2026-06-13 02:00:01
Yen-Ling Kuo always wanted to understand how things worked. When she was growing up in Taiwan, reading the story of Michael Faraday in elementary school piqued her curiosity about the natural world. During that time, she was introduced to Logo, a computer program with a turtle cursor to help children learn basic coding through hands-on experimentation.
It was Kuo’s introduction to programming logic.
Employer
University of Virginia in Charlottesville
Title
Assistant professor of computer science
Member grade
Member
Alma maters
National Taiwan University; MIT
In high school she learned the capacity computers held. She could write programs that completed tasks independently, she realized.
“Once I discovered how powerful computers could be,” she says, “I knew I wanted to focus on using them to solve real-world problems.”
Kuo, an IEEE member, never lost her interest in the “how” behind processes and tools. Her curiosity, combined with a stint working at a Silicon Valley company, led her to focus on innovations that live at the intersection of cognitive and computer sciences.
Kuo, now an assistant professor of computer science at the University of Virginia in Charlottesville, last year received the IEEE Robotics and Automation Society’s inaugural Outstanding Women in Robotics and Automation Early Career Contribution Award. The award is part of the IEEE-RAS Women in Engineering’s Outstanding Women in Robotics and Automation (WiRA) Paper Awards, which promote excellence and recognize the impact that female researchers have on robotics and automation fields at different stages in their academic careers.
Kuo’s winning paper, “Diff-DAgger: Uncertainty Estimation with Diffusion Policy for Robotic Manipulation,” demonstrates a novel method to help robots better identify and estimate uncertainty when faced with scenarios on which they’ve not been trained. The method reduces the amount of human supervision, improves a robot’s rate of successful task completion, and opens up a path to introduce more complex models with bigger data demands into interactive robot learning.
She says her research will help people working in the robotics and automation fields more efficiently collect the data needed for effective model training.
Kuo earned bachelor’s and master’s degrees in computer science at the National Taiwan University, in Taipei, in 2009 and 2012. As she was nearing completion of her master’s degree, she did what many computer science graduates do: She pursued a summer internship at a tech company.
She spent the summer of 2011 at Google’s campus in Kirkland, Wash., working on the company’s comparison ads project.
When her internship ended, she joined the MIT Media Lab as a visiting student, working on the Open Mind Common Sense project with Henry Lieberman.
As she was considering pursuing a Ph.D., a call from Google changed her plans. The company offered her a full-time role as a software engineer.
“I viewed the job offer as a positive development,” she says. “I believe it can never hurt your future research career to get some real-world experience under your belt.”
She was hired in 2012 and helped build techniques that incorporate computer vision and natural language processing to improve the customer shopping search experience. She led the company’s Shop the Look initiative, a predecessor to Google’s current AI-powered shopping experience. The project connected social media content with search results, something the company had struggled to do in the past.
Kuo and her team were tasked with building a connection between the natural language people use to describe an item and an image that matches the searcher’s intent. It was at a time when the neural network—using deep learning models to power Google products—was gaining momentum at the company. Integrating neural network tools into her work was a requirement—which raised questions for Kuo.
“I was applying the neural network tools,” she says. “But I didn’t have 100 percent certainty about how they actually worked.”
She considered how she could become more knowledgeable about deep learning models. It was a full-circle moment. She decided that after nearly four years at Google, it was time to earn a Ph.D. in computer science. She returned to MIT in 2016.
Boris Katz, one of Kuo’s Ph.D. advisors, is a principal research scientist and the head of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)’s InfoLab. He also led the creation of the START Natural Language System, the world’s first Web-based question-answering system.
When the two met, Katz asked Kuo why she wanted to pursue a doctorate degree. She explained her interest in understanding how neural networks work and in using that knowledge to connect the physical world with human language.
He suggested she attend a summer course at MIT’s Center for Brains, Minds, and Machines, a research initiative that ran from 2013 through 2025. CBMM’s objective was to bring together computer scientists, cognitive scientists, and neuroscientists to understand how human intelligence works. The goal was to use the resulting insights to establish an engineering practice to build artificial intelligence systems.
For Kuo, it was a chance to better understand human intelligence and identify ways it could be replicated in machines.
“It was an opportunity for me to interact with other scientists and gain insight into how people learn, understand, and figure things out in the world,” she says. “I saw it as a very useful and inspiring way to incorporate those ideas into my own research work.”
During her Ph.D. studies, she was a research assistant at CSAIL. The experience helped shape her doctoral research, which focused on building AI systems that apply past learning to new situations. She developed machine learning models to support the efforts, including language understanding and social interactions.
She completed her Ph.D. in computer science in 2022 with a minor in cognitive science.
After graduation, she continued her work and collaboration at CSAIL, particularly on projects that involved the “theory of mind” concept.
Theory of mind isn’t new, having originated with primatologists studying chimpanzees in the late 1970s. The theory recognizes that others have their own thoughts, beliefs, and perspectives. It’s a skill that allows humans to infer someone’s mental state and predict their behavior without verbal communication.
“It’s like when college roommates are moving into their dorm. They may not talk too much, but they work together naturally to coordinate their activities and accomplish goals,” Kuo says. “They can infer and mentally interpret each other’s behaviors and signals to make decisions and complete tasks without words.”
She brought her theory of mind research to the University of Virginia when she joined as an assistant professor in 2023.
Kuo conducts her research in UVA Engineering’s multidisciplinary cyberphysical Link Lab. Her broad focus is on developing computational models that help robots interpret both direct data and silent signals, from language and movements to a person’s gaze. If successful, it could give robots the same sort of physical and theory of mind reasoning capabilities that power physical and social interactions among humans.
“There are no computational frameworks yet available that will translate this kind of understanding into a robot efficiently,” she says.
She adds that the process to get there begins with improving how robots learn to perform tasks.
Historically, one way robots learned was to mimic humans. A researcher would manually guide a robot through a task, like cutting an apple, and it would repeat the movements. The robot was successful until the environment changed, such as when its hand was in a different position or the apple was at a different angle. The robot was then faced with a situation for which it hadn’t been trained. Without any data available to help it correct course, the robot would start making small errors that eventually led to a full system crash.
This diagram describes how the robotic gripper’s visual perception and tactile sensing prevents a potato chip from breaking.Xuhui Kang, Yen-Ling Kuo, et al.
To solve the problem, researchers developed the dataset aggregation (DAgger) method. As a robot performed a task, a researcher was on standby to provide real-time corrections during unexpected scenarios. The correction data was continuously added to the robot’s model, teaching it how to recover from mistakes.
To reduce the human monitoring effort, robot-gated DAgger was created to enable bots to query humans when the machines became uncertain.
The most popular approach to make the query decision is to train multiple models to consider when determining a course of action. If the models all agree, the robot proceeds. If they don’t agree, the robot is likely to get stuck and ask for help.
Although the multiple model approach was widely adopted, it has limitations. Practically speaking, as models become more complex, it is hard or impossible to train multiple copies. A more fundamental issue is that disagreement among models doesn’t always imply uncertainty; it could just mean there are different ways to accomplish a task.
That is the gap Kuo’s research team closed with the novel Diff-DAgger research. The approach builds on diffusion policy, a technique that helps robots account for different ways a task can be performed.
The new method repurposes diffusion loss, the signal a robot uses to improve its model during training, as a real-time confidence check. During task execution, the robot computes the signal and compares it against values from its training data using a statistical test. The signal spikes when the robot faces an unfamiliar situation and is uncertain how to proceed. The signal stays silent when the robot’s current action is close to what it learned before.
The spike represents the robot’s ability to self-diagnose and predict an imminent failure. Human intervention is triggered only when the signal spikes. No spike means the robot can be left to complete its decision-making process on its own.
Kuo’s team achieved significant results: Failure prediction rates were improved by 39 percent. Task completion rates were increased by 20 percent, and tasks were completed nearly eight times faster.
Her research at UVA gained attention from the National Science Foundation, which honored her last year with a Career Award, the foundation’s flagship grant for early-career researchers. The five-year US $665,000 grant supports her research that builds computational models for human-robot interactions through theory of mind reasoning.
She also received the Toyota Research Institute’s Young Faculty Researcher Award to teach cars to reason about interactions on the road and with the driver.
As service robots and self-driving vehicles become more available, such works are likely to make interactions between humans and robots more intuitive and useful.
Kuo ultimately wants to build more robust robots that are able to integrate into a social space with humans by engaging with us through grounded interactions, she says.
Like many IEEE members, Kuo was introduced to the organization as a student. In 2018 she submitted her first paper, “Deep Sequential Models for Sampling-Based Planning,” to the IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems while pursuing her Ph.D. at MIT. Her IEEE involvement grew alongside her professional career.
“It was a natural segue to transition from student to a full IEEE member,” she says. Today she is an active volunteer with the IEEE Robotics and Automation Society, a reviewer for submitted papers, and a presenter and panelist at conferences.
She says one of the best parts of attending conferences is having the opportunity to engage with students. She also enjoys participating as a panelist at luncheons, she says, because it gives her one-on-one time with student attendees. She can share her knowledge and offer insights as they prepare to embark on their career.
Her goal in the coming years, she says, is to broaden her involvement with IEEE initiatives and branch out to other technical committees. Sharing knowledge and learning from others is essential to anyone’s career growth, she says, and “IEEE offers a great opportunity for both.”
2026-06-11 21:00:02
“Space computing, the final frontier, has arrived,” Nvidia CEO Jensen Huang declared at the Nvidia GTC conference in March.
Indeed, the idea of data centers in orbit has gone from science fiction to a serious spending category. Elon Musk’s SpaceX has acquired xAI (also Musk’s) and is planning a constellation of space-based data centers. Google, not to be outdone, announced Project Suncatcher in partnership with Planet, planning to launch two satellites equipped with Google Tensor Processing Unit (TPU) AI chips by early 2027. Startup Starcloud has already filed a proposal with the Federal Communications Commission for an 88,000-satellite constellation for orbital data centers. As Starcloud’s filing suggests, these companies are all proposing fleets of satellites numbering in the thousands, each housing a rack or multiple racks of AI-grade GPUs, interconnected with each other through free-space optical links and communicating back to Earth via microwave links, either directly or through other satellites.
Proponents tout the many wonders of computing in space: abundant solar energy, free cooling, and freedom from Earth-based disturbances like earthquakes, floods, and protesters. But a sober look at the physics of space-based computing paints a much more nuanced picture.
Free cooling is perhaps the biggest misconception. Space is cold, but it also has no atmosphere. That means the best heat-removal mechanisms, conduction and convection, are off the table. The only option is radiation. To prevent a chip from overheating in space, a large, costly surface area is required to dissipate the energy and then radiate it.
Solar energy is abundant, but collecting it with functional solar panels that maintain perfect alignment toward the sun is a complex task requiring extensive attitude control systems. On top of that, ionizing radiation in space from cosmic rays and other sources poses a unique challenge, degrading the solar panels, the radiative coolers, and the chips themselves. Because regular maintenance in space is difficult, redundancy has to be built in at launch, and cost estimates have to account for efficiency degradation over time.
At ABI Research, where I work as an aerospace analyst, we did a rough total-cost-of-ownership comparison between a data center on Earth and one in space. It showed that the cost to launch and run a GPU in space for a year is at least an order of magnitude higher than the same feat in a terrestrial data center. Our model was simple, assuming an Nvidia H100 server rack launched with the requisite-size solar panel and radiator on a spacecraft akin to Starcloud’s pilot launch. We assumed SpaceX’s Starship was used at a highly optimistic launch cost per kilogram of US $44, and a terrestrial energy cost of $0.20 per kilowatt hour. This is a simple back-of-the-envelope calculation, but it does signal something real.
From our perspective, the cost of delivery and space hardening of the payload makes general-purpose space-based data centers difficult to justify economically today, despite the fact that data-center builders in many regions are scrambling for electric power. However, there are niche applications where the much higher costs of computing in space could be justified. Examples include preprocessing data from Earth-observation satellites, real-time detection and tracking of hypersonic missiles, and active collision avoidance in the increasingly crowded low Earth orbit. Even for these, though, contending with fundamental physics will still be a demanding challenge. And a technologically compelling one, too.
Cooling is where physics separates the science from the fiction. The governing equation for radiative cooling, the only type of cooling available in space, is known as the Stefan-Boltzmann Law. It states that the amount of power you can radiate is proportional to the area of the radiator times its temperature to the fourth power. For a space systems architect, the implications of this law are brutal. In orbit, the only variable we can control is area. This restriction creates a geometric penalty, or a “physics tax,” for cooling in space: The more power you need to reject, the bigger the area of the radiator you need to bring along from Earth.
The only cooling method available in space is radiation, and the radiator area required is derived using the Stephan-Boltzmann law. For a single chip drawing 700 watts, like Nvidia’s popular H100 GPU, the area required to keep it at 20 °C is just under 3 square meters, and it goes down to 1 square meter for an operating temperature of 85 °C. However, as the radiator surface is exposed to ionizing radiation, its emissivity decreases, and after 5 years in space the required area increases by about 40 percent.
To understand how big this baseline area is in practice, I used the Stefan-Boltzmann law to model the heat-rejection area needed to keep a single chip that draws 700 watts of power—such as the H100 GPU chip, an AI stalwart—at a constant 60 °C, usually considered the sweet spot for GPU longevity and stability. I further assumed that the radiator is perfectly facing deep space, at a chilly background temperature of 3 kelvins. By this calculation, a single chip would require 1.4 square meters of radiator surface.
To put this into perspective, consider that a common AI rack can hold approximately 32 GPUs (four H100 server boards). With CPUs, memory, and networking equipment, this rack would draw around 40 kilowatts of power. This single rack includes 2.5 terabytes of memory—enough capacity to serve over 20,000 concurrent users or run 16 simultaneous instances of Llama 3, an open-source AI model. But to cool this thermal load in a vacuum, that single rack would require an 80-square-meter radiator, roughly the size of a pickleball court. For an aggregate 100-megawatt data center, you’d need at least 2,500 of those radiators.
And that’s the best-case scenario. Additional problems are hidden in the low Earth orbit environment itself. Space exposes radiators and their coatings to a chemically hostile brew of ultraviolet light and atomic oxygen, quite the opposite of a clean-room environment. Over a LEO satellite’s typical 5-year lifespan, these elements degrade the radiator’s surface properties and lower its ability to shed heat.
Including this degradation in the model reveals that as the radiator degrades from a “fresh” state to an “end-of-life” state, the physics demands a further penalty. To maintain that same 60 °C operating temperature for the GPU chips, the required surface area jumps from about 1.4 square meters per chip to nearly 2.0 square meters. In other words, the physics tax rises by 40 percent. Therefore, you must launch at least 40 percent more radiator mass, endure higher atmospheric drag, and sacrifice valuable launch volume just to survive the degradation of the thermal coating. This increase adds significantly to the launch cost and further erodes the economics of a space-based data center.
Solving the heat problem is only part of the battle. The other significant challenge in low Earth orbit is ionizing radiation, which affects the computing hardware itself. Today’s satellites typically use radiation-hardened processors, which are very reliable but also much more expensive, and they perform poorly compared to commercial off-the-shelf processors.
A standard rad-hard chip doesn’t have the processing power to run a modern large language model (LLM). As a result, satellite operators aspiring to launch a data center have no choice but to make a risky compromise: to use hardware meant for terrestrial use. In order to achieve the necessary compute density, orbital data centers must use the same Nvidia H100s or Google TPUs found in terrestrial server farms. The problem is that these chips are “soft” targets in space. High-energy particles can flip bits in memory or cause “latch-ups” in logic that fry the circuit.
One possible option is to shield the computers from radiation with thick, absorbent panels. However, the shielding would add significantly to the already heavy satellites. The other option is to compensate for the radiation damage with redundancy. Indeed, edge computing architects are moving toward software-defined resilience, where instead of one perfectly hardened computer, operators fly a cluster of imperfect, commercial ones whose total cost could be as low as one-tenth to one-hundredth that of the rad-hard model.
This redundant approach is used in many spacecraft, including Artemis II, which recently carried astronauts around the moon, as well as SpaceX’s flight computers and the Hewlett Packard Enterprise edge servers for the International Space Station. By running three (or more) instances of the same calculation on three different nodes and comparing the answers, the system can detect a corrupted processor. If a node fails, the “orchestrator” reboots it while the others continue the mission. While this ensures resiliency, it also means that some fraction of the compute capacity is dedicated to redundancy, further increasing the costs.
An often-touted advantage of space-based data centers is the seemingly unlimited supply of free, clean energy from the sun. Solar energy in orbit is indeed abundant, at 1,361 watts per square meter. Of course, capturing that free energy is made possible only by the very costly launching of large solar panels into orbit. And those solar panels also degrade over time due to radiation exposure, typically losing 1 to 3 percent efficiency per year.
Let’s say a solar array collects 1 MW of power to run an AI cluster. The laws of physics demand that the satellite must eventually radiate 1 MW of waste heat. Because the square area needed to generate the solar power—around 400 W/m2—and to reject the heat—around 450 W/m2—are nearly equivalent, every square meter of power generation now demands approximately another square meter of cooling. The radiator needs to be a structural equal, not merely a passive coating on a surface used for something else.
As Elon Musk recently noted in Davos, the most efficient radiator is one that never sees the sun. By orienting the spacecraft so the solar panels face the sun and the radiators face the deep vacuum of space, efficiency skyrockets for both. But there’s a catch: Maintaining this perfect three-way alignment—panels to sun, radiator to the void, antennas to Earth—requires complex, high-torque attitude control systems. So this configuration means more payload and more computing power. Plus, these control systems are complex components with many failure modes, which is not optimal in a situation where maintenance is difficult.
Given all these challenges of deploying massive radiators for satellites in the hostile environment of space, why build data centers in space at all?
While training or inference on LLMs in space doesn’t seem economical today, there are other, very compelling applications for computing in space. Here are two: solving the downlink bottleneck from Earth-observation satellites and enabling collision-preventing maneuvers in the increasingly crowded low Earth orbit.
The latest Earth-observation satellites, equipped with hyperspectral and synthetic aperture radar sensors, are used for a range of important reconnaissance missions, such as battlefield intelligence, tracking the global shadow fleet of ships carrying contraband, and assessing earthquakes or infrastructure failures down to the millimeter. These systems can generate hundreds of terabytes of raw data per day that must be transmitted to Earth. However, the radio-frequency “pipes” used to downlink the data are congested, and the ground infrastructure cannot absorb the sheer volume of raw data.
Another immediate, mission-critical application for in-space computation is protecting the orbital environment. With over 17,000 satellites in orbit, the overwhelming majority of which are in low Earth orbit, avoiding collisions between these satellites is crucial. As NASA astrophysicist Donald Kessler pointed out back in 1978, a single space collision could cause a cascading effect that renders the entirety of LEO unusable.
According to SpaceX’s recent annual report, the Starlink constellation executes a collision avoidance maneuver every 2 minutes on average. Each maneuver already relies on onboard AI systems but still requires most of the processing to happen on the ground.
As low Earth orbit gets increasingly populated, collision avoidance will have to break the traditional ground-loop model. In the megaconstellation era of space, the OODA (observe, orient, decide, act) loop must happen onboard, thereby reducing the analysis turnaround from minutes to milliseconds.
The problem is that the flight computers standard on satellites are not built for this level of processing. The complex probability models required for maneuvering cannot currently be implemented by onboard computers in conjunction with their navigation systems. Clearly, more powerful computers are needed.
This is the true economic justification for moving compute to space: to move insight generation there. By placing high-performance computing adjacent to the sensors, we can process terabytes of data in orbit and downlink only the relevant data in real time, and we can do the computations necessary to avoid satellite collisions in real time.
So, assuming that some form of computing is inevitable in low Earth orbit in the foreseeable future, how will the heat be handled? The industry is currently experimenting with two main classes of solutions to cope with the Stefan-Boltzmann law.
One creative option is to use origami-inspired radiators, the kind used for the James Webb telescope. Companies are developing flexible, high-conductivity composite radiators that fold into a tight cube for launch and unfurl into enormous yet lightweight thermal wings in orbit.
Another possibility is to use liquid-droplet radiators. This concept proposes removing the rigid radiator structure completely and instead spraying a stream of coolant oil directly into the vacuum of space. The fluid travels through an open loop, exposed to the near-absolute zero of the void, maximizing radiative surface area before being caught by a collector and pumped back into the ship. It sounds like science fiction, but as the heat loads climb into the megawatts, liquid-droplet cooling may be the only way to cheat the mass limits of this exponential reality.
Our rough total-cost-of-ownership model uses optimistic versions of current numbers, such as launch cost, chip cost, and power use. A critic might point out that future technology will improve, both in efficiency, purpose-built designs, and costs.
Sure, the technology is bound to improve. But the critical factor isn’t just launch cost; it’s the computing power per unit mass and electric-power economics. Radiators and solar arrays can consume 65 to 70 percent of total satellite mass, and space-grade photovoltaics run orders of magnitude more expensive than terrestrial equivalents.
Chris Philpot
Even as launch costs fall, the mass and cost burden of power generation and thermal management will remain a fundamental problem.
Current space-grade solar panels rely on germanium substrates, whose supply is concentrated in China. It will be extremely difficult to scale up availability of these substrates. A transition to radiation-tolerant perovskite solar panels or a similar alternative could change the economics significantly, but that possibility is five years away or more. The technology will get cheaper, but the bottlenecks of power and thermal architecture will remain.
Recognizing the thermal reality of cooling in space forces us to shift how we view satellite operations. We are moving away from the “launch and forget” era toward an era of “autonomous logistics.” As our thermal model demonstrated, the harsh environment of space steadily attacks the hardware. UV radiation degrades thermal coatings; cosmic rays degrade silicon. In a traditional satellite model, when the radiator degrades or the memory fails, the satellite becomes space junk. For a multimillion-dollar data center, that disposal model is potentially ruinous.
To make the economics of orbital computation work, the infrastructure must be serviceable and the rockets to launch them reusable. The orbital domain will require automated servicing vehicles capable of swapping out degraded radiator panels and upgrading fried servers. In these ways, the future of the orbital data centers is dependent on the innovations of an emergent in-space economy.
There’s a good argument to be made that the need for space-based computation is less of a hype cycle and more of an enabler for the new space economy. Look no further than SpaceX’s recent regulatory filings proposing a constellation of up to a million satellites in low Earth orbit. At such a scale, routing all raw data back to Earth is physically impossible; the network itself must become the data center.
However, the winners in this sector will be determined by the systems architects who most cleverly accommodate the thermodynamics and the companies with sufficient vertical integration to take on the massive costs of operating data centers in orbit. Ultimately, the physics tax is universal. Whether managing heat rejection in the vacuum of low Earth orbit or managing power density in a hyperscale facility in Northern Virginia, the constraint is never the silicon. It’s the thermodynamics.
2026-06-11 18:00:01
An examination of how socially assistive wellness robots could support the seven dimensions of senior wellness, and how a framework can measure their autonomy.
What Attendees will Learn
2026-06-11 02:00:01
The EPICS (Engineering Projects in Community Service) in IEEE program, administered by IEEE Educational Activities, has launched the Excellent EPICS in IEEE Contributor Awards. The recognitions honor the program’s outstanding students and faculty volunteers in Excellent Team Leader and Excellent Faculty Advisor categories.
The awards recognize individuals whose leadership, mentorship, and commitment have meaningfully advanced the impact of EPICS projects. Candidates must demonstrate clear, measurable contributions that elevate both the student experience and the outcomes delivered to community partners. Reviewers also consider other awards, publications, presentations, and professional achievements that reinforce the nominee’s credibility and leadership.
Recipients must demonstrate outstanding project management and documentation, strong mentoring and collaboration, and high-quality outcomes.
Here are this year’s recipients.
Surattana Kakay is a computer engineering student at Rajamangala University of Technology Thanyaburi (RMUTT), located in IEEE Region 10 (Asia Pacific). Kakay, an IEEE student member, was honored for guiding her team in the design, development, and implementation of the Automatic Water Level Control System project, which aids rice farmers in Thailand.
As the team leader, Kakay played a pivotal role in transforming the student initiative into an operational, community‑centered solution. Her inspiration was purpose-driven, she says.
“My motivation was to apply engineering to real agricultural challenges, like water scarcity and climate change,” she says. “I wanted to bridge advanced technology with the tangible needs of local farmers.”
She managed the project end to end—coordinating workflow, assigning tasks based on team members’ strengths, and ensuring each phase of development aligned with the technical road map she created. She served as the primary liaison between the student team, the Pathum Thani Rice Research Center, and farmers to make sure the system was practical and user‑friendly, and that it addressed community needs.
“Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.” —Elizabeth Vidal-Duarte
Under her leadership, the team developed a low‑cost IoT‑based alternate wetting and drying (AWD) system that lets farmers remotely monitor and control water levels in rice paddies using smartphones. Kakay oversaw the integration of noncontact laser time‑of‑flight sensors to withstand harsh field conditions, and she championed the use of long-range technology connected to a free community Wi‑Fi network to eliminate Internet service fees.
The results were transformative, Kakay says.
“Our AWD system reduces water consumption by 63 percent and methane emissions by 7 percent annually,” she says. “Turning an academic assignment into a real‑world solution that delivers measurable, sustainable results has been incredibly meaningful.”
Her achievements advanced sustainability for Thailand’s most water‑intensive crop while demonstrating the potential of accessible engineering solutions.
Beyond technical innovation, Kakay cultivated a culture of learning, continuity, and empowerment within her team. She introduced a mentorship framework to support future student cohorts. She and her team produced academic papers, visual media, and presentations to communicate the project’s value to scientific audiences as well as the general public.
“Surattana Kakay is a pivotal figure in turning innovation into reality and delivering tangible benefits to the community,” says IEEE Member Thanasin Bunnam, her faculty advisor and an assistant professor at RMUTT.
Kakay’s leadership journey became a personal milestone, she says: “Leading this project transformed me from a student into a team leader. As a female engineer, it empowered me to advocate for women in engineering and show that gender is no barrier to technical excellence.”
Through her guidance, the AWD project evolved from a classroom assignment into a solution that illustrates IEEE’s mission of advancing technology for humanity.
Navid Shaghaghi, a lecturer and researcher at Santa Clara University, in California, was recognized for his dedication to integrating service learning into engineering education and fostering student innovation that benefits underserved communities in IEEE Region 6 (Western USA).
During his more than six years of engagement with EPICS in IEEE, Shaghaghi, an IEEE senior member, has demonstrated exceptional leadership in advancing sustainable, human‑centered engineering through the long‑running Hydration Automation (HA) project and the HiveSpy initiative. They are part of Santa Clara University’s Frugal Innovation Hub and EPIC Research Laboratory.
Since 2019, Shaghaghi has served as principal investigator for the HA project, guiding its evolution from prototype to a robust, field‑tested irrigation automation system that supports small ranches and community farms in California.
The HA project is a low‑cost system that helps reduce water waste by monitoring soil moisture and automating watering. By combining ultrasonic tank sensing, soil sensors, and ongoing technical support, the project improves efficiency, lowers operational costs, and promotes more sustainable urban agriculture.
Under Shaghaghi’s guidance, more than 30 undergraduate and graduate students have gained hands-on experience in IoT development, field deployment, testing, and client collaboration.
His commitment to frugal innovation and human‑centric design has resulted in solutions that are minimalist, affordable, sustainable, portable, and rugged—often challenging conventional approaches to agricultural technology.
“Turning an academic assignment into a real‑world solution that delivers measurable, sustainable results has been incredibly meaningful.” —Surattana Kakay
The HA project has produced new research publications and earned recognition, including a third-place finish by Shaghaghi’s graduate students at this year’s IEEE Rising Stars Project Showcase. During the annual event, students and young professionals present their technical innovations to industry leaders and peers.
The HiveSpy project is a low‑cost, frame‑level IoT monitoring system that helps beekeepers automate labor‑intensive tasks and prevent hive swarming by tracking production yield in real time. By collecting frame‑weight data and generating optimized harvest schedules, the system reduces manual workload while improving the hive’s health and boosting honey output.
Shaghaghi says his mentorship has been shaped by the realities of student turnover, a challenge he embraces with optimism and adaptability.
“The transient nature of student teams is a challenge but one you must embrace, bear‑hug style,” he says. “By energizing your student community and welcoming new contributors, you’ll be amazed by the brilliant solutions they bring.”
His philosophy has allowed him to cultivate a thriving pipeline of student innovators, he says, and he has strengthened his own professional practice as well.
“I’ve been mentoring EPICS in IEEE students since 2019,” he says. “It has taught me resilience and how to operate on a tight budget while still delivering real‑world results.”
Beyond the technical achievements, Shaghaghi’s work reflects a commitment to humanitarian technology and service learning. As the founder and director of the EPIC (Ethical, Pragmatic, and Intelligent Computer) lab, he has built a diverse, interdisciplinary community dedicated to innovation for the benefit of humanity.
For him, he says, the EPICS in IEEE award carries profound meaning: “Receiving this award validates my deepest conviction in humanitarian technology research and strengthens my commitment to service‑learning education.”
His students echo those sentiments. One team member said “Professor Shaghaghi is an engine of progress who keeps forging ahead.”
Through his leadership, Shaghaghi has created an enduring model of mentorship, innovation, and community partnership that is helping to shape the next generation of socially responsible engineers.
Elizabeth Vidal-Duarte is celebrated for her impactful mentorship and leadership in expanding EPICS in IEEE engagement across Peru and IEEE Region 9 (Latin America and Caribbean). Vidal-Duarte, a research professor at San Agustin National University Arequipa, in Peru, is a faculty advisor and technical mentor for two EPICS in IEEE projects. She encouraged students to apply to the EPICS program, helped them identify community needs, and supported them in crafting proposals grounded in service‑learning principles.
Under her leadership, the students developed a functional soft robotic glove used at Clínica San Juan de Dios to help patients improve their fine-motor skills. The clinic’s therapists use the device to measure the range of motion of joints at the beginning and end of each patient’s therapy session to improve their assessments. Compared with traditional manual measurements using a goniometer, the glove significantly reduces evaluation time and enables digitally recorded data, improving clinical efficiency and decision-making.
The second project is an emotion‑recognition system for people with visual impairment. The AI‑powered wearable helps recognize a person’s emotions through real‑time facial‑expression detection and haptic feedback.
The project has resulted in the “Emotion-Aware Assistive System With Wearable Haptic Feedback for Visual Impairment” research paper, which is to be presented at the IEEE International Symposium on Computer-Based Medical Systems, to be held from 3 to 5 June in Limassol, Cyprus.
Vidal-Duarte’s mentorship extends beyond the classroom. She visits rehabilitation centers and clinics to find people with visual impairments to ensure that the technologies she is helping to develop meet their needs.
“EPICS in IEEE has moved me beyond teaching concepts to truly living engineering as a tool for human impact,” Vidal-Duarte says. “Watching students grow as they design solutions that improve lives has been both inspiring and deeply humbling.”
Throughout the development of both projects, Vidal-Duarte provided sustained technical and organizational guidance, helping students define requirements, structure work plans, and overcome challenges in prototyping, testing, and validation.
Reflecting on the broader impact of EPICS, she says the program has given her “more than methodologies and tools—it has given me perspective, purpose, and a global community that constantly challenges me to grow as a mentor and as a human being.”
Her mentorship fostered not only technical excellence but also empathy, ethical awareness, and professional maturity among her students, she says. She guided them in preparing articles for submission to IEEE conferences, interdisciplinary collaboration, and hands-on fieldwork that bridged theory and real‑world constraints.
“Her constant support, her belief in each student’s potential, and her commitment to developing leaders who make a difference define [her] as a faculty advisor,” says Valentina Chabilla, an EPICS in IEEE student team member.
The EPICS recognition reflects her passion for teaching, her dedication to the community, and her impact on projects and students. Her commitment to accessible, sustainable innovation strengthened partnerships between the university and community groups, benefiting underserved populations.
“Receiving this award is both an honor and a responsibility,” she says. “It reminds me of the real impact engineering can have on people’s lives and strengthens my commitment to guiding students in creating meaningful change.”
Her leadership continues to inspire students to view engineering not just as a discipline but also as a powerful force for inclusion, dignity, and social impact.
The Excellent Contributor Award recipients exemplify the best of EPICS in IEEE. Through their leadership, they have strengthened the bridge between engineering education and community service, inspiring students to use their skills to create sustainable, real‑world impacts.
As EPICS continues to expand its global reach, the contributions of Kakay, Shaghaghi, and Vidal-Duarte serve as powerful reminders of what is possible when educators, volunteers, and students work together to improve the lives of others through engineering.
2026-06-10 21:00:00
A man raises his phone as police move into a crowd. The video is shaky, loud, immediate. Within minutes, it is online. Within hours, it is everywhere. This is how accountability works now. Something happens, someone records it, and that footage can show what really happened, sometimes contradicting official accounts. It can empower citizens and create consequences for officials.
But the footage’s life cycle does not end there.
In recent months, civil liberties groups have warned that adding facial recognition to consumer smart glasses could turn everyday recording into something more troubling: real-time facial identification. It reflects a broader shift already underway, where images and videos captured for one purpose can later be searched, matched, and used for another.
An ouroboros is an ancient Egyptian symbol, a snake or dragon eating its own tail. As I began to see patterns in my broader research on surveillance corporatism and governance lag, I began using the term “surveillance ouroboros” to describe this recursive pattern of observations intended to hold power accountable becoming new input for the same surveillance infrastructure.
During the George Floyd protests in 2020, people filmed police in real time. Phones were pointed at officers, not at each other. The goal was simple: to show what the state was doing. That footage spread quickly and became part of a much larger pool of public data.
At the same time, reporting from outlets including The New York Times and BuzzFeed News showed that law enforcement agencies were using facial-recognition tools, including systems built by Clearview AI. Those systems were built from billions of images scraped from across the internet, including publicly available photos and videos.
The basic approach is now routine: People record the state, or anything else (as in the January 6 attack on the U.S. Capitol), and the state compiles that footage and data into a searchable environment, which may later be used to identify some of the same people who made the footage.
Facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards.
A 2023 Government Accountability Office review found that federal law enforcement agencies continued to expand their use of facial-recognition systems for criminal investigations despite ongoing concerns around training, privacy protections, civil-liberties safeguards, and oversight. Earlier GAO findings showed that agencies had conducted roughly 60,000 facial-recognition searches before formal training requirements were put in place for personnel using the systems.
The American Civil Liberties Union and other groups have warned that these tools could be used to identify people from images shared online, including protest-related footage. Concerns about facial recognition led some U.S. states and cities, including San Francisco and Boston, to restrict or ban government use of the technology, while federal agencies have continued to face scrutiny over how such systems are tested, deployed, and audited. A 2024 analysis published in Internet Policy Review warned that facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards meant to govern them, creating growing tensions around data protection, oversight, and proportional use.
Surveillance used to require infrastructure. Cameras had to be installed, and data had to be collected deliberately. That is no longer the case. People carry cameras everywhere. They record constantly and upload in real time. Events are documented from multiple angles without planning or coordination. The cumulative result is a continuous stream of usable data: faces, locations, timestamps, and interactions. The Internet of Things (IoT) also waits all around us, gathering information and releasing it when people least expect it, as Andrew Guthrie Ferguson describes in a recent excerpt of his book Your Data Will Be Used Against You.
Similar dynamics are emerging globally. A recent analysis in the International Journal of Law and Information Technology examined how facial-recognition systems in China and Japan are expanding faster than the legal frameworks governing them. Reporting by The Guardian described the limited legal protections around the rapid deployment of AI-assisted surveillance infrastructure across parts of Africa.
There used to be a clear distinction between surveillance and accountability. Surveillance meant the powerful watching the people; authorities tended not to share their imagery except under duress or a court order and usually after a long delay. Accountability meant the people watching the powerful and often publishing imagery immediately to head off or counteract official mischief. That distinction no longer holds. The same footage can serve both roles. A recording meant to expose misconduct can later be used to identify someone else entirely.
Surveillance ouroboros is not a future risk. It is already here.
This dynamic persists because people still need to record. In many places, it is one of the only tools available when formal accountability breaks down. When oversight institutions weaken or fail, public documentation becomes a substitute. In that environment, people turn to visibility. But that visibility comes with a cost. The more people that document, the more data that exists. The more data that exists, the easier it is to search, match, and store. Every video feeds the ouroboros. People are not feeding the system because they trust it. They are feeding it because the alternative is silence.
Most of the people in these videos are not the focus. They are in the background, passing by or standing nearby. But that distinction does not matter once the footage enters a system. Today’s facial recognition can identify even a face that passed through the corner of a frame. Someone who did nothing can still become part of a dataset without ever knowing it. As recognition systems improve, older footage becomes more useful—and invasive.
No single decision created this outcome. It emerged gradually through more cameras, better recognition, larger datasets, and easier integration. Each step made sense on its own. Together, they changed what recording means.
Public recording is still necessary. Without it, many forms of abuse would remain hidden. But recording is no longer just exposure. It is also contribution. If you published imagery or video last year, you may already have contributed to a system you have never seen but the ouroboros has.
Surveillance ouroboros is not a future risk. It is already here. Every time someone presses publish, they are doing two things at once. They are exposing power, and they are helping build the system that the powerful will later use to track the less powerful.
2026-06-10 02:41:14
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Early in my career, I walked into a shared office space on my first day as a full stack software developer and sat down between the CTO and the CEO to get onboarded. There were four of us in total. Before the day was over, I received my first assignment.
This was one of the most formative—and most stressful—experiences of my professional life. In the decade since, I have worked at half a dozen companies including Fortune 100 firms, mid-size startups, and companies you’ve probably never heard of. I have also spoken with roughly a thousand developers at various stages of their careers.
Most engineers entering the field are obsessed with landing at Google, Meta, or Amazon. But those roles represent approximately 0.6 percent of software engineering positions. For most of us, the real choice is between a small startup, a mid-size company, and a large enterprise. Each comes with tradeoffs, and your experience will differ from mine. What follows is an honest account of what you might reasonably expect.
The Small Startup
Pros
Your work actually matters. A feature you build might determine whether the company closes its next funding round. You gain exposure to the full spectrum of the business, from deployment pipelines to sales and operations and everything in between. You wear many hats out of necessity. For engineers who want to grow quickly and understand how a product is built end to end, few environments move faster.
Cons
Everything is on fire, always. Work-life balance is difficult to maintain when every release feels critical. Priorities shift without warning and culture tends to reflect the personality of whoever has the most influence in a small room. Startups optimize for speed over craft which means engineers learn to move fast but don’t always learn to build well, and that gap can follow you into your next role.
The Mid-Size Company
Pros
“So this is how a real business works.” There is process, documentation, a quality assurance function, and some form of career structure. The team is large enough to offer a diversity of experience and perspective. Stability is a myth, especially nowadays, but it is considerably more predictable than an early-stage startup.
Cons
“So this is how a real business works?” Processes that enable quality also produce friction. Access controls, approval workflows, and cross-team dependencies slow things down. The career ladder exists but it might stop at senior engineer. Without significant organizational growth, your salary and title can plateau early.
The Large Enterprise
Pros
That badge on your LinkedIn profile just bought you credibility for the next five years. Compensation at this level can be meaningfully higher, particularly when equity is included. The career ladder is long and clearly defined. Engineering practices at mature organizations tend to be more rigorous, and a well-known employer carries market value in future job searches.
Cons
It’s slow. Technology stacks often lag industry trends by several years. Political dynamics shape advancement as much as technical ability does. Skill atrophy is a risk when you spend years on a narrow slice of a legacy system. You are now a small fish in a big pond and it will be harder to get noticed.
The Roadmap I Would Take If I Could Start Over
According to a recent Stack Overflow survey, 47 percent of professional developers work at companies with fewer than 100 employees. This may surprise you because social media is dominated by engineers who work at the most well known companies on the planet.
The path most engineers imagine for themselves and the path most engineers actually walk are two very different things.
If I could do it again, here’s the path I’d take: Start at a small company to build breadth and learn how a business works across functions. This also provides some room to experiment within different roles. Next, move to a mid-size organization with a clear goal of reaching a senior or leadership role. Making a lateral move is easier than trying to get up-leveled at the next company. Finally, target a more mature company where a leadership position opens the door to meaningful equity and long-term growth (aka stocks and bonuses).
Each stop builds something the others cannot. The startup gives you range. The mid-size company gives you a taste of how larger orgs operate. The enterprise gives you leverage, credibility and maybe even some stability.
Your path will not look like mine. At a five person startup, I had no idea what I was in for. Looking back, I would not trade it. Just know what you are signing up for before you sign.
—Brian
“Social engineering” is a concept that has become associated with phishing, in which scammers manipulate people into disclosing personal information. But shaping human behavior in this way doesn’t have to have such negative effects. Systems engineer Guru Madhavan argues that we need to reclaim the term and govern the practice to defend ourselves from bad actors and benefit from social engineering’s good side.
Smartphone apps are increasingly used to help manage medical conditions, but many of these have not been verified by any regulatory agencies. To help ensure these apps are credible, the IEEE Standards Association recently launched a directory listing apps that have been vetted by experts for technical soundness, ethical design, data security and privacy, and clinical efficacy. The registry will be publically available at no cost, and developers can now apply for approval.
A veteran chip designer reflects on what he learned when moving from academia to industry, where the goal changes from proof of concept to ensuring a design works reliably at scale. Differences in risk tolerance, he discovered, lead to varying approaches in the rapidly growing semiconductor industry.