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The University of Texas at Austin continues to be one of the premier schools for graduate studies, according to U.S. News & World Reports partial release of its most recent Best Graduate Schools. UT made gains in several disciplines, including computer science.

Overall, the University has 42 graduate schools and specialty programs ranked in the top 10 when combined with previous years, including a dozen in the College of Natural Sciences. The publication updates some of its specialty rankings each year and republishes the most recent rankings in other areas. Additionally, U.S. News delayed release of some rankings.

These numbers are meaningful. Having more than 40 schools, programs and specialties ranked in the top 10 in this partial release alone, including several that are the best in the country, if not the world, is reflective of our ability to continue to attract exceptional faculty and students, said President Jay Hartzell. Our talent is what puts UT at the leading edge of discovery in AI and robotics, life sciences, population research, and many other disciplines that are at the forefront of solving many of the worlds most pressing problems and bettering society.

The College of Natural Sciences has 12 programs and specialties ranked among the top 10 in the most recent ranking for a discipline, the most of any college or school at UT Austin. Computer Science (No. 7) scored top 10 rankings in all four disciplines specialties that are ranked within departments.

U.S. News & World Reports graduate rankings, which are published separately from the magazines yearly ranking of undergraduate programs, are considered the gold standard of graduate and professional rankings. They are based on surveys of academic leaders and, for select programs, additional quantitative measures including placement test scores, student/faculty ratios, research expenditures and job placement success.

UT Austin graduate schools, programs and specialties within Natural Sciences that U.S. News & World Report ranked this year or last are listed below.

Chemistry 16*

Computer Science 7

Mathematics 13*

Physics 13*

Statistics 27*

* Ranking not revised for 2024-25.

In the most recent life science rankings from U.S. News, UT Austin ranked in the top 25 for biological sciences, including ranking 8th in ecology/evolutionary biology; in the most recent ranking for plasma physics, UT ranked third.

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U.S. News Ranks UT Austin Computer Science Among Best in Graduate Program Rankings - College of Natural Sciences

Tech experts say that users produce more than34 million images per dayusing artificial intelligence, or AI, tools such asMidjourneyandDALL-E 2. The results are often inventive and astonishing.

While people might find making AI-generated art a relaxing, creative outlet, these images come at a cost. Server farms, giant data centers full of computers, will consume more energy each year processing AI art thanthe entire country of Argentina. In 2023, Google used5.6 billion gallons of waterjust cooling its servers.

The challenge of how to make these artistic tools available to those who want to use them while keeping an eye on sustainability is a problem that computer science doctoral student Maitreya Patel is keen to solve.

Patel has been working under the supervision ofYezhou YZ Yang, an associate professor of computer science and engineering in theSchool of Computing and Augmented Intelligence, part of theIra A. Fulton Schools of Engineeringat Arizona State University. Yang heads theActive Perception Group, a lab that studies computer vision and image generative AI.

Yang oversees several projects funded by grants from theNational Science Foundationdedicated to researching computer visual recognition tools. Some of the novel work being done there seeks to make a system that can create an image, check out what it has produced and learn from the comparison. The computer might draw a dog, scan the image, ask itself if the picture looks like a dog and then update its programming based on the results.

As part of his doctoral research, Patel has createdEclipse, a resource-efficient tool that takes in text prompts and then produces images. He made a demonstration website where a user can type in a short description of what they would like to see, and the AI tool will generate a picture.

The work deals with the central problem of training a model.

Today, most AI solutions have been created by feeding large sets of data into networks of computers and training models tweaking the algorithms, or sets of instructions, that the computers use to do their work. The software engineer supplies a computer with thousands of pictures of dogs and then tasks it with generating its own dog images.

But Patel and Yang believe there are better ways of harnessing the power of AI than simply using more and more computers to process more and more data.

We have created a new model pipeline, Patel says. Our model will use a small number of processing units, and it can be trained in one to two days.

The teams work is concerned with three basic issues: creating an image-generating model that requires less time and computational resources to train; producing a good open-source system that can be reused; and, finally, making software that users can train exclusively with their own images.

To make a more efficient image-generation model, they have a few new ideas. One is using a training strategy called contrastive learning, or teaching the computer what information is not relevant to get the right result.

Patel and Yang are also using adversarial training, a programming technique that deliberately attacks the image model and tries to get it to fail.

The advantage to this type of training is that we can discover the shortcomings of the current model, deal with its disadvantages and improve the system based on attacking the results, Yang says.

Patel and Yang are concerned about matters of privacy and ethics. Many AI image tools arerife with controversyabout the provenance of the art that was used to train their models.

The Eclipse team which also includes computer science doctoral students Sheng Cheng and Sangmin Jung, computer engineering doctoral student Changhoon Kim and Chitta Baral, a professor of computer science and engineering, who is serving in an advising role has a plan that could enable businesses to deploy their own versions of the Eclipse model and train it using only images owned by that enterprise. Concerns about lawsuits or image sources would be eliminated.

These are ideas that are interesting to artists working in the AI space.Erika Gronek, a Fulton Schools photographer, has used AI art tools and even written a book on AI art called "Uncanny: AI Speaks for Itself."

AI isnt going away, Gronek says. It has its critics, and rightly so, but it can also be viewed as another tool in the toolbox for an artist. At the very least, it should be wielded ethically by using proper datasets and sustainably because it can require such immense computing power.

In June, Yang and the research team will present their work at the prestigiousIEEE/CVF Conference on Computer Vision and Pattern Recognitionin Seattle. They are also on the lookout for enterprise partners who might want to back further development of their technology.

Were trying to figure out the sweet spot where vision and language meet to make critical improvements to the efficiencies of these models, Yang says.

He also hopes the project will inspire more doctoral and masters thesis students like Patel.

Exciting work in AI is being done here at the School of Computing and Augmented Intelligence, Yang says. We want to attract and inspire new doctoral and masters thesis students, help them develop professionally and showcase their efforts.

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Computer science student creates new tool to make AI-generated art more sustainable | ASU News - ASU News Now

Boston Universitys Metropolitan College seeks a dynamic and creative faculty member with expertise in data analytics or software development to join its nationally recognized Department of Computer Science (http://www.bu.edu/csmet/) at the rank of Associate Professor of the Practice starting July or September 1, 2024.

The Department is a leader in providing rigorous and industry-relevant education in areas such as information security, computer networks, computer information systems, financial informatics, digital forensics, and health informatics. Courses are offered in flexible face-to-face, blended and online formats. It offers the acclaimed Boston University online Master in Computer Information Systems program, consistently ranked in the top ten best online IT programs by US News & World Report for ten years. The department believes that the cultural and social diversity of our faculty, staff, and students is vitally important to the distinction and excellence of our research and academic programs. To that end, we are especially eager to have colleagues join our ranks who support our institutional commitment to ensuring BU is inclusive, equitable, and diverse.

Qualifications: The ideal candidate should hold a Ph.D. or equivalent degree in Computer Science or a related field, and have a demonstrated successful teaching and research record. We welcome applications from candidates with a terminal degree who have extensive professional and industry experience as well as an interest in teaching and applied research.

Responsibilities: Faculty duties include teaching up to six courses at the graduate and undergraduate levels per academic year, including blended and online courses, conducting active research, participating in course and curriculum development, and mentoring part-time faculty and students, as well as providing service to the department, college and the university. This is a full-time, non-tenure track appointment at the rank of Associate Professor of the Practice with renewable contracts of up to five years.

The faculty position offers significant opportunity for interdisciplinary and collaborative scholarly work, including research and curriculum development within Metropolitan College and Boston University, as well as the greater Boston area.

Salary: Commensurate with experience.

Application: Applicants should submit a cover letter stating career objectives, suitability for the position, research goals, and approach to teaching, as well as a curriculum vitae, and three letters of reference through the link below. Candidates who, within their application materials, substantively address their experiences, strengths, and opportunities for growth regarding diversity, inclusivity, and full participation at Boston University will receive the highest priority and consideration. Applications will be reviewed as received until the position is filled.

Please submit applications through this link: https://www.bu.edu/met/faculty-staff/forms/full-time-faculty-applications/

Department Contact:

Ms. Lyn Ariyakulkan

Boston University Metropolitan College

1010 Commonwealth Avenue, Floor 5

Boston, MA 02215

lyna@bu.edu

BU conducts a background check on all final candidates for certain faculty and staff positions. The background check includes contacting the final candidates current and previous employer(s) to ask whether, in the last seven years, there has been a substantiated finding of misconduct violating that employers applicable sexual misconduct policies. To implement this process, the University requires a final candidate to complete and sign the form entitled Authorization to Release Information after execution of an offer letter.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. We are a VEVRAA Federal Contractor.

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Associate Professor of the Practice, Computer Science job with Boston University Metropolitan College | 37636253 - The Chronicle of Higher Education

Leading Theoretical Computer Scientist Cited for Field-Defining Contributions

NEW YORK, April 10, 2024 /PRNewswire/ --ACM, the Association for Computing Machinery, today named Avi Wigderson as recipient of the 2023 ACM A.M. Turing Award for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science.

The ACM A.M. Turing Award, often referred to as the "Nobel Prize of Computing," carries a $1 million prize.

Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. He has been a leading figure in areas including computational complexity theory, algorithms and optimization, randomness and cryptography, parallel and distributed computation, combinatorics, and graph theory, as well as connections between theoretical computer science and mathematics and science.

The ACM A.M. Turing Award, often referred to as the "Nobel Prize of Computing," carries a $1 million prize with financial support provided by Google, Inc. The award is named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing.

What is Theoretical Computer Science?Theoretical computer science is concerned with the mathematical underpinnings of the field. It poses questions such as "Is this problem solvable through computation?" or "If this problem is solvable through computation, how much time and other resources will be required?"

Theoretical computer science also explores the design of efficient algorithms. Every computing technology that touches our lives is made possible by algorithms. Understanding the principles that make for powerful and efficient algorithms deepens our understanding not only of computer science, but also the laws of nature. While theoretical computer science is known as a field that presents exciting intellectual challenges and is often not directly concerned with improving the practical applications of computing, research breakthroughs in this discipline have led to advances in almost every area of the fieldfrom cryptography and computational biology to network design, machine learning, and quantum computing.

Why is Randomness Important?Fundamentally, computers are deterministic systems; the set of instructions of an algorithm applied to any given input uniquely determines its computation and, in particular, its output. In other words, the deterministic algorithm is following a predictable pattern. Randomness, by contrast, lacks a well-defined pattern, or predictability in events or outcomes. Because the world we live in seems full of random events (weather systems, biological and quantum phenomena, etc.), computer scientists have enriched algorithms by allowing them to make random choices in the course of their computation, in the hope of improving their efficiency. And indeed, many problems for which no efficient deterministic algorithm was known have been solved efficiently by probabilistic algorithms, albeit with some small probability of error (that can be efficiently reduced). But is randomness essential, or can it be removed? And what is the quality of randomness needed for the success of probabilistic algorithms?

These, and many other fundamental questions lie at the heart of understanding randomness and pseudorandomness in computation. An improved understanding of the dynamics of randomness in computation can lead us to develop better algorithms as well as deepen our understanding of the nature of computation itself.

Wigderson's ContributionsA leader in theoretical computer science research for four decades, Wigderson has made foundational contributions to the understanding of the role of randomness and pseudorandomness in computation.

Computer scientists have discovered a remarkable connection between randomness and computational difficulty (i.e., identifying natural problems that have no efficient algorithms). Working with colleagues, Wigderson authored a highly influential series of works on trading hardness for randomness. They proved that, under standard and widely believed computational assumptions, every probabilistic polynomial time algorithm can be efficiently derandomized (namely, made fully deterministic). In other words, randomness is not necessary for efficient computation. This sequence of works revolutionized our understanding of the role of randomness in computation, and the way we think about randomness. This series of influential papers include the following three:

Importantly, the impact of these three papers by Wigderson goes far beyond the areas of randomness and derandomization. Ideas from these papers were subsequently used in many areas of theoretical computer science and led to impactful papers by several leading figures in the field.

Still working within the broad area of randomness in computation, in papers with Omer Reingold, Salil Vadhan, and Michael Capalbo, Wigderson gave the first efficient combinatorial constructions of expander graphs, which are sparse graphs that have strong connectivity properties. They have many important applications in both mathematics and theoretical computer science.

Outside of his work in randomness, Wigderson has been an intellectual leader in several other areas of theoretical computer science, including multi-prover interactive proofs, cryptography, and circuit complexity.

MentoringIn addition to his groundbreaking technical contributions, Wigderson is recognized as an esteemed mentor and colleague who has advised countless young researchers. His vast knowledge and unrivaled technical proficiencycoupled with his friendliness, enthusiasm, and generosityhave attracted many of the best young minds to pursue careers in theoretical computer science.

"It's important to point out that Avi Wigderson also received the Abel Prize, which is considered the most important honor for lifetime achievements in the field of mathematics," explained ACM President Yannis Ioannidis. "Being selected for the ACM A.M. Turing Award is a fitting follow-upas mathematics is foundational to computer science and Wigderson's work has connected a wide range of mathematical sub-areas to theoretical computer science. Wigderson is a towering intellectual force in theoretical computer science, an exciting discipline that attracts some of the most promising young researchers to work on the most difficult challenges. This year's Turing Award recognizes Wigderson's specific work on randomness, as well as the indirect but substantial impact he has had on the entire field of theoretical computer science."

"Avi Wigderson's work on randomness and other topics has set the agenda in theoretical computer science for the past three decades," explained Jeff Dean, Senior Vice President, Google. "From the earliest days of computer science, researchers have recognized that incorporating randomness was a way to design faster algorithms for a wide range of applications. Efforts to better understand randomness continue to yield important benefits to our field, and Wigderson has opened new horizons in this area. Google also salutes Wigderson's role as a mentor. His colleagues credit him with generating great ideas and research directions, and then motivating a new generation of smart young researchers to work on them. We congratulate Avi Wigderson on receiving the ACM A.M. Turing Awardcomputing's highest honor."

Biographical BackgroundSince 1999, Avi Wigderson has been the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. Earlier, he was a Professor at the Hebrew University of Jerusalem and held visiting appointments at Princeton University, the University of California at Berkeley, IBM, and other institutions.

A graduate of The Technion Israel Institute of Technology, Wigderson earned MA, MSE, and PhD degrees in Computer Science from Princeton University. Wigderson's honors include the Abel Prize, the IMU Abacus Medal (previously known as the Nevanlinna Prize), the Donald E. Knuth Prize, the Edsger W. Dijkstra Prize in Distributed Computing, and the Gdel Prize. He is an ACM Fellow and a member of the U.S. National Academy of Sciences and the American Academy of Arts and Sciences.

About the ACM A.M. Turing AwardThe A.M. Turing Awardis named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing, and who was a key contributor to the Allied cryptanalysis of the Enigma cipher during World War II. Since its inception in 1966, the Turing Award has honored the computer scientists and engineers who created the systems and their underlying theoretical foundations that have propelled the information technology industry.

About ACMACM, the Association for Computing Machinery, is the world's largest educational and scientific computing society, uniting computing educators, researchers, and professionals to inspire dialogue, share resources, and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

SOURCE Association For Computing Machinery, Inc.

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AVI WIGDERSON RECEIVES ACM A.M. TURING AWARD FOR GROUNDBREAKING INSIGHTS ON RANDOMNESS - PR Newswire

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New work by researchers at CMU and the Indian Institute of Technology (IIT) Gandhinagar shows that adding an inexpensive thermal camera to wearable devices could substantially improve how accurately they estimate calories burned.

Credit: Carnegie Mellon University

Any fitness buff will tell you that the estimates of calories burned made by smartphones, smartwatches and other wearable devices vary wildly. That's because these devices lack the sensors required to gather all the information they need to make accurate estimates.

But new work by researchers at Carnegie Mellon University and the Indian Institute of Technology (IIT) Gandhinagar shows that adding an inexpensive thermal camera to these devices could substantially improve accuracy.

Using the thermal camera to monitor a person's breathing rate and body temperature could reduce inaccuracies in energy expenditure estimates from nearly 40% with a current smartwatch to just under 6%, saidMayank Goel, an associate professor in the School of Computer Science'sSoftware and Societal Systems Department(S3D) andHuman-Computer Interaction Institute(HCII).

But respiratory and heart rates aren't sufficient because they fail to take individual physical and contextual differences into account, Goel said.

The gold standard for clinically measuring energy expenditure is a calorimeter, which uses heart rate, respiration and the concentration of carbon dioxide in exhaled breaths to determine calories burned. Wearables already do a reasonable job at measuring heart rate and adding a thermal camera would provide a means for measuring respiratory rate. No solution exists for measuring CO2 concentrations with a wearable device, but the thermal camera could measure body temperature.

"We lose the ability to measure the concentration of oxygen and CO2, but we gained temperature measurements," Goel said.

The combination of those three data points, with help from machine learning, enabled the researchers to develop a system,called JoulesEye, for estimating energy expenditure. They recruited 54 participants who either cycled or ran for 15 minutes. Their tests showed that JoulesEye could estimate burned calories with an error rate of just 5.8% when compared to a clinical calorimeter.

In addition to helping fitness buffs, JoulesEye could be used in sports training, as well as for monitoring people with chronic diabetes or cardiovascular disease.

A report on JoulesEye, co-authored by Goel, Adhikary, Sadeh and Nipun Batra, an assistant professor of computer science at IIT Gandhinagar, has been published in the Proceedings of the ACM Mobile, Wearable and Ubiquitous Technologies and will be presented in October at theUbiComp 2024conference in Melbourne, Australia.

The cost of incorporating a low-resolution thermal camera into wearable devices should be feasible, as these cameras are already available for $45 or less. But Goel says the team is working to incorporate an even lower-resolution thermal camera into the system, which could lower the price of the sensor. It would also reduce privacy concerns about a camera being routinely pointed at the user's face.

The team also hopes to reduce the amount of time the thermal camera must be aimed at the user's face. It now takes about 40 seconds to make the necessary measurements.

"Our goal is that the time it takes to check your watch should be enough time to get the information we need," Goel said.

"When people see these numbers, they make changes in their behavior and that can be troublesome if the numbers are wrong," Goel said. Someone who thinks they just burned 400 calories on the treadmill, for instance, may eat more calories throughout the day, even though their actual expenditure was closer to 200 calories.

"That is a huge problem," he added.

Monitoring respiration has been a longtime interest of Goel and his Smart Sensing for Humans (SMASH) Lab, which develops technologies for such applications as health sensing and activity recognition. For instance, he previously developed ways to measure breathing using several methods ranging from wireless router data to custom wearables that analyze chest movements.

While pursuing a different project, Maite Sadeh, a Cornell information sciences major who was a SMASH Lab summer intern, found reports on how respiration could be measured using a thermal camera to detect exhalations of hot air. Goel's group then realized that inhalation leads to evaporation around the lips and nostrils. Both of these signals can be captured by a thermal camera.

Rishiraj Adhikary, a Ph.D. student in computer science at IIT Gandhinagar who was also a lab intern via a Fulbright Scholarship, then found studies showing that respiration combined with heart rate could be used to measure energy expenditure.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Thermal camera senses breathing to improve exercise calorie estimates - EurekAlert