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In particular, Anandkumar’s AI algorithms have accelerated weather forecasting and carbon capture and storage (CCS) modeling with an impressive four orde...")</span></div><div id="mw-revision-nav">(diff) ← Older revision | <a href="/Anima_Anandkumar" title="Anima Anandkumar">Latest revision</a> (<a href="/w/index.php?title=Anima_Anandkumar&diff=cur&oldid=203720" title="Anima Anandkumar">diff</a>) | <a href="/w/index.php?title=Anima_Anandkumar&direction=next&oldid=203720" title="Anima Anandkumar">Newer revision →</a> (<a href="/w/index.php?title=Anima_Anandkumar&diff=next&oldid=203720" title="Anima Anandkumar">diff</a>)</div></div></div><div id="jump-to-nav" class="mw-jump jump-to-nav">Jump to:<a href="#mw-navigation">navigation</a>, <a href="#p-search">search</a></div> </div> <div id="bodyContent" class="bodyContent">  <div id="mw-content-text" class="mw-body-content mw-content-ltr" lang="en" dir="ltr"><p><span survey-data-id="2" survey-data-expiry="2592000" survey-data-min-pages="2" survey-data-ratio="75" class="surveytag" survey-data-token="+\"></span> </p><div class="mw-parser-output"><div id="biography-box" class="infobox well" style="float: right; margin-left: 1em; max-width: 342px;"> <div id="biography-name" class="h2">Anima Anandkumar</div> <div id="biography-list"> <p><br /> </p> </div> </div> <h2><span class="mw-headline" id="Biography">Biography</span></h2> <p>Anima Anandkumar is a pioneering leader in artificial intelligence, particularly in “AI for science.” Her groundbreaking work on neural operators and tensor methods has revolutionized multiple scientific domains, including the replacement of traditional numerical solvers with unprecedented speedups. In particular, Anandkumar’s AI algorithms have accelerated weather forecasting and carbon capture and storage (CCS) modeling with an impressive four orders of magnitude speedups, which is extremely important for dealing with climate change. Additionally, her AI method Orbnet has transformed quantum chemistry, performing calculations thousands of times faster than traditional methods. Moreover, Anandkumar’s AI algorithms have enabled a more thorough understanding of coronavirus and accelerated drug discovery. </p><p>An <a href="/IEEE_Fellow_Grade_History" title="IEEE Fellow Grade History">IEEE Fellow</a>, Anandkumar is the Bren Professor and Senior Director of AI Research, California Institute of Technology, Pasadena, California, USA and is the 2025 recipient of the <a href="/IEEE_Kiyo_Tomiyasu_Award" title="IEEE Kiyo Tomiyasu Award">IEEE Kiyo Tomiyasu Award</a> for "contributions to AI, including tensor methods and neural operators with applications to scientific domains." </p><p><br /> </p>    </div></div><div class="printfooter"> Retrieved from "<a dir="ltr" href="https://ethw.org/w/index.php?title=Anima_Anandkumar&oldid=203720">https://ethw.org/w/index.php?title=Anima_Anandkumar&oldid=203720</a>"</div>   <div id='mw-data-after-content'> <p><div id="banner-slideshow"><a href="https://www.aiaa.org/SciTech" target="_blank" rel="nofollow" class="banner-slideshow--ad 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" 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