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From Game Engines to Robot Fingers: How One Researcher Mapped the Future of Computer Graphics
BlendEDBlendED TeamOn June 8, AI+X GTC hosted an industry workshop at the intersection of computer graphics, machine learning, and real-world careers. The session was designed for international students who want to understand not just what to study — but how technical skills actually land in industry, and what the path from classroom to cutting-edge research looks like from the inside. Our speaker was Yifei Li, a PhD graduate in Computer Science from MIT, where her research sits at the intersection of graphics, differentiable simulation, and physical AI. Before that, she completed her Bachelor's at Carnegie Mellon University — one of the top CS programs in the world. Over the course of her academic career, she completed seven internships at companies including Activision Blizzard, Google, Meta Reality Lab, Boston Dynamics AI Institute, and NVIDIA. Each one took her somewhere different: game engines, augmented reality, digital humans, robotics. She wasn't chasing trends. She was following the problems. This workshop was her telling us what she learned. Chapter 1: The First Internship — Graphics Is Engineering "Graphics means making things very fast to run in real time, writing code that other engineers can use, optimizing performance to its extreme." When Yifei was a sophomore at Carnegie Mellon, she spent a summer writing code that made grass move. Not metaphorically. Real grass — thousands of individually generated grass blades, swaying with wind, shifting in detail as the camera moved — all rendered in real time across the massive Battle Royale map of Call of Duty: Black Ops 4. It was her first internship, at Activision Blizzard. By the end of it, her work had shipped on every console and mobile platform the game touched. Before that summer, she thought graphics was mostly about visual algorithms. After it, she understood something different: graphics in industry means performance, systems thinking, and code that survives a production environment. The grass looked beautiful. But she spent most of those months optimizing it. Chapter 2: Google Maps — Making Virtual Objects Feel Real "Small realism errors can break immersion. There are actually many things that could go wrong." Her next stop was Google Maps, on the augmented reality navigation team. The challenge: when virtual information widgets float in front of real buildings on your phone screen, how do you make them look like they actually belong there? The answer was the sun. By knowing a user's GPS coordinates and exact time, you can calculate the sun's position and render accurate shadows on virtual objects — shadows that match the real world around them. Yifei built that lighting estimation pipeline. It had to work in sunshine, in overcast weather, across different cities, at any time of day. The engineering problem wasn't the idea. It was the robustness. Chapter 3: Meta Reality Lab — The Research Project That Went Viral "At the beginning, this was framed as a research project. I never imagined it would actually be used." Graduate school shifted Yifei's work from product-focused engineering to open-ended research. At Meta Reality Lab, she tackled an unlikely problem: teaching an AI to animate hand-drawn humanoid characters — the lopsided, expressive, anatomically approximate kind that children draw. The system had to detect a skeleton in an image that looked nothing like a real body, reconstruct the character's geometry, and apply real motion sequences to it. Nobody expected it to become a product. It did. Meta released it as a public web demo. Millions of people used it. Those users generated the data the team needed to keep improving the model. The lesson Yifei took: the distance between a research idea and something people love is sometimes shorter than you think. Chapter 4: The Bigger Picture — Riding Industry Shifts "The skills underneath stayed the same. What they were applied to kept changing." One of the most valuable parts of Yifei's talk wasn't about any single project. It was the pattern across all of them. She entered the industry in 2018, when games were the natural home for graphics talent. By 2020, the focus was shifting to AI. By 2021, every major tech company was building toward the metaverse — digital humans, virtual avatars, physics simulation fast enough to feel real. By 2024, the wave was robotics. Yifei followed each current. Her later internships at Boston Dynamics AI Institute and NVIDIA put her inside the physical world: designing robotic gripper shapes in simulation, optimizing finger geometry for manipulation, working on simulation-supervised visual reasoning for humanoid robots. The skills — geometry, simulation, differentiable physics, rendering — stayed constant. Their application just kept expanding. Chapter 5: Why Graphics Belongs Everywhere Now "Graphics tells us what structure should look like. That's exactly what modern AI needs." By the end of the session, the picture was clear: computer graphics isn't a niche skill. It's a language for describing the physical world. Geometry. Material properties. Light. Motion. Contact. Force. These are the building blocks graphics has always worked with — and they're exactly what modern AI systems need when they try to reason about the real world. Whether that's a robot learning to pick up an object, a simulation training a neural network, or a differentiable physics engine that lets you optimize a design by describing what you want it to do. The problems worth working on, Yifei said, are the ones where you have to convert messy real-world input into something structured and controllable. That's what graphics has always done. Chapter 6: Q&A — What the Interview Actually Looks Like "For research roles, it's more about a match on the skills. They want to make sure you have the right skills for the problem." The session closed with questions from attendees. The one that generated the most detail: how do engineering and research interviews actually differ? Engineering interviews test coding, systems design, and behavioral fit. Research interviews are a different shape: less focused on distributed systems or databases, more focused on whether your specific background matches the specific problem the team is working on. Expect a presentation round where you walk through your own research in depth — they want to see how you think. It's a small distinction that changes how you prepare, and how you choose what to apply for. Join the GTC AI+X GTC hosts sessions like this regularly — researchers, engineers, and builders sharing what the work actually looks like from the inside. It's free, open to all international students, and built around one idea: the earlier you understand what's happening at the frontier, the better positioned you are when you get there. If you want to go deeper — BlendED's NVIDIA PBL puts students inside the exact space Yifei described: visual computing, simulation, and AI systems that reason about the physical world. Learn more about the NVIDIA PBL → Yifei Li is a researcher at MIT working at the intersection of computer graphics, differentiable simulation, and physical AI. Her work spans cloth simulation, fluid dynamics, robotic manipulation, and digital humans. 06/12/2026 - Events & Collaboration
AI Careers in 2026: What College Students Should Actually Be Doing Now
BlendEDBlendED TeamWhat 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 04/27/2026 - Events & Collaboration
AI Hiring 2026: What MIT & Harvard AI Founders Actually Look For
BlendEDBlendED TeamThe 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 04/27/2026 - Learners' Stories
7 Projects, 7 Recommendation Letters, 1 US AI Internship Offer: How Did She Do It?
BlendEDBlendED TeamGood 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. 04/09/2026 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 04/09/2026- Learners' Stories
From Physics to Applied AI & Biotech: A Structured AI Transition Case
BlendEDBlendED TeamStudent: 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. 02/24/2026
Podcast - 【All Protocols Open】
Welcome to Episode 2 of 【All Protocols Open】, the official podcast of the AI+X Global Talent Community (GTC), powered by BlendED. In this thought-provoking episode, host Rumaisa sits down with Pedro Makhoul, Assistant Professor of Management at InSpur São Paulo, to explore his journey from finance to academia and the lessons he shares with undergraduates about career discovery through diverse experiences. Pedro dives deep into research management, offering insights on when to persist or pivot in projects, and reflects on the evolution of strategy consulting—from the origins of the BCG matrix to organizational design innovations. The conversation also examines how AI is reshaping management consulting, with Pedro drawing on his expertise in strategy and organizational design through his work with BlendED. 09/18/2025Welcome to Episode 1 of 【All Protocols Open】, the official podcast of the AI+X Global Talent Community (GTC), powered by BlendED. In this inspiring first episode, host Rumaisa Kashif (Computer Science, National University of Singapore, Pakistan🇵🇰 /Singapore 🇸🇬) sits down with Victory Yinka-Banjo, a 🇳🇬 Nigerian scholar, MIT graduate, and rising PhD student at Cornell University’s Tri-Institutional Program in Computational Biology & Medicine. This conversation is part of GTC’s founding podcast series, All Protocols Open — where we highlight interdisciplinary voices shaping the future of applied AI across borders, identities, and disciplines! 08/26/2025
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