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researcher

S. Dey

48 papers hereh-index 396k citations283 works total

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

author position
  • sole author1
  • middle author23
  • last author23

Across the 47 of 48 papers where every author was matched, so the position is known.

fields
  • eess.SY17
  • cs.IT14
  • math.OC10
  • cs.LG3
  • eess.SP2
  • stat.AP1
same name
  • S. Dey — 72 papers, h 11
  • S. Dey — 26 papers
  • S. Dey — 23 papers, h 61
  • S. Dey — 20 papers, h 8
  • S. Dey — 15 papers, h 9
  • S. Dey — 14 papers, h 17

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
20082026
most citedOptimal Energy Allocation for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgments and Energy Harvesting Constraints

82 citations · 166 across the 43 of their papers we have counts for

collaborators
Showing 2023 · math.OCShow all

3 papers · 2 filters

math.OC2023

Output Feedback Reinforcement Learning with Parameter Optimisation for Temperature Control in a Material Extrusion Additive Manufacturing system

Eleni Zavrakli, Andrew Parnell, Subhrakanti Dey

With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant…

math.OC2023

Data-driven Linear Quadratic Tracking based Temperature Control of a Big Area Additive Manufacturing System

Eleni Zavrakli, Andrew Parnell, Andrew Dickson +1

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation,…

math.OC2023

Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus

Alessio Maritan, Ganesh Sharma, Luca Schenato +1

This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimiza…

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