Duke Women's Golf Team Tied for Second in ACC Championship The Duke women's golf team began the 2026 ACC Championship with a strong performance, finishing 12-under-par over 36 holes and sitting tied for second place with 18 holes remaining in stroke play at the Porters Neck Country Club in Wilmington, North Carolina. The Blue Devils’ total score of 564 places them alongside SMU, while Stanford leads the field with a 23-under total of 553. The tournament, held at a 6,197-yard, par-72 course, will determine the top six teams advancing to match play, with an individual champion also being crowned following Friday’s final round. Duke’s freshman Rianne Malixi and junior Katie Li emerged as standout performers, securing top-six individual rankings. Malixi, who entered the final round two strokes behind leader Paula Martin Sampedro, carded bogey-free rounds of 67 and 68 to finish with a 9-under total of 135, placing her second overall. Her 564 total score also ties a Duke record for the lowest 36-hole score in ACC Championship play on a par-72 course. Malixi’s first-round 67, the lowest 18-hole score for Duke in the tournament’s history, showcased her dominance, with nine birdies over the two rounds. Li, meanwhile, finished with a 4-under total of 140, tying her for sixth. Her rounds of 69 and 71 included a strong start with three consecutive birdies in the first round, followed by a steady performance in the second round. Li’s 34th and 35th rounds of even or under par mark a career milestone, while her 15th round in the 60s highlights her consistency. The team’s performance was bolstered by contributions from other players. Freshman Avery McCrery, making her first tournament appearance since early March, posted a 3-under 69 in the first round, finishing with a 143 total (T14, -1).#smu #stanford #duke_womens_golf_team #porters_neck_country_club #wilmington_north_carolina
Martín Sampedro Wins Individual Title, Stanford Earns No. 1 Seed in Match Play - Atlantic Coast Conference Stanford junior Paula Martín Sampedro claimed the individual title at the 2026 ACC Women’s Golf Championship, finishing with a total of 14-under par (202) to secure medalist honors. This victory marks the second consecutive win for a Stanford player in the conference’s individual championship, following a similar achievement by a previous Cardinal athlete. The win propelled Stanford to the No. 1 seed in the upcoming match play tournament, granting the team a bye into the semifinals. The match play semifinals are set to begin on Saturday, April 18, at 1:30 p.m. ET, broadcast live on ACC Network Extra. Stanford’s team performance was equally impressive, finishing with a combined score of 33-under par (831), breaking the tournament’s 54-hole team record by six strokes. This surpassed the previous record set by Virginia in 2015 and Stanford’s own mark from the previous year. The Cardinal’s dominance was evident as they finished 11 strokes ahead of second-place SMU, both teams earning byes into the semifinals. Wake Forest secured the third seed with a total of 850 (-14), while North Carolina claimed the final match play spot by edging out Clemson by a single stroke. The match play bracket will see Wake Forest face North Carolina in the semifinals, with the winner advancing to face Stanford. Wake Forest’s Chloe Kovelesky finished third in the individual standings at 9-under par (207), contributing significantly to the team’s success. Duke, ranked fourth at 851 (-13), will face NC State, which finished fifth at 852 (-12). This matchup will determine the final two teams to compete against Stanford in the semifinals.#atlantic_coast_conference #stanford #paula_martn_sampedro #acc_network_extra #wake_forest
SMU Women's Golf Team Advances to ACC Championship Semifinals SMU women's golf secured a second-place finish in the stroke play portion of the 2026 ACC Women's Golf Championship, earning a bye to the match play semifinals. The Mustangs tied for the lowest round of the day at 10-under par, surpassing Duke to claim the No. 2 seed. This advancement positions them to compete for a championship spot without needing to play in the quarterfinals. Head coach Lauren Mason praised the team's focus and competitiveness, emphasizing their ability to perform under pressure. "This group has a lot of match play experience," Mason said, expressing confidence in the team's depth. The Mustangs finished ahead of nine nationally ranked teams, trailing only Stanford, the top-ranked squad in the country. They will face the winner of the 3-seed versus 6-seed matchup in the semifinals. Key players contributed significantly to the team's success. Mackenzie Lee, a senior from North Little Rock, Arkansas, shot five-under par in her final round to move into fourth place. Her performance marked her second consecutive top-five finish, including a victory at the Huntington Bank Collegiate. Lee also achieved her 500th career birdie during the tournament, lowering her career scoring average to 71.51. Emily Odwin, another standout, carded a 68 in the final round, securing three rounds of par or better at the tournament. Her five birdies and one bogey in the final round propelled her to a tie for eighth place, her fifth top-10 finish of the season and 13th of her career. Grace Jin finished 11th after a 72 in the final round, just one stroke off her best season finish of 10th. Jin’s performance included rounds of 69 and 71 in the first two rounds. Celine Chen, who moved into 21st place with a 74 on Friday, registered a 68 in the first round.#stanford #smu_womens_golf_team #lauren_mason #mackenzie_lee #emily_odwin
A generalizable deep learning system for cardiac MRI Cardiac MRI provides a detailed assessment of myocardial structure, function, and tissue properties. This study introduces a foundational vision system for cardiac MRI capable of representing the full spectrum of human cardiovascular disease and health. The deep-learning model is trained using self-supervised contrastive learning, where visual concepts from cine-sequence cardiac MRI scans are derived from the raw text of accompanying radiology reports. The model is trained and evaluated on data from four major U.S. academic clinical institutions and further tested on the UK BioBank and two additional public datasets. The system demonstrates strong performance across diverse tasks, including left-ventricular ejection fraction regression and the diagnosis of 39 conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. It achieves clinical-grade diagnostic accuracy with significantly less training data than traditional methods. Traditional deep-learning approaches for cardiovascular disease diagnosis rely on supervised training with curated datasets of predefined conditions. However, these systems often struggle with real-world clinical data, which is heterogeneous and includes multiple concurrent abnormalities. For example, patients with inherited cardiomyopathies may also have severe valvular disease, while those with ventricular thrombus may exhibit signs of heart failure from past ischemic events. Supervised models trained for one task rarely generalize to others, requiring retraining from scratch for each new clinical problem. This process demands thousands of labeled examples and lacks the contextual understanding that human clinicians inherently possess.#stanford #ucsf #medstar #acdc_dataset #uk_biobank
Atlassian CEO's Layoff Letter Is Good News for Graduates Atlassian’s CEO, Mike Cannon-Brookes, outlined three categories of employees the company prioritized retaining during recent layoffs, offering a positive outlook for recent graduates in the job market. The software firm announced it was cutting 1,600 jobs, or about 10% of its global workforce, to redirect resources toward its AI initiatives. Cannon-Brookes emphasized retaining high performers, employees with transferable skills, and graduates, signaling confidence in their value despite broader economic challenges. The decision contrasts with growing concerns about AI’s impact on entry-level roles. Recent studies suggest that younger workers, particularly those aged 22 to 25, face heightened risks as AI tools automate tasks traditionally handled by entry-level professionals. For instance, Stanford researchers noted a 16% relative employment decline for early-career workers in AI-exposed fields. Anthropic CEO Dario Amodei has also warned that up to half of entry-level white-collar jobs could be displaced by AI within the next 1 to 5 years. Despite these trends, Atlassian’s hiring practices suggest a different trajectory. Last October, Cannon-Brookes stated the company was increasing its recruitment of new graduates, citing the need for fresh talent to drive innovation in research and development. He argued that graduates bring a unique perspective to software development, capable of reshaping the industry. This stance aligns with the firm’s recent hiring numbers: 95 new graduates joined in February 2025, and 108 were set to start in February 2026. Cannon-Brookes’ letter to employees did not elaborate on the rationale for prioritizing graduates, but several possibilities exist.#dario_amodei #atlassian #mike_cannonbrookes #anthropic #stanford
