Tag: Higher Education

  • 10 Flexible College Majors for Multi-Industry Career Opportunities

    10 Flexible College Majors for Multi-Industry Career Opportunities

    What Makes a Major Flexible

    A major counts as flexible when it meets three conditions:

    • The skills transfer to different jobs.
    • Multiple sectors hire people with those skills.
    • Graduates can add specialization later without starting a new degree.

    Flexibility does not mean every job is open to every graduate. Regulated professions may require licenses, graduate degrees, or specific credentials. A transferable major supports several career directions. Additional qualifications provide the specialization.

    10 Majors That Travel Well

    The table below maps each major to its core transferable skill, the industries that use it, and where extra qualifications come into play.

    Major Core Transferable Skill Industries That Hire Additional Qualifications
    Computer Science Programming, data analysis, problem-solving Tech, finance, healthcare, manufacturing Some roles may require specialized certifications.
    Business Administration Management, operations, finance Virtually every industry MBA or specialized certifications for executive roles.
    Economics Quantitative reasoning, data interpretation Finance, government, consulting, tech Graduate degree for research positions.
    Mathematics Abstract reasoning, modeling Finance, data science, education Additional programming skills often give an edge.
    Communications Writing, public speaking, media Media, marketing, corporate, nonprofit Portfolio and professional experience are crucial.
    Psychology Human behavior analysis, research HR, marketing, healthcare, education Licensure is required for clinical practice.
    Engineering Systems thinking, technical design Manufacturing, tech, energy, construction Professional engineering license for certain roles.
    Biology Research, lab skills, data interpretation Healthcare, biotech, food safety, environment Advanced degree for independent research.
    Sociology Social research, statistical analysis Policy, community work, marketing, education Graduate degree for research-oriented roles.
    Data Science Machine learning, statistics, coding Tech, finance, healthcare, retail Certifications can boost employability and specialization.

    Why the Same Skill Works in Different Rooms

    The pattern across these ten majors is not the subject matter. It is the underlying capability. A mathematics graduate and an economics graduate both reason through uncertainty, just with different source materials. A sociology graduate and a psychology graduate both study behavior—one at the group level, the other at the individual level. Employers in unrelated sectors often need the same reasoning process, dressed in different vocabulary. That overlap lets a graduate move from a bank to a health system or from a media company to a policy think tank—no four-year restart required.

    Where the Degree Ends and Experience Begins

    A transferable major opens a door. It rarely walks a graduate through it. In many cross-industry roles, employers weigh the degree alongside what a candidate has actually built. Internships, projects, and applied work count. A computer science graduate who has only completed coursework competes differently than one who has shipped a working product. The degree signals aptitude; experience signals readiness. Both matter, and neither substitutes for the other.

    Specialization sits on top of the major rather than replacing it. A biology graduate does not need a new degree to move into food safety—but a nursing or clinical role would ask for one. An engineering graduate can move into risk analysis at a bank without retraining, while a licensed structural role demands formal certification. The major supplies the foundation; the credential supplies the door key for specific, regulated rooms.

    Final Thoughts

    The next shift in hiring will likely reward graduates who can name their transferable skills directly rather than let a job title imply them. A biology graduate who can say ‘I run controlled experiments and interpret data under uncertainty’ competes for more roles than one who simply lists a degree. The skill, stated plainly, travels further than the diploma ever could.

    FAQ

    1. Which college majors offer the most career flexibility?

    Majors such as computer science, business administration, economics, mathematics, communications, psychology, and engineering can support careers across multiple industries since they develop transferable skills.

    2. What makes a college major useful across multiple industries?

    A major is more versatile when it develops skills such as quantitative reasoning, research, communication, problem-solving, or systems thinking that employers use across different sectors.

    3. Can I change industries after completing college?

    Yes. Graduates can move between industries when their existing skills match the requirements of a new role. Internships, certifications, projects, and relevant work experience can make the transition easier.

    4. Does a flexible college major guarantee more career options?

    No. A versatile degree can provide a broad foundation, but career options also depend on experience, specialization, location, employer requirements, and additional qualifications.

    5. Do all careers accept graduates from different college majors?

    No. Some professions require specific degrees, licenses, certifications, or graduate-level education. Regulated fields may have additional eligibility requirements.

  • AI Regulation and Government Policy Are Rewriting India’s Data Science Curriculum

    AI Regulation and Government Policy Are Rewriting India’s Data Science Curriculum

    Governments worldwide are moving AI education from the margins to the center of national policy. Infrastructure, curriculum, and regulation are now being built together to expand the AI talent pool. Nowhere is this shift more visible than in India, where the IndiaAI Mission and the National Education Policy (NEP) 2020 are working as parallel engines, while regulators like AICTE and UGC determine how quickly institutions can respond.

    Why AI Education Has Become a Policy Priority

    As AI creates new demand for skilled workers, governments need people who can develop, apply, and manage AI systems across the economy. Education has therefore become a central pillar of national AI strategy. Governments are restructuring data science and AI courses, improving access to technical education, and working with industry to identify the skills employers actually need. The real challenge is no longer whether countries should build AI talent, but how policy can build that talent at scale — and whether it can reach beyond the institutions that already lead.

    Three Models, One Direction

    Three broad approaches to AI talent development are emerging around the world. Each starts from a different point, but all are moving toward the same goal: growing the supply of AI-capable professionals.

    India’s Two Engines: Infrastructure and Curriculum

    India is pursuing AI education through two engines moving side by side. The first is infrastructure. Launched in 2024 with an outlay of Rs. 10,372 crore over five years, the IndiaAI Mission supports computing infrastructure, datasets, and innovation ecosystems. Its education layer includes AI Centers of Excellence and efforts like the IndiaAI-Meta ‘Srijan’ Center at IIT Jodhpur, which turns national compute spending into practical student skills.

    The second engine is curriculum. NEP 2020 provides the broader framework for spreading AI and data science across higher education. IITs and NITs have moved from AI electives to full degree specializations, and the SWAYAM platform now offers more than 370 AI and analytics courses with more than 4,10,000 enrollments.

    Regulation as a Speed Mechanism

    Regulators are shaping how quickly institutions can adapt. AICTE sets curricular direction through Model B.Tech programs in AI and Data Science, pushing AI concepts into branches outside computer science. UGC promotes flexibility by allowing students to earn roughly 40% of their credits through online learning. Together, these policies give universities room to add emerging AI content without waiting for a full policy cycle. Regulation, in this sense, is not just about limits. It is about speed.

    Why Government Needs Industry

    Government provides scale, universities add academic structure, and industry brings current tools and workplace relevance. Digital platforms then extend access to a much wider audience. None of these can replace the others. Policy can fund compute and set standards, but it cannot supply live datasets or current hiring expectations at the pace that industry can.

    NASSCOM’s FutureSkills PRIME, which has trained over 1.6 million learners, along with ties to firms like Microsoft, IBM, Google, and SAP, keeps the policy framework current in practice rather than as an add-on.

    The Capacity Gap

    When AI infrastructure grows faster than institutions can absorb it, leading institutions have a clear advantage. They have stronger faculty, better access to computing resources, and closer industry connections. Smaller institutions face faculty shortages, weaker infrastructure, and less room to update curricula at pace. A national AI strategy can look successful on paper — with more courses and more funding — while still producing uneven outcomes across institutions.

    Final Thoughts

    India is not short on AI policy. It is short on reach. The mission is funded, the curriculum reforms are in place, and industry ties are growing. But none of that matters much if it stays confined to the same handful of top institutes. The real test is whether ordinary colleges get a fair shot too, or whether the gap between India’s best and everyone else keeps growing.

  • U.S. Universities Face Land Pressure as AI Data Center Demand Surges

    U.S. Universities Face Land Pressure as AI Data Center Demand Surges

    The rapid expansion of artificial intelligence is creating an unexpected challenge for American universities: soaring demand for large properties that can support energy-intensive data centers. Institutions that once reserved sprawling campuses for classrooms and research labs now find themselves competing with tech giants for the same real estate.

    George Washington University’s recent sale of its Virginia Science and Technology Campus to Amazon Data Services for $427 million exemplifies this trend. The 120-acre property in Ashburn, Virginia, lies in Loudoun County, the heart of Northern Virginia’s “Data Center Alley.” The university announced the sale in February without initially naming the buyer or disclosing the price, citing confidentiality. Amazon was later confirmed as the purchaser.

    GW stated the transaction would strengthen its long-term finances. It can continue using the campus for up to five years while relocating programs and offices. A substantial portion of the proceeds will fund a new endowment, and eligible faculty will receive one-time payments: $2,500 for full-time benefits-eligible staff and $1,250 for part-time faculty.

    Former GW professor Ellen Scully-Russ, who taught at the Virginia campus from 2009, described the property as struggling to become a central part of the university’s strategy. She noted a gradual reduction in resources, including unfilled faculty positions and eliminated teaching assistant roles due to budget constraints. Faculty and students were not informed about sale discussions in advance. “There was no transparency,” she told Fortune. She also questioned the long-term viability of large AI infrastructure projects, calling the trend potentially “a short-term bubble.”

    The University of Michigan is taking a different approach with a planned $1.25 billion high-performance computing and research center in Ypsilanti Township. Developed in partnership with Los Alamos National Laboratory, the facility will support research in medicine, materials science, clean energy, engineering, and national security. It is expected to require about 50 megawatts of power initially, eventually reaching 100 megawatts. However, local residents and officials oppose the project, and the Ypsilanti Township Board of Trustees unanimously passed a resolution against it.

    As AI continues to drive demand for data center infrastructure, universities must navigate the tension between preserving academic land and capitalizing on lucrative real estate opportunities. The outcome will shape the future of both higher education and the AI industry.

  • Why a Poorly Chosen MS in Data Science Can Derail Your Career

    Why a Poorly Chosen MS in Data Science Can Derail Your Career

    The boom in artificial intelligence has turned data science into one of the world’s most sought-after career paths. Universities across the globe have responded by launching hundreds of Master’s programs promising lucrative jobs, cutting-edge skills, and six-figure salaries. But not every MS in Data Science degree is built the same.

    For aspiring professionals, choosing the wrong program can have consequences that extend far beyond graduation. A poor curriculum, limited industry exposure, or weak placement support could leave graduates struggling to compete in a job market that increasingly values practical skills over academic credentials.

    As employers raise hiring standards, students are learning that the name of a degree matters far less than what they actually learn during it.

    Degree Alone No Longer Guarantees a Job

    Just a few years ago, having a master’s degree in data science was enough to grab recruiters’ attention. Today, the landscape has changed dramatically. When companies look to hire data scientists, they usually expect a certain level of programming skill, primarily in Python and SQL, plus the ability to build machine learning models. They also care that you can navigate cloud platforms without getting lost in today’s AI tools. Recruiters tend to scan your GitHub profile and want to see internships and projects from real life—stuff that quietly proves you can solve problems instead of only talking about them.

    Meanwhile, students who graduate from programs that focus largely on classroom theory often notice they’re missing the hands-on experience employers actually want. So a lot of people end up signing up for extra bootcamps or taking certification courses even though they already put a lot into a postgraduate degree.

    Outdated Curriculum Can Quickly Become Irrelevant

    Data science is one of the most rapidly changing areas of technology. Every year, new frameworks, AI model architectures, and other automation solutions completely change the industry. However, some courses still teach ideas that do not align with the present-day needs of the job market. Although basics are essential, companies demand more knowledge in cloud services, machine learning operations (MLOps), deep learning, large language models, and AI deployment. Two years of studying a curriculum that has become irrelevant may be a mistake for students, as they will enter the job market with obsolete knowledge.

    Industry Exposure Often Makes the Biggest Difference

    One of the major signs of a good data science program is industry interaction. Students get the chance to solve actual problems rather than examples through internships, projects, hackathons, collaborative research, and mentoring from the corporate world. This not only improves students’ technical skills but also their communication and analytical skills. Those programs that lack industry interaction would graduate students who know everything in theory but nothing about how data science is applied in real-world business. Experience weighs more than grades when hiring.

    Placement Support Matters More Than Rankings

    When some people select colleges based on rankings or the promise of high salaries, placements may be more complicated. A career service center, company contacts, an alum network, and recruitment days play important roles in helping people get placed. Colleges with strong industry connections give students a better chance of securing internships and on-campus placements. On the other hand, colleges without assistance in placing students face difficulty, making students’ lives difficult when entering the competitive labor market alone. In the opinion of experts, future college students need to look at the employment rates, average salaries, internships, and alum success stories.

    Financial Cost Extends Beyond Tuition Fees

    A foreign MS in Data Science costs lakhs, while a national MS in Data Science program is also substantial. Selecting a program that does not equip the candidate with valuable skills means they will have to invest money to gain those skills, either through certification courses, self-study, or low-paid jobs. Opportunity cost also becomes immense, since with each passing month, the individual becomes more employable yet earns less due to a lack of job-related skills. For some, the wrong program choice proves costlier than choosing a good course.

    What Prospective Students Need to Consider Before Applying

    Many experts advise going beyond college promotional materials when making decisions. The effective program must be characterized by modern courses and field practice, qualified lecturers, ongoing research, partnerships, and transparency concerning the placement of its alums. Prospective students should consult alums of the university, examine its programs, and find out whether its graduates work at major technology companies, rather than simply considering its placement statistics. Accreditation, faculty quality, and internship opportunities still matter.

    Final Thought

    Due to the high demand for specialists in artificial intelligence and data science, organizations have become very selective in hiring candidates with the required knowledge and competencies. MS in Data Science is an excellent choice, yet not every program provides its graduates with the required expertise. The hiring environment makes it necessary not only to earn the right degree but also to gain valuable skills and confidence and to pursue other opportunities after two years of study.

  • How Universities Are Redesigning Curricula for AI: Full Integration vs. Bolt-On Approaches

    How Universities Are Redesigning Curricula for AI: Full Integration vs. Bolt-On Approaches

    The AI revolution has exposed a critical divide in higher education. Some universities have redesigned learning across every discipline, while others have introduced AI as an additional subject. This comparison shows which approach is creating graduates ready for tomorrow’s workforce.

    Universities are splitting into two camps: those rebuilding degree programs around AI and those adding AI tools on top of unchanged curricula. Kogod, Arizona State, and Miami Dade show measurable gains from full redesign, including a 40% enrollment jump at Kogod. Barnard, Michigan, and Columbia chose speed and flexibility, but governance questions are still catching up to deployment.

    When AI becomes the curriculum, American University’s Kogod School of Business offers the clearest case study. In early 2024, its leadership gave faculty six weeks to rethink what AI-literate business education should look like. What emerged was not a bolt-on course but a full redesign. New AI Artisan classes cover foundational knowledge, while AI Sage courses embed hands-on AI work directly into finance, marketing, and accounting. Every incoming student now takes mandatory AI workshops. The school also created an AI Instruction Faculty Fellow role to keep teaching current and to train instructors before students were expected to use the tools. Three years on, undergraduate enrollment has climbed 40%, applications are up 50%, and more than 90% of faculty now use AI in their teaching. Kogod rebuilt courses first, invested in faculty second, and taught AI’s limitations before its strengths. The point was never just to teach students how to use AI—it was to teach them to question what it produces.

    Arizona State University took a different route to a similar outcome. After partnering with OpenAI in 2024, ASU rolled out ChatGPT Enterprise campus-wide but let individual departments decide how to use it. The AI Innovation Challenge invited faculty and staff to pitch their own projects rather than follow a top-down plan. Hundreds of proposals grew into more than 200 active initiatives spanning nearly every discipline, from research assistants to simulated patient conversations for health science students. By 2025, the partnership had expanded to ChatGPT Edu, backing more than 500 projects. ASU shows that large institutions can transform curricula without centralizing every decision, as long as shared tools and incentives exist.

    Miami Dade College proves that this kind of change doesn’t require a big budget. Starting in 2021, the college trained hundreds of faculty, partnered with IBM on AI for All, and launched Florida’s first associate’s and bachelor’s degrees in artificial intelligence. It later opened an AI center and is moving toward requiring every student to complete an applied AI course, with ethics built in from day one. Enrollment across its technology programs has hit record highs. The driver wasn’t honesty but leadership commitment.

    There is an interesting trend: not all of the universities that move faster are wealthy, but all of the ones that move faster have governance structures that enable curriculum decisions to be made quickly.

    Building skills without rewriting degrees: Barnard College chose a lighter touch. Instead of redesigning majors, it focused on giving every student access to AI tools through a partnership with Google, which brought Gemini and NotebookLM to campus, paired with a four-level literacy framework. This model moves faster and disrupts less. However, it has also exposed a weakness: faculty have raised concerns about privacy and oversight after the tools were already in students’ hands, a reminder that governance built after deployment tends to lag behind the technology it’s meant to manage.

    The University of Michigan and Columbia University follow a similar pattern. Michigan offers short AI courses through its Centre for Academic Innovation. Columbia provides professional AI certificates alongside tools like CU-GPT. In both cases, AI sits beside the degree rather than inside it, treated the way universities once treated coding or data science: a useful add-on, not a requirement.

    What the comparison shows: The overhaul schools moved on governance early. Kogod trained faculty before launching anything. Miami-Dade built ethics into required coursework from the outset. Barnard’s governance questions, by contrast, surfaced only after the tools were already deployed. Faculty adoption tells a similar story. Kogod tracks its numbers publicly and watched them rise from roughly half of the faculty to over 90% in a year. No comparable figures exist at the add-on schools, since participation there remains voluntary. The risks differ too. Curriculum redesign takes more coordination but produces consistent outcomes across students. Add-on strategies are quicker to launch but risk leaving preparation uneven and governance fragmented.

    The real divide in higher education isn’t public versus private, or large versus small. It’s between schools that treated AI as a reason to rebuild and schools that treated it as one more tool to fit into what already existed. Kogod, ASU, and Miami Dade can already point to enrollment gains and faculty adoption numbers. Barnard, Michigan, and Columbia bet on speed and flexibility, and their long-term results are still unfolding. The universities that remain competitive may not be those that adopted AI first but those that built academic systems capable of adapting every time AI evolves.

  • MIT Tops QS World University Rankings for 15th Consecutive Year

    MIT Tops QS World University Rankings for 15th Consecutive Year

    MIT Continues Its Reign as the World’s Best University

    The Massachusetts Institute of Technology (MIT) has once again been named the world’s top university by QS World University Rankings for the 2026-27 academic year. This marks the 15th consecutive year that MIT has held the number one spot, solidifying its position as a global leader in higher education and research.

    In addition to the overall ranking, MIT also placed first in 12 subject areas, showcasing its strength across a wide range of disciplines. The institute also ranked second in seven subject areas, further demonstrating its academic excellence.

    The QS World University Rankings are based on a variety of factors, including academic reputation, employer reputation, faculty-student ratio, citations per faculty, international faculty ratio, and international student ratio. MIT’s consistent performance across these metrics underscores its commitment to providing a world-class education and fostering groundbreaking research.

    This achievement is a testament to the hard work and dedication of MIT’s faculty, staff, and students. The institute’s continued success in the rankings reflects its ability to attract top talent from around the world and its commitment to pushing the boundaries of knowledge.

    For more details, visit the original article on MIT News.

  • MIT Anthropology: Bridging Local Knowledge and Global Research

    MIT Anthropology: Bridging Local Knowledge and Global Research

    The MIT Anthropology department is a leading force in community-based archaeology, cultural analysis, and human-centered research. Through innovative fieldwork and interdisciplinary collaboration, faculty and students explore how local expertise can inform academic inquiry and address pressing global challenges from climate change to AI ethics.

    Community-Based Archaeology

    Professor Sonya Atalay is pioneering community-based archaeology, linking local knowledge with academic research across the globe. Her work emphasizes participatory methods that empower indigenous communities and reshape archaeological practice.

    Climate and Community

    The Living Climate Futures Symposium and the Living Climate Futures Lab take a human-centered approach to climate research, investigating how communities experience and respond to environmental change. These initiatives highlight the importance of place-based pathways to a viable future.

    Technology and Health

    Anthropologist Amy Moran-Thomas studies overlooked insights from people that health care systems are meant to serve, bridging medical realities with technology and health research.

    AI, Chatbots, and Social Confidence

    A new MIT class uses anthropological methods to improve AI chatbots, helping computer science students design conversational agents that assist young users in becoming more social and socially confident.

    Human Insight Collaborative

    The MIT Human Insight Collaborative (MITHIC) launched its annual event showcasing projects that blend humanities, arts, and social sciences with other disciplines. The initiative also introduced the SHASS Faculty Fellows program to support research and mentorship.

    Other recent highlights include the Mind, Hand, and Harvest program bringing low-cost organic produce to campus, research on data visualizations as social artifacts, and MIT’s top ranking in arts and humanities by Times Higher Education.

  • MIT Accounting Research: Insights into Investor Behavior, Corporate Disclosures, and Global Rankings

    MIT Accounting Research: Insights into Investor Behavior, Corporate Disclosures, and Global Rankings

    Inspiring Student Growth

    Professors Xiao Wang and Rodrigo Verdi have been honored as “Committed to Caring” for their dedication to student development. June 30, 2025

    Playing a New Tune

    After stepping away from a family bagpiping tradition, Andrew Sutherland forged a new path as an innovative business scholar, making notable contributions to accounting research. September 24, 2024

    MIT Tops QS World University Rankings for 2021-22

    For the 10th consecutive year, MIT has been ranked the world’s No. 1 university, also placing first in 12 subject areas. June 8, 2021

    QS World University Rankings: MIT No. 1 in 12 Subjects for 2021

    The Institute ranked second in four additional subject areas. March 3, 2021

    MIT Named World’s Top University for 2020-21

    Securing the No. 1 spot for the ninth straight year, MIT also led in 12 subject areas. June 9, 2020

    QS World University Rankings: MIT No. 1 in 12 Subjects for 2020

    The Institute ranked second in five subject areas. March 3, 2020

    MIT Tops Global University Rankings for 2019-20

    For the eighth consecutive year, MIT was ranked first overall and led in 11 of 48 disciplines. June 18, 2019

    QS World University Rankings: MIT No. 1 in 11 Subjects for 2019

    The Institute placed within the top two in 17 of 48 subject areas. February 26, 2019

    MIT Professor Uncovers Secrets to Better Investing

    MIT Sloan’s Eric So sheds new light on investor behavior and stock market dynamics. December 17, 2018

    Why Private Firms’ Accounting Disclosures Help the Economy Grow

    MIT’s Nemit Shroff studies how corporate accounting information enables managers to make better business decisions. November 3, 2018

    MIT Ranks No. 1 Globally for 2018-19

    For the seventh year in a row, MIT was named the world’s top university, leading in 12 of 48 disciplines. June 6, 2018

    MIT No. 1 in 12 Subjects in 2018 QS Rankings

    MIT ranked within the top five in 19 out of 48 subject areas. February 28, 2018

    Work Continues Toward a More Diverse, Inclusive MIT

    Many recommendations from black students made in 2015 have been implemented, and departmental discussions continue. February 7, 2018

    MIT Tops QS World University Rankings for 2017-18

    For the sixth straight year, MIT was ranked No. 1 overall and first in 12 of 46 disciplines. June 8, 2017

    MIT No. 1 in 12 Subjects in 2017 QS Rankings

    The Institute ranked within the top five for 19 of 46 subject areas. March 8, 2017

  • MIT School of Science News: Black Hole Echoes, Climate Adaptation, and More

    MIT School of Science News: Black Hole Echoes, Climate Adaptation, and More

    Listening for the Echoes of Black Holes

    Erin Kara employs X-ray reverberations and other astrophysical data to investigate the most extreme objects in the universe. Read full story

    MIT in the Media: Curiosity-Driven Science and National Success

    Scientific American highlights the role of curiosity-driven research in America’s achievements, showcasing young scientists and MIT icons. Read full story

    Summer 2026 Recommended Reading from MIT

    Explore recent titles authored by MIT faculty and staff for the summer reading list. Read full story

    When Environmental Change Outpaces Adaptation

    A new model links Earth’s mass extinctions to mismatches between the rate of environmental change and biological adaptation. Read full story

    Characterizing Earth’s Damping Mechanisms

    PhD candidate Perrin Davidson studies the carbon cycle to understand the planet’s response to global disturbances. Read full story

    Meet the Leader of the Biology Department’s Kitchen

    Karen O’Leary, lab associate and acting supervisor of the Glassware Sterilization Facility, has become a cornerstone of department operations. Read full story

    QS Ranks MIT World’s No. 1 University for 2026-27

    MIT tops the ranking for the 15th consecutive year and places first in 12 subject areas. Read full story

    Susan Solomon Named 2026 Tang Prize Laureate

    The MIT professor’s work on atmospheric chemistry helped pave the way for ozone layer recovery and highlighted carbon emission impacts. Read full story

    MIT Open Learning Reaches the South Pole

    John Della Costa uses OpenCourseWare to teach physics and build community among Antarctica winterovers. Read full story

    Harriet Having It All: A Career in Molecular Biology

    Harriet Latham Robinson SM ’61, PhD ’65 balanced a career at the forefront of molecular biology with family and adventure. Read full story

    MIT Affiliates Win 2026 Hertz Foundation Fellowships

    Fellowships support doctoral students tackling pressing challenges in science, engineering, and mathematics. Read full story

    Would You Return a Favor? It Depends on the Relationship

    A study shows people expect reciprocal generosity only with friends or those of equal social status. Read full story

    Advancing Stem Cell Research and Building the Next Generation

    Biology PhD student Giselle Valdes studies stem cell regeneration while mentoring aspiring researchers. Read full story

    Myriam Heiman Named Director of Picower Institute

    Heiman, who studies neurodegenerative diseases, will lead the institute starting July 1. Read full story

    Pablo Jarillo-Herrillo Wins Kavli Prize in Nanoscience

    The MIT physicist shares the honor for foundational research establishing twistronics. Read full story

    For more news, visit the MIT School of Science news page.