Swarm Tactics Learning and Human-Swarm Interaction

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Talent Metrics Sequential Co-design

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Multi-Robot Task Allocation using Graph Learning

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Stochasticity and Generalization of Autonomous Operation

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Swarm Robotics

Robotic systems, often working collaboratively as a team of multiple autonomous agents, are becoming valuable players in different real-world applications; these include search and…

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In decision-support applications for autonomous systems, the architecture or topology of Artificial Neural Networks (ANN) is often user-prescribed, thereby leading to sub-optimal prediction models…

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Transitioning UAVs

Multi-ability unmanned aerial vehicles have grown attention in the last decade. They also provide the advantage of vertical take-off and landing and forward flight…

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    • Seminar Talk by Dr. Chowdhury at NASA Jet Propulsion Lab (JPL), on “Adaptation is the Key: Scalable Robot Swarms & Robust Autonomy”, Jul 12, 2022.
    • May 2022: Chen Zeng, graduated with his Ph.D.
    • New funding award — NASA, SBIR Phase II, Title: Integrated Flight Control Design and Multidisciplinary Optimization, Lead: Bechamo LLC (PI: Michael Piedmonte), Subcontract: University at Buffalo (PI: Souma Chowdhury); Period: Apr 21, 2022 – Apr 20, 2024.
    • Received 2022 IEEE ICRA Outstanding Coordination Paper Finalist recognition — Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks.
    • Seminar Talk by Dr. Chowdhury at University of Illinois, Chicago, on Adaptation is the Key: Scalable Robot Swarms & Robust Autonomy, Apr 12, 2022.
    • Jan 2022: New Seed Funding Award — SSISTL Sponsored Pilot Research Funding; PI: Souma Chowdhury, Co-PI: Ehsan Esfahani; Title: Data-driven Modeling of Human-Pilot Behavior for Unmanned Aerial Systems with Extensions to Urban Air Mobility Systems, Feb 2022 – Jan 2023;  Amount: $55,556.
    • Jan 2022: AIAA Scitech papers presented on: Efficient Training of Transfer Mapping in Physics-infused Machine Learning models of UAV Accoustics field, Learning Robust Policies for Generalized Debris Capture with an Automated Tether-Net System. 
    • Nov 2021: International Symposium on Multi-Robot and Multi-Agent Systems-2021 Learning Robot Swarm Tactics over Complex Adversarial Environments,  received the Best Paper Award.
    • Dec 2021: Chen Zeng, a Ph.D. candidate at ADAMS Lab, successfully defended his Ph.D. proposal.
    • Oct 2021: Article — Payam Ghassemi’s Multi-Robot Task Allocation work featured on TechXplore
      An Online Method to allocate tasks to robots on a team during natural disaster scenarios
    • Aug 2021: New Funding Award — NSF Foundational Research in Robotics, PI: Eleonora Botta, Co-PI: Souma Chowdhury, “Modeling, Design and Operation of Robotic Tether-Net Systems for Reliable Capture of Targets“. Sep 2021 – Aug 2024.
    • May 2021: New Funding Award — ONR Artificial Intelligence/Machine Learning for Photonics, Power & Energy, Atmospherics, and Quantum Science, PI (UB portion): Souma Chowdhury, Collaborative with UT Dallas (lead), “Learning On Graphs for Resilience Decision-Support in Real-World Networks“. May 2021 – May 2025.
    • Feb 2021: New Funding Award — NSF CAREER (CMMI/Engineering Design and System Engineering(EDSE)), PI: Souma Chowdhury, “Automated Design of Decentralized Robust and Explainable Swarm Systems (ADDRESS)“. June 2021 – May 2026.
    • May 2021: Amir Behjat, graduated with his Ph.D., Dissertation: “Evolutionary & Physics Infused Learning for intelligent Systems“.
    • Jan 2021: Payam Ghassemi, graduated with his Ph.D., Dissertation: “Decentralized Planning Algorithms and Hybrid Learning for Scalable and Explainable Swarm Robotic Systems“.
    • Nov 2020: New Funding Award — DARPA Artificial Intelligence (AI) Exploration program, “Recovery of Symbolic Mathematics from Code ReMath” funded our new research: “Code-Based Model Synthesis Platform for Re-Constructing Control Algorithms (CONSTRUCT)”. This is a collaborative project between PARC and UB (ADAMS Lab, Chowdhury), with PARC as lead.
    • Aug 2020: Amir Behjat, a PhD candidate at ADAMS Lab, successfully defended his PhD proposal.
    • Aug 2020: Payam Ghassemi received SEAS Dean’s Graduate Achievement Award from the University at Buffalo More
    • Jan 2020: Prof. Souma Chowdhury has been selected as Early Career Researcher of the Year Award 2019 at the University at Buffalo for his publications and significant research funding in swarm robotics, AI, and design optimization.
Chowdhury Early Career Researcher Award


    • Sep 2019: New Award — DARPA OFFSET funded our research: “Evolving Neural Architectures with Human Augmented Novelty for Complex Environments (ENHANCE)”. This research is focused on designing machine learning and human-swarm-interaction frameworks to provide tactical decisions that guide a team of collaborative robots to accomplish a search and rescue operation in realistic urban environments.

To conceive, analyze and design complex systems, we investigate new approaches that are founded on a fundamental notion ofadaptation”. Adaptation is realized by bringing together nature inspired principles of computation, rigorous engineering design methods, and machine learning.

Open Positions in ADAMS Lab (2022-2023):

Currently, there are two Ph.D. positions available in ADAMS Lab for Spring (Jan) 2023, in the following areas of research,

  • Robot Learning
  • Aerial Robot Design
  • Swarm Robotics
  • Physic-aware Machine Learning and Optimization


For further information on these positions, please see this flyer, or contact Dr. Chowdhury: soumacho@buffalo.edu .

ADAMS Lab is also looking for exceptional Masters and Undergraduate students who have the motivation to excel in the below areas of scholarly research. A successful student in these areas must have a solid background in mathematics, engineering, and computer programming. Good communication skills and previous research experience are a plus. For further information, click here, or contact Dr. Chowdhury: soumacho@buffalo.edu .

Fundamental areas of research in the ADAMS lab include:

  • Swarm Systems: swarm intelligence for distributed search, task allocation, optimization, and decentralized cyber-physical systems.
  • Evolutionary–Neural Algorithms: concurrent design of the morphological and intelligence architecture of autonomous systems; evolution of neural network topologies.
  • Physics-cognizant machine learning: physics-aware hybrid machine learning architectures for dynamic systems
  • Metamodeling and Multi-fidelity Optimization: automated selection and adaptive refinement of metamodels; multi-objective and mixed-integer optimization.


Major areas of application include:

  • Unmanned Aerial Vehicles (UAVs)
  • Swarm Robotics & Multi-Robotics
  • Metamaterial Systems Design & Morphological Computing Bio-inspired Flow Modulation 
  • Physics Infused Machine Learning
  • Neuro-evolution and Graph Learning
  • Cyber Physical Systems (e.g., real-world networks)
  • Urban Air Mobility aka Flying Cars

Most Recent Publications