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<div class=""><b class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">Dear Colleagues:</b></div>
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<div class=""><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">The Emory Theory and Modeling of Living Systems (TMLS) Initiative is elated to bring to you our next virtual workshop "Learning New Physics with Machine Learning". Please
join us on</span><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt; margin:0px"> </span><b class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt"><u class="">May 28th, 2020 at 10am - 1pm EDT (United States, East
Coast Time)</u></b><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt"> via YouTube live-streaming virtual experience.</span></div>
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<div class=""><b class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">Topic: </b></div>
<div class=""><i class="">"Can machine learning learn new physics, or do we need to put it in by hand?”</i></div>
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<div class="">There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their
biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess
about what we will find.</div>
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<div class="">Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in
contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be
broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.</div>
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<div class=""><b class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">Speakers:</b><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt"> </span></div>
<div class=""><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Lucy Colwell (Cambridge University)</div>
<div class=""><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Bryan Daniels (Arizona State University)</div>
<div class=""><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Sam Greydanus (Google Brain) <span class="x_x_x_Apple-tab-span" style="white-space:pre">
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<div class=""><span class="" style="color:rgb(50,49,48)"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Andrea Liu (University of Pennsylvania)</span></div>
<div class=""><span class="" style="color:rgb(50,49,48)"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Roger Melko (University of Waterloo)</span></div>
<div class=""><span class="" style="color:rgb(50,49,48)"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>David Schwab (CUNY)</span></div>
<div class=""><span class="" style="color:rgb(50,49,48)"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Max Tegmark (MIT) </span></div>
<div class=""><span class="" style="color:rgb(50,49,48)"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span>Aleksandra Walczak (CNRS/ENS Paris)</span></div>
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<div class=""><b class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt">To attend, register with this link:</b><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt"> </span></div>
<div class=""><span class="" style="font-family:Arial,Helvetica,sans-serif; font-size:12pt"><span class="x_x_x_Apple-tab-span" style="white-space:pre"></span></span><a href="https://bit.ly/3fC9uxr" class="">https://bit.ly/3fC9uxr</a></div>
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