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Krishma Rajesh Mehta

 

Krishma is a first-year graduate student in the MechE department. She completed her undergraduate from IIT Madras in May 2025. During her undergraduate, she worked on research topics spanning design optimization, vortex particle methods and flow instability. At MSEAS, she will be focusing on submesoscale dynamics, nonlinear dynamics and adaptive sampling for path planning. Outside of work, she likes to write poetry, travel and cook.

Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underpowered Systems Operating in Uncertain Ocean Currents

Killer, M., M. Wiggert, H. Krasowski, M. Doshi, P.F.J. Lermusiaux, and C. Tomlin, 2025. Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underpowered Systems Operating in Uncertain Ocean Currents. IEEE Robotics and Automation Letters 10(10), 10745-10752. doi:10.1109/LRA.2025.3604727

Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the expansive open oceans. However, in the open ocean neither anchored farming nor floating farms operating with powerful engines are economically viable. Recent studies have shown that vessels can navigate with low-power engines by going with the flow, utilizing minimal propulsion to strategically leverage beneficial ocean currents. In this work, we focus on low-power autonomous seaweed farms and design controllers that maximize seaweed growth by taking advantage of ocean currents. We first introduce a Dynamic Programming (DP) formulation to solve for the growth-optimal value function when the true currents are known. However, in reality only short-term imperfect forecasts with increasing uncertainty are available. Hence, we present three additional extensions. Firstly, we use frequent replanning to mitigate forecast errors. For that we compute the value function daily as new forecasts arrive, which also provides a feedback policy that is equivalent to replanning on the forecast at every time step. Second, to optimize for long-term growth, we extend the value function beyond the forecast horizon by estimating the expected future growth based on seasonal average currents. Lastly, we introduce a discounted finite-time DP formulation to account for the increasing uncertainty in future ocean current estimates. We empirically evaluate our approach with 30-day simulations of farms in realistic ocean conditions. Our method achieves 95.8% of the best possible growth using only 5-day forecasts. This confirms the feasibility of using low-power propulsion to operate autonomous farms in real-world conditions.

RSI Student Jayveer Kochhar Receives Distinguished Written Paper Award

Jayveer Kochhar, a rising high school senior who joined MSEAS during summer 2025 as an RSI scholar, has received one of five RSI Distinguished Written Paper Awards. His paper, “From Satellite Observations to Submesoscale Ocean Dynamics: Geostrophic Field Smoothing and Diffusion-Based Reconstruction,” focuses on how small-scale ocean currents, known as submesoscale features, can be predicted in the Gulf of Mexico. Congratulations Jayveer, and to his advisors Anantha and Akhil, as well as Chris and Pat for the data!

Tristan Kay

 

Hi, I’m Tristan a upcoming junior from Bellevue, Washington, studying Computer Science (6-3) and possibly doubling in Mathematics. At MSEAS, I’m currently exploring alternative schemes to DO, for computational speed / accuracy. Outside of academia, I enjoy reading, taking walks, and general hanging out with friends.

Aditya Awarded MathWorks Mechanical Engineering Fellowship

Congratulations to Aditya K. Saravanakumar, a Ph.D. candidate in the MSEAS group, for being awarded a MathWorks Mechanical Engineering Fellowship! The competitive MathWorks Engineering Fellowships are awarded to the top nominees from all of the academic departments in the School of Engineering, who use MATLAB and/or Simulink to advance discovery and innovation across disciplines. All the best to Aditya!