Enrique Mallada

Enrique Mallada is an Associate Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Mechanical Engineering, Applied Mathematics and Statistics, and Computer Science. He leads the Networks, Dynamics, and Learning Laboratory (NetDL2ab) and is a core member of MINDS (Mathematical Institute for Data Science) and ROSEI (Ralph O’Conner Sustainable Energy Institute). His research focuses on control systems, optimization, power systems, and machine learning, with applications in networked systems, energy grids, and distributed coordination.

Education: B.S. in Telecommunications Engineering from Universidad ORT (2005), Ph.D. in Electrical and Computer Engineering with a minor in Applied Mathematics from Cornell University (2014). Postdoctoral research at Caltech’s Center for the Mathematics of Information (2013–2015).

Research Interests: Networked systems (synchronization, distributed coordination), power systems (frequency control, grid resilience), optimization (time-varying algorithms, reinforcement learning), and machine learning (safety-critical applications, sparse recovery).

Key Projects: Real-time optimization for infrastructure networks, voltage collapse stabilization in power grids, control of distributed energy resources, and safety-aware reinforcement learning. His work integrates tools from control theory, optimization, and machine learning to address challenges in large-scale systems.

Awards: NSF CAREER Award, Caltech CMI Fellowship, Cornell Jacobs Fellowship, JHU Discovery/Catalyst Awards, and Excellence in Teaching Award.

Advising & Grants: Mentors graduate students in ECE and related fields. Active in grants focused on energy systems, control theory, and AI safety. Organizes conferences like CISS 2019 and serves on technical committees for IEEE Smart-GridComm and ACC.

Labs & Affiliations: NetDL2ab Lab (directed), MINDS (core member), ROSEI (core member), LCSR (affiliate), and Data Science & AI Institute (member). His research bridges academia and industry, addressing real-world challenges in energy and automation.