Projects per year
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Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy -
SDG 10 Reduced Inequalities -
SDG 16 Peace, Justice and Strong Institutions
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The Fingerprint is created by mining the titles and abstracts of the person's research outputs and projects/funding awards to create an index of weighted terms from discipline-specific thesauri.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Computational Mechanisms for State-Driven Active Sensing
Lamperski, A. (PI)
JOHNS HOPKINS UNIVERSITY, NIH NATIONAL INSTITUTE OF MENTAL HEALTH
8/1/25 → 6/30/30
Project: Research project
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Provably Correct Reconstruction of Dynamical Networks from Finite, Non-Ideal Data Streams
Lamperski, A. (PI) & Salapaka, M. V. (PI)
THE NATIONAL SCIENCE FOUNDATION
8/1/24 → 7/31/27
Project: Research project
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Extension of Robust Control Techniques for UAS Adaptive Control Systems
Caverly, R. (PI) & Lamperski, A. (PI)
3/8/23 → 9/30/23
Project: Research project
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Mechanics-Based Algorithms for Sampling, Control, and Learning in Non-Convex Domains
Lamperski, A. (PI)
THE NATIONAL SCIENCE FOUNDATION
9/1/21 → 8/31/24
Project: Research project
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Provably Convergent Game-Theoretic Coordination for Space Vehicle Swarms
Lamperski, A. (PI)
ASTER LABS, INC, NATIONAL AERONAUTICS & SPACE ADMIN
8/19/19 → 9/18/20
Project: Research project
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Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
Zheng, Y. & Lamperski, A., 2026, In: Proceedings of Machine Learning Research. 313Research output: Contribution to journal › Conference article › peer-review
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An augmented preference-based Bayesian approach for optimizing neuromodulation stimulation parameters using meta learning
Farooqi, H., Zhao, Z., Darrow, D., Lamperski, A. & Netoff, T. I., Dec 1 2025, In: Journal of neural engineering. 22, 6, 066033.Research output: Contribution to journal › Article › peer-review
Open Access1 Link opens in a new tab Scopus citations -
Function Gradient Approximation with Random Shallow ReLU Networks with Control Applications
Lamperski, A. & Salapaka, S., 2025, 2025 American Control Conference, ACC 2025. Institute of Electrical and Electronics Engineers Inc., p. 3844-3849 6 p. (Proceedings of the American Control Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Non-Asymptotic Analysis of Classical Spectrum Estimators with L-mixing Time-series Data
Zheng, Y. & Lamperski, A., 2025, 2025 American Control Conference, ACC 2025. Institute of Electrical and Electronics Engineers Inc., p. 1896-1901 6 p. (Proceedings of the American Control Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Link opens in a new tab Scopus citations -
Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control
Lamperski, A. & Lekang, T., 2024, 2024 IEEE 63rd Conference on Decision and Control, CDC 2024. Institute of Electrical and Electronics Engineers Inc., p. 7840-7845 6 p. (Proceedings of the IEEE Conference on Decision and Control).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
2 Link opens in a new tab Scopus citations