https://datascience.ucr.edu/ en https://datascience.ucr.edu/news/2019/04/05/some-optics-related-data-science-and-machine-learning-problems <span>Some optics-related data science and machine learning problems</span> <span><span>jhuh009</span></span> <span><time datetime="2019-08-08T14:42:29-07:00" title="Thursday, August 8, 2019 - 14:42">Thu, 08/08/2019 - 14:42</time> </span> <a href="https://datascience.ucr.edu/news">More News</a> <time datetime="2019-04-05T12:00:00Z">April 05, 2019</time> <p>In this talk, I'll first present a broad overview of several established fields in physical and quantum optics that are ripe for data science and machine learning applications. Secondly, I'll present some metadata related to different fields of optics in a mini science-technology-and-society study. I'll end by speaking about my current research, which presents novel approaches of optical computing for "smaller-brain" neural networks. This last segment relates to ME202, where we implement spectral computational methods for feature extraction in modern data analytics and machine learning problems.</p> <div class="tags-title">Tags</div> <div class="tags-list"> <div><a href="https://datascience.ucr.edu/tags/events" hreflang="en">events</a></div> <div><a href="https://datascience.ucr.edu/tags/spring2019" hreflang="en">Spring2019</a></div> </div> <div class="sharing-title">Share This</div><span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://datascience.ucr.edu/news/2019/04/05/some-optics-related-data-science-and-machine-learning-problems" data-a2a-title="Some optics-related data science and machine learning problems"><a class="a2a_button_facebook"></a><a class="a2a_button_x"></a><a class="a2a_button_linkedin"></a><a class="a2a_button_google_plus"></a><a class="a2a_button_email"></a><a class="a2a_button_printfriendly"></a><a class="a2a_dd addtoany_share" aria-label="more options to share" href="https://www.addtoany.com/share#url=https%3A%2F%2Fdatascience.ucr.edu%2Fnews%2F2019%2F04%2F05%2Fsome-optics-related-data-science-and-machine-learning-problems&amp;title=Some%20optics-related%20data%20science%20and%20machine%20learning%20problems"></a></span><script> (function () { const customClassName = 'show-for-sr'; const targetContainer = document.querySelector('.a2a_kit.addtoany_list'); if (!targetContainer) return; const addClassToLabels = () => { const labels = targetContainer.querySelectorAll('.a2a_label'); if (labels.length > 0) { labels.forEach(label => { if (!label.classList.contains(customClassName)) { label.classList.add(customClassName); } }); console.log('Successfully applied show-for-sr class to AddToAny labels.'); return true; } return false; }; const observerConfig = { childList: true, subtree: true }; const observer = new MutationObserver((mutationsList, observer) => { if (addClassToLabels()) { observer.disconnect(); } }); if (!addClassToLabels()) { observer.observe(targetContainer, observerConfig); } })(); </script> Thu, 08 Aug 2019 21:42:29 +0000 jhuh009 126 at https://datascience.ucr.edu https://datascience.ucr.edu/news/2019/04/12/estimating-determinants-child-growth-faltering-notes-measurement-models-and <span>Estimating the determinants of child growth faltering: notes on measurement, models and microdata</span> <span><span>jhuh009</span></span> <span><time datetime="2019-08-08T14:19:42-07:00" title="Thursday, August 8, 2019 - 14:19">Thu, 08/08/2019 - 14:19</time> </span> <a href="https://datascience.ucr.edu/news">More News</a> <time datetime="2019-04-12T12:00:00Z">April 12, 2019</time> <p><a href="https://www.josephrcummins.com/">Prof. Joseph Cummins</a></p> <p><span style="font-size: inherit;">Department of Economics, UCR</span></p> <p>Abstract: Early life growth faltering due to nutritional deficiencies and disease environment currently affects the health, productivity and lifespan of hundreds of millions of adults worldwide, with another 150 million children currently experiencing stunted growth.&nbsp; In this talk, I survey methods for estimating the effects of various individual- and ecological-level determinants of child growth faltering using individual-level data from repeated cross-sections of the Demographic and Health Surveys.&nbsp; I argue that the statistical models and outcome measures currently employed in the health and social science literatures often produce misleading, inappropriate, and/or highly biased estimates.&nbsp; Simultaneously, by aggregating results for children of various ages, these estimates tend to average over the exact dynamics of child growth that are the focus of many contemporary social science theories. I propose an empirical framework and a set of regression-based statistical models where the child growth profile itself becomes the outcome of interest.&nbsp;</p> <div class="tags-title">Tags</div> <div class="tags-list"> <div><a href="https://datascience.ucr.edu/tags/events" hreflang="en">events</a></div> <div><a href="https://datascience.ucr.edu/tags/spring2019" hreflang="en">Spring2019</a></div> </div> <div class="sharing-title">Share This</div><span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://datascience.ucr.edu/news/2019/04/12/estimating-determinants-child-growth-faltering-notes-measurement-models-and" data-a2a-title="Estimating the determinants of child growth faltering: notes on measurement, models and microdata"><a class="a2a_button_facebook"></a><a class="a2a_button_x"></a><a class="a2a_button_linkedin"></a><a class="a2a_button_google_plus"></a><a class="a2a_button_email"></a><a class="a2a_button_printfriendly"></a><a class="a2a_dd addtoany_share" aria-label="more options to share" href="https://www.addtoany.com/share#url=https%3A%2F%2Fdatascience.ucr.edu%2Fnews%2F2019%2F04%2F12%2Festimating-determinants-child-growth-faltering-notes-measurement-models-and&amp;title=Estimating%20the%20determinants%20of%20child%20growth%20faltering%3A%20notes%20on%20measurement%2C%20models%20and%20microdata"></a></span><script> (function () { const customClassName = 'show-for-sr'; const targetContainer = document.querySelector('.a2a_kit.addtoany_list'); if (!targetContainer) return; const addClassToLabels = () => { const labels = targetContainer.querySelectorAll('.a2a_label'); if (labels.length > 0) { labels.forEach(label => { if (!label.classList.contains(customClassName)) { label.classList.add(customClassName); } }); console.log('Successfully applied show-for-sr class to AddToAny labels.'); return true; } return false; }; const observerConfig = { childList: true, subtree: true }; const observer = new MutationObserver((mutationsList, observer) => { if (addClassToLabels()) { observer.disconnect(); } }); if (!addClassToLabels()) { observer.observe(targetContainer, observerConfig); } })(); </script> Thu, 08 Aug 2019 21:19:42 +0000 jhuh009 116 at https://datascience.ucr.edu https://datascience.ucr.edu/news/2019/04/26/plant-species-persistence-face-climate-change <span>Plant species persistence in the face of climate change</span> <span><span>jhuh009</span></span> <span><time datetime="2019-08-08T14:17:38-07:00" title="Thursday, August 8, 2019 - 14:17">Thu, 08/08/2019 - 14:17</time> </span> <a href="https://datascience.ucr.edu/news">More News</a> <time datetime="2019-04-26T12:00:00Z">April 26, 2019</time> <p>Climate change threatens the persistence of native plants across California, and strategies are needed to facilitate resilience and conserve the most vulnerable species. Conserving species under climate change is complicated, however, because the state’s native flora are threatened by other global changes, including altered disturbance regimes, land use change, and invasive species. We developed an integrated modeling framework to assess the relative impacts of multiple threats, and to rank management responses, for five endemic plant species in southern California. The modeling framework integrates land use change projections with projected species’ distribution shifts under climate change, which are subsequently linked to a stochastic population model. The population model predicts plant species persistence under alternative habitat change scenarios in addition to different fire regime and management scenarios. Overall, climate change was projected to produce large changes in species’ suitable habitat, although these changes varied by species and climate change scenario. Despite projections of large habitat shifts under climate change, too-frequent fire was the top-ranked threat for most species. Urban development often exacerbated habitat loss under climate change, but the relative impact on species’ persistence was largely a function of where the species is located on the landscape. In conclusion, if climate change impacts on species can be implied through altered distribution patterns, large changes can be expected in southern California. However, management strategies to address these habitat shifts would most likely not be effective unless other threats, particularly increased fire frequency, are not accounted for.</p> <div class="tags-title">Tags</div> <div class="tags-list"> <div><a href="https://datascience.ucr.edu/tags/events" hreflang="en">events</a></div> <div><a href="https://datascience.ucr.edu/tags/spring2019" hreflang="en">Spring2019</a></div> </div> <div class="sharing-title">Share This</div><span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://datascience.ucr.edu/news/2019/04/26/plant-species-persistence-face-climate-change" data-a2a-title="Plant species persistence in the face of climate change"><a class="a2a_button_facebook"></a><a class="a2a_button_x"></a><a class="a2a_button_linkedin"></a><a class="a2a_button_google_plus"></a><a class="a2a_button_email"></a><a class="a2a_button_printfriendly"></a><a class="a2a_dd addtoany_share" aria-label="more options to share" href="https://www.addtoany.com/share#url=https%3A%2F%2Fdatascience.ucr.edu%2Fnews%2F2019%2F04%2F26%2Fplant-species-persistence-face-climate-change&amp;title=Plant%20species%20persistence%20in%20the%20face%20of%20climate%20change"></a></span><script> (function () { const customClassName = 'show-for-sr'; const targetContainer = document.querySelector('.a2a_kit.addtoany_list'); if (!targetContainer) return; const addClassToLabels = () => { const labels = targetContainer.querySelectorAll('.a2a_label'); if (labels.length > 0) { labels.forEach(label => { if (!label.classList.contains(customClassName)) { label.classList.add(customClassName); } }); console.log('Successfully applied show-for-sr class to AddToAny labels.'); return true; } return false; }; const observerConfig = { childList: true, subtree: true }; const observer = new MutationObserver((mutationsList, observer) => { if (addClassToLabels()) { observer.disconnect(); } }); if (!addClassToLabels()) { observer.observe(targetContainer, observerConfig); } })(); </script> Thu, 08 Aug 2019 21:17:38 +0000 jhuh009 106 at https://datascience.ucr.edu https://datascience.ucr.edu/news/2019/04/19/scientific-data-chemical-engineers-perspective <span>Scientific Data: a Chemical Engineer’s Perspective</span> <span><span>jhuh009</span></span> <span><time datetime="2019-08-08T12:56:48-07:00" title="Thursday, August 8, 2019 - 12:56">Thu, 08/08/2019 - 12:56</time> </span> <a href="https://datascience.ucr.edu/news">More News</a> <time datetime="2019-04-19T12:00:00Z">April 19, 2019</time> <p><a href="https://jwulab.engr.ucr.edu/home/abouttheprofessor">Prof. Jianzhong Wu</a></p> <p>Department of Chemical and Environmental Engineering, UCR</p> <p>Abstract: Physics-based modeling and data science are complementary and can accelerate scientific progress for better understanding and more reliable predictions of the physicochemical properties and phase behavior of multicomponent chemical systems that are essential for engineering design of chemical products and processes. In this talk, I explore opportunities that may promote mutual understanding and effective collaboration between researchers from these intrinsically multidisciplinary fields. Illustrative examples will be discussed on how physics-based modeling leads scientific data useful for chemical engineering applications.</p> <div class="tags-title">Tags</div> <div class="tags-list"> <div><a href="https://datascience.ucr.edu/tags/events" hreflang="en">events</a></div> <div><a href="https://datascience.ucr.edu/tags/spring2019" hreflang="en">Spring2019</a></div> </div> <div class="sharing-title">Share This</div><span class="a2a_kit a2a_kit_size_32 addtoany_list" data-a2a-url="https://datascience.ucr.edu/news/2019/04/19/scientific-data-chemical-engineers-perspective" data-a2a-title="Scientific Data: a Chemical Engineer’s Perspective"><a class="a2a_button_facebook"></a><a class="a2a_button_x"></a><a class="a2a_button_linkedin"></a><a class="a2a_button_google_plus"></a><a class="a2a_button_email"></a><a class="a2a_button_printfriendly"></a><a class="a2a_dd addtoany_share" aria-label="more options to share" href="https://www.addtoany.com/share#url=https%3A%2F%2Fdatascience.ucr.edu%2Fnews%2F2019%2F04%2F19%2Fscientific-data-chemical-engineers-perspective&amp;title=Scientific%20Data%3A%20a%20Chemical%20Engineer%E2%80%99s%20Perspective"></a></span><script> (function () { const customClassName = 'show-for-sr'; const targetContainer = document.querySelector('.a2a_kit.addtoany_list'); if (!targetContainer) return; const addClassToLabels = () => { const labels = targetContainer.querySelectorAll('.a2a_label'); if (labels.length > 0) { labels.forEach(label => { if (!label.classList.contains(customClassName)) { label.classList.add(customClassName); } }); console.log('Successfully applied show-for-sr class to AddToAny labels.'); return true; } return false; }; const observerConfig = { childList: true, subtree: true }; const observer = new MutationObserver((mutationsList, observer) => { if (addClassToLabels()) { observer.disconnect(); } }); if (!addClassToLabels()) { observer.observe(targetContainer, observerConfig); } })(); </script> Thu, 08 Aug 2019 19:56:48 +0000 jhuh009 96 at https://datascience.ucr.edu