MIT - Data to AI Lab
@lab_dai
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Data to AI Lab at the Laboratory for Information and Decision Systems
Cambridge, MA
Joined June 2018
Excellent article in @Forbes today calling #syntheticdata “an all-too-rare example of…genuinely useful” generative AI, for the particular application of software testing. Read @jpwarren profile of @datacebo and @kveeramac : https://t.co/CpWQWShgFE
#bigdata #syntheticdata
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Young scientists regularly ask me for career advice. Academia or industry? Big company or startup? US or Europe? Good scientists in AI disciplines are fortunate to have many choices. But choosing can be stressful. I always give the same advice. 1/10
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#otd in 2004 Mark Zuckerberg gave his first-ever TV interview, where he said that his company “Thefacebook” had reached 100K users. “Who knows where we’re going to next?” https://t.co/VTsGos1CXc (v/@CNBC)
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Great article from @thenewstack about open source to business highlighting @datacebo a spin out from DAI lab.
What’s Next for Companies Built on Open Source? https://t.co/mOUGtG7tVO
@ha_joslyn #OpenSource #KubeCon #CloudNativeCon
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Congratulations @XuLeonard for having his paper on generating #syntheticdata using conditional GAN 1000 citations!
Really excited to announce that our NeurIPS 2019 paper on 'Modeling tabular data using conditional GAN' has surpassed 1k citations! It's inspiring to see researchers applying the model innovatively in the era of LLMs. #NeurIPS #GAN #SyntheticData #MIT
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Startup Spotlight: @DataCebo: https://t.co/XDZwrLE9jo < checkout my post & video interview with @kveeramac to learn more about how DataCebo uses GenAI to generate high quality data at scale. #SyntheticData #GenAI
clouddon.ai
This is a summary of conversation with Kalyan Veeramachaneni, CEO/ Co-Founder of DataCebo at KubeCon EU. Watch/ read to learn more about…
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CTGAN is a collection of #deeplearning #syntheticdata generators developed at MIT's @lab_dai and one of the most popular models in our #opensource SDV library. Read the what, why & how here 👉 https://t.co/evyDcFr0Et
#machinelearning
datacebo.com
It can be difficult to see the progress of a GAN. What if we verify it with metrics and visualizations?
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There is a reproducibility crisis in #healthcareAI — but it's not about #datasharing or #privacy. Here are some key learnings we found when developing a structured model-building process with automated tools 👉 https://t.co/2c7XReJk99
#autoML #machinelearning #automation
linkedin.com
There is a reproducibility crisis in healthcare AI but it's not about data sharing and privacy issues, as Dr. Jeffrey Funk recently pointed out in reaction to Nature's article The reproducibility...
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We had much to #celebrate in 2022: Alicia Sun (PhD SES) & Lei Xu (PhD EECS) (both toasting in top left image) successfully defended their doctoral theses. AND we were finally able to celebrate Micah Smith (PhD CS)’s 2021 @MIT graduation in person w/ @kveeramac #PhDCelebration
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Part of our mission is to identify #ML usability challenges. End-user comprehension is a major one. So we’ve developed a taxonomy to craft features non-#AI experts can understand & become more comfortable using #machinelearningmodel outputs. https://t.co/AHk3jpFozT
#ExplainableAI
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#Diversity, #equity, #inclusion AND #machinelearning! Way to go @MIT_SCC for encouraging the next generation of #ai pioneers.
news.mit.edu
Break Through Tech AI allows students to learn the basics of artificial intelligence and machine learning and apply their new skills to real-world industry projects. Hosted by the MIT Schwarzman...
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What’s in store for #AI in 2023? The #OpenSource revolution where startups and academia could (and should!) become the epicenters for fundamental AI research, no longer beholden to #BigTech.
technologyreview.com
Get a head start with our four big bets for 2023.
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Read the @VentureBeat article explaining our recently published research on creating high-quality sentences using #LLMs to train better robust #machinelearning models for language tasks. 👉 https://t.co/qbmNoGTIUc Read our research paper here 👉
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🥳Our very own #MIT Data-to-AI Lab alum Zara Perumal has just been named to the 2023 @Forbes #30under30 for #EnterpriseTechnology. Congrats, Zara! 🎉🍾Looking forward to seeing what great things you'll accomplish in 2023! 💫🚀
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It’s not always about data science & algorithms...But it’s always about camaraderie & collaboration. Last week, over bowling & drinks, @kveeramac and the @lab_dai team bid farewell to Mihir Thalanki (2nd from right), who was visiting us from @bitspilaniindia. #teamspirit #MIT
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We made it! After a year and half of no outing. Thanks @AlexandraZytek for organizing the hike and leading the way. And welcome back to Boston @lab_dai alum @n4atki and Katharine Xiao.
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Great chatting with @MattGoodingTM about privacy and how synthetic data can help. Did not get a chance to go into details, but good to highlight that one should avoid broad statements - this is good for privacy, this is not…
“Access to data is the number one issue and the first problem we encounter in any [AI] project,” says @kveeramac. Is synthetic data the answer?
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We love a good data science competition, but a winning, all-purpose ML model does not exist in the real world. Our users have taught us about trade-offs & we evolved to provide more choices. See our latest #syntheticdata article by @n4atki.
sdv.dev
Based on feedback from real users, SDV's machine learning models have evolved to offer more choices, understand sequential data, and encode business logic.
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