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L. Lindemann

13 papers hereh-index 596 citations17 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author8
  • last author5

Across the 13 of 13 papers where every author was matched, so the position is known.

fields
  • eess.SY7
  • cs.RO2
  • cs.AI1
  • cs.LG1
  • math.OC1
  • stat.ML1
same name
  • L. Lindemann — 9 papers, h 19
  • L. Lindemann — 6 papers
  • L. Lindemann — 4 papers, h 1
  • L. Lindemann — 1 paper, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.AI2025

Conformal Predictive Monitoring for Multi-Modal Scenarios

Francesca Cairoli, Luca Bortolussi, Jyotirmoy V. Deshmukh +2

We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic proper…

eess.SY2025

Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints

Junyang Cai, Weimin Huang, Brendan Long +4

Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specificati…

cs.RO2025

PCA-DDReach: Efficient Statistical Reachability Analysis of Stochastic Dynamical Systems via Principal Component Analysis

Navid Hashemi, Lars Lindemann, Jyotirmoy Deshmukh

This study presents a scalable data-driven algorithm designed to efficiently address the challenging problem of reachability analysis. Analysis of cyber-physical systems (CPS) reli…

eess.SY2025

Distributionally Robust Predictive Runtime Verification under Spatio-Temporal Logic Specifications

Yiqi Zhao, Emily Zhu, Bardh Hoxha +3

Cyber-physical systems (CPS) designed in simulators, often consisting of multiple interacting agents (e.g. in multi-agent formations), behave differently in the real-world. We want…

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