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research-statement.html
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<title>Research Statement – Sean I Young, PhD</title>
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https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h3 y2 ff2 fs1 fc0 sc0 ls1 ws0">SEAN I YOUNG, PhD<span class="ff3 ls0"> <span class="_ _0"></span> <span class="_ _0"></span><span class="ff4"> | <span class="ls2">About Me</span> | <span class="fc1 ls3">Curriculum Vit<span class="ls4">ae</span></span> | <span class="fc1">P<span class="ls4">ublication</span>s</span> | <span class="fc1 ls5">Google Scholar<span class="ls0"> </span></span>| <span class="fc1"> E-<span class="ls6">mail</span></span> </span></span></div><div class="t m0 x1 h4 y3 ff4 fs1 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h4 y4 ff4 fs1 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h4 y5 ff4 fs1 fc0 sc0 ls7 ws0">Page <span class="ls0">1 <span class="_ _1"> </span> <span class="_ _2"> </span></span><span class="fc1">Back to the top</span><span class="ff5 ls0"> </span></div><div class="t m0 x2 h5 y6 ff2 fs1 fc0 sc0 ls7 ws0">Biography<span class="ls0">.<span class="ff6 fs2"> </span><span class="ff4">I <span class="ls4">am</span> <span class="_ _3"></span><span class="ls8">Instructor<span class="ls0"> <span class="ls9">of <span class="lsa">Radiology</span></span> <span class="_ _3"></span><span class="ls4">at the Martinos Center<span class="lsb">, <span class="_ _3"></span>Harvard Medical </span></span></span></span></span></span></div><div class="t m0 x2 h4 y7 ff4 fs1 fc0 sc0 ls7 ws0">School<span class="ls0"> (<span class="lsc">supporte<span class="_ _3"></span>d<span class="ls0"> <span class="ls4">by</span> <span class="_ _3"></span>a <span class="_ _3"></span><span class="ls6">NIH K99/R00 <span class="_ _3"></span><span class="ls3">Career Development <span class="ls0">A<span class="ls2">ward</span>) <span class="_ _3"></span><span class="ls4">and <span class="lsa">Researc<span class="lsd">h </span></span></span></span></span></span></span></span></span></div><div class="t m0 x2 h4 y8 ff4 fs1 fc0 sc0 ls2 ws0">Affiliate<span class="ls0"> <span class="_ _0"></span></span>with<span class="ls0"> <span class="lse">the <span class="_ _0"></span>Computer <span class="_ _4"></span>Science <span class="_ _4"></span>and <span class="_ _4"></span>Artificial <span class="_ _4"></span>Intelligence <span class="_ _0"></span>Lab <span class="_ _4"></span>(CSAIL) <span class="_ _4"></span><span class="ls4">at <span class="_ _4"></span>the</span></span> </span></div><div class="t m0 x2 h4 y9 ff4 fs1 fc0 sc0 lsf ws0">Massachuset<span class="ls0">t<span class="lsc">s <span class="_ _3"></span>Institute <span class="_ _5"></span>of <span class="_ _5"></span>Technology<span class="ls0">, <span class="_ _3"></span><span class="ls2">where <span class="_ _5"></span><span class="ls0">I <span class="_ _6"></span><span class="ls4">design <span class="_ _6"></span>novel<span class="ls0"> <span class="_ _6"></span><span class="ls8">computational <span class="_ _5"></span>imaging<span class="ls0"> </span></span></span></span></span></span></span></span></span></div><div class="t m0 x2 h4 ya ff4 fs1 fc0 sc0 ls6 ws0">methods<span class="ls0"> <span class="ls10">for</span> <span class="lse">radiology<span class="lsb">. <span class="ls7">Pr<span class="_ _4"></span>eviously,</span></span></span> I <span class="ls2">was <span class="_ _4"></span>a </span>P<span class="ls9">ostdoctoral</span> S<span class="ls8">cholar</span> <span class="lsb">in the <span class="_ _4"></span>Department </span></span></div><div class="t m0 x2 h4 yb ff4 fs1 fc0 sc0 ls9 ws0">of <span class="_ _7"></span>Electrical <span class="_ _7"></span><span class="ls11">Engineering<span class="ls0">, <span class="_ _7"></span><span class="ls7">Stanford <span class="_ _7"></span>University<span class="lsb">, <span class="_ _7"></span>w<span class="lsd">here <span class="_ _7"></span></span></span></span>I <span class="_ _7"></span><span class="ls2">worked</span> <span class="_ _7"></span></span></span>on<span class="ls0"> <span class="_ _7"></span><span class="ls8">computationa<span class="ls12">l </span></span></span></div><div class="t m0 x2 h4 yc ff4 fs1 fc0 sc0 lsb ws0">imaging<span class="ls0"> <span class="ls4">and <span class="ls6">model</span></span> <span class="ls8">compression</span></span>. <span class="ls0">I <span class="lse">received <span class="ls6">my</span></span> <span class="ls7">PhD</span> </span>in <span class="ls0">e<span class="ls12">lectric<span class="ls4">al </span></span>e<span class="lsd">ngineering</span> <span class="ls10">from</span> </span></div><div class="t m0 x2 h4 yd ff4 fs1 fc0 sc0 lse ws0">the <span class="_ _6"></span><span class="ls5">University <span class="_ _6"></span>of <span class="_ _3"></span>New<span class="ls0"> <span class="_ _5"></span><span class="ls7">So<span class="_ _4"></span>uth <span class="_ _6"></span>Wales<span class="ls0">, <span class="_ _6"></span><span class="ls7">Sydney<span class="lsb">, <span class="_ _6"></span>N<span class="ls0">SW</span>, <span class="_ _6"></span>Australia.<span class="ls0"> <span class="_ _5"></span><span class="lsf">My<span class="ls0"> <span class="_ _6"></span><span class="lse">resear<span class="ls8">ch <span class="_ _6"></span><span class="ls13">exper<span class="_ _4"></span>tise<span class="ls0"> </span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 ye ff4 fs1 fc0 sc0 ls12 ws0">lie<span class="ls0">s <span class="lsb">in</span> <span class="_ _4"></span><span class="lse">the design <span class="_ _4"></span>of</span> <span class="_ _4"></span><span class="lsd">novel</span> <span class="ls6">methods</span> <span class="_ _4"></span><span class="ls10">for <span class="_ _4"></span><span class="ls8">computational <span class="lsb">imaging</span></span></span> <span class="ls4">and<span class="lsb">, <span class="_ _4"></span>in <span class="_ _4"></span>particular, <span class="ls9">3D <span class="_ _4"></span></span>image</span></span> <span class="lse">reconstruction</span> </span></div><div class="t m0 x1 h4 yf ff4 fs1 fc0 sc0 ls4 ws0">and <span class="_ _0"></span><span class="lse">related <span class="_ _0"></span>inverse <span class="_ _0"></span>problems <span class="_ _0"></span><span class="lsb">in<span class="ls0"> <span class="_ _0"></span><span class="ls6">medical <span class="_ _7"></span>imaging</span>. <span class="_ _0"></span><span class="ls8">In <span class="_ _0"></span>2018, <span class="_ _0"></span></span>I <span class="_ _0"></span></span></span>received <span class="_ _0"></span>the <span class="_ _0"></span>Australian <span class="_ _0"></span>Pattern <span class="_ _7"></span>Recognition </span></div><div class="t m0 x1 h4 y10 ff4 fs1 fc0 sc0 ls7 ws0">Society (APRS)’s best p<span class="_ _4"></span>aper award for <span class="ls6">my<span class="ls0"> <span class="ls2">work</span> <span class="ls9">on</span> “f<span class="ls4">ast optical flow extraction from c<span class="_ _4"></span>ompressed video”.</span></span></span></div><div class="t m0 x3 h6 y11 ff7 fs3 fc2 sc0 ls0 ws0">7</div><div class="t m0 x4 h4 y10 ff4 fs1 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h5 y12 ff2 fs1 fc0 sc0 ls13 ws0">Research <span class="_ _4"></span>Statement<span class="ls0">.<span class="ff6 fs2"> <span class="_ _4"></span></span><span class="ff4 ls8">I <span class="_ _4"></span>seek <span class="_ _4"></span>to <span class="_ _4"></span><span class="lsb">improve <span class="_ _4"></span><span class="lsd">healthcare<span class="ls0"> <span class="_ _4"></span><span class="ls9">outcomes <span class="_ _4"></span>by <span class="_ _4"></span><span class="ls6">mak</span></span></span></span>ing<span class="ls0"> <span class="_ _4"></span><span class="ls10">fundamental <span class="_ _4"></span>contribution</span>s <span class="lse">to <span class="_ _4"></span>the </span></span></span></span></span></div><div class="t m0 x1 h4 y13 ff4 fs1 fc0 sc0 lsc ws0">science <span class="_ _4"></span>of <span class="_ _4"></span><span class="ls8">computational <span class="_ _0"></span>radiology<span class="lsb">. <span class="_ _4"></span>Examples <span class="_ _4"></span>of <span class="_ _0"></span></span>computational <span class="_ _4"></span>radiology <span class="_ _4"></span>problems <span class="_ _0"></span>I <span class="_ _4"></span>have <span class="_ _0"></span></span>solved<span class="ls0"> <span class="_ _4"></span><span class="lsb">include </span></span></div><div class="t m0 x1 h4 y14 ff4 fs1 fc0 sc0 ls4 ws0">accurate <span class="_ _4"></span>MRI <span class="_ _0"></span><span class="ls9">of <span class="_ _4"></span><span class="ls6">mov<span class="_ _4"></span>ing <span class="_ _4"></span>sub<span class="_ _4"></span>ject<span class="ls0">s <span class="_ _4"></span><span class="ls10">from <span class="_ _0"></span>a <span class="_ _4"></span>single <span class="_ _0"></span><span class="lsf">MR <span class="_ _4"></span><span class="lsc">slice <span class="_ _0"></span>stack</span></span></span>; <span class="_ _4"></span><span class="lse">registration <span class="_ _0"></span>of <span class="_ _4"></span>medical <span class="_ _0"></span>images <span class="_ _0"></span>of <span class="_ _4"></span></span></span></span></span>different </div><div class="t m0 x1 h4 y15 ff4 fs1 fc0 sc0 ls8 ws0">contrast <span class="_ _6"></span>to <span class="_ _6"></span>deci<span class="ls0">-<span class="ls14">voxel <span class="_ _6"></span>accuracy<span class="ls0">; <span class="_ _6"></span><span class="ls4">and <span class="_ _6"></span><span class="lsc">supervised <span class="_ _6"></span><span class="lsb">image <span class="_ _6"></span><span class="lse">reconstruction<span class="ls0"> <span class="_ _6"></span><span class="ls2">with<span class="ls0"> <span class="_ _6"></span><span class="ls10">few <span class="_ _6"></span>labeled <span class="_ _6"></span><span class="lsb">imag<span class="ls13">es.<span class="ls0"> <span class="_ _6"></span><span class="ls3">Coming <span class="_ _6"></span>from </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y16 ff4 fs1 fc0 sc0 ls0 ws0">an <span class="lsb">imaging <span class="_ _4"></span>and <span class="_ _4"></span>signal processing <span class="_ _4"></span>background, I <span class="_ _4"></span>am <span class="_ _4"></span>also <span class="_ _4"></span>interested <span class="_ _4"></span>in solving <span class="_ _4"></span><span class="ls6">more <span class="_ _4"></span>general<span class="_ _4"></span> <span class="ls8">computational </span></span></span></div><div class="t m0 x1 h4 y17 ff4 fs1 fc0 sc0 lsb ws0">imaging <span class="_ _4"></span><span class="ls4">problems <span class="_ _0"></span>in <span class="_ _4"></span>hopes <span class="_ _0"></span>that <span class="_ _0"></span><span class="lse">the <span class="_ _4"></span>developed <span class="_ _0"></span>techniques <span class="_ _4"></span>will <span class="_ _0"></span>find <span class="_ _0"></span>use <span class="_ _4"></span>in <span class="_ _0"></span>radiology <span class="_ _0"></span>one <span class="_ _4"></span>day. <span class="_ _0"></span><span class="ls11">Examples <span class="_ _4"></span>of </span></span></span></div><div class="t m0 x1 h4 y18 ff4 fs1 fc0 sc0 ls8 ws0">computational imaging <span class="_ _4"></span>problems <span class="_ _4"></span>I <span class="_ _4"></span>have <span class="_ _4"></span>solved <span class="_ _4"></span>include <span class="_ _0"></span><span class="ls9">100<span class="ls0">x <span class="_ _4"></span><span class="ls10">faster motion <span class="_ _4"></span>estimation <span class="_ _4"></span><span class="ls4">using</span></span> <span class="_ _4"></span><span class="ls10">filtering</span>; <span class="_ _4"></span><span class="lsd">non</span>-</span></span></div><div class="t m0 x1 h4 y19 ff4 fs1 fc0 sc0 ls12 ws0">line<span class="ls0">-<span class="ls9">of</span>-<span class="lsc">sight surface <span class="_ _4"></span>reconstruction</span> <span class="ls4">using</span> <span class="_ _4"></span><span class="ls3">Cholesky</span>–<span class="ls15">Wie<span class="_ _4"></span>ner <span class="_ _4"></span>filterin<span class="_ _4"></span>g</span>; <span class="ls4">and</span> <span class="_ _4"></span>30x <span class="_ _4"></span><span class="lsc">smaller</span> <span class="ls8">convolutional <span class="_ _4"></span><span class="lsd">neural</span></span> </span></div><div class="t m0 x1 h4 y1a ff4 fs1 fc0 sc0 lsd ws0">networks <span class="_ _7"></span><span class="ls4">usi</span>ng <span class="_ _8"></span><span class="lse">transform <span class="_ _7"></span>quantization<span class="ls0"> <span class="_ _8"></span><span class="ls10">for <span class="_ _7"></span>real</span>-</span>time <span class="_ _7"></span>imag<span class="lsb">ing<span class="ls0">. <span class="_ _8"></span><span class="ls8">I <span class="_ _7"></span>detail <span class="_ _7"></span>some <span class="_ _8"></span>of <span class="_ _7"></span><span class="ls6">my</span></span> <span class="_ _8"></span><span class="ls2">work</span> <span class="_ _7"></span></span>in <span class="_ _8"></span>chronological </span></span></div><div class="t m0 x1 h4 y1b ff4 fs1 fc0 sc0 ls9 ws0">order<span class="ls0"> <span class="ls4">below</span> <span class="ls4">and <span class="ls8">conclude by</span></span> <span class="ls4">discuss<span class="lsb">ing</span></span> <span class="ls10">future directions</span> <span class="ls10">for</span> <span class="ls6">my</span> <span class="lse">research. </span> </span></div><div class="t m0 x1 h3 y1c ff2 fs1 fc0 sc0 ls16 ws0">PREVIOUS<span class="ls0"> <span class="_ _4"></span><span class="ls1">RESEARCH</span> </span></div><div class="t m0 x1 h5 y1d ff2 fs1 fc0 sc0 ls17 ws0">100<span class="ls0">x <span class="lsd">Faster <span class="_ _3"></span><span class="lsf">Motion<span class="ls0"> <span class="ls17">Estimation</span> <span class="_ _3"></span>(–<span class="ls17">2019)</span>.<span class="ff6 fs2"> </span><span class="ff4 lsf">My<span class="ls0"> <span class="_ _6"></span><span class="ls8">computational imaging<span class="ls0"> <span class="lse">re<span class="_ _3"></span>search <span class="ls4">began<span class="ls0"> <span class="_ _3"></span><span class="lsb">in <span class="ls2">with <span class="_ _3"></span><span class="ls6">my<span class="ls0"> <span class="ls7">PhD <span class="_ _3"></span><span class="ls2">work </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y1e ff4 fs1 fc0 sc0 ls0 ws0">o<span class="lsd">n video <span class="_ _6"></span><span class="ls8">compression<span class="lsb">, where <span class="_ _3"></span>I invest<span class="_ _3"></span>igat<span class="ls13">ed<span class="ls0"> <span class="lse">the <span class="_ _6"></span>use<span class="ls0"> <span class="ls9">of</span> <span class="lsd">high</span>-<span class="ls9">order</span> <span class="_ _6"></span><span class="ls4">parametric displacement<span class="ls0"> <span class="ls6">models</span> <span class="_ _6"></span><span class="ls10">for<span class="ls0"> <span class="ls6">more </span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y1f ff4 fs1 fc0 sc0 ls13 ws0">eff<span class="lsb">icient <span class="_ _4"></span><span class="lse">representation <span class="_ _0"></span>of <span class="_ _4"></span>video.<span class="ls0"> <span class="_ _0"></span><span class="ls8">In</span> <span class="_ _4"></span><span class="ls14">video <span class="_ _0"></span>compression, <span class="_ _4"></span>o<span class="lsd">ne <span class="_ _0"></span><span class="ls2">way <span class="_ _4"></span>to</span></span></span> <span class="_ _0"></span></span>reduce <span class="_ _4"></span><span class="ls4">data <span class="_ _0"></span></span>redundancy<span class="ls0"> <span class="_ _4"></span></span></span>is <span class="_ _0"></span>to <span class="_ _4"></span><span class="lse">transmit<span class="ls0"> </span></span></span></div><div class="t m0 x1 h4 y20 ff4 fs1 fc0 sc0 ls4 ws0">an <span class="_ _4"></span>earlier<span class="ls0"> <span class="_ _4"></span><span class="ls14">video <span class="_ _4"></span><span class="ls10">frame</span></span>, <span class="_ _0"></span><span class="ls10">followed <span class="_ _4"></span>by</span> <span class="_ _4"></span><span class="lse">the <span class="_ _4"></span></span></span>displacement<span class="ls0"> <span class="_ _0"></span></span>and <span class="_ _4"></span>difference<span class="ls0"> <span class="_ _4"></span><span class="ls10">field</span>s <span class="_ _4"></span><span class="lse">that</span> <span class="_ _0"></span><span class="ls2">warp <span class="_ _4"></span>the <span class="_ _4"></span><span class="ls13">earl<span class="_ _4"></span>ier</span></span> <span class="_ _4"></span><span class="ls10">frame <span class="_ _4"></span></span></span>and </div><div class="t m0 x1 h4 y21 ff4 fs1 fc0 sc0 lsc ws0">synthesize<span class="ls0"> <span class="ls4">a <span class="_ _3"></span>later<span class="ls0"> <span class="ls10">frame</span>—<span class="lse">the <span class="_ _3"></span><span class="lsc">same<span class="ls0"> <span class="ls4">principle</span>s <span class="_ _3"></span><span class="ls2">which<span class="ls0"> <span class="ls4">underl<span class="lsb">ie</span></span> <span class="ls8">change <span class="_ _3"></span><span class="ls4">detection<span class="ls0"> <span class="lsb">in</span> <span class="_ _3"></span><span class="ls6">medical <span class="lsb">imaging<span class="ls0">. <span class="lsf">My</span> <span class="_ _3"></span><span class="ls5">US<span class="ls0"> </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y22 ff4 fs1 fc0 sc0 ls4 ws0">paten<span class="ls0">t</span></div><div class="t m0 x5 h6 y23 ff7 fs3 fc2 sc0 ls0 ws0">3</div><div class="t m0 x6 h4 y22 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="ls10">filed</span> <span class="lsb">in</span> <span class="ls9">2018 <span class="ls4">demonstrate</span></span>s <span class="lse">the <span class="ls4">utility</span></span> <span class="ls9">of <span class="lsd">high<span class="ls13">er</span></span></span>-<span class="_ _3"></span><span class="ls9">order<span class="ls0"> <span class="ls12">(generalization of affine)</span> <span class="ls4">displacement models</span> </span></span></div><div class="t m0 x1 h4 y24 ff4 fs1 fc0 sc0 ls10 ws0">for <span class="_ _6"></span><span class="ls14">video <span class="_ _6"></span><span class="ls8">compression<span class="ls0">, <span class="_ _3"></span><span class="ls4">accompanied <span class="_ _6"></span>by<span class="ls0"> <span class="_ _6"></span><span class="ls4">a <span class="_ _3"></span>procedure <span class="_ _6"></span><span class="lse">that<span class="ls0"> <span class="_ _3"></span><span class="ls8">can<span class="ls0"> <span class="_ _6"></span><span class="lse">recover <span class="_ _6"></span>the<span class="_ _4"></span><span class="ls0"> <span class="_ _6"></span><span class="ls14">values <span class="_ _6"></span>of <span class="_ _3"></span><span class="ls4">displacement<span class="ls0"> <span class="_ _6"></span><span class="ls4">parameter<span class="ls0">s </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y25 ff4 fs1 fc0 sc0 lse ws0">to<span class="ls0"> <span class="ls4">a <span class="lsd">high precision.</span></span> </span></div><div class="t m0 x1 h4 y26 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span><span class="ls3">Concurrently <span class="ls2">with</span></span> <span class="ls6">my</span> <span class="ls2">wor<span class="_ _4"></span>k on <span class="ls4">parametric displacement</span></span> <span class="ls13">estimation techniques<span class="_ _4"></span><span class="lsb">, </span></span>I <span class="lsb">investigated <span class="lse">the use <span class="ls9">of</span></span></span> </div><div class="t m0 x1 h4 y27 ff4 fs1 fc0 sc0 ls4 ws0">den<span class="lsc">se <span class="_ _0"></span></span>deformation<span class="ls0"> <span class="_ _7"></span><span class="ls10">field<span class="lsc">s <span class="_ _7"></span>to <span class="_ _7"></span><span class="lsb">improve</span></span></span> <span class="_ _0"></span><span class="lse">the <span class="_ _7"></span>performance <span class="_ _7"></span>of <span class="_ _7"></span><span class="ls14">video <span class="_ _0"></span>compression</span></span> <span class="_ _7"></span><span class="lsc">systems</span>. <span class="_ _7"></span>I <span class="_ _0"></span></span>demonstrated<span class="_ _4"></span><span class="ls0"> <span class="_ _0"></span><span class="lse">that</span> </span></div><div class="t m0 x1 h4 y28 ff4 fs1 fc0 sc0 ls4 ws0">deformation estimation<span class="ls0"> </span>and many<span class="ls0"> <span class="lse">related inverse problems <span class="_ _4"></span>in imaging</span> </span>and vision<span class="ls0"> <span class="ls8">can be <span class="lsc">solved <span class="ls2">with</span></span></span> <span class="lsd">hi<span class="_ _4"></span>gh</span>-</span></div><div class="t m0 x1 h4 y29 ff4 fs1 fc0 sc0 ls4 ws0">dimensiona<span class="ls12">l <span class="_ _4"></span><span class="ls5">Gaussian <span class="_ _0"></span><span class="ls10">filtering <span class="_ _4"></span>t<span class="ls9">o <span class="_ _4"></span>obviate<span class="ls0"> <span class="_ _4"></span><span class="lse">th<span class="ls13">e <span class="_ _0"></span><span class="lsd">need <span class="_ _4"></span>for</span></span></span> <span class="_ _4"></span><span class="lsc">slow, <span class="_ _4"></span><span class="lsb">iterative <span class="_ _4"></span></span></span></span>optimiz<span class="_ _4"></span>ation <span class="_ _4"></span>procedures<span class="ls0">. <span class="_ _4"></span></span>For <span class="_ _4"></span>certain<span class="ls0"> </span></span></span></span></span></div><div class="t m0 x1 h4 y2a ff4 fs1 fc0 sc0 lse ws0">tasks, <span class="_ _4"></span>this <span class="_ _4"></span><span class="ls10">filtering<span class="ls0"> <span class="_ _4"></span></span></span>technique <span class="_ _4"></span><span class="ls4">accelerated<span class="ls0"> <span class="_ _4"></span></span>displacement <span class="_ _0"></span>estimation<span class="ls0"> <span class="_ _4"></span></span>by <span class="_ _4"></span>a <span class="_ _4"></span>factor <span class="_ _4"></span>of <span class="_ _0"></span><span class="ls9">100<span class="ls0"> <span class="_ _4"></span><span class="ls8">compared <span class="_ _4"></span>to <span class="_ _4"></span>iterative </span></span></span></span></div><div class="t m0 x1 h4 y2b ff4 fs1 fc0 sc0 ls4 ws0">approaches<span class="ls0">.</span></div><div class="t m0 x7 h6 y2c ff7 fs3 fc2 sc0 ls0 ws0">7</div><div class="t m0 x8 h4 y2b ff4 fs1 fc0 sc0 ls0 ws0"> <span class="_ _8"> </span><span class="lsf">My</span> <span class="_ _a"> </span><span class="ls4">doctoral <span class="_ _a"> </span><span class="ls2">work <span class="_ _8"></span>resulted <span class="_ _a"> </span>in <span class="_ _8"> </span></span>a <span class="_ _a"> </span>PhD <span class="_ _8"> </span>thesis</span> <span class="_ _a"> </span><span class="ls9">on <span class="_ _8"> </span>non</span>-<span class="ls12">linear <span class="_ _a"> </span>optimization <span class="_ _8"></span>and <span class="_ _a"> </span>regular<span class="_ _3"></span>ization </span></div><div class="t m0 x1 h4 y2d ff4 fs1 fc0 sc0 lse ws0">techniques <span class="_ _3"></span>for <span class="_ _3"></span>inverse <span class="_ _3"></span>problems in <span class="_ _6"></span>imaging<span class="lsb">, <span class="_ _3"></span><span class="ls4">and<span class="ls0"> <span class="_ _3"></span><span class="ls10">four<span class="ls0"> <span class="_ _3"></span>f<span class="lsb">irst</span>-<span class="ls4">author</span> <span class="_ _3"></span><span class="lsb">journal <span class="_ _3"></span><span class="ls4">publication<span class="ls0">s</span></span></span></span></span></span></span></span></div><div class="t m0 x9 h6 y2e ff7 fs3 fc2 sc0 ls0 ws0">4<span class="fc0">,</span>6<span class="fc0">,</span>7<span class="fc0">,</span>8</div><div class="t m0 xa h4 y2d ff4 fs1 fc0 sc0 ls0 ws0"> <span class="_ _3"></span><span class="ls4">as <span class="_ _3"></span>well <span class="_ _3"></span>as <span class="lsc">sm<span class="_ _3"></span>aller<span class="ls0"> </span></span></span></div><div class="t m0 x1 h4 y2f ff4 fs1 fc0 sc0 ls8 ws0">conference<span class="ls0"> <span class="ls4">and workshop</span> </span>contributions<span class="ls0">. </span></div><div class="t m0 x1 h3 y30 ff2 fs1 fc0 sc0 lsd ws0">Fast <span class="ls6">Non<span class="ls0">-</span></span>Lin<span class="_ _3"></span>e<span class="ls0">-<span class="ls18">of</span>-<span class="ls13">Sight Surface <span class="_ _3"></span>Reconstruction<span class="ls0"> <span class="lse">(20<span class="ls17">19</span></span>).<span class="ff4"> <span class="_ _6"></span>A<span class="ls10">fter <span class="ls6">my</span></span> <span class="_ _6"></span><span class="ls7">PhD, <span class="ls0">I <span class="_ _6"></span><span class="ls6">mov<span class="_ _4"></span>ed to <span class="_ _6"></span>Stanford University<span class="ls0"> <span class="ls4">and </span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y31 ff4 fs1 fc0 sc0 ls8 ws0">continued <span class="_ _7"></span><span class="lse">to <span class="_ _7"></span>work<span class="ls0"> <span class="_ _8"></span><span class="ls9">on <span class="_ _7"></span><span class="ls13">efficient</span></span> <span class="_ _7"></span><span class="ls6">met<span class="_ _4"></span>hods</span> <span class="_ _7"></span><span class="ls10">for <span class="_ _7"></span></span></span></span>computational <span class="_ _8"></span>imaging<span class="ls0"> <span class="_ _7"></span><span class="ls4">problems.</span> <span class="_ _7"></span><span class="ls3">Collaborati<span class="_ _4"></span>ng <span class="_ _7"></span>closel<span class="_ _4"></span>y <span class="_ _7"></span><span class="ls2">with </span></span></span></div><div class="t m0 x1 h4 y32 ff4 fs1 fc0 sc0 ls5 ws0">Gordon <span class="_ _4"></span>Wetzstein<span class="_ _4"></span>, <span class="_ _4"></span><span class="ls0">I <span class="_ _4"></span><span class="ls2">worked <span class="_ _0"></span>on <span class="_ _4"></span><span class="lse">the <span class="_ _4"></span><span class="lsd">non</span></span></span>-<span class="ls12">line</span>-<span class="ls9">of</span>-<span class="lsc">sight <span class="_ _4"></span>imaging <span class="_ _4"></span></span>(<span class="ls14">“looking <span class="_ _4"></span>around <span class="_ _0"></span>the <span class="_ _4"></span>corner”</span>) <span class="_ _4"></span><span class="ls4">problem <span class="_ _4"></span>and </span></span></div><div class="t m0 x1 h4 y33 ff4 fs1 fc0 sc0 ls4 ws0">developed <span class="_ _7"></span>an <span class="_ _8"></span>efficient<span class="ls0"> <span class="_ _7"></span><span class="ls6">method</span></span></div><div class="t m0 xb h6 y34 ff7 fs3 fc2 sc0 ls0 ws0">9</div><div class="t m0 xc h4 y33 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="_ _7"></span><span class="lse">that</span> <span class="_ _8"></span><span class="ls8">can <span class="_ _7"></span><span class="lse">resolve <span class="_ _7"></span>surf<span class="_ _4"></span>aces</span></span> <span class="_ _7"></span><span class="ls9">of <span class="_ _8"></span><span class="lsd">hidden <span class="_ _7"></span><span class="lsc">scene <span class="_ _7"></span></span></span>object</span>s <span class="_ _8"></span><span class="ls2">with <span class="_ _7"></span>an <span class="_ _8"></span><span class="ls4">unprecedented</span></span> </div><div class="t m0 x1 h4 y35 ff4 fs1 fc0 sc0 ls12 ws0">level<span class="ls0"> <span class="ls9">of detail</span> <span class="ls4">by recovering <span class="_ _4"></span>both albedo and surface <span class="_ _4"></span>normals, <span class="lsb">in addition to</span></span> <span class="ls4">a <span class="ls9">1000</span></span>-<span class="ls10">fo</span></span>ld<span class="ls0"> s<span class="ls4">pee</span>d-<span class="ls4">up</span> <span class="ls9">over</span> <span class="lse">the</span> </span></div><div class="t m0 x1 h4 y36 ff4 fs1 fc0 sc0 lsc ws0">stat<span class="ls0">e-<span class="ls9">of</span>-<span class="lse">the</span>-<span class="ls4">art</span> <span class="_ _3"></span><span class="ls6">methods<span class="lsb">. <span class="ls7">Similar to <span class="_ _6"></span><span class="ls6">my<span class="_ _4"></span><span class="ls0"> <span class="ls10">filter</span>-<span class="_ _3"></span><span class="ls4">based<span class="ls0"> <span class="ls6">motion estimation <span class="_ _3"></span><span class="ls4">approach, <span class="ls8">I <span class="_ _3"></span>formulate <span class="lse">the <span class="_ _6"></span><span class="lsc">solution of </span></span></span></span></span></span></span></span></span></span></span></span></span></div><a class="l" href="index.html"><div class="d m1" style="border-style:none;position:absolute;left:314.117647px;bottom:1125.882353px;width:49.920000px;height:13.440000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="cv.html"><div class="d m1" 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class="t m0 x1 h4 y37 ff4 fs1 fc0 sc0 ls7 ws0">Page <span class="ls0">2 <span class="_ _1"> </span> <span class="_ _2"> </span></span><span class="fc1">Back to the top</span><span class="ff5 ls0"> </span></div><div class="t m0 x1 h4 y38 ff4 fs1 fc0 sc0 lse ws0">the underlying <span class="_ _3"></span><span class="lsb">inverse pr<span class="_ _3"></span>oblem as <span class="_ _3"></span><span class="ls10">filter<span class="lsb">ing<span class="ls0"> <span class="ls9">of <span class="_ _6"></span>the obser<span class="ls14">ve<span class="ls4">d signal<span class="ls0"> <span class="_ _3"></span><span class="ls4">and apply<span class="ls0"> <span class="_ _3"></span>“<span class="ls3">Cholesky</span>–<span class="ls15">Wiene<span class="_ _4"></span>r <span class="ls10">filtering</span></span>” <span class="_ _6"></span><span class="lse">to </span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y39 ff4 fs1 fc0 sc0 ls13 ws0">evaluate t<span class="_ _4"></span>he analytic <span class="_ _4"></span>solution <span class="_ _4"></span>in <span class="_ _4"></span>a <span class="_ _4"></span>non<span class="ls0">-<span class="lsb">iterative manner</span>. <span class="_ _4"></span><span class="ls5">This <span class="_ _4"></span>work <span class="_ _4"></span>led <span class="_ _4"></span>to <span class="_ _4"></span>a <span class="_ _4"></span>first <span class="_ _4"></span>author <span class="_ _4"></span><span class="ls4">publication</span></span></span></div><div class="t m0 xd h6 y3a ff7 fs3 fc2 sc0 ls0 ws0">9</div><div class="t m0 xe h4 y39 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="lsb">in <span class="_ _4"></span>the </span></div><div class="t m0 x1 h4 y3b ff4 fs1 fc0 sc0 ls8 ws0">IEEE<span class="ls0"> <span class="ls3">CVPR <span class="ls12">(oral presentation) <span class="ls4">among other unpublished contributions.</span></span></span> </span></div><div class="t m0 x1 h3 y3c ff2 fs1 fc0 sc0 ls0 ws0">30x <span class="_ _4"></span><span class="ls13">Smaller</span> <span class="_ _0"></span><span class="lse">Convolutional <span class="_ _4"></span>Neural <span class="_ _0"></span>Networks</span> <span class="_ _0"></span><span class="lse">(2021)</span>.<span class="ff4"> <span class="_ _0"></span><span class="ls15">While <span class="_ _0"></span>a<span class="lse">t <span class="_ _0"></span>Stanford<span class="lsb">, <span class="_ _0"></span></span></span></span>I <span class="_ _0"></span><span class="ls4">also</span> <span class="_ _0"></span><span class="ls2">worked <span class="_ _0"></span>with <span class="_ _0"></span>Bernd <span class="_ _0"></span>Girod </span></span></div><div class="t m0 x1 h4 y3d ff4 fs1 fc0 sc0 lse ws0">to <span class="_ _4"></span><span class="ls4">develop<span class="ls0"> <span class="_ _0"></span><span class="ls8">compression <span class="_ _0"></span></span></span></span>techniques<span class="ls0"> <span class="_ _4"></span><span class="ls10">for <span class="_ _0"></span><span class="ls8">convolutional <span class="_ _0"></span><span class="lsd">neural <span class="_ _4"></span>networks</span></span></span></span></div><div class="t m0 xf h6 y3e ff7 fs3 fc2 sc0 ls19 ws0">10</div><div class="t m0 x10 h4 y3d ff4 fs1 fc0 sc0 ls0 ws0">. <span class="_ _4"></span><span class="ls15">With <span class="_ _0"></span>the<span class="_ _4"></span> <span class="_ _4"></span><span class="lse">rise <span class="_ _0"></span>of <span class="_ _0"></span>portable <span class="_ _0"></span>medical </span></span></div><div class="t m0 x1 h4 y3f ff4 fs1 fc0 sc0 lsb ws0">imaging <span class="_ _6"></span><span class="ls4">devi<span class="_ _4"></span>ces<span class="lsb">, <span class="_ _3"></span>n<span class="ls13">eural <span class="_ _3"></span>network compression <span class="_ _6"></span><span class="ls8">continues to <span class="_ _6"></span>be <span class="ls4">an <span class="_ _6"></span>important <span class="_ _3"></span>problem in <span class="_ _6"></span>radiology due <span class="_ _6"></span>to <span class="ls0">t<span class="lsd">he </span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y40 ff4 fs1 fc0 sc0 ls4 ws0">demand <span class="_ _3"></span>for <span class="_ _3"></span><span class="lse">real<span class="ls0">-</span>time <span class="_ _3"></span><span class="lsb">image <span class="_ _6"></span>processing <span class="ls9">on <span class="_ _6"></span><span class="ls13">embedded<span class="ls0"> <span class="lsb">im<span class="_ _3"></span>aging <span class="_ _3"></span>devices<span class="ls0">. <span class="_ _3"></span><span class="ls8">I <span class="_ _3"></span>first <span class="_ _3"></span>developed <span class="_ _6"></span>a theory <span class="_ _6"></span><span class="ls9">of rate <span class="_ _6"></span>and </span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y41 ff4 fs1 fc0 sc0 ls4 ws0">distortion <span class="_ _4"></span><span class="ls10">for <span class="ls12">linear <span class="_ _4"></span>operators<span class="ls0">, <span class="_ _4"></span><span class="lse">together <span class="_ _4"></span>with decorrelating <span class="_ _4"></span>transforms <span class="_ _4"></span>for <span class="_ _4"></span>provably <span class="_ _4"></span>optim</span></span></span></span>al<span class="ls0"> <span class="_ _4"></span><span class="lse">rate</span>–</span>distortion </div><div class="t m0 x1 h4 y42 ff4 fs1 fc0 sc0 ls4 ws0">performance<span class="lsb">. <span class="ls5">The <span class="_ _4"></span>weight <span class="_ _4"></span>tensor<span class="_ _4"></span>s were<span class="_ _4"></span> </span></span>quantized <span class="_ _4"></span>in <span class="_ _4"></span>the <span class="_ _4"></span>transform domain <span class="_ _4"></span>again <span class="_ _4"></span><span class="lsc">subject to <span class="_ _4"></span>rate<span class="ls0">–</span></span>distortion </div><div class="t m0 x1 h4 y43 ff4 fs1 fc0 sc0 ls8 ws0">constraints, <span class="ls4">and the resulting network model was optionally fine<span class="ls0">-<span class="lse">tuned, for a 3</span>0x <span class="lse">reduction in network <span class="lsc">size </span></span></span></span></div><div class="t m0 x1 h4 y44 ff4 fs1 fc0 sc0 ls4 ws0">all <span class="_ _4"></span><span class="ls2">without <span class="_ _4"></span>loss <span class="_ _0"></span>of <span class="_ _4"></span>accuracy. <span class="_ _4"></span><span class="ls5">This<span class="ls0"> <span class="_ _4"></span></span></span>wor<span class="_ _4"></span>k <span class="_ _4"></span><span class="ls12">led <span class="_ _4"></span>to<span class="ls0"> <span class="_ _4"></span><span class="ls9">one</span> <span class="_ _4"></span><span class="ls10">first</span>-</span></span></span>author <span class="_ _4"></span>publication<span class="ls0"> <span class="_ _4"></span><span class="lsb">in <span class="_ _4"></span><span class="lse">the <span class="_ _4"></span><span class="ls8">IEEE <span class="_ _4"></span>Trans <span class="_ _0"></span>Pattern <span class="_ _4"></span>Anal </span></span></span></span></div><div class="t m0 x1 h4 y45 ff4 fs1 fc0 sc0 lsf ws0">Mach Intell</div><div class="t m0 x11 h6 y46 ff7 fs3 fc2 sc0 ls19 ws0">10</div><div class="t m0 x12 h4 y45 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="ls4">among other conference publications.</span> </div><div class="t m0 x1 h3 y47 ff2 fs1 fc0 sc0 lsd ws0">Few<span class="ls0">-<span class="ls13">Shot <span class="_ _4"></span>Supervised <span class="_ _0"></span>Image <span class="_ _4"></span>Rec<span class="_ _4"></span>onstruction</span> <span class="_ _4"></span><span class="lse">(202</span>2).<span class="ff8"> <span class="_ _0"></span><span class="ff4">I<span class="lsd">n <span class="_ _4"></span><span class="lse">the <span class="_ _0"></span>wake <span class="_ _0"></span>of</span></span> <span class="_ _4"></span><span class="lse">the <span class="_ _0"></span><span class="ls3">COVID</span></span>-<span class="ls9">19</span> <span class="_ _0"></span><span class="ls4">pandemic<span class="lsb">, <span class="_ _4"></span></span></span>I <span class="_ _0"></span><span class="ls4">became</span> </span></span></span></div><div class="t m0 x1 h4 y48 ff4 fs1 fc0 sc0 ls0 ws0">d<span class="ls13">etermined <span class="_ _7"></span>to <span class="_ _7"></span><span class="ls4">pursue</span></span> <span class="_ _0"></span><span class="lse">research <span class="_ _7"></span>that <span class="_ _7"></span>can <span class="_ _7"></span>touch</span> <span class="_ _7"></span><span class="ls4">people’s <span class="_ _7"></span>lives</span>. <span class="_ _7"></span>I <span class="_ _7"></span><span class="ls6">moved <span class="_ _7"></span>to <span class="_ _7"></span><span class="ls7">Boston</span></span> <span class="_ _7"></span><span class="lsb">in <span class="_ _7"></span><span class="ls12">late <span class="_ _0"></span><span class="ls9">2020 <span class="_ _7"></span>for <span class="_ _7"></span>medical </span></span></span></div><div class="t m0 x1 h4 y49 ff4 fs1 fc0 sc0 lsb ws0">imaging <span class="_ _5"></span>research <span class="_ _6"></span>in <span class="_ _6"></span>the <span class="_ _6"></span>labs <span class="_ _6"></span>of<span class="ls0"> <span class="_ _6"></span><span class="ls7">Bruce <span class="_ _6"></span>Fischl<span class="ls0"> <span class="_ _6"></span>(<span class="lsf">Martinos <span class="_ _3"></span>Center, <span class="_ _6"></span>Harvard <span class="_ _6"></span>Medic<span class="_ _4"></span>al <span class="_ _6"></span>School) <span class="_ _6"></span><span class="ls4">and <span class="_ _6"></span>Polina <span class="_ _6"></span>Golland<span class="ls0"> </span></span></span></span></span></span></div><div class="t m0 x1 h4 y4a ff4 fs1 fc0 sc0 ls0 ws0">(<span class="ls3">CSAIL<span class="lsb">, <span class="lsf">MIT)</span></span></span> <span class="lse">to</span> <span class="ls2">work <span class="ls9">on <span class="lsc">semi</span></span></span>-<span class="lsc">supervised <span class="lsb">imaging</span></span></div><div class="t m0 x13 h6 y4b ff7 fs3 fc2 sc0 ls19 ws0">11</div><div class="t m0 x14 h4 y4a ff4 fs1 fc0 sc0 lsb ws0">, <span class="ls2">which<span class="ls0"> <span class="ls4">allows</span> <span class="lsd">neural networks <span class="lse">to be trained on a mix </span></span></span></span></div><div class="t m0 x1 h4 y4c ff4 fs1 fc0 sc0 ls9 ws0">of <span class="_ _3"></span>labeled <span class="_ _3"></span>and <span class="_ _3"></span>unlabeled<span class="ls0"> <span class="_ _6"></span><span class="lsb">images<span class="ls0">—<span class="ls4">a <span class="_ _3"></span>scenario <span class="_ _3"></span>that <span class="_ _3"></span><span class="lsb">is <span class="_ _3"></span><span class="ls10">frequently <span class="_ _6"></span><span class="ls13">encountered<span class="_ _4"></span><span class="ls0"> <span class="_ _6"></span><span class="lsb">in <span class="ls6">medical<span class="ls0"> <span class="_ _6"></span><span class="lsb">imaging. <span class="_ _3"></span><span class="ls8">I <span class="_ _3"></span>formulate </span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y4d ff4 fs1 fc0 sc0 lsc ws0">semi<span class="ls0">-</span>supervis<span class="lsb">ion <span class="ls4">as <span class="lse">the <span class="ls6">machine <span class="ls12">learning equivalent of </span></span>the <span class="ls0">“</span>regularization by denoising<span class="ls0">” <span class="ls12">(R<span class="_ _3"></span>ED) <span class="ls10">formalism </span></span></span></span></span></span></div><div class="t m0 x1 h4 y4e ff4 fs1 fc0 sc0 ls4 ws0">used to solve inverse problems, and alternate<span class="ls0"> </span>denoisi<span class="_ _4"></span>ng and weight update steps <span class="ls10">for <span class="lsc">supervis<span class="lsb">ion<span class="ls0">. <span class="ls5">This work </span></span></span></span></span></div><div class="t m0 x1 h4 y4f ff4 fs1 fc0 sc0 ls12 ws0">led to <span class="ls9">one<span class="ls0"> <span class="ls10">first author publication in the IEEE Trans Pattern Anal Mach Intell</span></span></span></div><div class="t m0 x15 h6 y50 ff7 fs3 fc2 sc0 ls19 ws0">11</div><div class="t m0 x16 h4 y4f ff4 fs1 fc0 sc0 ls0 ws0"> <span class="ls4">as well as</span> <span class="ls9">other workshop </span></div><div class="t m0 x1 h4 y51 ff4 fs1 fc0 sc0 ls4 ws0">publications<span class="ls0"> <span class="ls2">with collaborators at the Uni<span class="_ _4"></span>versity of Maryland</span>. </span></div><div class="t m0 x1 h3 y52 ff2 fs1 fc0 sc0 ls13 ws0">Single<span class="ls0">-</span>Stack <span class="_ _4"></span>Slice<span class="ls0">-<span class="lse">to</span>-<span class="ls9">Volume <span class="_ _4"></span>Reconstruction</span> <span class="_ _0"></span><span class="lse">(2023)</span>.<span class="ff8"> <span class="_ _4"></span><span class="ff4">A<span class="lse">t <span class="_ _0"></span>Harvard <span class="_ _4"></span>Medical <span class="_ _4"></span>School <span class="_ _0"></span>and <span class="_ _4"></span>MIT<span class="lsb">, <span class="_ _4"></span>I <span class="_ _0"></span><span class="ls4">developed </span></span></span></span></span></span></div><div class="t m0 x1 h4 y53 ff4 fs1 fc0 sc0 ls0 ws0">a <span class="_ _4"></span><span class="ls10">fully <span class="_ _4"></span>convolutional <span class="_ _4"></span>approach <span class="_ _4"></span>to <span class="_ _0"></span>slice</span>-<span class="lse">to</span>-<span class="ls14">volume <span class="_ _4"></span>reconstruction <span class="_ _4"></span>(SVR), <span class="_ _4"></span>enabling <span class="_ _4"></span><span class="lsb">imaging <span class="_ _0"></span>of <span class="_ _4"></span>subjects <span class="_ _4"></span>with </span></span></div><div class="t m0 x1 h4 y54 ff4 fs1 fc0 sc0 ls4 ws0">uncontrollable motion (such as in <span class="_ _3"></span>the case of the fetal <span class="_ _3"></span>population)<span class="ls0"> <span class="ls10">from a single <span class="_ _3"></span>stack of MR <span class="_ _3"></span>slices<span class="ls0"> <span class="ls4">acquired </span></span></span></span></div><div class="t m0 x1 h4 y55 ff4 fs1 fc0 sc0 ls4 ws0">using e.g., HASTE <span class="_ _3"></span>or <span class="ls7">SSFSE <span class="_ _3"></span>sequences.</span></div><div class="t m0 x17 h6 y56 ff7 fs3 fc2 sc0 ls0 ws0">12</div><div class="t m0 x18 h4 y55 ff4 fs1 fc0 sc0 ls0 ws0"> <span class="ls7">Previous</span> <span class="ls7">SVR <span class="_ _3"></span><span class="ls6">methods require multiple slice stacks for accurate </span></span></div><div class="t m0 x1 h4 y57 ff4 fs1 fc0 sc0 lse ws0">reconstruction<span class="lsb">, <span class="_ _4"></span>precluding their<span class="ls0"> <span class="_ _4"></span><span class="ls4">use <span class="_ _4"></span>in <span class="_ _4"></span>applic<span class="_ _4"></span>ation</span>s <span class="_ _4"></span><span class="lsc">such <span class="_ _4"></span>as <span class="_ _4"></span>fetal <span class="_ _4"></span>fMRI</span></span>, <span class="_ _4"></span>where <span class="_ _4"></span></span>the <span class="_ _4"></span>time<span class="ls0">-<span class="lsc">sensitive nature <span class="_ _4"></span>of </span></span></div><div class="t m0 x1 h4 y58 ff4 fs1 fc0 sc0 lse ws0">the acquisiti<span class="_ _4"></span>on <span class="ls9">often <span class="_ _4"></span>prohibits <span class="_ _4"></span>the <span class="_ _4"></span>use <span class="_ _4"></span>of<span class="ls0"> <span class="ls6">mul<span class="_ _4"></span>tiple <span class="_ _4"></span>slice <span class="_ _4"></span>stacks. <span class="_ _4"></span><span class="ls5">This <span class="_ _4"></span>work <span class="_ _4"></span>led <span class="_ _4"></span>to <span class="_ _4"></span>a <span class="_ _4"></span>first <span class="_ _4"></span>author <span class="_ _4"></span>submi<span class="_ _4"></span>ssion <span class="_ _4"></span></span></span></span></span>to<span class="ls0"> </span></div><div class="t m0 x1 h4 y59 ff4 fs1 fc0 sc0 lse ws0">the IEEE CVPR<span class="ls0"> <span class="ls12">(under review)</span></span></div><div class="t m0 xc h6 y5a ff7 fs3 fc2 sc0 ls0 ws0">12</div><div class="t m0 x19 h4 y59 ff4 fs1 fc0 sc0 ls0 ws0">. </div><div class="t m0 x1 h3 y5b ff2 fs1 fc0 sc0 ls1a ws0">CURRENT RESEARCH<span class="ls0"> </span></div><div class="t m0 x1 h3 y5c ff2 fs1 fc0 sc0 ls7 ws0">Universal<span class="ls0"> <span class="_ _0"></span><span class="ls1b">Image <span class="_ _7"></span>Registration</span> <span class="_ _0"></span><span class="lse">(K99<span class="ls12">, <span class="_ _0"></span>2023</span></span>–).<span class="ff8"> <span class="_ _7"></span><span class="ff4 ls3">Certain <span class="_ _7"></span>change<span class="ls0">s <span class="_ _7"></span><span class="lsb">in <span class="_ _0"></span>human <span class="_ _0"></span><span class="ls9">organs</span></span> <span class="_ _7"></span><span class="ls4">are</span> <span class="_ _7"></span><span class="lsc">strongly <span class="_ _0"></span>predictive <span class="_ _0"></span>of </span></span></span></span></span></div><div class="t m0 x1 h4 y5d ff4 fs1 fc0 sc0 ls13 ws0">early <span class="_ _3"></span>disease processes <span class="_ _6"></span>(such as<span class="ls0"> <span class="_ _3"></span><span class="ls8">cortical<span class="ls0"> <span class="_ _3"></span><span class="ls4">atrophy <span class="_ _3"></span>in the <span class="_ _6"></span>case of <span class="_ _6"></span>Alzheimer’s Disease). <span class="_ _3"></span>However, <span class="_ _3"></span>using<span class="ls0"> <span class="ls13">existing</span> </span></span></span></span></span></div><div class="t m0 x1 h4 y5e ff4 fs1 fc0 sc0 lsb ws0">image processing <span class="lse">tool<span class="_ _4"></span>s to <span class="_ _4"></span>detect thi<span class="_ _4"></span>s anatomical <span class="_ _4"></span>change <span class="_ _4"></span>in clini<span class="_ _4"></span>cal MR <span class="_ _4"></span>scans <span class="_ _4"></span>taken across <span class="_ _4"></span>time <span class="lsd">has<span class="_ _4"></span> <span class="ls4">proven </span></span></span></div><div class="t m0 x1 h4 y5f ff4 fs1 fc0 sc0 ls4 ws0">difficult<span class="ls0"> <span class="_ _3"></span><span class="ls4">due <span class="_ _3"></span>to <span class="_ _3"></span><span class="lsd">numerous<span class="ls0"> <span class="_ _3"></span><span class="lsf">MR <span class="_ _3"></span>effects and <span class="_ _6"></span>subject<span class="_ _4"></span> <span class="_ _3"></span>motion, which <span class="_ _6"></span>can mask <span class="_ _6"></span>the<span class="_ _4"></span> <span class="_ _3"></span>anatomical change <span class="_ _6"></span>of interest </span></span></span></span></span></div><div class="t m0 x1 h4 y60 ff4 fs1 fc0 sc0 lse ws0">relevant to diagnosis and research. <span class="lsf">My K99 pr<span class="_ _4"></span>oject<span class="ls0"> <span class="ls4">aims of the proposed project <span class="_ _4"></span>are to leverage the recent </span></span></span></div><div class="t m0 x1 h4 y61 ff4 fs1 fc0 sc0 ls4 ws0">advances <span class="_ _4"></span>in <span class="_ _0"></span>deep <span class="_ _0"></span>learning <span class="_ _4"></span>to <span class="_ _0"></span>design, <span class="_ _0"></span>develop <span class="_ _0"></span>and <span class="_ _4"></span>evaluate <span class="_ _0"></span>an <span class="_ _4"></span>imaging <span class="_ _0"></span>and <span class="_ _0"></span>analysis <span class="_ _0"></span>framework <span class="_ _4"></span><span class="ls2">which<span class="ls0"> <span class="_ _0"></span><span class="ls8">can </span></span></span></div><div class="t m0 x1 h4 y62 ff4 fs1 fc0 sc0 lse ws0">resolve <span class="_ _0"></span>anatomical <span class="_ _7"></span>change<span class="ls0">s <span class="_ _7"></span><span class="ls2">with <span class="_ _7"></span>high <span class="_ _0"></span>accurac<span class="_ _4"></span>y, <span class="_ _0"></span>aidin<span class="_ _4"></span>g <span class="_ _0"></span>in <span class="_ _7"></span>the <span class="_ _7"></span>early <span class="_ _7"></span>diagnosis <span class="_ _7"></span>and <span class="_ _7"></span>intervention <span class="_ _7"></span>before <span class="_ _0"></span>th<span class="_ _4"></span>e </span></span></div><div class="t m0 x1 h4 y63 ff4 fs1 fc0 sc0 ls9 ws0">onset of dysfunction<span class="ls0"> <span class="lsb">in the case of neurological diseases, for example</span>. </span></div><div class="t m0 x1 h3 y64 ff2 fs1 fc0 sc0 ls13 ws0">Research <span class="ls2">Outlook.<span class="ff8 ls0"> <span class="_ _3"></span><span class="ff4 lsf">My<span class="ls0"> <span class="ls13">expertise in <span class="_ _3"></span>computational imaging and <span class="_ _3"></span>modeling coupled <span class="_ _3"></span>with<span class="ls0"> a <span class="lsd">newly <span class="_ _3"></span>discovered </span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y65 ff4 fs1 fc0 sc0 ls4 ws0">aptitude <span class="_ _4"></span>for <span class="_ _0"></span>deep <span class="_ _4"></span>learning <span class="_ _0"></span>in <span class="_ _4"></span>MRI <span class="_ _0"></span>makes <span class="_ _4"></span><span class="ls6">me<span class="ls0"> <span class="_ _0"></span></span></span>an <span class="_ _4"></span>extremely <span class="_ _0"></span>suitable <span class="_ _4"></span>candidate <span class="_ _0"></span><span class="ls10">for <span class="_ _4"></span>a <span class="_ _4"></span>junior <span class="_ _0"></span>faculty <span class="_ _4"></span>position<span class="ls0"> </span></span></div><div class="t m0 x1 h4 y66 ff4 fs1 fc0 sc0 lsb ws0">in <span class="_ _4"></span>radiology.<span class="ls0"> <span class="_ _0"></span><span class="lsf">My</span> <span class="_ _0"></span><span class="ls6">mentors <span class="_ _0"></span>from <span class="_ _0"></span>Harvard</span> <span class="_ _0"></span><span class="ls4">and</span> <span class="_ _0"></span><span class="lsf">MIT <span class="_ _0"></span><span class="lsd">have <span class="_ _0"></span>already</span></span> <span class="_ _4"></span><span class="ls4">prov<span class="_ _4"></span>ided</span> <span class="_ _4"></span><span class="ls4">guidance <span class="_ _0"></span>and <span class="_ _0"></span>mentoring <span class="_ _0"></span>in <span class="_ _0"></span>the </span></span></div><div class="t m0 x1 h4 y67 ff4 fs1 fc0 sc0 ls10 ws0">fields of neurology <span class="_ _3"></span>and radiology to <span class="_ _3"></span>ensure that appropr<span class="_ _3"></span>iate methods are <span class="_ _3"></span>used to address clinically <span class="_ _3"></span>relevant </div><div class="t m0 x1 h4 y68 ff4 fs1 fc0 sc0 ls4 ws0">questions. The NIH K99/R00 award <span class="lsd">has<span class="ls0"> </span>not only provide<span class="ls0">d <span class="ls6">me</span> <span class="ls2">with </span></span></span>an opportunity to work with mentors </div><div class="t m0 x1 h4 y69 ff4 fs1 fc0 sc0 ls4 ws0">and solve <span class="_ _6"></span>a problem <span class="_ _3"></span>of utmost<span class="ls0"> <span class="_ _6"></span><span class="lsb">importance but <span class="_ _6"></span><span class="lsd">has <span class="ls4">also <span class="_ _3"></span>support<span class="ls13">ed me<span class="ls0"> <span class="_ _6"></span><span class="lse">to grow <span class="_ _3"></span>as <span class="_ _3"></span>an independent <span class="_ _6"></span>researcher </span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y6a ff4 fs1 fc0 sc0 ls4 ws0">and ultimately raise the next generation of researchers to carry on <span class="_ _4"></span>our <span class="ls8">collective <span class="lsc">scientific legacy.<span class="ls0"> </span></span></span></div><div class="t m0 x1 h4 y6b ff4 fs1 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span><span class="ls5">Transitioning <span class="_ _8"></span>to</span> <span class="_ _8"></span><span class="ls10">faculty, <span class="_ _7"></span></span>I <span class="_ _8"></span><span class="ls2">will</span> <span class="_ _7"></span><span class="lsc">start <span class="_ _8"></span><span class="ls6">my</span></span> <span class="_ _7"></span><span class="ls9">own <span class="_ _8"></span><span class="lse">research <span class="_ _7"></span>lab</span></span> <span class="_ _8"></span><span class="ls10">foc<span class="ls4">use</span></span>d <span class="_ _8"></span><span class="ls9">on</span> <span class="_ _7"></span><span class="ls4">developing <span class="_ _8"></span>novel <span class="_ _7"></span>computational<span class="_ _4"></span> </span></div><div class="t m0 x1 h4 y6c ff4 fs1 fc0 sc0 lsb ws0">imaging <span class="_ _5"></span><span class="ls6">met<span class="_ _4"></span>hods <span class="_ _6"></span>for <span class="_ _6"></span>radio<span class="_ _4"></span>logy<span class="ls0">. <span class="_ _6"></span>I <span class="_ _6"></span><span class="ls2">will <span class="_ _6"></span>utilize <span class="_ _6"></span><span class="ls6">my<span class="ls0"> <span class="_ _3"></span><span class="ls13">experience<span class="ls0"> <span class="_ _6"></span><span class="ls2">with <span class="_ _6"></span>a <span class="_ _6"></span><span class="lse">range <span class="_ _6"></span>of<span class="ls0"> <span class="_ _6"></span><span class="ls8">computational <span class="_ _6"></span>imaging<span class="ls0"> <span class="_ _6"></span><span class="ls4">problems<span class="ls0"> </span></span></span></span></span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y6d ff4 fs1 fc0 sc0 ls4 ws0">and <span class="_ _4"></span>a <span class="_ _0"></span>deep <span class="_ _4"></span>understanding <span class="_ _0"></span><span class="ls9">of <span class="_ _0"></span><span class="lsc">signal <span class="_ _4"></span>processing <span class="_ _4"></span>theory <span class="_ _4"></span><span class="lse">to<span class="ls0"> <span class="_ _0"></span></span></span>spearhead <span class="_ _4"></span>the<span class="ls0"> <span class="_ _4"></span><span class="ls8">computational <span class="_ _0"></span>imaging <span class="_ _4"></span>and <span class="_ _0"></span><span class="ls14">vision</span></span> </span></span></span></div><div class="t m0 x1 h4 y6e ff4 fs1 fc0 sc0 lse ws0">research<span class="ls0"> <span class="_ _4"></span><span class="ls9">of <span class="_ _0"></span><span class="ls6">my</span></span> <span class="_ _0"></span><span class="ls10">future <span class="_ _0"></span>lab</span> <span class="_ _0"></span><span class="lsb">in <span class="_ _0"></span>the <span class="_ _4"></span>R00 <span class="_ _0"></span>phase</span>. <span class="_ _0"></span>I <span class="_ _0"></span><span class="lsc">see <span class="_ _0"></span></span></span>the <span class="_ _4"></span>increase <span class="_ _0"></span><span class="lsb">in<span class="ls0"> <span class="_ _0"></span><span class="ls4">demand <span class="_ _0"></span>for <span class="_ _0"></span><span class="ls13">engineering <span class="_ _0"></span></span>academics</span> <span class="_ _0"></span><span class="ls2">with </span></span></span></div><a class="l" href="#pf1" data-dest-detail='[1,"XYZ",69.84,702.48,null]'><div class="d m1" style="border-style:none;position:absolute;left:769.411765px;bottom:58.823529px;width:71.280000px;height:12.000000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.633987,0.000000,0.000000,1.633987,0.000000,0.000000]}'></div></div>
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class="t m0 x1 h4 y37 ff4 fs1 fc0 sc0 ls7 ws0">Page <span class="ls0">3 <span class="_ _1"> </span> <span class="_ _2"> </span></span><span class="fc1">Back to the top</span><span class="ff5 ls0"> </span></div><div class="t m0 x1 h4 y6f ff4 fs1 fc0 sc0 ls6 ws0">medical <span class="_ _4"></span>knowled<span class="_ _4"></span>ge <span class="_ _4"></span>as <span class="_ _4"></span>an <span class="_ _0"></span>opportunity <span class="_ _4"></span>f<span class="_ _4"></span>or <span class="_ _4"></span>translational<span class="_ _4"></span> <span class="_ _4"></span>research, <span class="_ _0"></span>either <span class="_ _4"></span><span class="lsb">in<span class="ls0"> <span class="_ _4"></span>an <span class="_ _4"></span><span class="ls13">engineering <span class="_ _4"></span><span class="ls4">department</span></span> <span class="_ _4"></span><span class="ls2">with </span></span></span></div><div class="t m0 x1 h4 y70 ff4 fs1 fc0 sc0 lsc ws0">strong ties to <span class="ls4">a <span class="ls8">clinic<span class="ls0"> <span class="ls9">or a radiology departme<span class="lsd">nt having</span></span> </span></span></span>strong <span class="_ _3"></span>ties to engineering. <span class="ls5">Transition<span class="ls0"> <span class="lse">to faculty will </span></span></span></div><div class="t m0 x1 h4 y71 ff4 fs1 fc0 sc0 ls4 ws0">also <span class="_ _6"></span>allow <span class="_ _6"></span><span class="ls6">me<span class="ls0"> <span class="_ _6"></span><span class="lse">to <span class="_ _3"></span>pursue <span class="_ _6"></span><span class="ls6">my<span class="ls0"> <span class="_ _6"></span><span class="ls4">passion <span class="_ _6"></span>as <span class="_ _3"></span>an <span class="_ _6"></span>educator <span class="_ _6"></span>to <span class="_ _6"></span>motivate <span class="_ _3"></span>and <span class="_ _6"></span>inspire <span class="_ _6"></span>the <span class="_ _3"></span><span class="lsd">next<span class="ls0"> <span class="_ _6"></span><span class="ls4">generation <span class="_ _6"></span>of <span class="_ _6"></span>researchers<span class="ls0"> </span></span></span></span></span></span></span></span></span></span></div><div class="t m0 x1 h4 y72 ff4 fs1 fc0 sc0 ls4 ws0">and<span class="ls0"> </span>pass on to <span class="_ _4"></span>them <span class="ls14">very <span class="ls8">carefully </span></span>distilled <span class="_ _4"></span>knowledge <span class="lsb">in hopes that they will <span class="_ _4"></span><span class="lse">take our scientific di<span class="_ _4"></span>scoveries </span></span></div><div class="t m0 x1 h4 y73 ff4 fs1 fc0 sc0 ls4 ws0">and innovations further<span class="ls0">. </span></div><div class="t m0 x1 h3 y74 ff2 fs1 fc0 sc0 ls1 ws0">REFERENCES<span class="ls0"> </span></div><div class="t m0 x1 h4 y75 ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls9">1. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _3"></span>I. <span class="_ _3"></span>Young, <span class="_ _3"></span>David <span class="_ _3"></span><span class="ls5">Taubman. <span class="_ _3"></span>Rate<span class="ls0">-<span class="ls4">distortion <span class="_ _3"></span>optimized <span class="_ _3"></span>optical <span class="_ _3"></span>flow <span class="_ _3"></span>estimation. <span class="_ _6"></span>Proc<span class="_ _4"></span><span class="ls0">. <span class="_ _6"></span><span class="ls8">ICIP; 2<span class="_ _3"></span>015. </span></span></span></span></span></span></span></span></div><div class="t m0 x1a h4 y76 ff4 fs1 fc0 sc0 ls4 ws0">p. 1677<span class="ls0">–<span class="ls9">1681. </span> </span></div><div class="t m0 x1 h4 yb ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">2</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean I. <span class="_ _4"></span>Young, <span class="_ _4"></span>Reji K. <span class="_ _4"></span>Mathew, <span class="_ _4"></span>David Ta<span class="_ _4"></span>ubman. Optimi<span class="_ _4"></span>zing block</span>-<span class="ls8">coded motion <span class="_ _4"></span>parameters with </span></span></div><div class="t m0 x1a h4 y77 ff4 fs1 fc0 sc0 ls4 ws0">block<span class="ls0">-</span>partition graphs. Proc<span class="ls0">. <span class="ls8">ICIP; 2016. p. 2037</span>–<span class="ls9">2041. </span> </span></div><div class="t m0 x1 h4 y78 ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">3</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean I. Young, Xiaoyu Xi<span class="_ _4"></span>u, Yuwen He, Rahul Vanam.<span class="_ _4"></span> Higher</span>-<span class="ls9">order motion models and graduated </span></span></div><div class="t m0 x1a h4 y79 ff4 fs1 fc0 sc0 ls6 ws0">motion parameter esti<span class="_ _4"></span>mation for video c<span class="_ _4"></span>oding. United States<span class="_ _4"></span> Patent US201762504963P; <span class="_ _4"></span>2018.<span class="ls0"> </span></div><div class="t m0 x1 h4 y7a ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">4</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_"> </span>I. <span class="_ _c"> </span><span class="ls2">Young, <span class="_"> </span>Aous <span class="_ _c"> </span>T</span></span>. <span class="_"> </span><span class="ls6">Naman, <span class="_ _c"> </span><span class="ls7">David <span class="_"> </span><span class="ls5">Taubman.<span class="_ _4"></span> <span class="_"> </span>COGL: <span class="_ _c"> </span>Coefficient <span class="_ _c"> </span>Graph <span class="_"> </span>L<span class="_ _4"></span>aplacians <span class="_ _c"> </span>for </span></span></span></span></div><div class="t m0 x1a h4 y7b ff4 fs1 fc0 sc0 ls1c ws0">Optimized JPEG Im<span class="_ _4"></span>age Decoding. IEEE<span class="_ _4"></span> Trans Image Pro<span class="_ _4"></span>cess. 2019 Jan;<span class="_ _4"></span>28(1):343<span class="ls0">–<span class="ls9">355. </span> </span></div><div class="t m0 x1 h4 y7c ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">5</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _3"></span>I. <span class="_ _3"></span>Young, Aous <span class="_ _6"></span>T. Naman, <span class="_ _3"></span>Bernd <span class="_ _3"></span>Girod, David <span class="_ _6"></span>Taubma<span class="_ _4"></span>n. <span class="_ _3"></span>Solving vision <span class="_ _6"></span>pr<span class="_ _4"></span>oblems <span class="_ _3"></span>via filtering. </span></span></div><div class="t m0 x1a h4 y7d ff4 fs1 fc0 sc0 ls7 ws0">Proc<span class="ls0">. <span class="ls8">ICCV; 2019. p. 5592</span>–<span class="ls9">5601.</span> </span></div><div class="t m0 x1 h4 y7e ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">6</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean I. <span class="ls2">Young, Aous T. <span class="ls6">Naman, </span></span>David <span class="ls5">Taubman. Graph Laplacian regularization for robu<span class="_ _4"></span>st optical </span></span></span></div><div class="t m0 x1a h4 y7f ff4 fs1 fc0 sc0 ls10 ws0">flow estimation. IEEE Trans Image<span class="_ _3"></span> Process. 2020;29:3970<span class="ls0">–<span class="ls9">3983. </span> </span></div><div class="t m0 x1 h4 y15 ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">7</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _4"></span>I. <span class="_ _0"></span>Young, <span class="_ _0"></span>Bernd <span class="_ _0"></span>Girod, <span class="_ _0"></span>David <span class="_ _0"></span>Taubman. <span class="_ _0"></span>Fast <span class="_ _0"></span>optical<span class="_ _4"></span> <span class="_ _4"></span>flow <span class="_ _0"></span>extracti<span class="_ _4"></span>on <span class="_ _4"></span>fro<span class="_ _4"></span>m <span class="_ _4"></span>compres<span class="_ _4"></span>sed <span class="_ _4"></span>vi<span class="_ _4"></span>deo. </span></span></div><div class="t m0 x1a h4 y80 ff4 fs1 fc0 sc0 ls8 ws0">IEEE Trans Image Process. 2020;29:6409<span class="ls0">–<span class="ls9">6421. </span> </span></div><div class="t m0 x1 h4 y81 ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls9">8. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean I. <span class="_ _4"></span>Young, Bernd <span class="_ _4"></span>Girod, David <span class="_ _4"></span>Taubman. Ga<span class="_ _4"></span>ussian lift<span class="_ _4"></span>ing for <span class="_ _4"></span>fast bilateral<span class="_ _4"></span> and non<span class="_ _4"></span>local means </span></span></span></div><div class="t m0 x1a h4 y82 ff4 fs1 fc0 sc0 ls10 ws0">filtering. IEEE Trans Image Proce<span class="_ _3"></span>ss. 2020;29:6082<span class="ls0">–<span class="ls9">6095. </span> </span></div><div class="t m0 x1 h4 y83 ff4 fs1 fc0 sc0 lsb ws0"> <span class="ls0">9</span>. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _7"></span>I.<span class="_ _4"></span> <span class="_ _8"></span><span class="ls2">Young, <span class="_ _7"></span></span>David<span class="_ _4"></span> <span class="_ _7"></span>B.<span class="_ _4"></span> <span class="_ _7"></span><span class="ls2">Li<span class="_ _4"></span>ndell, <span class="_ _8"></span></span>Bernd <span class="_ _7"></span><span class="ls5">Gi<span class="_ _4"></span>rod, <span class="_ _8"></span></span>David <span class="_ _8"></span><span class="ls5">Taubman, <span class="_ _8"></span>Gordon <span class="_ _7"></span><span class="ls15">We<span class="_ _4"></span>tzstein. <span class="_ _8"> </span>Non</span></span></span>-<span class="ls12">line</span>-<span class="ls9">of</span>-</span></div><div class="t m0 x1a h4 y84 ff4 fs1 fc0 sc0 lsc ws0">sight <span class="_ _4"></span>surface <span class="_ _4"></span>reconstruction using <span class="_ _4"></span>the <span class="_ _4"></span>directional <span class="_ _4"></span>light<span class="ls0">-<span class="ls8">cone <span class="_ _4"></span>transform. <span class="_ _4"></span>Proc</span>. <span class="_ _4"></span><span class="ls3">CVPR; <span class="_ _0"></span>2020. <span class="_ _4"></span>p. <span class="_ _0"></span>1407</span>–</span></div><div class="t m0 x1a h4 y85 ff4 fs1 fc0 sc0 ls9 ws0">1416. <span class="ls0"> </span></div><div class="t m0 x1 h4 y86 ff4 fs1 fc0 sc0 ls9 ws0">10<span class="lsb">. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_"> </span>I. <span class="_"> </span><span class="ls2">Young, <span class="_"> </span>Wang <span class="_"> </span>Z<span class="lsd">he</span></span></span></span>, <span class="_ _d"> </span><span class="ls7">David <span class="_"> </span><span class="ls5">Taubman, <span class="_"> </span></span>Bernd <span class="_"> </span><span class="ls5">Girod. <span class="_"> </span>Transform <span class="_"> </span>quantization <span class="_"> </span>for <span class="_"> </span>CNN </span></span></span></div><div class="t m0 x1a h4 y87 ff4 fs1 fc0 sc0 ls8 ws0">compression. IEEE Trans Pattern Anal Mach Intell. 2021;1<span class="ls0">–<span class="ls9">1. </span> </span></div><div class="t m0 x1 h4 y88 ff4 fs1 fc0 sc0 ls9 ws0">11<span class="lsb">. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _8"></span>I. <span class="_ _8"> </span>Youn<span class="_ _4"></span>g</span></span>., <span class="_ _8"></span>Adrian <span class="_ _8"></span>V. <span class="_ _8"> </span>Dalca, <span class="_ _8"> </span>Enzo <span class="_ _8"> </span>Ferrante, <span class="_ _8"> </span>Polina <span class="_ _8"> </span>Golland, <span class="_ _8"> </span>Christopher <span class="_ _8"> </span>A. <span class="_ _8"> </span>Metzler, <span class="_ _8"> </span>Bruce </span></div><div class="t m0 x1a h4 y89 ff4 fs1 fc0 sc0 ls9 ws0">Fischl, <span class="_ _4"></span>and <span class="_ _4"></span>Juan <span class="_ _4"></span>Eugenio <span class="_ _0"></span>Iglesias.<span class="ls0"> <span class="_ _4"></span><span class="ls7">Supervision <span class="_ _0"></span>by</span> <span class="_ _4"></span><span class="ls7">Denoising<span class="lsb">. <span class="_ _4"></span>IEEE <span class="_ _4"></span>Trans <span class="_ _4"></span>Pattern <span class="_ _4"></span>Anal <span class="_ _4"></span><span class="lsf">Mach <span class="_ _4"></span>Int<span class="_ _4"></span>ell. </span></span></span></span></div><div class="t m0 x1a h4 y8a ff4 fs1 fc0 sc0 ls9 ws0">202<span class="ls0">3<span class="lsb">;1</span>–</span>1. <span class="ls0"> </span></div><div class="t m0 x1 h4 y8b ff4 fs1 fc0 sc0 ls9 ws0">12<span class="lsb">. <span class="ls0"> <span class="_ _b"> </span><span class="ls7">Sean <span class="_ _e"> </span>I.</span> <span class="_ _e"> </span><span class="ls2">Young</span></span>, <span class="_ _e"> </span><span class="ls2">Yaël <span class="_ _e"> </span><span class="ls7">Balbastre, <span class="_ _e"> </span>Bruce <span class="_ _e"> </span>Fischl,<span class="_ _4"></span> <span class="_ _e"> </span>Polina <span class="_ _e"> </span>Golland, <span class="_ _e"> </span>Juan <span class="_ _e"> </span>Eugenio<span class="ls0"> <span class="_ _e"> </span><span class="ls8">Iglesias</span></span></span></span>. <span class="_ _e"> </span></span>Fully </div><div class="t m0 x1a h7 y8c ff4 fs1 fc0 sc0 ls8 ws0">convolutional slice<span class="ls0">-<span class="lse">to</span>-<span class="ls14">volume reconstruction for <span class="lsc">single</span></span>-<span class="lsc">stack<span class="_ _3"></span> MRI<span class="lsb">. <span class="ls7">Proc. CVPR<span class="ls0">; s<span class="ls4">ubmitted</span></span></span>. <span class="ff9 fs4 ls0"> </span></span></span></span></div><a class="l" href="#pf1" data-dest-detail='[1,"XYZ",69.84,702.48,null]'><div class="d m1" style="border-style:none;position:absolute;left:769.411765px;bottom:58.823529px;width:71.280000px;height:12.000000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.633987,0.000000,0.000000,1.633987,0.000000,0.000000]}'></div></div>
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