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Manikandan Padmanaban - IBM Research
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class="yX5Q1"><section><h3 class="_8VEPP">Title</h3><div class="Lk57l">Senior Research Engineer | Master Inventor</div></section></div><div class="yX5Q1"><section><h3 class="_8VEPP">Location</h3><div class="Lk57l SfenW">IBM Research - India Bengaluru, India</div></section></div></div></div></section></div><aside class="RxbOK"><div class="fV3Nx"><section data-autoid="dds--tableofcontents" class="bx--tableofcontents bx--tableofcontents--white"><section data-autoid="dds--layout" class="bx--grid"><div class="bx--row"><div class="bx--tableofcontents__sidebar bx--col-lg-4"><div class="bx--tableofcontents__mobile-top"></div><div style="position:sticky;top:0"><div class="bx--tableofcontents__desktop" data-autoid="dds--tableofcontents__desktop"><ul></ul></div><div class="bx--tableofcontents__mobile" data-autoid="dds--tableofcontents__mobile"><div class="bx--tableofcontents__mobile__select__wrapper"><select class="bx--tableofcontents__mobile__select"><option 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class="_8MUnA">Kumar Saurav</li><li class="_8MUnA">Jagabondhu Hazra</li><li class="_8MUnA">et al.</li></ul></li><li class="EEJK9">2024</li><li class="EEJK9 XuP8p">GHGT 2024</li></ul><footer class="mwzz0"><div class="_U3CT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></footer></article></li><li class="vGCFo"><article class="PpV0z _4VvpY ywX4g"><h3 class="d0naJ jjmX_"><a class="_1kOEn" href="/publications/towards-net-zero-emission-harnessing-foundation-models-for-enterprise-decarbonisation-project-recommendation">Towards Net-zero Emission: Harnessing Foundation Models for Enterprise Decarbonisation project recommendation</a></h3><ul class="VgSQD"><li class="EEJK9"><ul class="_8ydsY"><li class="_8MUnA"><a class="WvbPS" href="/people/manikandan-padmanaban">Manikandan Padmanaban</a></li><li class="_8MUnA">Jagabondhu Hazra</li><li class="_8MUnA">et al.</li></ul></li><li class="EEJK9">2024</li><li class="EEJK9 XuP8p">GHGT 2024</li></ul><footer class="mwzz0"><div class="_U3CT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></footer></article></li><li class="vGCFo"><article class="PpV0z _4VvpY ywX4g"><h3 class="d0naJ jjmX_"><a class="_1kOEn" href="/publications/a-framework-for-sugarcane-biomass-estimation-by-capturing-growth-dynamics-using-sar-and-optical-imageries">A FRAMEWORK FOR SUGARCANE BIOMASS ESTIMATION BY CAPTURING GROWTH DYNAMICS USING SAR AND OPTICAL IMAGERIES</a></h3><ul class="VgSQD"><li class="EEJK9"><ul class="_8ydsY"><li class="_8MUnA"><a class="WvbPS" 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al.</li></ul></li><li class="EEJK9">2023</li><li class="EEJK9 XuP8p">INFORMS 2023</li></ul><footer class="mwzz0"><div class="_U3CT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></footer></article></li><li class="vGCFo"><article class="PpV0z _4VvpY ywX4g"><h3 class="d0naJ jjmX_"><a class="_1kOEn" href="/publications/scope-3-emission-estimation-using-large-language-models">Scope 3 emission estimation using large language models</a></h3><ul class="VgSQD"><li class="EEJK9"><ul class="_8ydsY"><li class="_8MUnA"><a class="WvbPS" href="/people/ayush-jain">Ayush Jain</a></li><li class="_8MUnA"><a class="WvbPS" href="/people/manikandan-padmanaban">Manikandan Padmanaban</a></li><li class="_8MUnA">et al.</li></ul></li><li class="EEJK9">2023</li><li class="EEJK9 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6z"></path></svg></a></li></ul></nav></section><section class="_80MRl _WUMa"><h2 class="Ji5Eq CvY_O">Patents</h2><div class="_wrOu"><ul class="csXgP"><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2024-10-14T00:00:00.000Z">14 Oct 2024</time></li><li class="U4UFg">US</li><li class="U4UFg">12118626</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US12118626/en">Generating Context-aware Process-based Model Determinations For Greenhouse Gas Emissions From Agricultural Fields</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li 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Gas Emission Estimates For Agricultural Fields Using Cohort Analysis Techniques</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2024-04-22T00:00:00.000Z">22 Apr 2024</time></li><li class="U4UFg">US</li><li class="U4UFg">11965482</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US11965482/en">Maximizing Solar Panel Power Generation With Micro Wind Turbines</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2022-07-18T00:00:00.000Z">18 Jul 2022</time></li><li class="U4UFg">US</li><li class="U4UFg">11393194</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US11393194/en">Selective Analysis For Field Boundary Detection</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2021-05-03T00:00:00.000Z">03 May 2021</time></li><li class="U4UFg">US</li><li class="U4UFg">10998853</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US10998853/en">Internet Of Things-enabled Solar Photovoltaic Health Monitoring And Advising Related Thereto</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2021-02-15T00:00:00.000Z">15 Feb 2021</time></li><li class="U4UFg">US</li><li class="U4UFg">10921028</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US10921028/en">Solar Structure</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2020-11-09T00:00:00.000Z">09 Nov 2020</time></li><li class="U4UFg">US</li><li class="U4UFg">10832353</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US10832353/en">Determining Intermittent Renewable Energy Penetration Limits In A Grid</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2020-10-05T00:00:00.000Z">05 Oct 2020</time></li><li class="U4UFg">US</li><li class="U4UFg">10796283</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US10796283/en">Dynamically Deleting Received Documents Based On A Generated Expiration Deadline For An Event Lapsing</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li><li class="J0_xW"><article class="u8xcP HepWi"><ul class="f7xyQ"><li class="U4UFg"><time dateTime="2020-01-06T00:00:00.000Z">06 Jan 2020</time></li><li class="U4UFg">US</li><li class="U4UFg">10531560</li></ul><div class="UvF6F"><h3 class="euhTO"><a class="q8YpP" href="https://patents.google.com/patent/US10531560/en">Use Of Conducting Fluid In Printed Circuits</a></h3></div><div class="lzakT"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="_1nFq8 _DIFW"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li></ul><nav aria-label="pagination" class="_0Fe_k mbGgE"><ul class="tgnrU"><li><a aria-current="location" class="QCIlI" href="/people/manikandan-padmanaban">1</a></li><li><a class="QCIlI" href="/people/manikandan-padmanaban?patents-page=2">2</a></li><li><a class="mTtGB" aria-label="Next page" href="/people/manikandan-padmanaban?patents-page=2"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" 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style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/><noscript><img alt="Decarbonization-Accelerator.gif" loading="lazy" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover" sizes="(min-width: 99rem) calc((((99rem - 50rem) * 0.5 + 21rem) + 2rem) * (480 / 270) * (1 / (3 / 2))), (min-width: 82rem) calc((((100vw - 50rem) * 0.5 + 21rem) + 2rem) * (480 / 270) * (1 / (3 / 2))), (min-width: 66rem) calc((((100vw - 49rem) * 0.5 + 21rem) + 2rem) * (480 / 270) * (1 / (3 / 2))), (min-width: 42rem) calc((((100vw - 27rem) * 1 + 21rem) + 2rem) * (480 / 270) * (1 / (3 / 2))), calc(100vw * (480 / 270) * (1 / (3 / 4)))" srcSet="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=16&q=75 16w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=32&q=75 32w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=48&q=75 48w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=64&q=75 64w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=96&q=75 96w, 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https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=828&q=75 828w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=1080&q=75 1080w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=1200&q=75 1200w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=1920&q=75 1920w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=2048&q=75 2048w, https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=3840&q=75 3840w" src="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2FDecarbonization_Accelerator_f566f971ad.gif&w=3840&q=75"/></noscript></span></div><div class="RxG9U pN08U kQ_b1 fl1Ur YT0D_ OS51V"><h3 class="f5A_A"><a class="KFMUe" href="/projects/carbon-performance-engine">Carbon Performance Engine</a></h3><div class="z3jU_ iGP2P">An enterprise-scale initiative to measure, track, and reduce carbon emissions</div><ul class="XBsQU tIdYx H_THJ pFSjT _2bQNq sOAT8" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="82" href="/topics/climate-and-sustainability"><span title="Climate and Sustainability">Climate and Sustainability</span></a></li></ul></div></div></div><div class="dfjBt fgEQs" style="--background:#F2F4F8"><div class="wrY9S"><div class="_1AT5d"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img alt="greenhouse-gas-monitoring.png" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/><noscript><img alt="greenhouse-gas-monitoring.png" loading="lazy" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover" sizes="(min-width: 99rem) calc((((99rem - 50rem) * 0.25 + 9rem) + 2rem) * (2712 / 1528) * (1 / (3 / 2))), (min-width: 82rem) calc((((100vw - 50rem) * 0.25 + 9rem) + 2rem) * (2712 / 1528) * (1 / (3 / 2))), (min-width: 66rem) calc((((100vw - 49rem) * 0.25 + 9rem) + 2rem) * (2712 / 1528) * (1 / (3 / 2))), (min-width: 42rem) calc((((100vw - 27rem) * 0.5 + 9rem) + 2rem) * (2712 / 1528) * (1 / (3 / 2))), calc(100vw * (2712 / 1528) * (1 / (3 / 2)))" srcSet="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2Fgreenhouse_gas_monitoring_6fe4f2a283.png&w=16&q=85 16w, 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src="https://research.ibm.com/_next/image?url=https%3A%2F%2Fresearch-website-prod-cms-uploads.s3.us.cloud-object-storage.appdomain.cloud%2Fgreenhouse_gas_monitoring_6fe4f2a283.png&w=3840&q=85"/></noscript></span></div><div class="RxG9U"><h3 class="f5A_A"><a class="KFMUe" href="/projects/greenhouse-gas-monitoring">Greenhouse Gas Monitoring</a></h3><div class="z3jU_ iGP2P"></div><ul class="XBsQU tIdYx H_THJ pFSjT _2bQNq sOAT8" style="--gap-sm:0.5rem;--gap-md:0.5rem;--gap-lg:0.5rem;--gap-xlg:0.5rem;--gap-max:0.5rem"><li class="iHbnT vRTEX l8Im1 er023 EOn_C MtRtY"><a class="bx--tag ZvHdV sCEvD _7_FxK _9ckLP bx--tag--green bx--tag--interactive" id="82" href="/topics/climate-and-sustainability"><span title="Climate and Sustainability">Climate and Sustainability</span></a></li></ul></div></div></div><div class="hGGJj" style="--display-sm:none;--display-md:block;--display-lg:none;--display-xlg:none;--display-max:none"><div class="_1ssQU"><a class="aTLYO" href="/projects">View more projects</a><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="32" height="32" viewBox="0 0 32 32" aria-hidden="true" class="BmaJQ"><path d="M18 6L16.57 7.393 24.15 15 4 15 4 17 24.15 17 16.57 24.573 18 26 28 16 18 6z"></path></svg></div></div></div><div class="_6jUuY" style="--display-sm:grid;--display-md:none;--display-lg:grid;--display-xlg:grid;--display-max:grid"><a href="/projects" class="W_kkd oJAQ2 bx--btn bx--btn--secondary">View more projects<svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" aria-hidden="true" width="16" height="16" viewBox="0 0 16 16" class="bx--btn__icon"><path d="M9.3 3.7L13.1 7.5 1 7.5 1 8.5 13.1 8.5 9.3 12.3 10 13 15 8 10 3z"></path></svg></a></div></section><section class="_5QLND"><a class="xHBSN" name="collaborators" style="--top:0px;--top-offset:0rem"></a><h2 class="Ji5Eq">Top collaborators</h2><div class="YT7ZZ" style="--column-count-sm:4;--item-span-sm:2;--column-count-md:8;--item-span-md:4;--column-count-lg:12;--item-span-lg:3;--column-count-xlg:12;--item-span-xlg:3;--column-count-max:12;--item-span-max:3"><article class="_4ygEB ALUAy wjYBe _2099_"><div class="_7JblE" style="--diameter-sm:128px;--diameter-md:128px;--diameter-lg:128px;--diameter-xlg:128px;--diameter-max:128px"><div class="_32VBU">AJ</div><div class="Hf3tG"><span style="box-sizing:border-box;display:block;overflow:hidden;width:initial;height:initial;background:none;opacity:1;border:0;margin:0;padding:0;position:absolute;top:0;left:0;bottom:0;right:0"><img alt="Ayush Jain" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover"/><noscript><img alt="Ayush Jain" loading="lazy" decoding="async" data-nimg="fill" style="position:absolute;top:0;left:0;bottom:0;right:0;box-sizing:border-box;padding:0;border:none;margin:auto;display:block;width:0;height:0;min-width:100%;max-width:100%;min-height:100%;max-height:100%;object-fit:cover" sizes="128px" srcSet="https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=16&q=75 16w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=32&q=75 32w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=48&q=75 48w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=64&q=75 64w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=96&q=75 96w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=128&q=75 128w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=256&q=75 256w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=384&q=75 384w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=640&q=75 640w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=750&q=75 750w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=828&q=75 828w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=1080&q=75 1080w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=1200&q=75 1200w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=1920&q=75 1920w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=2048&q=75 2048w, https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=3840&q=75 3840w" src="https://research.ibm.com/api/image/ayush-jain.webp?url=https%3A%2F%2Fresearch-website-prod-ibmers-images.s3.us-east.cloud-object-storage.appdomain.cloud%2F316&w=3840&q=75"/></noscript></span></div></div><div><h3 class="qS8LQ"><a class="MhAku" href="/people/ayush-jain">Ayush Jain</a></h3><div class="TNwAp">Research Software Engineer</div></div></article><article class="_4ygEB ALUAy wjYBe _2099_"><div class="_7JblE" style="--diameter-sm:128px;--diameter-md:128px;--diameter-lg:128px;--diameter-xlg:128px;--diameter-max:128px"><div class="_32VBU">SG</div><div class="Hf3tG"><span 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Emission","type":{"__typename":"PublicationType","displayValue":"Poster"},"published":"2024-10-20","publishedMeta":{"__typename":"PublishedMeta","source":"GHGT 2024","year":"2024"},"abstract":"The GHG Protocol Corporate Standard [1] offers a structured framework for quantifying an enterprise's greenhouse gas (GHG) emissions, categorizing them into three scopes. Scope 1 emissions are direct emissions from organizations, Scope 2 emissions result from generation of purchased energy, while Scope 3 encompasses indirect emission from both upstream and downstream of the value chain. Around 80% of a company's greenhouse gas impacts stem from Scope 3 emissions across their value chain [2]. To develop a comprehensive greenhouse gas emissions inventory, it is crucial for companies to account for Scope 3 emissions in conjunction with their Scope 1 \u0026 2 emissions. This inclusive approach allows companies to grasp their complete environmental impact and strategically target the most significant opportunities for its reduction. While many of the world's largest companies routinely track and report emissions from their direct operations (Scopes 1 \u0026 2), only a few extend their reporting to include Scope 3 emissions, and even then, it is often limited to specific categories. This disparity arises primarily due to the formidable complexity associated with tracking and accounting for Scope 3 emissions [3]. Presently, enterprises face several key challenges in this realm such as lack of cooperation and transparency between stakeholders, difficulty in data collection due to large number of stake holders and lack of personal resources to collect the data [4].\n\nThe latest developments in deep learning-based foundation models designed for natural language processing (NLP) have demonstrated superior performance compared to traditional machine learning models in various NLP classification tasks, especially in situations where labelled data is scarce [5, 6]. In the present context, financial transaction text data can be used as a proxy for acquired goods \u0026 services, thereby leveraging domain adapted Natural Language Processing (NLP) foundation models to estimate Scope 3 emissions. However, existing approaches in the literature uses the default setting of using equal weight for all the classes for fine-tuning their foundation models [5]. This ignores the fact that classes with higher emission factor introduce larger error in scope-3 emission calculation in an event of a misclassification.","linkCode":null,"source":{"__ref":"Source:24226"},"sourceInstance":{"__ref":"SourceInstance:22702"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16073"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Author:60258":{"__typename":"Author","id":"60258","ibmer":null},"AuthorName:52494":{"__typename":"AuthorName","id":"52494","firstName":"Vinamra","firstNameInitials":null,"lastName":"Baghel","author":{"__ref":"Author:60258"}},"Publication:154803":{"__typename":"Publication","id":"154803","slug":"towards-net-zero-emission-harnessing-foundation-models-for-enterprise-decarbonisation-project-recommendation","title":"Towards Net-zero Emission: Harnessing Foundation Models for Enterprise Decarbonisation project recommendation","type":{"__typename":"PublicationType","displayValue":"Poster"},"published":"2024-10-20","publishedMeta":{"__typename":"PublishedMeta","source":"GHGT 2024","year":"2024"},"abstract":"As the urgency associated with climate change grows at an unprecedented pace, it has become imperative for enterprises and organizations to align their goals with sustainability values. One of the focal points for many organizations today is decarbonization, i.e., progressing towards carbon neutrality or a net zero carbon emission output resulting from their operations. Achieving net-zero emissions is a critical goal for organizations across all industries. The Net Zero target refers to completely negating the amount of greenhouse gases (GHGs) produced by reducing emissions and implementing methods to reduce the amount of GHG emitted directly or indirectly into the atmosphere. Modern regulations and economic pressures make it necessary for enterprises to exercise GHG emission accounting and disclosure and accelerate their progress towards Net Zero targets. In fact, over 20 percent of the world’s largest companies have set long term net-zero by the next decade. Unfortunately, most of them are struggling with identifying the right set of carbon reducing initiatives suitable for their organization given the budgets and sustainable goals. Ideating the right initiatives to accelerate progress in net zero requires domain expertise, experience, creativity, diversity of thought, and collaboration. This makes the process of innovating slow, dependent, and limited.\n\nDecarbonization of supply chain is an important area of study, which includes accounting of carbon footprints, identifying an inefficient process, understanding the factors which attributes to low performance and recommending the feasible intervenable actions for overall carbon reductions. This enables businesses to identify sustainability impacts across a range of attributes such as economic, environment, social and governance. It allows decision-makers to identify sustainability opportunities and prioritize reduction actions. In the contemporary literature, most of the decarbonization works revolves around carbon accounting and hotspot identification by using either qualitative [1] or quantitative approaches [2]. Reference [3] utilised Hot Spot Analysis (HAS) to integrate social and environmental dimensions along the entire value chain and to identify relevant aspects for a product specific sustainability management. Works like [4], [5] and [6] utilized LCA to evaluate carbon dioxide equivalent (CO2e) emissions and identify carbon hotspots in bio-diesel, maize silage and beef supply chains respectively. However, both HSA and LCA methodologies do not provide explainable insights of the identified hotspots.\n\nFoundation models represent a recent advancement in artificial intelligence. They involve large models trained on extensive datasets through self-supervised learning. After training, these models can be fine-tuned for different tasks, surpassing conventional machine learning and deep learning models in performance. The remarkable success of foundation models in numerous domains has prompted researchers to explore their applications in the field of climate and sustainability. In this paper, we proposed a novel prompt-tuning methodology for large language models for recommending decarbonization project based on context like type of industry, decarbonization targets, decarbonization projects already taken in past, investment amount, etc and demonstrated the efficacy of the LLM to recommend enterprise specific decarbonization projects.","linkCode":null,"source":{"__ref":"Source:24226"},"sourceInstance":{"__ref":"SourceInstance:22702"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:52494"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16073"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:163":{"__typename":"Source","id":"163","longName":"IEEE International Geoscience and Remote Sensing Symposium","shortName":"IGARSS","type":"CONFERENCE"},"SourceInstance:20257":{"__typename":"SourceInstance","id":"20257","name":"IGARSS 2024"},"Author:18459":{"__typename":"Author","id":"18459","ibmer":{"__ref":"Ibmer:3759"}},"AuthorName:16092":{"__typename":"AuthorName","id":"16092","firstName":"Kamal","firstNameInitials":null,"lastName":"Das","author":{"__ref":"Author:18459"}},"Author:55782":{"__typename":"Author","id":"55782","ibmer":null},"AuthorName:48006":{"__typename":"AuthorName","id":"48006","firstName":"Hendrik","firstNameInitials":null,"lastName":"Hamann","author":{"__ref":"Author:55782"}},"Publication:147467":{"__typename":"Publication","id":"147467","slug":"a-framework-for-sugarcane-biomass-estimation-by-capturing-growth-dynamics-using-sar-and-optical-imageries","title":"A FRAMEWORK FOR SUGARCANE BIOMASS ESTIMATION BY CAPTURING GROWTH DYNAMICS USING SAR AND OPTICAL IMAGERIES","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2024-07-07","publishedMeta":{"__typename":"PublishedMeta","source":"IGARSS 2024","year":"2024"},"abstract":"As the sense of urgency regarding climate change continues to mount with growing regulatory pressure across the globe, natural carbon sequestration is becoming increasingly cru- cial as climate mitigation measure. Agriculture is one the most promising natural way of carbon sequestration and farm- ers can generate additional income by selling carbon credits. Therefore, accurate estimation of crop biomass is crucial. In this paper, we proposed a novel framework to accurately mea- sure the crop biomass by capturing ecological, environmental and phenological information derived from SAR and optical imageries. We have evaluated the efficacy of the approach on a set of sugarcane farms in South-East Asia and demonstrated an accuracy of more than 85%.","linkCode":null,"source":{"__ref":"Source:163"},"sourceInstance":{"__ref":"SourceInstance:20257"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16092"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:48006"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Author:177724":{"__typename":"Author","id":"177724","ibmer":null},"AuthorName:204218":{"__typename":"AuthorName","id":"204218","firstName":"Subhojit","firstNameInitials":null,"lastName":"Mandal","author":{"__ref":"Author:177724"}},"Affiliation:3020":{"__typename":"Affiliation","id":"3020","isIbm":false},"Author:177725":{"__typename":"Author","id":"177725","ibmer":null},"AuthorName:204219":{"__typename":"AuthorName","id":"204219","firstName":"Mainak","firstNameInitials":null,"lastName":"Thakur","author":{"__ref":"Author:177725"}},"Publication:147468":{"__typename":"Publication","id":"147468","slug":"improved-dissolved-organic-carbon-prediction-in-diverse-inland-water-bodies-utilizing-machine-learning-and-remote-sensing","title":"IMPROVED DISSOLVED ORGANIC CARBON PREDICTION IN DIVERSE INLAND WATER BODIES: UTILIZING MACHINE LEARNING AND REMOTE SENSING","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2024-07-07","publishedMeta":{"__typename":"PublishedMeta","source":"IGARSS 2024","year":"2024"},"abstract":"The pool of dissolved organic carbon (DOC) is a pivotal influencer in the ecology and bio-geochemistry of inland water ecosystems, constituting a significant factor in the carbon budgets of terrestrial ecosystems. Employing a machine learning approach and leveraging a large curated dataset (AquaSat), this study delves into the importance of multiple spectral remote sensing data and spatio-temporal information in contributing to the variability of in situ DOC concentrations in inland water bodies. The research underscores the critical role of spatial and temporal information in enhancing the accuracy of DOC predictions, revealing a remarkable 200% increase in machine learning model performance when spatio-temporal information is incorporated. With a substantial coverage of inland water bodies in the dataset, the results offer valuable insights and findings for effective environmental monitoring and resource management.","linkCode":null,"source":{"__ref":"Source:163"},"sourceInstance":{"__ref":"SourceInstance:20257"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:204218"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16092"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:204219"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:452":{"__typename":"Source","id":"452","longName":"International Conference on Learning Representations","shortName":"ICLR","type":"CONFERENCE"},"SourceInstance:11200":{"__typename":"SourceInstance","id":"11200","name":"ICLR 2024"},"Author:44400":{"__typename":"Author","id":"44400","ibmer":{"__ref":"Ibmer:2365"}},"AuthorName:35612":{"__typename":"AuthorName","id":"35612","firstName":"Shantanu","firstNameInitials":"S.","lastName":"Godbole","author":{"__ref":"Author:44400"}},"Publication:99281":{"__typename":"Publication","id":"99281","slug":"empowering-sustainable-finance-leveraging-large-language-models-for-climate-aware-investments","title":"Empowering Sustainable Finance: Leveraging Large Language Models for Climate-Aware Investments","type":{"__typename":"PublicationType","displayValue":"Workshop paper"},"published":"2024-05-07","publishedMeta":{"__typename":"PublishedMeta","source":"ICLR 2024","year":"2024"},"abstract":"With the escalating urgency of climate change, it is becoming more imperative for businesses and organizations to align their objectives with sustainability goals. Financial institutions also face a critical mandate to fulfill the Sustainable Development Goals (SDGs), particularly goal 13, which targets the fight against climate change and its consequences. Mitigating the impacts of climate change requires a focus on reducing supply chain emissions, which constitute over 90% of total emission inventories. In the financial industry, supply chain emissions linked to lending and investments emerge as the primary source of emissions, posing challenges in tracking financed emissions due to the intricate process of collecting data from numerous suppliers across the supply chain. To address these challenges, we propose an emission estimation framework utilizing a Large Language Model (LLM) to drastically accelerate the assessment of the emissions associated with lending and investment activities. This framework utilizes financial activities as a proxy for measuring financed emissions. Utilizing the LLM, we classify financial activities into seven asset classes following the Partnership for Carbon Accounting Financials (PCAF) standard. Additionally, we map investments to industry categories and employ spend-based emission factors (kg-CO2/$-spend) to calculate emissions associated with financial investments. In our study, we compare the performance of our proposed method with state-of-the-art text classification models like TF-IDF, word2Vec, and Zero-shot learning. The results demonstrate that the LLM-based approach not only surpasses traditional text mining techniques and performs on par with a subject matter expert (SME) but most importantly accelerates the assessment process.","linkCode":null,"source":{"__ref":"Source:452"},"sourceInstance":{"__ref":"SourceInstance:11200"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35612"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:48006"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Publication:100090":{"__typename":"Publication","id":"100090","slug":"building-sustainable-futures-tutorial-on-carbon-footprint-analysis-and-mitigation-strategies-using-counter-factual-queries","title":"Building Sustainable Futures: Tutorial on Carbon Footprint Analysis and Mitigation Strategies Using Counter Factual Queries","type":{"__typename":"PublicationType","displayValue":"Workshop paper"},"published":"2024-05-07","publishedMeta":{"__typename":"PublishedMeta","source":"ICLR 2024","year":"2024"},"abstract":"As the sense of urgency regarding climate change continues to mount with growing regulatory pressure across the globe, it has become increasingly crucial for enterprises and governments to align their goals with sustainability values. They face a crucial imperative to act on climate change mitigation by disclosing their GHG emissions and committing to reduction and optimization of emissions from their industrial activities including operations, infrastructure, logistics, and supply chains. The world's largest enterprises have set long-term net-zero targets but lacks an integrated view of how their key business operations and processes contribute to their sustainability journey, which makes it difficult for them to embark on a well-planned journey to achieve their sustainability goals. With the recent advancement, AI intervention becomes imperative to measure, track, and improve ESG performance to achieve sustainability goals. \n\nThis tutorial aims to provide a comprehensive guide on leveraging advanced AI techniques for analysing and mitigating carbon footprints in various sectors. The tutorial covers the utilization of a generalized framework that integrates sector-specific and cross-sector enterprise data, including assets and operations, to derive actionable insights. The framework also uses additional data such as weather parameters and contextual information to facilitate a holistic approach to carbon footprint analysis and its mitigation strategies. The tutorial will delve into the working of a framework which comprises of an LLM driven carbon accounting engine, predictive models for carbon emissions, anomaly detection models, and counterfactual models. It identifies the emission hotspots, thereafter provides actionable recommendations to mitigate the carbon emission. The proposed tutorial aims to empower participants with the knowledge and skills to make informed decisions towards building a more sustainable future","linkCode":null,"source":{"__ref":"Source:452"},"sourceInstance":{"__ref":"SourceInstance:11200"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16073"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:459":{"__typename":"Source","id":"459","longName":"INFORMS Annual Meeting","shortName":"INFORMS","type":"CONFERENCE"},"SourceInstance:5824":{"__typename":"SourceInstance","id":"5824","name":"INFORMS 2023"},"Author:18445":{"__typename":"Author","id":"18445","ibmer":{"__ref":"Ibmer:3389"}},"AuthorName:16076":{"__typename":"AuthorName","id":"16076","firstName":"Ranjini","firstNameInitials":null,"lastName":"Bangalore","author":{"__ref":"Author:18445"}},"Publication:22420":{"__typename":"Publication","id":"22420","slug":"from-disclosure-to-action-data-driven-decarbonization-for-enterprises","title":"From Disclosure to Action: Data-Driven Decarbonization for Enterprises","type":{"__typename":"PublicationType","displayValue":"Talk"},"published":"2023-10-15","publishedMeta":{"__typename":"PublishedMeta","source":"INFORMS 2023","year":"2023"},"abstract":"Enterprises face immense pressure to address climate change by disclosing and reducing their GHG emissions across activities. While many companies have committed to long-term net-zero targets, there is a need for a systematic approach that can help enterprises to identify opportunities that can help them reduce their emissions.\nWe address this gap by leveraging enterprise environmental data available in open domain and using data-driven approaches to create a sustainability knowledge base. We further perform clustering of enterprises based on parameters such as sector, geography, size, etc. We use the knowledge base and clusters to learn from similar enterprises and supply chains to identify enterprise decarbonization projects.","linkCode":null,"source":{"__ref":"Source:459"},"sourceInstance":{"__ref":"SourceInstance:5824"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:52494"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16076"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Publication:22421":{"__typename":"Publication","id":"22421","slug":"disambiguation-of-product-expense-data-for-carbon-emission-estimation","title":"Disambiguation of product expense data for carbon emission estimation","type":{"__typename":"PublicationType","displayValue":"Invited talk"},"published":"2023-10-15","publishedMeta":{"__typename":"PublishedMeta","source":"INFORMS 2023","year":"2023"},"abstract":"Estimating the carbon emission embodied in the supply chain product is imperative for understanding the climate impact and its actions. The US Environmentally Extended Input-Output (USEEIO) model provides a carbon emission factor per dollar spend for a set of industry sectors. This provides an opportunity to classify the product transaction data into one of the predefined USEEIO industry classes. But the presence of acronym and the limited words in the transaction data poses a challenge in mapping an expense data to an industry sector. To address this challenge, we propose to incorporate the enterprise specific novel embeddings into the NLP foundation model leveraging enterprise contextual data. This helps to improve the disambiguation with industry abbreviations and the results show an improved accuracy in comparable to annotation by domain expertise.","linkCode":null,"source":{"__ref":"Source:459"},"sourceInstance":{"__ref":"SourceInstance:5824"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Author:44766":{"__typename":"Author","id":"44766","ibmer":null},"AuthorName:35855":{"__typename":"AuthorName","id":"35855","firstName":"Kommy","firstNameInitials":"K.","lastName":"Weldemariam","author":{"__ref":"Author:44766"}},"Publication:22422":{"__typename":"Publication","id":"22422","slug":"estimate-supply-chain-emission-using-large-language-model","title":"Estimate supply chain emission using large language model","type":{"__typename":"PublicationType","displayValue":"Talk"},"published":"2023-10-15","publishedMeta":{"__typename":"PublishedMeta","source":"INFORMS 2023","year":"2023"},"abstract":"Estimating the carbon emission embodied in the supply chain product is imperative for understanding the climate impact and its actions. The US Environmentally Extended Input-Output (USEEIO) model provides a carbon emission factor per dollar spend for a set of industry sectors. This provides an opportunity to classify the product transaction data into one of the predefined USEEIO industry classes. But the presence of acronym and the limited words in the transaction data poses a challenge in mapping an expense data to an industry sector. To address this challenge, we propose to incorporate the enterprise specific novel embeddings into the NLP foundation model leveraging enterprise contextual data. This helps to improve the disambiguation with industry abbreviations and the results show an improved accuracy in comparable to annotation by domain expertise.","linkCode":null,"source":{"__ref":"Source:459"},"sourceInstance":{"__ref":"SourceInstance:5824"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35612"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35855"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:183":{"__typename":"Source","id":"183","longName":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","shortName":"KDD","type":"CONFERENCE"},"SourceInstance:5670":{"__typename":"SourceInstance","id":"5670","name":"KDD 2023"},"Publication:22470":{"__typename":"Publication","id":"22470","slug":"scope-3-emission-estimation-using-large-language-models","title":"Scope 3 emission estimation using large language models","type":{"__typename":"PublicationType","displayValue":"Workshop paper"},"published":"2023-08-06","publishedMeta":{"__typename":"PublishedMeta","source":"KDD 2023","year":"2023"},"abstract":"Large enterprises face a crucial imperative to achieve the Sustainable Development Goals (SDGs), especially goal 13, which focuses on combating climate change and its impacts. To mitigate the effects of climate change, reducing enterprise Scope 3 (supply chain emissions) is vital, as it accounts for more than 90\\% of total emission inventories. However, tracking Scope 3 emissions proves challenging, as data must be collected from thousands of upstream and downstream suppliers. To address the above mentioned challenges, we propose a first-of-a-kind framework that uses domain-adapted NLP foundation models to estimate Scope 3 emissions, by utilizing financial transactions as a proxy for purchased goods and services. We compared the performance of the proposed framework with the state-of-art text classification models such as TF-IDF, word2Vec, and Zero shot learning. Our results show that the domain-adapted foundation model outperforms state-of-the-art text mining techniques and performs as well as a subject matter expert (SME). The proposed framework could accelerate the Scope 3 estimation at Enterprise scale and will help to take appropriate climate actions to achieve SDG 13.","linkCode":null,"source":{"__ref":"Source:183"},"sourceInstance":{"__ref":"SourceInstance:5670"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16080"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16082"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:1524"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35612"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35855"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"ROOT_QUERY":{"__typename":"Query","blogPostsSearch({\"filterBy\":{\"ibmerSlug\":\"manikandan-padmanaban\"},\"limit\":4,\"start\":0})":{"__typename":"BlogPostsSearchConnection","nodes":[],"totalCount":0},"ibmersBySlug({\"slugs\":[\"manikandan-padmanaban\"]})":[{"__ref":"Ibmer:1181"}],"patents({\"after\":\"-1\",\"filterBy\":{\"ibmerSlug\":\"manikandan-padmanaban\"},\"first\":10,\"sortBy\":{\"direction\":\"DESC\",\"option\":\"DATE\"}})":{"__typename":"QueryPatents_Connection","nodes":[{"__typename":"Patent","num":"12118626","title":"GENERATING CONTEXT-AWARE PROCESS-BASED MODEL DETERMINATIONS FOR GREENHOUSE GAS EMISSIONS FROM AGRICULTURAL FIELDS","country":"US","date":"2024-10-14"},{"__typename":"Patent","num":"12095411","title":"VARIABLE SOLAR PANEL ASSEMBLY","country":"US","date":"2024-09-16"},{"__typename":"Patent","num":"12031964","title":"ENHANCING SPATIAL AND TEMPORAL RESOLUTION OF GREENHOUSE GAS EMISSION ESTIMATES FOR AGRICULTURAL FIELDS USING COHORT ANALYSIS TECHNIQUES","country":"US","date":"2024-07-08"},{"__typename":"Patent","num":"11965482","title":"MAXIMIZING SOLAR PANEL POWER GENERATION WITH MICRO WIND TURBINES","country":"US","date":"2024-04-22"},{"__typename":"Patent","num":"11393194","title":"SELECTIVE ANALYSIS FOR FIELD BOUNDARY DETECTION","country":"US","date":"2022-07-18"},{"__typename":"Patent","num":"10998853","title":"INTERNET OF THINGS-ENABLED SOLAR PHOTOVOLTAIC HEALTH MONITORING AND ADVISING RELATED THERETO","country":"US","date":"2021-05-03"},{"__typename":"Patent","num":"10921028","title":"SOLAR STRUCTURE","country":"US","date":"2021-02-15"},{"__typename":"Patent","num":"10832353","title":"DETERMINING INTERMITTENT RENEWABLE ENERGY PENETRATION LIMITS IN A GRID","country":"US","date":"2020-11-09"},{"__typename":"Patent","num":"10796283","title":"DYNAMICALLY DELETING RECEIVED DOCUMENTS BASED ON A GENERATED EXPIRATION DEADLINE FOR AN EVENT LAPSING","country":"US","date":"2020-10-05"},{"__typename":"Patent","num":"10531560","title":"USE OF CONDUCTING FLUID IN PRINTED CIRCUITS","country":"US","date":"2020-01-06"}],"totalCount":14},"publications({\"after\":\"-1\",\"filterBy\":{\"ibmerSlug\":\"manikandan-padmanaban\"},\"first\":10,\"sortBy\":\"NEWEST\"})":{"__typename":"QueryPublications_Connection","totalCount":15,"nodes":[{"__ref":"Publication:154800"},{"__ref":"Publication:154803"},{"__ref":"Publication:147467"},{"__ref":"Publication:147468"},{"__ref":"Publication:99281"},{"__ref":"Publication:100090"},{"__ref":"Publication:22420"},{"__ref":"Publication:22421"},{"__ref":"Publication:22422"},{"__ref":"Publication:22470"}]}}},"ssrDateString":"2024-11-30T11:09:40.465Z"},"__N_SSP":true},"page":"/people/[personSlug]","query":{"personSlug":"manikandan-padmanaban"},"buildId":"iOcPTflN99LxtqEOvJ5Aj","isFallback":false,"gssp":true,"locale":"en-US","locales":["en-US"],"defaultLocale":"en-US","scriptLoader":[]}</script></body></html>