, connect with me on LinkedIn.

Conferences / Talks

December 17-19, 2022 15th International Conference of the ERCIM WG on Computational and Methodological Statistics CMStatistics 2022. Virtual. [Slides]
November 28 - December 1, 2022 NeurIPS 2022: Thirty-sixth Conference on Neural Information Processing Systems NIPS2022. In-person. [Poster Session]
December 10, 2021 Talk at National & Kapodistrian University of Athens. In-person. [Announcement]
August 12, 2021 2021 Joint Statistical Meetings (JSM). Virtual. [Video presentation]
August 12, 2021 DEVCOM ARL Summer Student Symposium. Virtual. [Poster]
July 10, 2021 NETWORKS 2021: A Joint Sunbelt and NetSci Conference. Virtual.

Papers

For a full list of articles, please check my Google Scholar and/or ResearchGate.

“The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks” Pantazis, K., Athreya, A., Arroyo, J., Frost, W. N., Hill, E. S., and Lyzinski, V. Journal of Machine Learning Research (JMLR) 23(141):1−77, 2022. journal.
“Multiplex graph matching matched filters” Pantazis, K., Sussman, D.L., Park, Y. et al. _Applied Network Science 7, 29 (2022) journal.
- A shorter version of the paper appeared in GTA³ 3.0: The 3rd workshop on Graph Techniques for Adversarial Activity Analytics, 2019.
“Clustered Graph Matching for Label Recovery and Graph Classification” Li, Z., Arroyo, J., Pantazis, K., and Lyzinski, V. _IEEE Transactions on Network Science and Engineering (2023) journal.
“Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings” Pantazis, K., Trosset, M., Frost, W. N., Priebe, C. E., Lyzinski, V. arxiv (2024).

Reviewer

Journal of Machine Learning Research (JMLR)
Knowledge-based Systems (KBS)

Teaching

Course instructor

Johns Hopkins University

Introduction to Optimization (EN.553.361), Spring 2023
Probability and Statistics for the Biological Sciences and Engineering (EN.553.311), Fall 2022

Discussion Sections (TA instructor)

University of Maryland College Park

Introduction to Linear Algebra (MATH240), Spring 2022
Applied Probability and Statistics (STAT400), Spring 2020 & 2021
Linear Algebra for Scientists & Engineers (MATH461), Fall 2019

University of Massachusetts, Amherst

Ordinary Differential Equation (MATH331), Fall 2017

Internships

Summer 2022 Research Intern: Graph Matching (Microsoft) (12 weeks) [[LinkedIn announcement]](https://www.linkedin.com/jobs/view/research-intern-graph-matching-at-microsoft-3073489771/)

Aim attention at modeling security incidents via graphs and applying modern machine learning and graph theoretic methods toward building interactive systems.
Summer 2021 CCDC-ARL Summer Student Experience: Computational and Information Sciences Directorate, Adelphi MD (12 weeks)

Focused on developing a learning algorithm for collaboratively prioritizing and filtering information object in dynamic contextual environment.

Extracurricular

Volunteering

I am a probationary / associate member of the Hyattsville Volunteer Fire Department, Inc since October 2021.
I am a platelet donor at American Red Cross Consider Platelet Donation
I am an active member of Potomac Appalachian Trail Club since 2020. During Spring 2021, I was a crew member for trail maintenance at Sugarloaf Mountain, MD.

Well-being activities

If you are living in (or visiting) the DMV area and you are interested in spending your leisure time exploring nearby mountains or rivers via hiking, running, cycling, rock climbing or any other activity, let me know! Also, follow me on Strava. 😁