Alaa Selim is a postdoctoral researcher at Dartmouth Engineering and the Arthur L. Irving Institute for Energy and Society, and serves as member of the Dartmouth Postdoctoral Association (DPA) executive board. His work on AI-driven grid resilience has earned him the Best Poster Award at Postdoc Research Day, the DPA Professional Development Award, and a spot in the Irving Institute’s 2026 New Energy Summer Summit cohort.
What do you consider your hometown, and what brought you to Dartmouth?
I consider Cairo, Egypt my hometown. Cairo is a big, vibrant hub with so much history, culture, energy, and many things to do. Growing up there shaped how I think about engineering and infrastructure because you see every day how large systems such as transportation, electricity, communication, buildings, and public services support people’s lives at a massive scale. That made me curious about how complex systems work and what happens when they are stressed.
What brought me to Dartmouth was the opportunity to work at the intersection of power systems, artificial intelligence, resilience, and real-world energy challenges. Through Thayer School of Engineering and the Irving Institute, Dartmouth offered a unique environment where engineering connects naturally with entrepreneurship, sustainability, medicine, and community impact. For someone interested in critical infrastructure, it felt like the right place to grow as a researcher while also thinking about how research can serve people beyond the lab.
What drew you to resilient power systems and AI-driven grid operations; was there a moment or experience that pointed you in this direction?
I was drawn to power systems because they sit quietly behind almost everything we do, from hospitals and homes to transportation, communication networks, universities, and now even AI data centers. The electric grid is one of the largest engineered systems in the world, but it has to operate in real time under uncertainty, extreme weather, cyber risks, equipment failures, and growing demand. That combination of scale, complexity, and public importance really attracted me.
During my PhD, I became fascinated by the question: can AI help such a complex system make better decisions when conditions are changing quickly? That question pushed me toward resilient grid operations, where learning-based methods, control, and optimization can help protect critical services before small disruptions become major outages. The “aha” moment was realizing that AI should not only improve efficiency or prediction accuracy; it should also support reliable decisions when the grid is under pressure and people are depending on it most.
Your research on power grid resilience was recently featured by Dartmouth Engineering. What problem are you trying to solve, and why is it urgent?
My research focuses on helping power grids become more resilient to disruptions they were not originally designed for: severe storms, cyberattacks, equipment failures, fast-changing demand, and the growing integration of solar energy, batteries, electric vehicles, microgrids, and data centers. These technologies are exciting and necessary, but they also make the grid more complex and harder to operate during emergencies.
I work on AI-driven decision-making tools for grid restoration, proactive protection, and resilient operation. The goal is to help operators restore power faster, protect critical loads, and prevent cascading failures when the system is stressed. This is urgent because our society is becoming more electrified and more dependent on digital infrastructure at the same time. When the grid is disrupted, the effects can spread quickly into healthcare, communication, transportation, education, and daily life. In simple terms, my work is about helping the grid make smarter decisions when the stakes are high, and when there is no time for a long committee meeting.
You’ve presented your work at Cornell, the University of Maine, and the University of Vermont. What has taking your research on the road taught you?
Presenting my work at neighboring universities gave me the chance to connect with academic researchers across New England and learn how different groups think about energy resilience, AI, power systems, and infrastructure challenges. Each visit taught me something new, not only from the questions I received, but also from hearing about their research directions, lab activities, students, and regional energy priorities.
It reminded me that resilient energy systems are a shared challenge. No single lab, algorithm, or discipline can solve it alone. These talks also helped me become better at communicating my work to audiences outside my immediate research area. As researchers, we sometimes love our equations, simulations, and acronyms a little too much. Taking research on the road reminds you to explain why the work matters, who it can help, and what problem it is actually solving. It also made me appreciate how much collaboration can begin from one good question after a seminar.
You won the Best Poster Award at Postdoc Research Day and received the DPA Professional Development Award. What has professional development looked like for you at Dartmouth?
Professional development at Dartmouth has meant much more than adding another line to a CV. It has meant learning how to communicate technical research clearly, build collaborations, mentor others, and connect academic work with real-world impact. Winning the Best Poster Award at Postdoc Research Day was meaningful because it showed that complex topics like grid resilience, AI-driven restoration, and cyber-physical energy security can connect with a broad audience when the story is clear.
The DPA Professional Development Award supported my engagement with external energy communities, including the Energy Data Competition at the Institute of Industrial and Systems Engineers, where I was selected as a finalist. I was also selected for the 2026 New Energy Summer Summit cohort at the Irving Institute, which has been an exciting opportunity to think about energy challenges across disciplines. Dartmouth has helped me grow as a researcher, speaker, collaborator, mentor, and future faculty member or research scientist. It has also taught me that explaining research without using 40 acronyms is its own engineering challenge, and probably deserves a workshop.
You’ve been active in the Dartmouth Postdoc Association. What does that community mean to you, and what would you want incoming postdocs to know?
The Dartmouth Postdoc Association has been one of the most meaningful parts of my time here. Postdocs are in a unique stage: independent enough to lead, but still building the next chapter of their careers. That can be exciting, but also uncertain. The DPA creates a space where people can share opportunities, challenges, advice, and sometimes just coffee and encouragement, which, honestly, is also a form of infrastructure.
Being involved in the DPA helped me meet postdocs from many fields and better understand their experiences, goals, and concerns. It also gave me a chance to contribute to the community, support professional development, and help make postdocs more visible across campus. For incoming postdocs, I would say: do not treat your postdoc as only a research position. It is also a chance to build community, practice leadership, explore career paths, mentor others, and learn from people whose work may be completely different from yours. Some of the best ideas come from conversations you did not plan.
What’s next after Dartmouth, and how has this postdoc shaped where you’re headed?
After Dartmouth, I hope to continue building an independent career around learning-based approaches for solving complex power-system problems. My research direction focuses on how data centers impact grid operation and resilience, how large language models and AI agents can support energy-system decision-making, and how control and optimization methods can improve grid restoration and proactive network protection before disruptions become major outages.
Dartmouth helped me connect these research directions with real-world needs, interdisciplinary collaboration, and startup potential. I became more interested in moving from algorithms and simulations toward scalable tools that utilities, campuses, data centers, hospitals, and communities can actually use. Through my work at Thayer and the Irving Institute, I have also been able to think about critical-facility resilience and how energy research can connect with healthcare, entrepreneurship, and public impact. This postdoc shaped how I think about scholarship: strong research should produce papers, but it should also create tools, partnerships, students, and impact. That is the kind of career I hope to build.
Outside the lab, what do you enjoy about life in the Upper Valley?
I have really enjoyed the natural beauty and calmness of the Upper Valley. Hiking in places like Quechee Gorge trails in Vermont and Pine Park, walking near the Connecticut River, exploring small towns around Dartmouth, and trying to enjoy winter through skiing have all been great ways to reset after long days of coding, writing, or debugging power-grid simulations. Sometimes stepping away from a simulation is exactly what helps you figure out why it was not working.
I also enjoy meeting people here from very different backgrounds and experiences. That has been one of the best surprises of life in the Upper Valley: it is quiet, but never boring if you are open to conversations. The area has a special mix of nature, community, and intellectual energy. It is the kind of place where you can think deeply, make new friends, enjoy the outdoors, and quickly learn that good winter boots are not optional.
