{"id":4247,"date":"2026-05-07T12:12:16","date_gmt":"2026-05-07T16:12:16","guid":{"rendered":"https:\/\/blogs.mathworks.com\/matlab\/?p=4247"},"modified":"2026-05-07T12:12:16","modified_gmt":"2026-05-07T16:12:16","slug":"mathworks-call-for-research-proposals-latest-news","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/matlab\/2026\/05\/07\/mathworks-call-for-research-proposals-latest-news\/","title":{"rendered":"MathWorks Call for Research Proposals: Latest News"},"content":{"rendered":"
Today\u2019s guest blogger is Deepak Bhatia. Deepak is the Program Manager of the MathWorks Call for Research Proposals program. <\/span><\/div>
In <\/span>March 2025<\/span><\/a> MathWorks launched <\/span>the <\/span>MathWorks Call for Research Proposals<\/span><\/a> which aims to support researchers who are tackling industry-relevant challenges using MATLAB and Simulink. The <\/span>winners of that first round were announced in October 2025<\/span><\/a> alongside another application round. Today, I am happy to announce both the Spring 2026 call and the winners of the Fall 2025 call.<\/span><\/div>

The Spring 2026 Call is open<\/span><\/h2>
The Spring 2026 cycle is currently open! If you are a full\u2011time faculty member working with an industry partner and have a bold research idea, we encourage you visit the <\/span>MathWorks Call for Research Proposals page<\/span><\/a> for details on eligibility, timelines, and application guidelines. <\/span><\/div>
\"The<\/img><\/div>

Fall 2025 Award Recipients<\/span><\/h2>
Covariance Completion for Adaptive Beamforming and Direction Finding Applications<\/span><\/div>
Daniel Jakubisin<\/span><\/a>,<\/span> <\/span>Tarun Cousik Suman<\/span><\/a>, and <\/span>Nishith Tripathi<\/span><\/a> <\/span>(Virginia Polytechnic Institute & State University), in collaboration with <\/span>Analog Devices<\/span>, are developing novel frameworks for hybrid beamformers aimed at achieving the performance and adaptability traditionally associated with digital beamformers. The project introduces a unified methodology to quantify hybrid beamforming effects, analyze key performance trade\u2011offs, and demonstrate benefits for advanced communication, sensing, and direction\u2011finding systems operating in demanding, reliability\u2011critical environments.<\/span><\/div>
Main tools: <\/span>RF Toolbox<\/span><\/a>, <\/span>Phased Array System Toolbox<\/span><\/a>, <\/span>Antenna Toolbox<\/span><\/a> <\/span><\/div>
Learn more about the group\u2019s work <\/span>here<\/span><\/a>.<\/span><\/div>
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Advanced Robotic Manipulation and Control in Enhanced Automated Lines and Machinery<\/span><\/div>
Marco Carricato<\/span> and <\/span>Edoardo Ida' <\/span>(University of Bologna) are collaborating with <\/span>IMA Industria Macchine Automatiche S.p.A.<\/span>, in addressing challenges for using advanced robotic systems within high\u2011performance automated production lines. This project focuses on advanced modeling and control of robotic systems for high\u2011speed, high\u2011acceleration handling of liquid\u2011filled containers, as well as trajectory planning and cooperative manipulation for bulky and rigid products. The results aim to improve both productivity and reliability in next\u2011generation manufacturing systems.<\/span><\/div>
Main tools: <\/span>Robotics System Toolbox<\/span><\/a>, <\/span>Parallel Computing Toolbox<\/span><\/a>, <\/span>MATLAB Coder<\/span><\/a><\/div>
Learn more about the research group <\/span>here<\/span><\/a>. <\/span><\/div>

Honorable Mentions<\/span><\/h2>
In addition to the winning proposals, many other proposals demonstrated exceptional technical merit and innovative use of MATLAB and Simulink. The following projects are recognized for their creativity, rigor, and strong potential for real\u2011world impact.<\/span><\/div>

Fall 2025 Honorable Mentions<\/span><\/h3>
Principal Investigator:<\/span> <\/span>Guilherme Vieira Hollweg<\/span><\/a> (University of Michigan\u2013Dearborn)<\/span><\/div>
Proposal:<\/span> <\/span>Robust Adaptive MPC\u2011RMRAC\u2011Based Control with Real\u2011Time Efficiency Optimization for Electric Vehicles<\/span><\/div>
This project proposes a robust model reference adaptive control framework to improve the efficiency of electric vehicles under real\u2011world operating conditions, including varying loads and temperatures. The approach targets autonomous and fleet EV applications, emphasizing performance, robustness, and computational efficiency.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>.<\/span><\/div>
<\/span><\/div>
Principal Investigator:<\/span> <\/span>Usman Hadi<\/span><\/a> (Ulster University)<\/span><\/div>
Proposal:<\/span> <\/span>6G Secure Hybrid Intelligence for Enhanced Layered Defense (SHIELD)<\/span><\/div>
This work focuses on improving the reliability and security of sixth\u2011generation (6G) wireless networks, which are expected to play a critical role in future industrial systems and disaster\u2011response scenarios. The proposed framework integrates secure hybrid intelligence to support resilient and trustworthy communications.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>.<\/span><\/div>
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Principal Investigator:<\/span> <\/span>Christos Verginis<\/span><\/a> (Uppsala University)<\/span><\/div>
Proposal:<\/span> <\/span>Real\u2011Time Learning and Control of Multi\u2011Robot Systems<\/span><\/div>
This project aims to develop a framework that enables teams of robots to learn and operate collaboratively in highly dynamic environments. By combining real\u2011time learning with control strategies, the work seeks to enhance adaptability and coordination in multi\u2011robot systems.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>.<\/span><\/div>
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Principal Investigator:<\/span> <\/span>Pavithra Prabhakar<\/span><\/a> (University of New Mexico)<\/span><\/div>
Proposal:<\/span> <\/span>An Abstraction\u2011Based Verification Framework for AI\u2011Enabled Automotive Systems<\/span><\/div>
This project aims to develop a rigorous verification framework for cyber-physical automotive systems that integrate neural network\u2013based components. By leveraging abstraction techniques, the proposed work seeks to enable scalable and systematic analysis of AI-enabled modules, thereby enhancing the assurance, reliability, and safety of automated driving systems.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>.<\/span><\/div>

Spring 2025 Honorable Mentions<\/span><\/h3>
In addition to the Fall 2025 submissions, we also want to recognize outstanding proposals from the Spring 2025 cycle.<\/span><\/div>
Principal Investigator:<\/span> <\/span>Arnaud Malan<\/span><\/a> (University of Cape Town)<\/span><\/div>
Proposal:<\/span> <\/span>Simulink Co\u2011Simulation Demonstrator: CFD Modeling of Aircraft Liquid Hydrogen Tanks with Active Pressure Control<\/span><\/div>
This project advances active pressure control strategies for liquid hydrogen tanks in aerospace applications. By integrating active pressure control <\/span>into the validated <\/span>AlphaFlow<\/span><\/a> CFD <\/span>model, the work enables co\u2011simulation studies of pressurization strategies and their effectiveness in mitigating pressure collapse during violent slosh events.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>.<\/span><\/div>
<\/span><\/div>
Principal Investigator:<\/span> <\/span>Kirti M. Yenkie<\/span><\/a> (Rowan University)<\/span><\/div>
Proposal:<\/span> <\/span>Framework for Efficient Operations in Multiproduct Pipeline Systems: A Case Scenario of Lubricant Oil Flushing<\/span><\/div>
This research addresses pipeline flushing operations by developing computational models and optimal control strategies to reduce product losses and improve operational efficiency. The approach replaces trial\u2011and\u2011error methods with predictive, data\u2011driven strategies validated through real\u2011time testing.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>. <\/span><\/div>
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Principal Investigator:<\/span> <\/span>Manish Kumar<\/span><\/a> (University of Cincinnati)<\/span><\/div>
Proposal:<\/span> <\/span>Control of Multiple Crazyflies with Unreal Engine\u2013Based Simulation and Digital Twin Environment Using MATLAB<\/span><\/div>
This project develops a framework for controlling fleets of drones using high\u2011fidelity simulation and digital twin technology. By integrating Unreal Engine with MATLAB, the work supports sim\u2011to\u2011real validation and safe, cost\u2011effective testing of drone path planning for automated inspection and inventory monitoring tasks.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>. <\/span><\/div>
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Principal Investigator:<\/span> <\/span>Qiugang (Jay) Lu<\/span><\/a> (Texas Tech University)<\/span><\/div>
Proposal:<\/span> <\/span>Knowledge\u2011Guided and Hierarchical Graph Structure Learning for Process Monitoring of Complex Industrial Systems<\/span><\/div>
This proposal introduces graph structure learning methods for monitoring large\u2011scale, interconnected industrial systems. By combining domain knowledge with data\u2011driven techniques and hierarchical graph neural networks, the work aims to enable earlier and more accurate fault detection and diagnosis.<\/span><\/div>
Learn more <\/span>here<\/span><\/a>. <\/span><\/div>

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