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Events & Collaboration

AI Careers in 2026: What College Students Should Actually Be Doing Now

What does it actually take to build a career in AI in 2026? Not the theory — the real, practical answer for a college student navigating a job market that changes every few weeks? On March 19, 2026, BlendED hosted a live dialogue bringing together two seasoned industry builders and six student leaders from universities across the UK and Europe. The result was one of the most grounded, honest conversations we have had about what college students should genuinely be doing right now. Our featured speakers were Robin Singh, Senior Optical Scientist at Apple and MIT PhD graduate, and Piotr Swierczynski, CTO of Nodar, an MIT spin-off working on 3D perception for autonomous driving and robotics. The student leaders — Nam, Alessandro, Dylan, Ella, Ben, and Nadeem — each brought questions from their own communities, ranging from robotics clubs to data science societies and hackathon teams. Here is what came out of it. Standing Out Has Become a Logistics Problem — and a Skills Problem Nam Pham, a master's student in AI at the University of Tokyo currently interning at Audi in autonomous driving, opened the discussion with a pointed question: What actually moves the needle when applying to companies like Apple today? Robin was direct. The applicant pool has multiplied — for one position, you now compete with hundreds of people, not ten. That changes everything. His advice had two parts. On the logistics side: proactive, personalised outreach matters far more than a mass application. A generic resume edited through AI tools is immediately recognisable, and does nothing. What does work is a strong web presence — a personal site, a consistent GitHub history, a Google Scholar profile — something that tells a story of sustained work over time. When Robin reviews a candidate, the first thing he does after reading the resume is search their name. What comes up matters. On the skills side: stop thinking about one fixed skill set. The ability to learn — quickly, from the right sources — is now more valued than any particular specialisation. Robin put it simply: you should be open, not blinded. Whether it is computer vision, photonics, quantum algorithms, or something else, curiosity and adaptability are what hiring managers are actually looking for. Piotr added a sharper filter: the candidates who stand out are the ones who show genuine ownership. Not a forked GitHub repo with two changes. Something built from scratch, designed, iterated, and brought to a polished result. Real craftsmanship is visible, and rare. In Robotics, the Biggest Breakthroughs Will Come from Integration Alessandro Sica, president of the UAM Robotics Society at the University of Manchester and a final-year mechatronics student, asked: Where robotics development is actually heading — toward better AI models, better hardware, or something else? Piotr gave the clearest answer of the session. The constraint is not models and it is not hardware. It is the interface between them. We already have models smarter than our robots can use, and hardware more capable than software can exploit. The engineers who understand both sides — who can work across that intersection — are the ones who will succeed. The reason is practical: models and hardware are too often designed independently, by different communities with different assumptions, and they do not speak the same language. Robotics also introduces real-world constraints — latency, power, safety — that benchmark performance alone does not account for. The remedy is co-design: hardware and software developed together, with each side shaping the other. Robin reinforced this with a concrete example. The reason Apple Silicon outperforms competitors is precisely because macOS and Apple's chips are built together. The same principle applies to robotics at every level. For students, both speakers agreed: being on that hardware-software intersection makes you genuinely harder to replace — by both companies and AI. Passion Projects Can Become Products — But Only If Someone Else Has the Problem Dylan Kainth, head of events at KCL's tech society and organiser of Hack London and the UK's largest student tech fair, asked: How student passion projects can actually become products, businesses, or careers. Robin emphasised motivation and honesty. Before anything else, ask yourself: is this a hobby, or does it solve a real problem in the world? And if it is the latter, can it attract funding? The current moment is unusually good for prototyping — AI companions can fill skill gaps between hardware and software in ways that compress development cycles significantly. Piotr gave the most direct advice of the session: before you write a single line of code, find one real person other than yourself with the same problem. Most projects do not fail on execution. They fail because no one needed them in the first place. He described watching a friend spend 18 months building something that no one wanted — a genuinely soul-crushing experience that was entirely avoidable. The practical sequence he recommended: validate first, build an MVP as fast as you can, get it in front of real users with no shame, and let people pay. Perfection is not the goal at that stage. The Questions Students Are Not Asking Enough Ella Tekeli, a final-year arts and sciences student at UCL and head of research at the Data Science Society, asked: What techniques, applications, or implications they wish more people were thinking about? On the technical side, Piotr flagged three areas he sees as underexplored: world models (which will attract significant investment beyond Jan LeCun's own work), Bayesian deep learning with proper uncertainty quantification, and continual learning — the ability for models to update over time without drifting. Robin pointed to wearable AI as a hardware-software integration challenge with enormous room to grow, and to the need for numerical LLMs — models trained to reason about quantitative data with measurable confidence. On implications, both speakers were measured but honest. The labour market disruption is real but slower than the headlines suggest. The biggest near-term effect Piotr sees is not mass unemployment but scope reduction — fewer people doing the same work. He also raised a second-order effect that gets less attention: if companies stop hiring junior engineers today, where do senior engineers come from in ten years? Robin framed AI's current trajectory the way historians might frame the industrial revolution. Mechanical jobs were replaced by machines, but new roles emerged. This is the same shift, one level up — from manual coding to AI-assisted building. The question is not whether the shift happens, but whether you are positioned to move with it. Should You Do a Postgraduate Degree? An Honest Answer Ben Chesworth, founder of the Turing Society and a third-year maths student, asked a question many students avoid asking directly: Is there any point in graduate school right now, when you could be building things instead? Robin's answer depended on where you are and what you want. If you feel underprepared for the direction you want to go, a postgraduate degree can provide structure that the internet cannot. There is real value in the discipline of working through a problem systematically over months — a muscle that is harder to build independently in a world full of distraction. But if your goal is a startup or early employment, your undergrad time is enough — use it to build projects and apply. Piotr added something important: the real output of a PhD is not a thesis. It is meta-skills — the ability to struggle productively in solitude, maintain focus on a single problem for months, and develop genuine work ethic. Those are undervalued, and they compound over time. His recommendation for most graduates, though: get employed first. Work with the best people you can find. Learn what problems exist and what has already been solved. Spend two or three years just learning, then return to academia if something specific calls you back. Solo projects can happen on the side — but it is very hard to sustain them without income. Cloud AI or Edge AI? Where to Build for the Future Nadeem Ahmad, treasurer of the Robotics Society at the University of Manchester — the UK's largest, with over 550 members — closed the student questions with one that combined career strategy and environmental responsibility: given the growing water footprint of data centres, should students be doubling down on cloud AI or pivoting to edge AI? Piotr's answer was clear. Both are here to stay, but the talent shortage is on the edge side. If technical strength and long-term resilience are your goals, edge AI is a stronger bet — it requires both hardware and software understanding, demands real-world optimisation under constraints, and is significantly harder for LLMs to replace than cloud API work. The cloud will remain important, but most cloud AI jobs will ultimately come down to calling APIs rather than building. Robin agreed, adding that the current generation of AI infrastructure is essentially a prototype at scale — the next wave will be about efficient models running on edge devices, not just stacking more GPUs. The sustainability concern Nadeem raised is real, and the field has not adequately addressed it. That gap is an opportunity. On a separate note, Nadeem also asked about ethical engineering — how to ensure that your work, over time, is something you are proud of. Piotr's answer was direct: ask yourself whether, looking back in ten years, what you built made a positive impact. Not just whether it was technically interesting, but whether it was good. Robin added that ethical principles should not change with the technology — they need to be foundational, not adapted to whatever the current opportunity is. Watch the recording ↓ https://us02web.zoom.us/rec/share/4HYX-kv9ubzNS_d0Cr8jAbbJZjRt19-cClJhL0txszxOL3g8am59-c0gKSn35rH-.rqDwWBdQtt0mN2gL?startTime=1773946861000 Passcode: p7^=ks!Q About BlendED BlendED is an AI education platform based in Kendall Square, Boston — right next to MIT. We help college students from over 20 countries build a real, tangible profile in AI through project-based learning, MIT-certified coursework, and mentorship from industry experts and researchers. Our AI+X Learning Plan runs 6 to 12 months and connects students with projects across computer vision, NLP, robotics, finance, biotech, and more. Students who complete the programme leave with a portfolio that admission offices and recruiting teams recognise. Beyond the programme, BlendED co-founded the AI+X Global Talent Community — a network now spanning partnerships with over 150 universities and 100 student clubs across Brazil, Mexico, South Korea, France, Singapore, South Africa, Taiwan, Japan, the UAE, and more. Events like this one are what the community makes possible. If today's conversation made you think about what you should be building next, that is exactly what we are here to help with. 👉 Explore the BlendED program catalog | Join our next event | Connect with our team
Events & Collaboration

AI Hiring 2026: What MIT & Harvard AI Founders Actually Look For

The AI job market has never been more exciting — or more confusing. Every week, a new model launches, a new tool goes viral, and another job description raises the bar impossibly higher. Students and early-career professionals are left asking: what does it actually take to get hired in AI today? To find out, BlendED hosted a live panel — AI Hiring 2026: What MIT & Harvard AI Founders Look For — bringing together three builders from the MIT and Harvard ecosystem alongside a student representative from the National University of Singapore. The result was one of the most candid conversations we've had on the realities of AI hiring. Here are the key insights. 1. "Entry-Level" Has Changed — But It Hasn't Disappeared One of the most frequently asked questions from our student audience: Is entry-level actually real anymore, or are companies quietly expecting mid-level performance from fresh graduates? The short answer: Entry-level still exists, but the definition has expanded. Anya Panagala, co-founder and CEO of Wise AI, put it plainly — what once meant "recently graduated with some coursework" now means someone who has already built something real. Internships, side projects, open-source contributions, or even a high school app with real users all count. The youngest engineer on her team is 15. Alex Benjamin, imaging scientist at Novartis and a former MIT PhD graduate, added an important nuance: most hiring decisions aren't really structured around "entry vs. senior" at all. They're driven by goodness of fit — a precise match between what a candidate can demonstrate and what the team actually needs. Peter Yu, co-founder and CTO of XYZ Robotics, framed it around depth. The ability to identify a real challenge, articulate it clearly, and attempt a creative solution is what signals readiness, regardless of your title or graduation year. Coding Is Table Stakes — Here's What Actually Differentiates Candidates With AI making code generation faster and easier than ever, what actually separates a genuinely strong candidate from someone who just looks strong on paper? All three panelists agreed: it's not more technical knowledge. Alex pointed to first-principles problem solving as the most undervalued skill in the market. The ability to receive an ambiguous problem statement, break it into its fundamental components, consider risks and trade-offs, and think toward a solution is something you cannot fake, and you cannot speed-run. Anya highlighted something equally critical: knowing how and where to learn. In a field where new tools launch every week, the candidate who can orient themselves quickly and implement new knowledge rapidly is far more valuable than one who knows every current tool by heart. Peter rounded out the picture with communication. The ability to communicate precisely — to know your message, speak at the right level of abstraction for your audience, and make it easy for others to collaborate with you — is one of the most consistently underestimated skills in the industry. Depth vs. Breadth: Know Which Path You're On One thread that ran through the entire conversation: Do you want to be a specialist or a leader? In deep tech, specialists are sought after with laser precision. But if your ambition is leadership — managing teams, building products, founding companies — then breadth and narrative matter more. The practical takeaway: get clear on which path you're building toward, and let that guide which experiences, projects, and courses you pursue. For Students: Enjoy Your 20s, But Make the Investment Alex said plainly that many job descriptions today are "completely absurd," and holds hiring managers partly responsible. Students should not read the market as a signal that they need to simulate a decade of professional life during their undergrad years. What they should do is balance depth with exploration. All three agreed: your 20s are a gift. The goal is to make real investments in your craft while staying curious, maintaining social capital, and enjoying the freedom that won't always be there. Starting a Company? Solve a Real Problem First The only startups worth starting are those solving a real, observable problem — not a problem you invented to justify founding something. Alex, who co-founded two companies that both failed, was direct: a business solves a problem and asks for money in return. If you think you've found a real problem you can monetize, that's the point at which you should form a company. Watch the recording ↓ https://us02web.zoom.us/rec/share/CO2DpyU5AUSxZra2IhEqBiju3e469skNwp-90NIEHMh9_SD3LAWPWkRCT3rcE-fN.ySpu4p_FhN_sYhsZ Passcode: VycF*Z8m About BlendED This webinar was hosted by BlendED, an AI education platform based in Kendall Square, Boston — right next to MIT. Our mission is to help students from all backgrounds, STEM and non-STEM alike, build genuine AI foundations through real-world projects with founders, researchers, and industry experts in the Boston AI ecosystem. Through our AI+X Learning Plan (6 or 12 months), students gain hands-on project experience, industry mentorship, and the kind of validation that actually shows up on a portfolio — not just a certificate. We've connected over 120 student clubs across 6 countries and co-founded the AI Plus X Global Talent Community with students from around the world. Events like this one are just a part of what we offer — virtual and in-person programming throughout the year, covering everything from AI foundations to NLP to applied machine learning. If you're serious about positioning yourself in the AI industry over the next 12 months, start building — and let BlendED help you do it. 👉 Explore the BlendED program catalog | Join our next event | Connect with our team
Learners' Stories

7 Projects, 7 Recommendation Letters, 1 US AI Internship Offer: How Did She Do It?

Good grades alone don't open doors anymore. Jessie Chang knew this better than most. A first-year Computer Science student at the National University of Singapore, PSC full scholarship recipient, GPA 4.77 — she had everything a strong student is supposed to have. And yet, when she looked at her resume honestly, she saw the same gap that quietly haunts thousands of high-achieving CS students: zero AI/ML experience, zero research, zero recommendation letters. Just one internship in server operations. Nothing close to the field she actually wanted to enter. In just seven months, she turned every one of those zeros into something real — 7 PBL projects, 2 research projects with Novartis/MIT and Stanford mentors, 7 genuine recommendation letters, and a 6-month remote internship offer from a Harvard/MIT-founded Health Tech startup backed by millions in Series A funding. This is not someone else's story. It is a replicable path. The Starting Point: Strong on Paper, Missing the Layer That Matters Jessie arrived at BlendED with credentials most students would envy. PSC full scholarship. GPA 4.77. Solid programming skills in Python, JavaScript, and Java. But her resume told a more honest story: Experience: 1 internship — server operations, not AI AI/ML experience: Zero Research: Zero Recommendation letters: Zero This is actually the reality for the majority of students who want to enter AI. Good grades, but nothing AI-related on the resume. An interest in the field, but no clear path in. Short-term projects that admissions officers and HR don't take seriously. The credentials look fine — but that critical layer of genuine industry experience is missing. Jessie's story is about how she filled every one of those gaps — not by waiting, but by doing. 7 Projects in 7 Months: From Zero to AI + Biotech In April 2025, Jessie joined BlendED's AI+X Program. By November, she had completed all 7 projects — each one a real collaboration with industry experts and global peers. J.P. Morgan (Apr–Jun 2025) — Machine learning in quantitative finance Genentech (May–Jul 2025) — Quantitative analysis in biotech MIT xPRO (Jul–Aug 2025) — ML, modeling & simulation On-Campus Boston (Aug 2025) — Summer research experience at MIT Tableau (Aug 2025) — Visual data science, on-campus Novo Nordisk (Aug 2025) — AI computer vision × biotech Hugging Face (Sep–Nov 2025) — AI natural language processing Through this process, a clear direction emerged — not because someone told her what to choose, but because she had done enough real work across enough domains to know with certainty. "AI + Biotech & Healthcare. From finance to NLP to biotech, I clarified my focus through broad exploration. Not 'I'm interested in this' — but 'I've done it, and I know this is the direction I want.'" Research That Goes Deeper: Recommendation Letters That Actually Mean Something Beyond the PBL projects, Jessie entered two mentor-guided research projects — and earned recommendation letters that are categorically different from anything a standard academic program produces. Research Project 1 — Medical Imaging AI: Retinal Vessel Segmentation with Limited Data Working under a Senior Imaging Scientist at Novartis and an MIT-affiliated researcher, Jessie designed a retinal vessel segmentation pipeline comparing three classes of methods, and evaluated how pretraining scale affects robustness and generalization. The result was a recommendation letter covering her research capability and independent thinking. Research Project 2 — Cardiomyocyte Cell-Type Classification from scRNA-seq Data Under a Bioengineering Researcher at Stanford University, she designed a scRNA-seq classification pipeline to distinguish 11 cardiac cell types, incorporating Harmony batch correction and scPred probabilistic label transfer. This letter covered her teamwork and learning ability. What makes these letters different? They are not templated "this student performed well" assessments. They are specific capability evaluations based on weeks of genuine, substantive collaboration. Admissions officers and HR professionals can tell the difference — and they do. The Resume, Before and After BlendED helped Jessie rebuild her resume from the ground up — converting project experience into verifiable evidence of real capability. Before After Experience 1 internship (non-AI) 7 PBL + 2 research + 1 internship Technical skills Python, JS, Java + PyTorch, OpenCV, scikit-learn, C, MATLAB AI/ML projects None 3 deep-focus projects Research None Novartis/MIT + Stanford mentors Recommendation letters None 7 (based on real collaboration) Industry validation None Harvard/MIT-backed startup Direction Unclear AI + Biotech & Smart Healthcare The shift is not just in what the resume lists — it is in what it proves: "I participated" → "I can demonstrate it" "Certificate + recommendation letter" → "Capability + evidence + industry recognition" "Learning experience" → "Real deliverable" The Internship: A Harvard Startup Chose Her This is where Jessie's story moves beyond impressive credentials into something genuinely rare for a first-year student: a real internship at a real company, matched by BlendED based on demonstrated capability. Based on the Computer Vision work Jessie had done throughout her PBL projects, BlendED matched her to a Health Tech startup founded by Harvard and MIT graduates — a company building contactless health monitoring technology that checks heart rate, breathing rate, and blood pressure directly through video calls. The company's CEO personally reviewed student profiles and selected Jessie. Her 6-month project: using smartphone cameras to estimate human BMI. The scope was serious: Full end-to-end CV pipeline from data collection to model deployment Human segmentation, keypoint detection, and BMI regression model training Opportunity to co-publish findings with Harvard and MIT researchers Final deliverables: a working prototype and a full validation report How it happened matters as much as the outcome itself. Jessie did not send out a cold application. BlendED proactively matched her profile to the company based on her demonstrated skills. The CEO reviewed the shortlist and chose her. An alignment call followed, and the project launched. "This is not 'sending your own resume.' BlendED matched her based on what she could actually do." Why This Story Matters If you are preparing to apply for graduate school or looking for your first serious role, the landscape has shifted. The competition is no longer about who has done the most projects. It is about who can prove they can actually do the work. For graduate admissions, the question is not how many projects you listed — it is whether you can demonstrate independent research capability and genuine domain understanding. For jobs and internships, HR is increasingly focused on what you built and what problem you solved, not where you studied. For the long term, Industry Validation status is valid for life. Even after graduation, alumni can continue to apply. Jessie's path makes the logic clear: 7 PBLs → direction established → research + recommendation letters → real industry placement. None of it was accidental. It was the result of a systematic process, applied consistently over seven months. Start Your Own Path Jessie started with a 4.77 GPA and zero AI experience. She ended her first year with a 6-month internship at a Harvard/MIT-founded startup, two research projects mentored by scientists from Novartis, MIT, and Stanford, and seven recommendation letters grounded in real collaboration. The gap between where you are and where you want to be does not close by waiting. It closes by doing — with real projects, real mentors, and real industry exposure. BlendED's AI+X Program is built to take students from zero AI experience to genuine, industry-validated capability — whether you are a CS student like Jessie, a physics major, a social science student, or anything in between. 👉 Apply Now to the AI+X Program Submit your application today. After reviewing your submission, the BlendED team will be in touch to guide you through the next steps. This is not someone else's story. This is a replicable path — and it can start with you.
Learners' Stories

12 Projects, 6 Weeks in Boston, One Life Turning Point: A Tourism Student's AI Exploration Journey

What happens when a student who dislikes her major, has zero technical background, and no clear direction decides to stop waiting and start doing? In Mika Hayashi's case, the answer is: everything changes. Over the course of eight months, this Tourism Science student from Tokyo Metropolitan University completed 12 real-world industry AI projects, spent six weeks doing cross-disciplinary research in Boston, discovered her true passion in psychology and neuroscience, and ultimately transferred from Tokyo to Boston to pursue it. This is not a hypothetical success story. It is a blueprint for how exploration — systematic, hands-on, and brave — can transform uncertainty into direction. The Starting Point: Zero Experience, Zero Direction Mika Hayashi grew up in Japan, born to Chinese parents. On paper, she looked like she had it together — a second-year Tourism Science student at Tokyo Metropolitan University, a part-time model, even a title holder as "International Tourism Miss." But beneath the surface, she carried a quiet frustration that many students know all too well: she didn't like her major, and she had no idea what she actually wanted to do instead. Her resume at the time told the whole story: Major: Tourism Science — completely unrelated to technology Tech experience: Zero. AI experience: Zero. Programming: Zero Skills: Word, Excel, and Canva Direction: Completely uncertain She wasn't alone in this. For countless students, choosing a major they don't enjoy — while feeling unqualified to pivot toward something like AI because they're "not an engineering type" — becomes paralyzing. The fear of making the wrong move leads to making no move at all. Mika recognized this trap and chose a different path. "Don't wait until you've figured it out. Start doing first." 12 Projects, 12 Doors Opened In December 2024, Mika joined BlendED's AI+X program. Over the next eight months, she completed 12 enterprise-level industry projects — each one in a different field, with a different team, tackling a different real-world problem. The breadth was remarkable. Over eight months, she completed 12 enterprise-level projects spanning virtually every major industry: Meta Project — AI-driven social media analytics BCG Project — Corporate strategy consulting Headspace Project — Applied psychology with human data Shell Project — AI for energy and sustainability Deloitte Project — Innovation management and strategic leadership AI in Hardware — AI in hardware systems Amazon Project — Operations strategy and supply chain analysis J.P. Morgan Project — Machine learning in quantitative finance Tableau Project — Data visualization for social impact Novo Nordisk Project — Computer vision in biotech Headspace Project (Advanced) — Applied psychology, on-campus deep dive Boston Dynamics Project — AI and robotics for robot manipulation From finance to consulting, from psychology to robotics — twelve projects that transformed all her uncertainty into real, hands-on exploration. This was not résumé-padding. Each project meant collaborating with industry experts and peers from around the world to solve genuine problems. As BlendED describes it, she turned all her "uncertainty" into real exploration. One Summer, Six Weeks in Boston In the summer of 2025, Mika took things further. She flew to Boston and participated consecutively in all three sessions of BlendED's on-campus experience — six weeks in total. She wasn't there to observe. She was there to do real cross-disciplinary research. During those six weeks, she completed four on-campus projects that illustrated just how far she had traveled from her tourism science roots: Tourism Route Optimization — Designed an algorithm to generate optimal 24-hour New York City itineraries, applying tourism science through the lens of linear programming Emotion Regulation Strategy Research — Conducted a psychology study comparing "objective distancing" versus "positive reappraisal" as emotion regulation methods Bitcoin Price Prediction — Built a prediction model combining time-series analysis with NLP-based social media sentiment analysis Retinal Vessel Segmentation — Trained a vessel detection model using self-supervised learning on 35,000 unlabeled medical images A tourism major student, doing medical imaging, financial forecasting, and psychology research in Boston. This is the power of exploration. The Discovery: It Was the Brain All Along After completing 12 vastly different projects, something clicked for Mika. She found herself most drawn to one thread running beneath all the domains she had explored — finance, consulting, energy, robotics, biotech, psychology. That thread led her to the same place every time: the brain, and the foundations of human cognition. In her own words: "Through exploring such diverse fields, I realized I wanted to dive deeper into the brain, the foundation underlying the advancement of all these disciplines. That's what led me to switch my focus to psychology and neuroscience." This was not a snap decision made on a whim. It was a conclusion earned through twelve real projects across twelve different industries. Her choice wasn't based on vague interest — it was grounded in direct experience. She had done the work, verified each direction firsthand, and arrived at a clear answer. This is the difference between exploration and confusion. Exploration is systematic. Confusion is aimless. The Bold Move: Tokyo ✈️ to Boston Finding her direction gave Mika the clarity to do something most people wouldn't dare. In winter 2026, she officially transferred from Tokyo Metropolitan University to Boston, simultaneously changing both her country and her field of study — from Tourism Management to Psychology and Neuroscience. Changing countries is already a significant act of courage. Changing majors on top of that is even harder. Doing both at the same time requires genuine clarity and real courage. Mika had both. She got there through four concrete steps: Broad exploration across 12 projects to confirm her true interest Six weeks on-campus in Boston to build a genuine professional network A project portfolio that demonstrated her cross-disciplinary capabilities The transfer itself in winter 2026, stepping fully into Psychology and Neuroscience "This was not impulsive. It was a clear choice built on a foundation of real exploration." Where She Is Now The transformation is striking when laid out side by side: Before After Major Tourism Science Psychology & Neuroscience Location Tokyo Boston Tech Experience None 12 industry real-world projects AI Experience None ML / CV / NLP / Self-Supervised Learning Direction None AI + Psychology & Neuroscience Mindset None Clear, determined, already in action And she keeps moving forward. She was invited back to BlendED's holiday on-campus event after transferring. She is about to join a US Physical AI startup. She remains an active member of the GTC community, attending events in both Tokyo and Boston. Her focus continues to deepen at the intersection of robotics, artificial intelligence, and cognitive science. Why This Story Matters Mika's journey speaks directly to a feeling that is far more common than most people admit: "I don't know what I want to do." "I'm interested in AI but I'm not a science or engineering student." "I feel like my major is wrong but I don't know where to go." "I'm afraid of making the wrong choice, so I keep waiting." Her story offers three clear lessons: 1. You don't need to figure it out before you start. Direction comes from doing, not thinking. Mika needed 12 projects to find hers. 2. Your background is not a barrier. A tourism major with zero technical experience completed medical imaging, quantitative finance, and robotics projects. 3. Action beats waiting. Being afraid of making the wrong choice is understandable. But the biggest mistake is doing nothing at all. Mika didn't wait until she had the perfect plan. She started exploring, let the work reveal her direction, and then had the courage to follow it — all the way from Tokyo to Boston. "You don't need to know your direction first. You need to start exploring first." 👉 Apply Now to the AI+X Program
Learners' Stories

From Physics to Applied AI & Biotech: A Structured AI Transition Case

Student: Sean Joe Lee University: National University of Singapore Positioning Theme: From Physics to Applied AI & Biotech Case Type: Structured AI Transition Not a Computer Science Student When Sean entered the AI+X program in April 2025, he was not a “pure CS student.” His background was in Physics at NUS. He wasn’t trying to deepen an existing AI identity. He was exploring whether AI could become one. That distinction matters. Sean represents a growing category of transition talent — students with strong quantitative foundations but without formal computer science training, looking to build a legitimate path into applied AI. 2025: Building Breadth Before Specialization In one year, Sean completed four AI+X PBLs: Machine Learning in Quantitative Finance – J.P. Morgan AI in Visual Computing AI in Natural Language Processing – Hugging Face AI & Computer Vision in Biotech – Novo Nordisk Signal: Cross-domain AI exposure (Finance, CV, NLP, Biotech) Technical breadth before narrowing Execution stamina across multiple cycles He wasn’t chasing one trend. He was stress-testing his fit across domains. When Simpler Models Win One of Sean’s most notable outcomes: “Why Simple Augmentation Outperforms CycleGAN for Pneumonia Detection” https://program.blendedlearn.org/outcome-spotlight/when-complexity-fails%3A-why-simple-augmentation-outperforms-cyclegan-for-pneumonia-detection This project demonstrated something deeper than technical execution. It showed: Model robustness thinking Experimental comparison discipline Understanding when complexity is unnecessary He didn’t default to advanced architectures. He evaluated trade-offs. That’s research maturity. Technical Depth Signals Visual Computing – Deepfake Detection Adversarial robustness testing Domain shift evaluation Amazon Operations Strategy (On-Site) Multi-criteria decision-making (MCDM) modeling Supply chain disruption simulation Systems-level framing He received explicit praise for his holistic analytical structure — not just coding ability. The Inflection Point: AI + Biotech Across projects — especially in computer vision and biotech — Sean’s direction crystallized. He began focusing on: Machine learning research Computer vision applications Biotech and medical AI This is where exploration turned into conviction. The transition wasn’t accidental. It was structured. Winter 2026: On-Campus Activation Sean joined the AI+X Winter On-Campus Experience in Boston. He worked on: Amazon Operations (on-site) AI in Visual Computing (on-site) But the real shift happened outside the classroom. He met researchers affiliated with: Broad Institute Harvard University Fresh off his Winter On-campus experience, Sean Joe Lee is already diving deep into discussions with Caleb J. Kumar and Caroline Bulstra, continuing the synergy sparked at their recent Boston BlendED Meetup. Many of them had transitioned from non-CS backgrounds. For the first time, Sean saw that his path was not unusual. It was viable. Post-Program Momentum After returning: He independently followed up with Broad researchers Scheduled deeper discussions Submitted a research application Began considering a potential academic move to Boston He described the experience as “transformative.” More than skills. It changed how he saw himself. Founder-Level Match Strategically, we matched Sean with: Founder of a hardware innovation lab Recently completed Series A Focused on Computer Vision + Biotech Now: They are in direct contact Conversations ongoing Exploring a potential 2026 internship This is not hypothetical networking. It is a live pipeline. Program → Domain Clarity → Founder Connection → Internship Possibility. Public Credibility Moment Sean presented his generative medical model work at: AI+X Meetup @ Kendall Square (Winter 2026) Audience included: Founders Harvard researchers Industry practitioners He later followed up independently with a Harvard researcher for coffee. That’s ecosystem activation. What This Case Represents Sean’s journey is not about “learning AI.” It represents: Non-CS → AI transition proof Longitudinal learning (4 online + 2 on-campus) Cross-domain AI capability Ecosystem access beyond classroom Founder-level relationship building Real internship pathway (2026) A physics student entered exploring AI and exited with technical depth across ML domains, a focused interest in computer vision + biotech, active researcher relationships in Boston, and a live founder-level connection potentially leading to a 2026 internship. That is AI identity transformation.
Events & Collaboration

Event Recap｜Applied Quantum Simulation with IBM Qiskit | From Circuits to Fault-Tolerant Quantum Systems

Feb 2, 2026 — In this BlendED GTC workshop, learners and practitioners joined a deep and friendly exploration of quantum computing fundamentals, practical hybrid algorithms, and the future of error-tolerant quantum systems. Hosted by William (standing in for Vanessa) and featuring Dr. Stefano, a quantum computing researcher from University of Oxford, the session bridged intuitive explanations with real examples from research and industry, making quantum concepts accessible for learners across backgrounds. What Is Quantum Computing and Why It Matters The workshop opened by contrasting classical bits (which encode either a 0 or 1) with qubits — quantum bits that can exist in a superposition of both states simultaneously. Stefano emphasized that this isn’t just abstract math: qubits represent real physical systems (e.g., atoms manipulated by lasers) whose behavior defies everyday intuition but obeys quantum laws. This superposition leads to interference effects (famously illustrated by the double-slit experiment) and enables entanglement, where qubits become interdependent even across distance. These phenomena underpin the exponential scaling of quantum state spaces, giving quantum computing its theoretical edge over classical simulation. Quantum Circuits & Computational Intuition Stefano then showed how quantum computation is performed through quantum circuits: sequences of operations (gates) that rotate qubit states and create entanglement, followed by measurements that convert quantum information back into classical bits. Each added qubit doubles the number of representable states, so a 50-qubit system can embody over 11 million billion combinations — far beyond what even powerful classical supercomputers can track directly. This exponential growth explains the promise of quantum speed-ups for certain problems. Hybrid Algorithms & Real-World Use Cases The talk then shifted from theory to variational quantum algorithms, a leading class of near-term quantum applications. These hybrid models pair parameterized quantum circuits with classical optimizers in a loop: the quantum hardware encodes a problem state, the classical processor evaluates and updates parameters, and the cycle repeats to improve results. Stefano highlighted examples from chemistry simulations to quantum-enhanced machine learning, noting that while rigorous quantum advantage remains an active research area, such hybrid approaches illustrate how quantum devices can contribute to practical workflows today. Error Correction: A Roadmap for Scalable Quantum Computing Recognizing the fragility of quantum states, the final major theme was quantum error correction, essential for future large-scale advantage. Unlike classical bits, qubits are highly susceptible to noise. Stefano explained the basic intuition: by encoding logical information redundantly across many physical qubits, systems can detect and correct errors before they corrupt computation. However, this overhead is substantial, and building full fault-tolerant quantum computers remains one of the field’s biggest challenges. Looking Ahead: Community & Learning Pathways The workshop concluded with a lively Q&A addressing questions about entanglement, quantum simulation techniques, and tools such as Qiskit — which learners can explore further in the upcoming PBL quantum simulation track. Participants were also reminded that quantum computing is evolving fast, with community and research efforts pushing both hardware and algorithms forward. Key Takeaways Quantum systems differ fundamentally from classical ones due to superposition and entanglement. Quantum circuits model computation through qubit rotations, entangling operations, and measurements. Variational hybrid algorithms offer promising near-term applications by combining quantum and classical strengths. Error correction is critical for practical, large-scale quantum computing and remains an active research frontier. Community engagement and ongoing learning opportunities connect learners with emerging quantum development workflows. Interested in exploring quantum computing hands-on? Join our workshop series and PBL tracks to build projects, simulate quantum circuits, and deepen your understanding of next-generation computing paradigms. https://program.blendedlearn.org/pbls/applied-quantum-simulation-%E2%80%93-ibm-qiskit-project 📺 Watch the Replay Couldn’t join live? Don’t miss this in-depth discussion and Q&A.
Events & Collaboration

Event Recap｜AI in Marketing Personalization (Spotify) | Generative AI for Real-World Growth

If your inbox is full of “personalized” emails that still feel generic, you’re not alone. The real question is: what does personalization look like when it actually works—at scale, in real products? In this AI+X Global Talent Community workshop, Abhinav Kumar (Staff Research Scientist working on large-scale personalization and advertising systems) walked us through how modern personalization is built—from recommendation engines to LLM-powered decision-making—and where things can go wrong if context and brand safety aren’t handled carefully. Why Marketing Personalization Matters — and Why AI Now People are overwhelmed by messages across social, email, and search. Personalization isn’t just “nice to have”—it’s how brands earn attention without shouting louder. What’s changed recently is user expectation: experiences like Netflix, Amazon, Spotify, and modern search have trained us to expect content that is fast, relevant, and context-aware. “Personalization is how you make the message feel relevant—without making it feel creepy.” (Workshop theme) What LLMs Add to Personalization (Beyond Keywords) A key takeaway from the session: LLMs can move personalization beyond “keyword matching” toward understanding intent and context—especially useful when users ask questions, compare options, or interact with a chatbot. The workshop highlighted how LLMs can help systems: Use longer context (not just one query at a time) Adapt in real time based on what’s happening now Generate tailored content (copy, responses, variations of messaging) “Context is the difference between ‘relevant’ and ‘risky.’” Practical Techniques Mentioned (Simple Map) To make these systems work in real products, the talk outlined a few common approaches: Fine-tuning / domain adaptation: shape a general model to a specific task or audience RAG (Retrieval-Augmented Generation): combine an LLM with your product/user knowledge base for more grounded outputs Few-shot prompting: teach behavior with a handful of good examples Governance + human feedback loops: reduce hallucinations and inappropriate outputs This is how “Spotify-style personalization” becomes more than a concept—it becomes something you can build, test, and improve. The Trade-offs: Cost, Safety, and Trust The session also emphasized that better personalization isn’t free. Real deployments involve trade-offs: Operational cost & complexity (running models at scale) Brand safety risks (context mismatch can backfire fast) Privacy & trust (users may feel uncomfortable if personalization feels too invasive) “Great personalization doesn’t just predict what you want—it respects what you’re comfortable with.” Speaker Spotlight: Why This Perspective Matters What made this workshop especially practical was the speaker’s lens: Abinav’s work focuses on personalization systems that impact very large user populations, where small errors can quickly become big trust issues. Instead of treating “LLMs in marketing” as a buzzword, the session framed it as an engineering + product problem: How do you keep outputs useful, safe, and scalable—without losing the human side of the user experience? Related Learning Opportunity If you’re interested in applying the ideas from this session in a real-world setting, you can join our upcoming AI in Marketing Personalization (Spotify) project-based learning (PBL) track. In this project, learners will work alongside an industry expert to design, build, and evaluate AI-driven personalization systems—exploring how user behavior, generative AI, and business metrics come together in modern marketing platforms. You’ll gain hands-on experience with real datasets, personalization pipelines, and performance measurement while collaborating with peers from around the world. Join the AI+X Community Become part of a global network of learners exploring AI in biology, engineering, business, hardware, and more. Join our future AI+X workshops Create your free GTC account to stay updated on global events Explore upcoming PBL projects, including AI & Cybersecurity Visit us in Boston for the 2026 Winter or Summer AI+X On-Campus Experience 📺 Watch the Replay Couldn’t join live? Don’t miss this in-depth discussion and Q&A.
Events & Collaboration

Event Recap｜AI in Hardware: Building Smarter Systems from Chips to Robots

As artificial intelligence continues to advance, its real-world impact increasingly depends on how well it integrates with physical systems. In this AI+X Global Talent Community workshop, “AI in Hardware: Building Smarter Systems from Chips to Robots,” participants explored how AI moves beyond software—into chips, sensors, wearables, and autonomous machines that interact directly with the world around us. The session was led by Robin Singh, Optical Scientist and AI & Hardware Researcher (Ph.D. and M.S. from MIT), who brings experience spanning academic research and industry applications in AI-driven hardware systems, AR/VR devices, and autonomous technologies. Why AI in Hardware—and Why Now? The workshop opened with a fundamental question: Why does AI need hardware to truly matter? While AI models are becoming increasingly powerful, they only become real products when embedded into physical systems that operate under real-world constraints. Robin emphasized that AI is “only as good as its hardware.” Power limits, latency requirements, thermal constraints, and form factors fundamentally shape what AI systems can do in practice. Understanding AI in isolation is no longer enough—successful innovation requires co-designing AI and hardware together. Three Domains Where AI Meets the Physical World The session focused on three major domains that demonstrate how AI and hardware converge in real products: Wearables and AR/VR Systems Using AR/VR smart glasses as a primary example, Robin walked through how AI operates on edge devices. Participants learned about system-on-chip (SoC) architectures, sensor stacks, and edge computing, as well as the strict power and latency constraints that govern wearable AI systems. Topics such as perception, gesture and eye tracking, voice interaction, and intelligent rendering highlighted how AI must be carefully optimized to run in real time on small, battery-powered devices. AI for Chip Design The workshop then moved down to the silicon level, exploring how AI is increasingly used to design AI chips themselves. From architectural exploration and RTL optimization to placement, routing, and manufacturability, AI techniques such as reinforcement learning, graph neural networks, and generative models are transforming the chip design process. Rather than replacing human engineers, AI acts as a co-designer—helping navigate the massive design space of modern chips. Autonomous Vehicles as Cyber-Physical Systems The third domain examined autonomous driving as a large-scale, safety-critical AI system. Robin explained how self-driving cars rely on perception, prediction, and planning pipelines that must operate under extremely low latency and high reliability constraints. Participants gained insight into how custom hardware accelerators and optimized AI models are essential for enabling real-time decision-making in autonomous vehicles. From Concepts to Hands-On System Design Beyond theory, the workshop highlighted how these ideas translate into hands-on learning through the AI in Hardware Project-Based Learning (PBL) track. In this project, students work in simulated environments that closely mirror real hardware systems, allowing them to: Design AR/VR systems under realistic power and latency constraints Build and optimize custom chip architectures using AI-assisted tools Develop autonomous driving pipelines in simulation platforms This approach allows learners to engage directly with the challenges engineers face when deploying AI in the real world—even without access to physical hardware. Key Takeaways This workshop reinforced a critical insight: AI innovation does not happen in isolation from hardware. Real impact comes from understanding how algorithms, systems, and physical constraints interact as a whole. By learning to design AI with hardware in mind, students gain skills that are increasingly essential for careers in AI research, hardware engineering, robotics, and intelligent systems. As AI continues to move into everyday devices and infrastructure, the ability to build intelligent systems that operate reliably in the real world will define the next generation of innovation. Related Learning Opportunity｜AI in Hardware (Mar. 09 - May 03, 2026) In early 2026, Robin Singh will lead a hands-on, project-based AI in Hardware track within the AI+X Learning Plan. This experience focuses on designing AI systems that operate under real-world hardware constraints. Project tracks include: Wearable & AR/VR Systems – Edge AI design under power, latency, and form-factor limits AI for Chip Design – Using AI models to assist architecture exploration and physical design Autonomous Systems – Perception, prediction, and planning in simulated self-driving environments Students work in simulation-based environments and build portfolio-ready projects, gaining practical experience in AI–hardware co-design. Join the AI+X Community Become part of a global network of learners exploring AI in biology, engineering, business, hardware, and more. Join our future AI+X workshops Create your free GTC account to stay updated on global events Explore upcoming PBL projects, including AI & Cybersecurity Visit us in Boston for the 2026 Winter or Summer AI+X On-Campus Experience 📺 Watch the Replay Couldn’t join live? Don’t miss this in-depth discussion and Q&A.
Events & Collaboration

Event Recap | Seeing with AI: From 3D Models to Intelligent Vision

As part of the AI+X Global Talent Community series, BlendED hosted a workshop exploring one of today’s most transformative technologies: computer vision. Students from the UK, Korea, Singapore, Europe, and the U.S. joined to learn how modern AI systems interpret the world, generate realistic imagery, and reconstruct 3D environments. This session offered a comprehensive introduction to the foundations, evolution, and future directions of visual intelligence. About the Speaker Researcher at MIT Media Lab; NSF Graduate Research Fellow Sid specializes in vision systems for extreme environments, including low-light, high-speed, and non-line-of-sight imaging. His research spans: Single-photon sensing Physics-based and neural vision Generative modeling 3D reconstruction He holds an MS from MIT and a BS in Electrical Engineering from UCLA. Sid also collaborates with MIT Media Lab researchers and contributes to next-generation vision systems that extend beyond human capabilities. “Vision is the process of discovering from an image what is present in the world and where it is.” — Sid, referencing David Marr’s classic definition Why This Topic? Computer vision sits at the intersection of data, algorithms, and sensing, powering everything from robotics and autonomous vehicles to AR/VR, medical imaging, and industrial inspection. With rapid advances in deep learning, 3D modeling, and generative AI, understanding how machines “see” is becoming a foundational skill for the next wave of innovators. This workshop helps students build intuition for: How AI interprets images Why 3D reconstruction has become mainstream The evolution from classical vision → deep learning → generative modeling How modern sensors go beyond human limits Key Insights from the Session 1. What Does It Mean to See? Sid explained that human and machine vision share the same goal: understanding what is in the world and where. Vision allows systems to interpret meaning without touching or interacting physically — a core requirement for robotics and autonomous systems. 2. Three Levels of Vision Sid explains low-, mid-, high-level vision (depth, edges, segmentation, semantics). Sid introduced the classical pipeline: Low-level: depth, materials, edges Mid-level: regions, boundaries, motion High-level: objects, semantics, pose estimation 3. Why Computer Vision Is Hard Humans see meaning; computers see numbers. Translating pixel values into semantic understanding is the central challenge of the field. 4. The History of AI Comes in Waves From perceptrons in the 1950s → the AI Winter → CNN breakthroughs (AlexNet) → today’s foundation models, Sid walked through how progress has repeatedly surged after periods of stagnation. 5. Generative Modeling Is Transforming Vision Diffusion models, GANs, and VAEs now generate imagery with realism once thought impossible. Sid showed examples of: Realistic cityscapes Scene simulation for autonomous driving Multi-scenario predictions from a single frame 6. From 2D → 3D: The Rise of NeRF Slides showing 2D → 3D reconstruction and multi-view scenes. The breakthrough of Neural Radiance Fields (NeRF) enables AI to construct 3D worlds from ordinary images. This technique now powers: Google Maps 3D views AR/VR content Medical imaging Automotive visualization 7. Inverse Graphics Explained Slide showing projections, materials, and shape recovery. Computer vision is fundamentally about reversing the rendering process—inferring real-world shape, material, and structure from 2D projections. 8. Discriminative vs. Generative Intelligence Examples of accurate segmentation, boundary detection, and label outputs. A complete vision system must both recognize (discriminative) and predict/simulate (generative). Foundation models now unify both capabilities within a single framework. 9. Beyond Human Sensing AI vision isn’t limited to RGB cameras. Sid demonstrated advanced sensing that lets machines: See in total darkness See around corners See through fog Measure heat signatures Track light propagation inside materials 10. The Future of Vision: AI + Sensing + Simulation Modern computer vision sits at the intersection of physics-based sensing, neural learning, and simulation—opening the door to robotics, digital twins, AR, and science applications. Q&A Highlights Participants asked: “Is computer vision overhyped?” Sid explained that the opposite is true—progress is so strong that researchers sometimes worry the field is “solved,” but robotics and real-world deployment continue to reveal open challenges. “What tools power the demos?” Techniques include optical flow networks (RAFT), keypoint tracking, thermal imaging, depth sensors, and high-speed cameras depending on the task. “How can students enter this field?” Graduate study, research internships, and project-based learning—such as BlendED’s AI+X Learning Plan—are strong pathways. Related Learning Opportunity: AI in Visual Computing (2026) Sid will be leading a hands-on, project-based AI in Visual Computing course within the AI+X Learning Plan in early 2026. Tracks include: 3D Modeling & Neural Radiance Fields Object Detection & Classification Image Synthesis & Generative AI Extreme Sensing & Physics-based Vision Students work on real datasets and build portfolio-ready projects. Looking Ahead Computer vision continues to evolve rapidly, with breakthroughs emerging in 3D reconstruction, generative simulation, and sensing technologies. As Sid emphasized, innovation comes in cycles—but the field is far from saturated. The next wave will shape robotics, biomedical imaging, AR/VR, and beyond. Join the AI+X Community Become part of a global network of learners exploring AI in biology, engineering, business, hardware, and more. Join our future AI+X workshops Create your free GTC account to stay updated on global events Explore upcoming PBL projects, including AI & Cybersecurity Visit us in Boston for the 2026 Winter or Summer AI+X On-Campus Experience 📺 Watch the Replay Couldn’t join live? Don’t miss this in-depth discussion and Q&A. https://www.loom.com/share/525c813ed7f048f4bd856dbdf0c97ccd
Events & Collaboration

Connecting People, Creating Opportunities: Reflections from a Meetup Organizer

As the lead organizer of the AI+X GTC Meetup @ SEOUL, held just over a week ago, I had the opportunity to reflect on what this gathering truly represented. Beyond the logistics and programming, the meetup offered several meaningful insights about student-driven communities, collaboration, and the power of human connection. 1. Students and student clubs are already creating opportunities — this meetup became a space to share them with the world Across universities, passionate students and student-led clubs are already building something of their own — regardless of scale. Projects are being developed, research is ongoing, and communities are taking shape. However, in a world that is rapidly evolving through technology and increasingly global in nature, keeping these processes and outcomes contained within closed circles feels limiting. Engaging with peers who share similar ambitions, learning from experienced professionals, and openly sharing current work with a broader audience can significantly accelerate growth. Knowing the depth of passion within the Korean student community was a key reason this meetup was conceived. I believe this gathering marked the beginning of new opportunities — one that can meaningfully accelerate the pace at which many students grow, learn, and connect. 2. Shared “X” strengthens my own X — and expands through convergence While networking with participants, I found myself engaging with individuals whose X overlapped with my own — AI + Business, or more broadly, AI + Society. Through these conversations, I learned how others frame questions, define variables, and approach research from perspectives both similar to and distinct from mine. These exchanges helped sharpen my own focus and deepen my understanding of my interests. At the same time, interacting with participants from entirely different domains — such as AI + Medicine — expanded my thinking in unexpected ways. Listening to experimental, evidence-based research prompted me to consider whether similar underlying principles could be interpreted through a social or human-centered lens. In return, I was able to share perspectives on the intersection of technology and humanity, offering insight into how certain technical principles might ultimately affect people and society. Through this reciprocal exchange, I experienced my own X becoming more integrated, more expansive, and more grounded. 3. Our potential is far beyond our expectations We often constrain ourselves within the boundaries of being a “student.” Our activities become limited to our own universities, our peers, or the paths most commonly taken by others our age. This meetup challenged those assumptions. Regardless of school, age, nationality, or academic background, participants engaged openly and broadly. The GTC community helped dissolve these perceived boundaries, creating space for connection across fields and experiences. For me personally, this meetup became an opportunity to step beyond the identity of a student and act as a community manager — actively seeking, shaping, and creating opportunities on a broader scale. Designing a space where momentum can continue As the organizer of the Seoul meetup, the goal was simple but intentional: to create a space where individual efforts could meet, to design an environment grounded in mutual trust, and to provide a structure that allows meaningful exchange to continue beyond a single event. Despite the endless advancement of technology, this experience reaffirmed how essential human connection remains. These moments of exchange may not produce immediate, visible outcomes, but they often mark the starting point of possibilities that unfold over time. I am genuinely excited about what may emerge after the meetup — the conversations that continue, the collaborations that form, and the paths that begin here. This was only the beginning.

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