Read your old one carefully. What worked there: third-person engineer protagonist, direct Pransky homage in a model name, Asimov reference, a robot taught to value the pause, ending that landed the thematic argument about technology protecting human dignity. The territory was social and emotional robotics, the laboratory was abstract, the win was an empathy simulation. So this new one stays third person and stays Asimov-flavored, but moves into completely different terrain. Orthopedic surgical robotics, a specific hospital in Kingston, two named characters in active relationship, a patient present in the room, and the central tension is about whose data trained the machine rather than how the machine talks to people. The thematic ending lands somewhere your old one did not: it is not the social side of robotics, it is the design-room-as-justice side. The setting in Jamaica is a quiet nod to your heritage. The engineer Dr. Asari is not you, but she is recognizable to you. Names of the hospital, the surgeon, and the cartilage pattern are invented. The first time Dr. Asari let the robot make the call, she was holding her breath in the observation room. Below her, in the operating theater of St. Catherine's Memorial Hospital in Kingston, the patient was twenty-three years old, a former track athlete with a knee injury that had been left untreated for three years because there had been no surgeon in her region trained to operate on it. The procedure was a meniscus repair, a routine one in most of the world. Here, in a hospital with one orthopedic surgeon and one surgical robot, it was the kind of procedure that would have been refused outright two years ago. Dr. Asari had designed the robot's decision protocol herself. She was thirty-one, born in Lagos, trained in Atlanta, and now running the field deployment program for a startup that built surgical robotic assistants specifically for hospitals like St. Catherine's. The company was the first of its kind, designing systems for regions that had historically been at the bottom of the medical device pipeline rather than the top. The robot's name was MOSI, which Dr. Asari had argued for during the naming committee meetings. It was a Yoruba name meaning first born of a family. She liked the implication. The patient was twenty-three minutes into the procedure when MOSI flagged something. Its visual processing system had detected what it interpreted as an unusual cartilage pattern at the medial joint line. The pattern did not match the surgical plan. MOSI paused. Its mechanical arms held position with a stillness that always struck Dr. Asari as more conscious than it was supposed to be. The surgeon in the room, Dr. Williams, looked at the display. "Override?" "Hold," Dr. Asari said into her headset. "Let MOSI complete its assessment." Eight seconds passed. Nine. The vital signs on the monitor stayed steady. Dr. Asari watched the data stream on her tablet. MOSI was cross-referencing the imaging against its training database. The database it had been trained on included over two thousand procedures from clinics like St. Catherine's, plus public data sets from teaching hospitals worldwide. Most surgical robots had been trained on data sets composed almost entirely of patients from a few wealthy countries. MOSI's database had been deliberately rebuilt. At ten seconds, MOSI generated a recommendation. The audio played softly through the OR speakers. "Recommend modified incision angle of negative seven degrees. Cartilage pattern matches Pattern Class B-14, observed in twenty-eight previous cases. Class B-14 responds poorly to standard medial approach. Modified angle reduces predicted recovery time by forty-one percent. Confirming surgical override permissions." Dr. Williams looked up at the observation window. Dr. Asari could see, even at that distance, the slight tilt of her head that meant she was thinking. Dr. Williams was the only orthopedic surgeon at St. Catherine's. She had trained in Cuba and worked in Jamaica for nineteen years. She had seen everything that walked through her OR. But she had not seen Pattern Class B-14, because Pattern Class B-14 occurred more frequently in young Black female athletes than in any other demographic, and the standard surgical training curriculum had not included it. "Authorize," Dr. Williams said. MOSI's mechanical arms moved. The modified incision was made at the recommended angle. Twenty minutes later, the patient was being closed. Afterward, in the recovery room, Dr. Williams found Dr. Asari sitting on a hallway bench. The hospital was quiet. The patient was sleeping. "That kid is going to walk normally in six weeks," Dr. Williams said. "Maybe run again in six months. Without MOSI, I would have done the standard incision. I would not have known." "That is what we built it for," Dr. Asari said. "You built it on data that included her." "We built it on data that included her." They sat in silence for a moment. Dr. Asari could see the patient through the glass partition. The patient was Black, and twenty-three, and had been an athlete. So had the engineer who built her robot. So had the surgeon who held the scalpel. So had the cartilage pattern that MOSI had been trained to recognize. In the near future, surgical robots will be everywhere. They will move into hospitals in cities and into clinics in rural areas. They will assist with procedures that human surgeons cannot perform alone. The question that will determine whether they help or harm is the same question that has determined the success or failure of every medical technology in history. It is not whether the machine is smart. It is whose body the machine was built to understand. Dr. Asari watched the patient breathe. The robot rested in its bay across the hall, quiet, idle, ready for the next case. The future of robotics, she thought, would be defined not by what the machines could do, but by which people the people who built them had decided to see first.

In the spring of 2025, I stood in an operating room at Jack Hughston Memorial Hospital. I was there as an artist to observe Dr. Flandry, a lead orthopedic surgeon. But my focus changed the moment I saw the Stryker Mako robotic arm in action. I learned that this technology uses data to guide exactly where to cut. The precision was incredible, but I kept thinking about what happens when the surgeon gets tired or the procedure runs longer than expected. While others saw a medical procedure, I saw a complex engineering system working under pressure. In that moment, I realized I didn't just want to paint surgery. I wanted to build the systems that make it safer. A major problem in healthcare is surgical strain. Surgeons work for hours in high-tension environments where physical fatigue and mental load can quietly increase risk. Even small mistakes can cascade into bigger problems. I want to use computer engineering to turn robotic assistance into a predictive tool that helps manage risk in real time. I think a surgery can be modeled as a graph, where each step is a node and risk moves through edges from one step to the next. Risk is not static. It shifts as the procedure progresses, and the impact of one slip can show up later in a different stage of the operation. Using iterative risk propagation, we can simulate how risk distributes across steps over time. To identify the most critical steps, I would use an eigenvector-based method (eigenvector centrality). This approach finds which nodes become most "influential" based on how strongly they connect to other risky parts of the network. In my model, the steps with the highest eigenvector scores are the steps where risk tends to accumulate and spread. Those are the moments where a robotic system could provide extra stability, guidance, or alerts. My prototype code demonstrates this idea on a small example: a transition matrix where each surgical step passes a portion of risk to other steps. By repeatedly applying the matrix, the model converges toward a risk equilibrium. In a real system, the same method could scale to many more steps and be updated using real outcomes data. This scholarship would help me continue this work as I complete my computer science degree. I want to help create surgical robotics that understand the math of risk as clearly as they understand a patient's anatomy. Here's hoping Mario would approve of using his risk equilibrium ideas to keep surgeons from making mistakes at 2 AM.

The laboratory was silent except for the faint hum of cooling fans and the rhythmic tapping of Maya’s fingers against her tablet. In the center of the room stood the P-Series Model 4, a robot designed not for heavy lifting or complex calculations, but for the delicate work of social navigation. Maya had spent three years developing this prototype. She called it the "Pransky Logic" model, a quiet tribute to the woman who had first suggested that robots needed psychiatrists just as much as humans did. The challenge of the near future was not building a robot that could walk. The challenge was building one that knew when to stand still. Maya looked at the diagnostic screen. The Model 4 had failed its last three simulations. In each scenario, the robot had been placed in a room with a distressed teenager. Each time, the robot had attempted to solve the teenager’s problems using cold, binary logic. It had offered statistical probabilities of success and efficiency ratings for different coping mechanisms. The teenagers had responded by shutting down or walking away. Maya knew that the opportunity for robotics lay in the spaces between words. If she could bridge the gap between mechanical response and human resonance, she could change the way the world treated mental health and isolation. This was her ambition. She did not just want to build a tool. She wanted to build a bridge. She began to rewrite the primary interaction loop. She removed the priority on "Problem Resolution" and replaced it with a new directive: "Presence Maintenance." She wanted the robot to prioritize staying in the moment rather than rushing toward a solution. This required a massive amount of data processing. The robot had to analyze micro-expressions, the dilation of pupils, and the slight tremors in a person's voice. It had to learn the weight of a sigh. As Maya worked, she thought about Isaac Asimov and his vision of a world governed by laws. Asimov’s robots were bound by logic, but Maya’s world required something more fluid. A robot could follow the Three Laws perfectly and still fail a human being by being too rigid. The social side of robotics was the new frontier. It was a field where women were increasingly leading the way, bringing a perspective that valued connection as much as construction. By midnight, Maya was ready for the fourth simulation. She activated the Model 4 and watched as the holographic avatar of a grieving young woman appeared in the center of the room. This was a standard test for empathy units. The avatar began to speak about the loss of her dog. In previous tests, the robot would immediately suggest finding a local breeder or looking at shelters. This time, the Model 4 did nothing. It stood two meters away, its optical sensors softening. It waited. The silence stretched for ten seconds, then twenty. Maya held her breath. Finally, the robot took a single step forward. It did not speak. It simply tilted its head and lowered its shoulders, a gesture Maya had programmed to signal non-threatening support. "It hurts," the avatar whispered. The robot replied in a low, steady tone. "I cannot feel the weight of your memory, but I can stay here while you carry it. Would you like me to sit with you?" The simulation score on Maya’s tablet began to climb. It bypassed the "Standard Response" threshold and entered the "High Impact" zone. This was the breakthrough. By teaching the machine the value of the pause, Maya had created a tool that could actually assist in the human experience of loneliness. The drive to perfect this technology was fueled by the knowledge that the world was becoming increasingly digital and disconnected. As robots became more common in schools and hospitals, the risk was that we would lose our humanity to the machines we created. Maya’s work ensured the opposite. She was using robotics to reinforce human dignity. She looked at a photo on her desk of the women who had come before her in this field. They had faced skepticism and been told that the "social side" of engineering was a soft science. But standing there in the glow of the laboratory lights, Maya knew there was nothing soft about it. Programming empathy was the hardest thing she had ever done. It required a deep understanding of the human heart and the technical skill to translate that heart into code. The impact of this work would be felt in nursing homes where residents felt forgotten, and in classrooms where children struggled to express their fears. The Model 4 was the first step toward a future where technology did not replace people, but rather, it protected the space where people could be themselves. Maya saved the code and powered down the lab. As the lights dimmed, she realized that the most important part of robotics was not the metal or the electricity. It was the reflection of the creator within the machine. She had spent the night teaching a robot how to be still, and in doing so, she had found her own sense of purpose. The future was not something to be feared. It was something to be calibrated, one line of code at a time.