Research

Best paper award named for retired CU �鶹ӰԺ professor

Emily Adams — Fri, 08 May 2026 20:41:44 +0000

Best paper award named for retired CU �鶹ӰԺ professor Emily Adams Fri, 05/08/2026 - 14:41

Harold "Hal" Gabow retired from CU �鶹ӰԺ in 2008, but his impact on computer science theory is still going strong.

In addition to continuing to publish research papers in his retirement, in 2025 Gabow endowed a new best paper award for the Association of Computer Machinery's Transactions on Algorithms, a journal for which he served as a founding editor.

We caught up with Gabow to learn more about the award, his continued passion for computing theory and what he sees as some of CU �鶹ӰԺ's most important contributions to the field.

Tell us about how Transactions on Algorithms got started.

The Journal of Algorithms (JoA) was founded in 1980 by Herb Wilf and Don Knuth. It became a prominent venue for publication of research articles in the emerging field of algorithm design. A special feature was David Johnson’s NP-Completeness Column, which covered the latest results on the central problem of theoretical computer science: “Does P equal NP? If one can check the solution to a problem quickly, can one find the solution of the problem quickly?” (a Millennium Prize Problem of the Clay Mathematics Institute, worth $1 million).

In 2003, the JoA editorial board decided to move the journal to a publisher with broader appeal. I worked with the Association for Computing Machinery to get the journal approved as ACM Transactions on Algorithms (TALG). We continued the mission of disseminating the best papers dealing with the mathematics of discrete algorithms, their design and their analysis. We also continued the NP-Completeness Column, due to its popularity and importance on a question with great implications for algorithm design, computation and mathematics in general.

What is your goal for the Harold N. Gabow Best Paper Award?

Working with the current editor of TALG, we created a new type of award. The theoretical computer science community has two common types of awards for research papers: best paper awards (best paper presented at a given conference or a journal’s best paper of the year) and test-of-time awards (for papers at a conference that was 10 years ago, or 20 or 30).

This award is a combination – it goes to an article published in the last three years in TALG. This time frame means that the article is recent (like a best paper award) but has begun to influence current research in the field (like a test-of-time award). My goal for the award is to maintain TALG’s dominance in publishing the best articles on algorithm design and to help researchers, both young and more established, to gain recognition for their work.

How will the winners be chosen, and what do they receive?

Winners are chosen by a committee centering around the TALG editorial board, following a detailed procedure. The cash award of $1,024 is a tribute to binary numbers, the language of computers and discrete mathematics (1024 = 2^10).

While you're retired from CU �鶹ӰԺ, you have remained active in the computer science theory community. What have you been up to?

After retiring in 2008, I had the luxury of going back to my first love, matching algorithms. These algorithms find the best ways to pair up compatible entities – current real-life applications include kidney and liver donation (transplant chains and paired donor exchanges); assignment of students to schools (eg, Denver Public School System’s SchoolChoice program) and doctors to hospitals (National Resident Matching Program). In retirement, I published 10 research papers culminating in best-known algorithms for several related types of matching. I probably could have researched these algorithms decades earlier, but the pressures of daily academic life didn’t allow it. I count retirement as an academic benefit!

What are the most exciting things you’re seeing in the theory space right now?

The progress of AI in mathematical research and algorithm design! An example from just last month is Anthropic Claude’s solution to a discrete mathematics problem that was stumping Donald Knuth, one of JoA’s founding editors. It is undoubtedly just a matter of time before an AI agent is listed as a contributing author in a TALG paper. Although the entire field of mathematics will evolve to incorporate AI, there will always be a niche for traditional algorithms, guaranteed by their simplicity and beauty to endure forever.

What do you see as CU �鶹ӰԺ’s most important contributions to computer science theory?

Gene Meyers, my PhD student with co-advisor Andrzej Ehrenfeucht, designed the widely used BLAST tool for human genome sequencing analysis. Very recently, my algorithm for maximum cardinality matching was adopted by the Library of Efficient Data Types and Algorithms.

In 2025, Professor Emeritus Harold "Hal" Gabow endowed a new best paper award for the Association of Computer Machinery's Transactions on Algorithms, a journal for which he served as a founding editor.

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Toward safer self-driving cars

joze4324 — Mon, 20 Apr 2026 15:20:41 +0000

Toward safer self-driving cars joze4324 Mon, 04/20/2026 - 09:20

Jeff Zehnder

Majid Zamani is designing safer self-driving car technology with math.

An associate professor of computer science at the �鶹ӰԺ, Zamani is working on a European Research Council (ERC) proof-of-concept project that uses mathematical models to guide autonomous vehicles, rather than relying on testing to capture every possible crash scenario.

“Existing autonomy software are not formally proven to work all the time,” Zamani said. “Waymo taxis carry sensor suites worth hundreds of thousands of dollars and are marketed as self-driving, yet they do not always operate autonomously. At times, they become stuck and require remote operator intervention, a limitation that can undermine public trust in the system.”

The issue is edge cases. Existing autonomous driving software incorporates results from millions of miles of travel, but cars still encounter new situations regularly. While humans easily adapt to unforeseen road conditions, machines do not.

When those incidents arise, automakers update their software to address the new scenario, each time adding more lines of codes. Some vehicles now exceed 100 million lines of computer codes, Zamani said.

“One might say that 98 percent of the challenge of autonomy has been solved, leaving only 2 percent unresolved. But that remaining 2 percent is still enormous. When measured against the millions of miles driven each day, even a small fraction of failure cases translates into a significant real-world problem,” Zamani said.

What if there was a better way?

Utilizing an ERC grant awarded through his visiting-professorship at the Ludwig-Maximilians-Universität München in Germany, Zamani wants self-driving cars to rely on concrete physics and mathematical formulas rather than endless testing of scenarios.

“These are Newton’s laws. We understand the relationship between velocity and acceleration, and we can calculate how long it will take a car to stop once it detects an obstacle. The mathematics is clean, and if we succeed, we can certify the system’s effectiveness,” he said.

Zamani and his team are focused specifically on lane changes and have made significant progress. Through the grant, they plan to soon test their work on an embedded NVIDIA Jetson AGX Orin platform using MORAI, a high-fidelity driving simulator that mimics real-world conditions.

“We have proven that our software is formally correct. Now we need to demonstrate it in practice,” he said.

Designing self-driving cars around mathematics and logic may seem like the obvious approach, but it requires substantial computation, which is one reason current systems do not fully rely on it.

“Imagine a busy intersection in a large city, with bicycles, pedestrians, traffic signals, other vehicles, and road conditions that shift with the weather. A mathematically grounded system must decide in real time how to respond, but the sheer number of interacting variables makes the problem extraordinarily complex, even though many of those interactions are ultimately governed by physics,” he said.

The team is developing methods to make its physics- and mathematics-based approaches more scalable. That includes both refining its algorithms and exploring neural networks and other machine learning techniques.

“Sometimes, a very small change in the model architecture can lead to an algorithm that scales much more effectively,” he said. “It is challenging, but we have made meaningful progress. Implementing the MORAI high-fidelity simulator is an important step toward showing that what we promise is possible and demonstrating provable safety in complex autonomous driving scenarios.”

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Research shines light on potential biases in Science paper acceptance

Emily Adams — Wed, 24 Sep 2025 16:55:59 +0000

Research shines light on potential biases in Science paper acceptance Emily Adams Wed, 09/24/2025 - 10:55

Unusual study highlights some notable disparities in the fate of the more than 68,000 submissions the journal received over the 6-year study period, says co-author Aaron Clauset.

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Alumnus receives international recognition for undergraduate thesis

Alexandra Grace Wilson — Thu, 21 Nov 2024 21:42:41 +0000

Alumnus receives international recognition for undergraduate thesis Alexandra Grac… Thu, 11/21/2024 - 14:42

Nathaniel Collins (Math'23) received the Outstanding Undergraduate Thesis Award from the Vienna Center for Logic and Algorithms for his work, "Count-Free Weisfeiler–Leman and Group Isomorphism" completed under supervision from Associate Professor Joshua Grochow.

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Robots can save us if they can see us: Heckman receives CAREER award

Emily Adams — Tue, 15 Oct 2024 20:07:19 +0000

Robots can save us if they can see us: Heckman receives CAREER award Emily Adams Tue, 10/15/2024 - 14:07

Grace Wilson

Autonomous robots could save human lives more easily if they could “see” and react better in adverse environmental conditions. By pursuing the possibilities of using millimeter wave radar for robotic perception, Christoffer Heckman is making this fundamental shift possible.

An associate professor of computer science at CU �鶹ӰԺ, Heckman will receive $600,000 over the next five years through the National Science Foundation's CAREER award for this research.

Currently, most robots use sensors based on the visible spectrum of light, like cameras or lasers. In environments with smoke, fog or dust, however, visible light bounces off these particles.

Robots, like humans, can't plan their movements accurately if they don't know where they are or what is around them.

"Humans operating in a visually degraded environment are in trouble. We cannot solve that problem, but incorporating millimeter wave radar could enable our robots to do things that even humans can't do," Heckman said.

This is because millimeter waves pass through smoke, fog and dust.

A new path

Traditionally, Heckman explained, radar has been viewed with skepticism for these kinds of tasks. The sensors have been too large and energy-intensive for agile robots. The long wavelength of radar creates complex, confused signals.

With the advent of new, smaller system-on-a-chip radar sensors, the traditional energy and size limitations have been removed. This leaves the complexity of radar waveform signals.

"This is a fascinating problem," Heckman explained. "People really understand how radar works, down to equations that have existed for almost a century, but radar can be difficult to precisely interpret in cluttered environments. It bounces around within an enclosed area, and can pass right through small objects."

Heckman's solution is to fuse the knowledge we have about electromagnetic waves with supervised machine learning.

Datasets from high-fidelity optical sensors are paired with low-fidelity radar signals of the same scene. Machine learning then cleans the radar signal to match the high-fidelity scene. This training then can be used to build clear radar reconstructions of environments where optical sensors are obscured.

This powerful synthesis of physics and computer science stands to dramatically improve the capability of radar as a perception sensor.

Beyond sensing

Heckman has further plans as well. He wants to use this advance to support quick and accurate actions and replanning for autonomous systems.

Robotic thinking has traditionally followed the saying "sense, plan, act." A robot understands a scene, plans its route according to its inputs, and acts on that plan. Segmenting these activities, however, can lead to slow movement and inability to react to changes.

Heckman seeks to use radar in conjunction with optical and lidar sensors to improve navigation strategies as a robot is navigating a space, allowing it to respond more quickly to changes.

Robots that can plan for themselves better and can see into obscured spaces have a valuable role in search-and-rescue, firefighting and space missions.

Heckman's MARBLE team has used robots to explore dark caves through the DARPA Subterranean Challenge and as a firefighting assistant finding active embers. As the research advances made possible by this CAREER Award take shape, where will robots be able to see next?

Humans operating in a visually degraded environment are in trouble. We cannot solve that problem, but incorporating millimeter wave radar could enable our robots to do things that even humans can't do." - Chris Heckman

Radar breakthrough in robotic sensing to help systems see and act in smoke, darkness recognized by $600,000 National Science Foundation award.

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A SPOT robot with a light enters a dark mine tunnel.

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Avinash Ratnavel wins alumni engagement medal

Anonymous — Mon, 29 Apr 2024 18:23:48 +0000

Avinash Ratnavel wins alumni engagement medal Anonymous (not verified) Mon, 04/29/2024 - 12:23

Ratnavel (MCompSci'18) was recognized by the College of Engineering and Applied Science for bringing support to networking events, mock interviews, events for career exploration and on-campus job interviews, among other accomplishments.

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Video - ChatGPT: Fear, Hype, or Hope? Education and research practices and ethics in the generative AI era

Anonymous — Thu, 20 Apr 2023 23:26:18 +0000

Video - ChatGPT: Fear, Hype, or Hope? Education and research practices and ethics in the generative AI era Anonymous (not verified) Thu, 04/20/2023 - 17:26

Grace Wilson

Three leading experts discuss how the disruptive and powerful elements of ChatGPT and other generative AI stand to transform our world. Jim Martin clarifies what a large language model like ChatGPT actually is, Diane Sieber urges the creation of norms around the usage of these tools while Tom Yeh focuses on its potential impacts on education.

[video:https://youtu.be/6nkKFmFOoOE]

Panelists:

Jim Martin (Department of Computer Science and Institute of Cognitive Science), an expert on natural language processing and large language models
Diane Sieber (Herbst Program for Engineering, Ethics & Society), a pioneer in education bridging technology, humanities and arts
Tom Yeh (computer science), a leading researcher in human-computer interaction who has studied the use of generative AI in introductory programming and K-12 settings.

Moderator:

Bobby Schnabel, external chair of computer science, founding director of the ATLAS Institute and former CEO of the Association for Computing Machinery.

Through this panel discussion attended by over 300 people from the university and general public, hear from leading experts on the technical areas underlying ChatGPT and other generative AI, the uses of generative AI in university and K-12 education, and the ethical and societal issues associated with generative AI tools.

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AI has social consequences, but who pays the price? Tech companies’ problem with ‘ethical debt’

Anonymous — Thu, 20 Apr 2023 23:05:20 +0000

AI has social consequences, but who pays the price? Tech companies’ problem with ‘ethical debt’ Anonymous (not verified) Thu, 04/20/2023 - 17:05

Casey Fiesler, Associate Professor of Information Science with a courtesy appointment in Computer Science, writes for The Conversation about how we can tackle possible negative consequences and societal harms from AI development. Links to external article.

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Computer science PhD student leads research into secrets of honeycomb formation

Anonymous — Thu, 20 Apr 2023 22:37:56 +0000

Computer science PhD student leads research into secrets of honeycomb formation Anonymous (not verified) Thu, 04/20/2023 - 16:37

Josh Rhoten

Researchers in the Department of Computer Science and BioFrontiers Institute are studying honeycomb formation in bees with the hope of one day recreating the same intricate and impressive hexagonal structures for other uses.

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Talking with the fireflies: Orit Peleg receives CAREER Award and Alfred P. Sloan Fellowship

Anonymous — Fri, 17 Feb 2023 17:36:27 +0000

Talking with the fireflies: Orit Peleg receives CAREER Award and Alfred P. Sloan Fellowship Anonymous (not verified) Fri, 02/17/2023 - 10:36

Grace Wilson

If we as humans can understand how fireflies in swarms synchronize, we can understand their communication more deeply. Their on-off flickers of light are more similar to our binary computational logic than the tremulous variations of a frog croak or pitches of bugling elk calls.

Fireflies' elegant, distributed communication systems could eventually help us with our own telecommunications through new ideas about compressing information and distributed networks.

Assistant Professor of computer science Orit Peleg has just received $900,000 over the next five years to learn how fireflies in a swarm synchronize their lighting displays. The funding was provided by a National Science Foundation CAREER award, a highly prestigious early-career grant for junior faculty members.

Peleg, a member of the Biofrontiers Institute and Department of Computer Science, seeks to create testable theories about animal communication with her lab by merging tools from physics, biology, math and computer science.

Female fireflies judge the blinking displays of males to determine their mate. When there are swarms of small, blinking insects that can stretch over miles in the dark, any way to cut down on the visual clutter is important, and this is where synchronizing their flash patterns comes together.

"There are some really interesting questions about how really similar signals on the level of individual fireflies can result in a different collective signal," Peleg said.

Some groups of fireflies begin to flash in a burst together and stop the burst together, while others flash in a matching pattern but offset in time from one another so that the overall swarm always has some members flashing.

Currently our telecommunication networks and other manmade technology require synchronization to a central clock, which means that the network is really sensitive to failures of that clock.

"But that's not how biology does it. Biology achieves synchronization in a distributed way, which is more robust to failures of individual nodes," Peleg said

Changing the Working Model

We currently describe fireflies' brilliant displays of synchronizing bioluminescence through mathematical models that don't fully account for the possible agency of fireflies to change their lighting patterns in response to other fireflies.

The models are also tied to limited experimental data, giving Peleg and her lab rich ground for research and experimentation.

In the field, the lab sets up dark tents in the middle of the firefly swarms. They bring fireflies inside and use LED lights to mimic their signal patterns. They've already seen that, by changing the LED flashes, they can change the responses of living fireflies.

"They kind of treat it as if it were another firefly. It's really fun to watch how the firefly responds and communicates back to the artificial light," Peleg said.

They have also had success in training fireflies to create light patterns they have never been observed making in the wild in response to the LED light's patterns.

Outside of the carefully controlled environment in the tent, the lab will also use low-cost video equipment throughout the swarms to create robust 3-D video that can be analyzed and turned into 3D models that match the observed behavior of the fireflies.

By creating a model from field-data, Peleg can create more testable and verifiable theories around the ways that fireflies manage their synchronization.

The field data can be used to create simulations of firefly lighting patterns where the actions of real fireflies placed in the system impact the response of the program's lights, which involves advanced image processing and hardware.

In addition, Peleg is excited to use the data created from these field recordings to offer a class called "Physics, Artificial Intelligence, and Generative Art of Agent-Based Models," where undergraduate and graduate students will be able to craft their own visualizations of the data captured by the cameras in the field. By bringing experiential learning to the forefront, Peleg hopes to support their joy of learning.

Assistant Professor of computer science Orit Peleg has just received $900,000 over the next five years to learn how fireflies in a swarm synchronize their lighting displays. She's using LEDs, VR and big tents in the wilderness to signal to the fireflies... and they're signaling back.

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