Research

My research group works on networked decision systems, where teams of agents collaborate to perform complex tasks. Within this broad area I focus on optimal strategy design and fundamental performance limits under extreme constraints in communication, power, and rationality. I develop optimal design principles and algorithms for intelligent decision-making agents applications as diverse as microbiology, autonomous vehicles, robotics, the internet-of-things, and social networks. 

My long term goal is to unveil fundamental limits and design principles for distributed intelligent systems, that cooperate, learn and adapt in constrained environments.

My research incorporates tools and insights from many different disciplines, such as engineering, economics and biology.  During my academic career I have had the priviledge of collaborating with bright minds across these disciplines. My modeling approach is characterized by non-conventional problem formulations combining controls and communications, and by introducing non-conventional elements to well-known problems, capturing new features. I often work on problems that are inherently challenging, which makes them intellectually interesting and lead to counter-intuitive solutions and insights. 

News:

• Aug 2024: New grant! I have received a Research Initiation Grant from the Southeastern Center for Electrical Engineering Education (SCEEE) to develop a new and fast distributed machine learning architecture based on maximum disagreement principle.

• July 2024: New journal paper! My recent work on robust networked estimation in the presence of cybersecurity attacks has been accepted for publication at the IEEE Transactions on Automatic Control.

• April 2024: New journal paper! Our work on extremum information for remote estimation and distributed learning has been published at Frontiers in Complex Systems.

• February 2024: New grant! I have received a First Year Assistant Professor Grant from the Council on Research and Creativity (CRC) at Florida State University to develop new distributed learning principles for human-robot networks.

• December 2023: IEEE CDC Invited Session - Together with Prof. Shinkyu Park I organized an invited session on "Strategic Coordination of Multi-agent Systems and Its Applications" at the IEEE CDC in Singapore, 2023.

• September 2023: Invited Talk - I gave an invited talk at the Allerton Conference, 2023.

• January 2023: New journal paper! Our paper on Maximum Dissent for distributed optimization has been accepted for publication at the IEEE Transactions on Network Systems.

• January 2023: I joined Florida State University as an Assistant Professor!

Open GRA and Postdoc positions:

I am currently looking for self-motivated students interested in pursuing a Ph.D. under my guidance. Our lab has several open Ph.D. positions and one postdoc position to work on robotic networks, IoT and social networks. If you are interested, please send your CV and brief statement telling me a bit about yourself to m.vasconcelos@fsu.edu.

Selected Publications:

The list below includes a few selected recent publications and pre-prints. An up-to-date list of publications can be found on Google Scholar.

• Fast networked data selection via distributed smoothed quantile estimation
  X. Zhang and M. M. Vasconcelos
  Submitted to IEEE Transactions on Automatic Control (TAC), 2024

• Robust remote estimation over shared networks in the presence of denial-of-service attacks
  X. Zhang and M. M. Vasconcelos
  Accepted at IEEE Transactions on Automatic Control (TAC), 2024

• Neuroscheduling for Remote Estimation
  M. M. Vasconcelos and Y. Zhang
  Accepted for presentation at the Asilomar Conference on Signals, Systems, and Computers, 2024

• Extremum information transfer over networks for remote estimation and distributed learning
  M. M. Vasconcelos and U. Mitra
  Frontiers in Complex Systems (Control and Engineering of Complex Systems), 2024

• On the role of network structure in learning to coordinate with bounded rationality
  Y. Zhang and M. M. Vasconcelos
  Accepted for presentation at the IEEE Conference on Decision and Control (CDC), 2024

• Rationality and Connectivity in learning to coordinate with bounded rationality
  Y. Zhang and M. M. Vasconcelos
  American Control Conference (ACC), 2024

• On the coordination efficiency of strategic multi-agent robotic teams
  M. M. Vasconcelos and B. Touri
  IEEE Conference on Decision and Control (CDC), 2023

• Data-Driven Classification of Low-Power Communication Signals by an Unauthenticated Agent Using a Software-Defined Radio
  T. R. Keshabhoina and M. M. Vasconcelos
  Asilomar Conference on Signals, Systems, and Computers, 2023

• Top-k data selection via distributed sample quantile inference
  X. Zhang and M. M. Vasconcelos
  Learning for Dynamic and Control Conference (L4DC), 2023

• Strategic multi-task coordination over regular networks of robots with limited computation and communication capabilities
  Y. Wei and M. M. Vasconcelos
  Annual Conference on Information Sciences and Systems (CISS), 2023

• Maximal dissent: a state-dependent way to agree in distributed convex optimization
  A. Verma, M. M. Vasconcelos, U. Mitra and B. Touri
  IEEE Transactions on Control of Network Systems (TCNS), 2023

• Robust remote estimation over the collision channel in the presence of an intelligent jammer
  X. Zhang and M. M. Vasconcelos
  IEEE Conference on Decision and Control (CDC), 2022

• Learning distributed channel access policies for networked estimation: data-driven optimization in the mean-field regime
  M. M. Vasconcelos
  Learning for Dynamic and Control Conference (L4DC), 2022

• Intertemporal trade-off between population growth rate and carrying capacity during public good production
  M. S.  Gangan, M. M. Vasconcelos, U. Mitra, O. Câmara and J. Q. Boedicker
  iScience (Cell Press), 2022

• Distributed remote estimation over the collision channel with and without local communication
  X. Zhang, M. M. Vasconcelos, W. Cui and U. Mitra
  IEEE Transactions on Control of Network Systems (TCNS), 2022

• Bio-inspired multi-agent coordination games with Poisson observations
  M. M. Vasconcelos
  IFAC Conference on Networked Systems (NecSys), 2022

Teaching:

• Fall 2024 - EEE 4872/5875 – Introduction to Artificial Intelligence

• Spring 2024 - EEL4930/5930 – Convex Optimization

• Fall 2023 - EEL4930/5930 – Networked Multi-Agent Systems

Team:

• Zhewei Wang (PhD student, FSU Mechanical Engineering) - Topic: Coordination of Robotic Networks under Bounded Rationality and Communication Constraints

• Sara Mirza (Undergraduate researcher, FSU Information Technology) - Topic: Interaction between Humans and Autonomous Vehicles 

• Bilal Taleb (Undergraduate researcher, FSU Computer Science) - Topic: Interaction between Humans and Autonomous Vehicles 

• Ann-Marie Gilliam (Undergraduate researcher, FSU Cell & Molecular Neuroscience) - Topic: New Models and Efficiency Metric of Bacterial Quorum Sensing 

• Arin Matos (Undergraduate researcher, FSU Medical Sciences) - Topic: New Models and Efficiency Metric of Bacterial Quorum Sensing 

• Vicenzo DiCorpo (Undergraduate researcher, FSU Chemical Engineering) - Topic: New Models and Efficiency Metric of Bacterial Quorum Sensing 

Alumni:

• Yifei Zhang (Postdoc) - Topic: Coordination of Robotic Networks under Bounded Rationality

• Tarun Rao Keshabhoina (MSc, VT Computer Engineering) - Topic: Cybersecurity of Low Power Communication Networks

• Yi Wei (MSc, VT Computer Engineering) - Topic: Multi-task coordination games

Outreach:

I am passionate about making science and technology accessible to everyone. If you would like to get involved with my work at any capacity (student, collaborator, educator, media, etc.), please get in touch!