Research

Best paper award named for retired С�� Boulder professor

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

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

Harold "Hal" Gabow retired from С�� Boulder 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 С�� Boulder'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 С�� Boulder, 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 С�� Boulder’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 University of Colorado Boulder, 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 С�� Boulder, 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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A delicate touch: teaching robots to handle the unknown

Anonymous — Tue, 02 Apr 2024 19:39:31 +0000

A delicate touch: teaching robots to handle the unknown Anonymous (not verified) Tue, 04/02/2024 - 13:39

Grace Wilson

William Xie, a first-year PhD student in computer science, is teaching a robot to reason how gently it should grasp previously unknown objects by using large language models (LLMs).

DeliGrasp, Xie's project, is an intriguing step beyond the custom, piecemeal solutions currently used to avoid pinching or crushing novel objects.

In addition, Deligrasp helps the robot translate what it can 'touch' into meaningful information for people.

"William has gotten some neat results by leveraging common sense information from large language models. For example, the robot can estimate and explain the ripeness of various fruits after touching them." Said his advisor, Professor Nikolaus Correll.

Let's learn more about DeliGrasp, Xie's journey to robotics, and his plans for the conference Japan and beyond.

[video:https://www.youtube.com/watch?v=OMzTgY1gxLw]

How would you describe this research?

As humans, we’re able to quickly intuit how exactly we need to pick up a variety of objects, including delicate produce or unwieldy, heavy objects. We’re informed by the visual appearance of an object, what prior knowledge we may have about it, and most importantly, how it feels to the touch when we initially grasp it.

Robots don’t have this all-encompassing intuition though, and they don’t have end-effectors (grippers/hands) as effective as human hands. So solutions are piecemeal: the community has researched “hands” across the spectrum of mechanical construction, sensing capabilities (tactile, force, vibration, velocity), material (soft, rigid, hybrid, woven, etc…). And then the corresponding machine learning models and/or control methods to enable “appropriately forceful” gripping are bespoke for each of these architectures.

Embedded in LLMs, which are trained on an internet’s worth of data, is common sense physical-reasoning that crudely approximates a human’s (as the saying goes: “all models are wrong, some are useful”). We use the LLM-estimated mass and friction to simplify the grasp controller and deploy it on a two-finger gripper, a prevalent and relatively simple architecture. Key to the controller working is the force feedback sensed by the gripper as it grasps an object, and knowing at what force threshold to stop—the LLM-estimated values directly determine this threshold for any arbitrary object, and our initial results are quite promising.

How did you get inspired to pursue this research?

I wouldn’t say that I was inspired to pursue this specific project. I think, like a lot of robotics research, I had been working away at a big problem for a while, and stumbled into a solution for a much smaller problem. My goal since I arrived here has been to research techniques for assistive robots and devices that restore agency for the elderly and/or mobility-impaired in their everyday lives. I’m particularly interested in shopping (but eventually generalist) robots—one problem we found is that it is really hard to determine, let alone pick ripe fruits and produce with a typical robot gripper and just a camera. In early February, I took a day to try out picking up variably sized objects via hand-tuning our MAGPIE gripper’s force sensing (an affordable, open-source gripper developed by the Correll Lab). It worked well; I let ChatGPT calibrate the gripper which worked even better, and it evolved very quickly into DeliGrasp.

What would you say is one of your most interesting findings so far?

LLMs do a reasonable job of estimating an arbitrary object’s mass (friction, not as well) from just a text description. This isn’t in the paper, but when paired with a picture, they can extend this reasoning for oddballs—gigantic paper airplanes, or miniature (plastic) fruits and vegetables.

With our grasping method, we can sense the contact forces on the gripper as it closes around an object—this is a really good measure of ripeness, it turns out. We can then further employ LLMs to reason about these contact forces to pick out ripe fruit and vegetables!

What does the day-to-day of this research look like?

Leading up to submission, I was running experiments on the robot and picking up different objects with different strategies pretty much every day. A little repetitive, but also exciting. Prior to that, and now that I’m trying to improve the project for the next conference, I spend most of my time reading papers, thinking/coming up with ideas, and setting up small, one-off experiments to try out those ideas.

How did you come to study at С�� Boulder?

For a few years, I’ve known that I really wanted to build robots that could directly, immediately help my loved ones and community. I had a very positive first research experience in my last year of undergrad and learned what it felt like to have true personal agency in pursuing work that I cared about. At the same time I knew I’d be relocating to Boulder after graduation. I was very fortunate that Nikolaus accepted me and let me keep pursuing this goal of mine.

It’d be unfathomable if I could keep doing this research in academia or industry, though of course that would be ideal. But I’m biased toward academia, particularly teaching. I’ve been teaching high school robotics for 5 years now, and now teaching/mentoring undergrads at С��—each day is as fulfilling as the first. I have great mentors across the robotics faculty and senior PhD students we work in ECES 111, a giant, well-equipped space that 3 robotics labs share, and it’s great for collaboration and brainstorming.

What are your hopes for this international conference (and what conference is it?)

The venue is a workshop at the 2024 International Conference on Robotics and Automation (ICRA 2024), happening in Yokohama, Japan from May 13-17. The name of the workshop is a mouthful: Vision-Language Models for Navigation and Manipulation (VLMNM).

A workshop is detached from the main conference, and kind of is its own little bubble (like a big supermarket—the conference—hosting a pop-up food tasting event—the workshop). I'm really excited to meet other researchers and pick their brains. As a first-year, I’ve spent the past year reading papers from practically everyone on the workshop panel, and from their students. I’ll probably also spend half my time exploring (eating) around the Tokyo area.

William Xie, a first-year PhD student in computer science, is teaching a robot to reason how gently it should grasp previously unknown objects by using large language models (LLMs).

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Harsh workplace climate is pushing women out of academia

Anonymous — Tue, 24 Oct 2023 16:17:15 +0000

Harsh workplace climate is pushing women out of academia Anonymous (not verified) Tue, 10/24/2023 - 10:17

Yvaine Ye

Women faculty are more likely to leave academia than men faculty throughout all career stages in U.S. universities found Katie Spoon, the paper’s first author and a doctoral student in the Department of Computer Science.

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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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Research

Best paper award named for retired С���� Boulder professor

Tell us about how Transactions on Algorithms got started.

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

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

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

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

What do you see as С���� Boulder’s most important contributions to computer science theory?

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Toward safer self-driving cars

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Research shines light on potential biases in Science paper acceptance

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Alumnus receives international recognition for undergraduate thesis

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

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Avinash Ratnavel wins alumni engagement medal

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A delicate touch: teaching robots to handle the unknown

How would you describe this research?

How did you get inspired to pursue this research?

What would you say is one of your most interesting findings so far?

What does the day-to-day of this research look like?

How did you come to study at С���� Boulder?

What are your hopes for this international conference (and what conference is it?)

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Harsh workplace climate is pushing women out of academia

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

Panelists:

Moderator:

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

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Best paper award named for retired С�� Boulder professor

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

What do you see as С�� Boulder’s most important contributions to computer science theory?

How did you come to study at С�� Boulder?