Interview with Haimin Hu: Sport-theoretic integration of security, interplay and studying for human-centered autonomy

On this interview sequence, we’re assembly a number of the AAAI/SIGAI Doctoral Consortium members to search out out extra about their analysis. On this newest interview, Haimin Hu tells us about his analysis on the algorithmic foundations of human-centered autonomy and his plans for future initiatives, and provides some recommendation for PhD college students seeking to take the following step of their profession.

Might you give us an summary of the analysis you carried out throughout your PhD?

My PhD analysis, performed underneath the supervision of Professor Jaime Fernández Fisac within the Princeton Secure Robotics Lab, focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic sport concept with machine studying and safety-critical management, my work goals to make sure autonomous programs, from self-driving automobiles to drones and quadrupedal robots, are performant, verifiable, and reliable when deployed in human-populated house. The core precept of my PhD analysis is to plan robots’ movement within the joint house of each bodily and knowledge states, actively making certain security as they navigate unsure, altering environments and work together with people. Its key contribution is a unified algorithmic framework—backed by sport concept—that enables robots to securely work together with their human friends, adapt to human preferences and objectives, and even assist people refine their expertise. Particularly, my PhD work contributes to the next areas in human-centered autonomy and multi-agent programs:

• Reliable human–robotic interplay: Planning secure and environment friendly robotic trajectories by closing the computation loop between bodily human-robot interplay and runtime studying that reduces the robotic’s uncertainty concerning the human.
• Verifiable neural security evaluation for complicated robotic programs: Studying strong neural controllers for robots with high-dimensional dynamics; guaranteeing their training-time convergence and deployment-time security.
• Scalable interactive planning underneath uncertainty: Synthesizing game-theoretic management insurance policies for complicated and unsure human–robotic programs at scale.

Was there a mission (or facet of your analysis) that was notably attention-grabbing?

Security in human-robot interplay is very tough to outline, as a result of it hinges on an, I’d say, virtually unanswerable query: How secure is secure sufficient when people would possibly behave in arbitrary methods? To provide a concrete instance: Is it enough if an autonomous automobile can keep away from hitting a fallen bike owner 99.9% of the time? What if this price can solely be achieved by the automobile all the time stopping and ready for the human to maneuver out of the way in which?

I’d argue that, for reliable deployment of robots in human-populated house, we have to complement normal statistical strategies with clear-cut strong security assurances underneath a vetted set of operation situations as effectively established as these of bridges, energy crops, and elevators. We want runtime studying to attenuate the robotic’s efficiency loss attributable to safety-enforcing maneuvers; this requires algorithms that may scale back the robotic’s inherent uncertainty induced by its human friends, for instance, their intent (does a human driver wish to merge, lower behind, or keep within the lane?) or response (if the robotic comes nearer, how will the human react?). We have to shut the loop between the robotic’s studying and decision-making in order that it could possibly optimize effectivity by anticipating how its ongoing interplay with the human might have an effect on the evolving uncertainty, and in the end, its long-term efficiency.

What made you wish to examine AI, and the realm of human-centered robotic programs particularly?

I’ve been fascinated by robotics and clever programs since childhood, after I’d spend whole days watching sci-fi anime like Cellular Swimsuit Gundam, Neon Genesis Evangelion, or Future GPX Cyber Components. What captivated me wasn’t simply the futuristic expertise, however the imaginative and prescient of AI as a real accomplice—augmenting human talents quite than changing them. Cyber Components particularly planted the thought of human-AI co-evolution in my thoughts: an AI co-pilot that not solely helps a human driver navigate high-speed, high-stakes environments, but additionally adapts to the driving force’s fashion over time, in the end making the human a greater racer and deepening mutual belief alongside the way in which. Right now, throughout my collaboration with Toyota Analysis Institute (TRI), I work on human-centered robotics programs that embody this precept: designing AI programs that collaborate with individuals in dynamic, safety-critical settings by quickly aligning with human intent by multimodal inputs, from bodily help to visible cues and language suggestions, bringing to life the very concepts that after lived in my childhood creativeness.

You’ve landed a school place at Johns Hopkins College (JHU) – congratulations! Might you speak a bit concerning the technique of job looking out, and maybe share some recommendation and insights for PhD college students who could also be at the same stage of their profession?

The job search was positively intense but additionally deeply rewarding. My recommendation to PhD college students: begin considering early concerning the sort of long-term impression you wish to make, and act early in your software package deal and job speak. Additionally, be sure to speak to individuals, particularly your senior colleagues and friends on the job market. I personally benefited loads from the next sources:

Do you will have an thought of the analysis initiatives you’ll be engaged on at JHU?

I want to assist create a future the place people can unquestionably embrace the presence of robots round them. In the direction of this imaginative and prescient, my lab at JHU will examine the next subjects:

• Uncertainty-aware interactive movement planning: How can robots plan secure and environment friendly movement by accounting for his or her evolving uncertainty, in addition to their means to cut back it by future interplay, sensing, communication, and studying?
• Human–AI co-evolution and co-adaptation: How can embodied AI programs be taught from human teammates whereas serving to them refine current expertise and purchase new ones in a secure, personalised method?
• Secure human-compatible autonomy: How can autonomous programs guarantee prescribed security whereas remaining aligned with human values and attuned to human cognitive limitations?
• Scalable and generalizable strategic decision-making: How can multi-robot programs make secure, coordinated choices in dynamic, human-populated environments?

How was the expertise attending the AAAI Doctoral Consortium?

I had the privilege of attending the 2025 AAAI Doctoral Consortium, and it was an extremely useful expertise. I’m particularly grateful to the organizers for curating such a considerate and supportive setting for early-career researchers. The spotlight for me was the mentoring session with Dr Ming Yin (postdoc at Princeton, now school at Georgia Tech CSE), whose insights on navigating the unsure and aggressive job market had been each encouraging and eye-opening.

Might you inform us an attention-grabbing (non-AI associated) reality about you?

I’m captivated with snowboarding. I realized to ski primarily by vision-based imitation studying from a chairlift, although I’m positively paying the worth now for poor generalization! Someday, I hope to construct an exoskeleton that teaches me to ski higher whereas protecting me secure on the double black diamonds.

About Haimin

Haimin Hu is an incoming Assistant Professor of Laptop Science at Johns Hopkins College, the place he’s additionally a member of the Information Science and AI Institute, the Institute for Assured Autonomy, and the Laboratory for Computational Sensing and Robotics. His analysis focuses on the algorithmic foundations of human-centered autonomy. He has obtained a number of awards and recognitions, together with a 2025 Robotics: Science and Programs Pioneer, a 2025 Cyber-Bodily Programs Rising Star, and a 2024 Human-Robotic Interplay Pioneer. Moreover, he has served as an Affiliate Editor for IEEE Robotics and Automation Letters since his fourth yr as a PhD pupil. He obtained a PhD in Electrical and Laptop Engineering from Princeton College in 2025, an MSE in Electrical Engineering from the College of Pennsylvania in 2020, and a BE in Digital and Info Engineering from ShanghaiTech College in 2018.