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<News hasArchived="false" page="4" pageCount="8" pageSize="10" timestamp="Fri, 01 May 2026 23:46:09 -0400" url="https://dev.my.umbc.edu/groups/iharp/posts.xml?mode=pawpularity&amp;page=4">
  <NewsItem contentIssues="true" id="144068" important="true" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/144068">
  <Title>iHARP represented at 3rd Annual NSF HDR Ecosystem Conference</Title>
  <Tagline>Thank you to those who attended and represnted iHARP!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div><img src="https://iharp.umbc.edu/wp-content/uploads/sites/686/2024/09/LinkedIn-Story-banner-HRD-2024.png" style="max-width: 100%; height: auto;"></div><div><br></div><div><br></div><div><p><span>iHARP recently participated in the </span><a href="https://hdr-ecosystem.github.io/hdr2024/" rel="nofollow external" class="bo"><span>3rd Annual Conference of the NSF HDR Ecosystem</span></a><span> which was held on the campus of the University of Illinois Urbana-Champaign from 9th to the 12th of September 2024. </span></p><br><p><span>Thank you to those iHARP members and affiliates who were able to attend this year’s HDR  conference. We would like to recognize the following iHARP members for their contribution as speakers, session chairs, presenters, and organizers:</span></p><ul><li><p><span>Dr. Vandana Janeja, </span><span>UMBC</span><span></span><span></span></p></li><li><p><span>Dr. Karen Chen, </span><span>UMBC</span><span></span></p></li><li><p><span>Dr. Mohamed Mokbel, </span><span>UMN</span></p></li><li><p><span>Dr. Aneesh Subramanian, </span><span>CU Boulder</span></p></li><li><p><span>Dr. Jianwu Wang, </span><span>UMBC</span><span></span></p></li><li><p><span>Dr. Sharad Sharma, </span><span>UNT</span></p></li><li><p><span>Dr. Josephine Namayanja, </span><span>UMBC</span><span></span><span></span></p></li><li><p><span>Dr. Bayu Tama, </span><span>UMBC</span><span></span></p></li></ul><ul><li><p><span>Subhankar Ghosh, </span><span>UMN</span></p></li><li><p><span>Maloy Kumar Devnath, </span><span>UMBC</span><span></span></p></li><li><p><span>Chhaya Kulkarni, </span><span>UMBC</span><span></span></p></li><li><p><span>Akila Sampath, </span><span>UMBC</span><span></span><span></span></p></li><li><p><span>Tolulope Ale, </span><span>UMBC</span><span></span></p></li></ul></div><img src="https://iharp.umbc.edu/wp-content/uploads/sites/686/2024/09/Express-collage.png" style="max-width: 100%; height: auto;"><div><br><span>Check out the </span><a href="https://hdr-ecosystem.github.io/hdr2024/" rel="nofollow external" class="bo"><span>conference website</span></a><span> to catch the highlights. </span><a href="https://indico.cern.ch/event/1364455/timetable/#all.detailed" rel="nofollow external" class="bo"><span>Click here to see the detailed agenda</span></a><span> and conference materials.</span></div><div><span><br></span></div><div>To learn more about iHARP and the research being conducted, please visit<a href="http://iharp.umbc.edu" rel="nofollow external" class="bo">iharp.umbc.edu</a>.</div><br></div>
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  <Summary>iHARP recently participated in the 3rd Annual Conference of the NSF HDR Ecosystem which was held on the campus of the University of Illinois Urbana-Champaign from 9th to the 12th of September...</Summary>
  <Website>https://iharp.umbc.edu/</Website>
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  <PostedAt>Thu, 19 Sep 2024 15:07:16 -0400</PostedAt>
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  <NewsItem contentIssues="true" id="143614" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/143614">
  <Title>Maloy Kumar Devnath successfully defends his PhD Proposal</Title>
  <Tagline>Congratulatoins Maloy!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>Maloy Kumar Devnath, iHARP Research Assistant, successfully defended his PhD 
    Proposal on Wednesday, August 28, 2024. Join iHARP in congratulating Maloy on his
     successful PhD proposal defense. <br></div><div><br></div><div><strong>Title</strong></div><div>Deciphering the Polar Puzzle: Exploring the Relationship Between Sea Ice Retreat and Ice Sheet Melting in the Antarctic</div><div><strong><br></strong></div><div><strong>Committee</strong></div><div>Dr. Vandana P. Janeja, Chair/Advisor (UMBC)<br>Dr. Sudip Chakraborty, Co-Advisor (UMBC)<br>Dr. James Foulds (UMBC)<br>Dr. Md Osman Gani (UMBC)<br>Dr. Aneesh Subramanian (University of Colorado Boulder)</div><div><br></div><div><strong>Abstract</strong><br></div><div>The dynamics of the Antarctic region which holds 90% of the Earth’s ice volume, particularly the interplay between sea ice retreat and land ice or ice sheets melting, demand rigorous scientific examination due to the alarming changes observed in recent decades. Antarctica’s ice mass has been diminishing rapidly, with an estimated average loss of approximately ∼ 146 billion tons annually since 2002. The reduction in sea ice extent raises critical questions about its repercussions on ice sheet melting, as sea ice provides a protective barrier separating ice sheets from warm ocean currents and wave action. While Antarctic sea ice was expanding until 2015, recent trends show a dramatic reversal with record low extents in February 2023. Understanding the relationship between sea ice changes and ice sheet dynamics is pivotal for deciphering the broader implications of global sea-level rise, a pressing concern for coastal communities, ecosystems, and policymakers. To address this, this thesis employs machine learning algorithms and remote sensing techniques to analyze temporal and spatial variations in the polar climate system. This research aims to develop innovative machine learning algorithms to detect anomalous melt events, variations in melt onset and duration, and quantify the feature similarity and associations between sea ice retreat and land ice or ice sheet melting, particularly in regions experiencing anomalous melts with earlier retreat and late accumulation periods. This thesis specifically aims to:<br><br>1. Understand how anomalous melt events and variations in melt onset and melting period length impact the dynamics of sea ice retreat.<br>2. Assess the significance of anomalous melt events compared to steady-state conditions in influencing sea ice retreat.<br>3. Quantify feature similarity and associations (co-occurrence) between sea ice retreat and land ice or ice sheet melting in regions experiencing anomalous<br>melts, with particular attention to earlier retreat and late accumulation periods.<br><br>By addressing these questions, this thesis contributes to a comprehensive understanding of the intricate interactions between sea ice retreat and ice sheet melting in the Antarctic region and their broader implications for global sea-level rise.<br><br></div><div><br></div><div><em><br></em></div><div><em>Pictured is Maloy Kumar Devnath with Committee members<br>Top Row, left to right:  Dr. Vandana Janeja, Dr. Md Osman Gani, Dr. Aneesh Subramanian </em></div><div><em>Bottom Row, left to right: Maloy Kumar Devnath, Dr. James Foulds,  Dr. Sudip Chakraborty </em><br></div></div>
]]>
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  <Summary>Maloy Kumar Devnath, iHARP Research Assistant, successfully defended his PhD  Proposal on Wednesday, August 28, 2024. Join iHARP in congratulating Maloy on his  successful PhD proposal defense....</Summary>
  <Website>https://iharp.umbc.edu/</Website>
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  <PostedAt>Thu, 05 Sep 2024 10:32:39 -0400</PostedAt>
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  <NewsItem contentIssues="true" id="143446" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/143446">
    <Title>Data Science Scholars Call for Applications</Title>
    <Tagline>Fall 2024 applications are now open &amp; due Sept 6</Tagline>
    <Body>
      <![CDATA[
          <div class="html-content"><p><span>The Data Science Scholars Program (DSS) is  </span><span>partnering with the </span><span>Center for Social Science Scholarship (CS3) and The Center for Women in Technology (CWIT) </span><span> in </span><span>Fall 2024</span><span>. </span></p><p><span>Students admitted to the program will work as teaching fellows (TFs) and peer mentors. They will also network with UC Berkeley data science students, attend special events, and be mentored by faculty. </span></p><p><span>The DSS program welcomes undergraduate scholars from multiple disciplines, including the social sciences, looking at all aspects of data and its impact on society. </span><span>This year scholars will have the opportunity to be teaching fellows or research fellows. Scholars will have an opportunity to work on research projects in Climate Science, Deep fake detection, </span><a href="https://sites.google.com/umbc.edu/storystudio/" rel="nofollow external" class="bo"><span>data storytelling</span></a><span>, </span><a href="https://umbc.edu/stories/karen-chen-nsf-career-award-student-data/" rel="nofollow external" class="bo"><span>learning analytics</span></a><span> and other data science related projects. For some projects look at these websites,</span><a href="https://www.umces.edu/chesapeake-global-collaboratory" rel="nofollow external" class="bo"><span>https://www.umces.edu/chesapeake-global-collaboratory</span></a><span>, </span><a href="https://iharp.umbc.edu/" rel="nofollow external" class="bo"><span>https://iharp.umbc.edu/</span></a><span>, </span><a href="https://mdata.umbc.edu/deep-fake-detection/" rel="nofollow external" class="bo"><span>https://mdata.umbc.edu/deep-fake-detection/</span></a></p><p><br></p><ul><li>COIET Students please reference the CWIT flyer for additional details. </li><li>Humanities students please reference the CS3 flyer for additional details. </li></ul><p><br></p><p><strong>Interested students should complete the Google submission form,<br><a href="https://forms.gle/gKLakVQMEbWPHUtt6">https://forms.gle/gKLakVQMEbWPHUtt6</a> , by Sept 6, 2024. </strong><br></p><p><br></p><p>If you have any questions, please email <a href="mailto:iHARP@UMBC.edu">iHARP@UMBC.edu</a>.<br></p></div>
      ]]>
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    <Summary>The Data Science Scholars Program (DSS) is  partnering with the Center for Social Science Scholarship (CS3) and The Center for Women in Technology (CWIT)  in Fall 2024.   Students admitted to the...</Summary>
    <Website>https://datasciencescholars.umbc.edu/</Website>
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    <PostedAt>Thu, 29 Aug 2024 08:55:59 -0400</PostedAt>
    <EditAt>Thu, 29 Aug 2024 09:02:31 -0400</EditAt>
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  <NewsItem contentIssues="true" id="143204" important="true" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/143204">
  <Title>2024 iHARP All Hands Meeting</Title>
  <Tagline>Convergence and Community Engagement</Tagline>
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    <div class="html-content"><p><span>In summer of 2024, iHARP conducted its All Hands Meeting on June 20 through June 21, 2024 with the theme of “Convergence and Community Engagement”. The All Hands Meeting took place in Fairbanks, Alaska at the University of Alaska Fairbanks (UAF) and included a virtual option. Over 50 attendees in multiple time zones attended this year’s presentation and discussion packed meeting. The meeting brought together iHARP researchers and collaborators who shared their knowledge, insights, current state of research and future research goals. The meeting also served as a launching pad for new connections through the Pan-HDR Ice-Melt Community Council (</span><a href="https://iharp.umbc.edu/phicc/" rel="nofollow external" class="bo"><span>PHICC</span></a><span>).</span></p><p><span><br></span></p><div><img src="https://iharp.umbc.edu/wp-content/uploads/sites/686/2024/08/All-Hands-June-2024.png" style="max-width: 100%; height: auto;"></div><div><br></div><div><p><a href="https://iharp.umbc.edu/events/ahm2024/agenda-2/" rel="nofollow external" class="bo"><span>Day one</span></a><span> of the meeting premiered with the state of the institute, twelve (12) research presentations, a multi-disciplinary panel featuring senior researchers, a convergence discussion where members discussed their vision for iHARP’s future activities at the intersection of multidisciplinary activities, and a poster session where additional research was shared. The selected research presentations and poster presentation only highlighted a small portion of the work that is being conducted through iHARP and its eight collaborating institutions. Day two of the event featured Dr. Vipin Kumar as keynote speaker, and guest speakers from the External Advisory Board and the newly formed PHICC. </span></p><p><span><br></span></p><img src="https://iharp.umbc.edu/wp-content/uploads/sites/686/2024/08/All-Hands-June-2024-2.png" style="max-width: 100%; height: auto;"></div><div><br></div><div><p><span>Throughout the meeting, attendees and presenters were excited to celebrate and reflect on the progress that iHARP has made over the last few years. iHARP members and collaborators are encouraged and excited about the potential growth of research opportunities within iHARP  while taking into careful consideration the longevity and sustainability of research and </span><span>while</span><span> seeking input from its advisory board. With that in mind, iHARP members have begun the ground work in considering the lasting impact of the center and the research being produced and shared. </span></p><br><br><p><span>iHARP is looking forward to another successful year of research and collaboration while continuing to build new relationships within the community and strengthening its existing relationships. </span></p><br><p><span>iHARP extends its deepest gratitude to all those who made this meeting possible and a huge success. A special thank you to all presenters and contributors for sharing their research and knowledge. </span></p><br><p><span>—--- </span></p><p><span>iHARP Mission:</span></p><p><span>iHARP conducts data intensive research, education, outreach, and cyberinfrastructure development that will transform understanding of the effects of climate change in polar regions. This institute brings together stakeholders and leading scholars in data science and polar science to reduce uncertainties in projecting Greenland and Antarctica’s future mass balance, associated sea-level rise, and impacts on global communities.</span></p><br><p><span>Missed the meeting or want to rewatch? </span></p><p><span>You can watch the All Hands Meeting Presentations by visiting the iHARP </span><a href="https://www.youtube.com/@nsfiHARP/videos" rel="nofollow external" class="bo"><span>YouTube channel </span></a><span>under the</span><a href="https://youtube.com/playlist?list=PL_gs0pcOObIWLhEldvYm2lvlN1A7mWZ1Y&amp;si=XRH6v2e0TLr8EyBx" rel="nofollow external" class="bo"><span>playlist titled 2024 iHARP All Hands Meeting hosted at UAF</span></a><span>. Please note that breaks, meal times, and a select portion of the meeting has been edited out. </span></p><br><p><span>Interested in collaborating with iHARP researchers?</span></p><p><span>Email us at </span><a href="mailto:iharp@umbc.edu" rel="nofollow external" class="bo"><span>iharp@umbc.edu</span></a><span> to let us know how you would like to collaborate and we will connect you with a researcher. Please be sure to include your interest and how you would like to partner or collaborate, and where applicable which researcher(s) you would like to work with. </span></p></div><br></div>
]]>
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  <Summary>In summer of 2024, iHARP conducted its All Hands Meeting on June 20 through June 21, 2024 with the theme of “Convergence and Community Engagement”. The All Hands Meeting took place in Fairbanks,...</Summary>
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  <NewsItem contentIssues="true" id="142364" important="true" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/142364">
  <Title>Sahara Ali Successfully Defends her Dissertation</Title>
  <Tagline>Congratulations Sahara!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>Please join us in congratulating Sahara on her successful dissertation defense. Sahara successfully defended her PhD dissertation on Thursday, June 5, 2024 in front of her committee members and attendees. It was a well-attended event with approximately 40 attendees. <br></div><div><br></div><div>Sahara has been a invaluable member of iHARP and the UMBC community. Starting Fall 2024, Sahara will be joining the Department of Information Science at the 
    University of North Texas (an R1 university) as an Assistant Professor. We look forward to seeing Sahara's continue success as a researcher and 
    watching her grow as Assistant Professor position.</div><div><br></div><div>Congratulations to Sahara on all of her hard work!!! iHARP wishes her great success in her next adventure!<br></div><div><br></div><div>-------------------------------------------------</div><div><br></div><div><div><strong>PhD Candidate: </strong>Sahara Ali<br><strong>Dissertation Title: </strong>Spatiotemporal Forecasting and Causality Methods for the Arctic Amplification<br><br><strong>Committee:</strong></div><div>- Dr Jianwu Wang (Committee Chair, UMBC)</div><div>- Dr Vandana Janeja (UMBC)</div><div>- Dr Sanjay Purushotham (UMBC)</div><div>- Dr Md. Osman Gani (UMBC)</div><div>- Dr Aneesh Subramanian (UC Boulder)</div>- Dr Yiyi Huang (UMBC / Bloomberg)</div><div><br></div><div><div><strong>Abstract:</strong></div><div>The
     Arctic is a region with unique climate features where warming has been 
    almost twice as fast as the rest of the world. The warming of the 
    Arctic, also known as Arctic amplification, is led by several 
    atmospheric and oceanic drivers. The essential part of Arctic 
    amplification is the unprecedented sea ice loss as demonstrated by 
    satellite observations. However, understanding the causes of sea-ice 
    variations and its feedback on the atmospheric processes is a complex 
    task. Therefore, accurately forecasting Arctic sea ice from sub-seasonal
     to seasonal scales has been a major scientific challenge and has gained
     interest of Data Scientists to develop novel statistical and machine 
    learning approaches for this task. This brings us to three key research 
    areas studied in this dissertation: (i) accurate forecasting of climate 
    data in the Arctic on sub-seasonal to seasonal scales, (ii) estimating 
    the influence of atmospheric processes and their time-varying effects on
     the sea-ice and ice-sheet variations. (iii) estimating the spatial 
    interference of atmospheric processes and their time-varying effects on 
    the sub-regional sea-ice variations.</div><div>For the first topic, this 
    research explores the potential of data-driven approaches to study sea 
    ice variations by proposing custom deep learning modeling techniques to 
    learn spatiotemporal variations in the sea ice. By combining the spatial
     skills of convolutional neural networks (CNNs), predictive power of 
    recurrent neural networks (RNNs) and other hybrid modeling techniques 
    (ConvLSTM), the proposed models reduce the prediction error by 60% as 
    compared to the state-of-the-art approaches. The work further 
    contributes to accurate long-term forecasting beyond the seasonal 
    barrier.</div><div><br></div><div>For the second topic, the research utilizes potential 
    outcome framework to infer causation in climate data on daily and 
    monthly temporal scales using custom deep learning based forecasting 
    models. In comparison with related work, the framework proposes a deep 
    learning based causal inference model to infer causation under 
    continuous treatment using recurrent neural networks, and a novel 
    probabilistic balancing technique to reduce the confounding bias.<br>For
     the third topic, this work formalizes the concept of spatial 
    interference in case of time-varying treatment assignments by extending 
    the potential outcome framework under the assumption of no unmeasured 
    confounding. The extended framework utilizes latent factor modeling to 
    reduce the bias due to time-varying confounding while leveraging the 
    power of U-Net architecture to capture global and local spatial 
    interference in data over time. The proposed causal estimators are an 
    extension of average treatment effect (ATE) for estimating direct (DATE)
     and indirect effects (IATE) of spatial interference on treated and 
    untreated data. Being the first of its kind deep learning based 
    spatiotemporal causal inference technique, the proposed approach shows 
    advantages over several baseline methods based on the experiment results
     on synthetic datasets, with and without spatial interference. The 
    empirical results on the Arctic dataset not only align with domain 
    knowledge, but also pave paths in quantifying the impact of causal 
    drivers of climate change in the Arctic. <br></div><div><br></div><div>Overall, these 
    predictive and inferential models have the potential to generalize for 
    multiple downstream tasks and can be extended to other domains beyond 
    Earth Science.</div><br><br></div></div>
]]>
  </Body>
  <Summary>Please join us in congratulating Sahara on her successful dissertation defense. Sahara successfully defended her PhD dissertation on Thursday, June 5, 2024 in front of her committee members and...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <NewsItem contentIssues="true" id="142207" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/142207">
  <Title>Sahara Ali PhD Dissertation Defense</Title>
  <Tagline>Thursdasy, June 6, 2024 at 2p [virtual]</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><div>We are excited to share that Sahara Ali will be defending her PhD 
    Dissertation on Thursday, June 6 at 2p (est). <strong><br></strong></div><div><strong><br></strong></div><div><strong>PhD Candidate: </strong>Sahara Ali<br><strong>Dissertation Title: </strong>Spatiotemporal Forecasting and Causality Methods for the Arctic Amplification<br><br><strong>Committee:</strong></div><div>- Dr Jianwu Wang (Committee Chair, UMBC)</div><div>- Dr Vandana Janeja (UMBC)</div><div>- Dr Sanjay Purushotham (UMBC)</div><div>- Dr Md. Osman Gani (UMBC)</div><div>- Dr Aneesh Subramanian (UC Boulder)</div><div>- Dr Yiyi Huang (UMBC / Bloomberg)<br><br><strong>Date and Time: </strong>June 6th, 2024, 2-4pm EST</div><div><br></div><div><strong>Meeting Information:</strong></div><div>Meeting link:<br><a href="https://umbc.webex.com/umbc/j.php?MTID=me7c6a976cb64159dd2cf7944e72a250a" rel="nofollow external" class="bo">https://umbc.webex.com/umbc/j.php?MTID=me7c6a976cb64159dd2cf7944e72a250a</a><br><br>Meeting number:<br>2632 192 0478<br>Meeting password:<br>gxF8cQCRT77<br><br>Join by phone<br>+1-202-860-2110 United States Toll (Washington D.C.)<br>Access code: 26321920478<br><br>Global call-in numbers<br><a href="https://umbc.webex.com/umbc/globalcallin.php?MTID=m922682770606a3cedacc1779cee0a25f" rel="nofollow external" class="bo">https://umbc.webex.com/umbc/globalcallin.php?MTID=m922682770606a3cedacc1779cee0a25f</a><br><br></div><div><strong>Abstract:</strong></div><div>The
     Arctic is a region with unique climate features where warming has been 
    almost twice as fast as the rest of the world. The warming of the 
    Arctic, also known as Arctic amplification, is led by several 
    atmospheric and oceanic drivers. The essential part of Arctic 
    amplification is the unprecedented sea ice loss as demonstrated by 
    satellite observations. However, understanding the causes of sea-ice 
    variations and its feedback on the atmospheric processes is a complex 
    task. Therefore, accurately forecasting Arctic sea ice from sub-seasonal
     to seasonal scales has been a major scientific challenge and has gained
     interest of Data Scientists to develop novel statistical and machine 
    learning approaches for this task. This brings us to three key research 
    areas studied in this dissertation: (i) accurate forecasting of climate 
    data in the Arctic on sub-seasonal to seasonal scales, (ii) estimating 
    the influence of atmospheric processes and their time-varying effects on
     the sea-ice and ice-sheet variations. (iii) estimating the spatial 
    interference of atmospheric processes and their time-varying effects on 
    the sub-regional sea-ice variations.</div><div>For the first topic, this 
    research explores the potential of data-driven approaches to study sea 
    ice variations by proposing custom deep learning modeling techniques to 
    learn spatiotemporal variations in the sea ice. By combining the spatial
     skills of convolutional neural networks (CNNs), predictive power of 
    recurrent neural networks (RNNs) and other hybrid modeling techniques 
    (ConvLSTM), the proposed models reduce the prediction error by 60% as 
    compared to the state-of-the-art approaches. The work further 
    contributes to accurate long-term forecasting beyond the seasonal 
    barrier.</div><div><br></div><div>For the second topic, the research utilizes potential 
    outcome framework to infer causation in climate data on daily and 
    monthly temporal scales using custom deep learning based forecasting 
    models. In comparison with related work, the framework proposes a deep 
    learning based causal inference model to infer causation under 
    continuous treatment using recurrent neural networks, and a novel 
    probabilistic balancing technique to reduce the confounding bias.<br>For
     the third topic, this work formalizes the concept of spatial 
    interference in case of time-varying treatment assignments by extending 
    the potential outcome framework under the assumption of no unmeasured 
    confounding. The extended framework utilizes latent factor modeling to 
    reduce the bias due to time-varying confounding while leveraging the 
    power of U-Net architecture to capture global and local spatial 
    interference in data over time. The proposed causal estimators are an 
    extension of average treatment effect (ATE) for estimating direct (DATE)
     and indirect effects (IATE) of spatial interference on treated and 
    untreated data. Being the first of its kind deep learning based 
    spatiotemporal causal inference technique, the proposed approach shows 
    advantages over several baseline methods based on the experiment results
     on synthetic datasets, with and without spatial interference. The 
    empirical results on the Arctic dataset not only align with domain 
    knowledge, but also pave paths in quantifying the impact of causal 
    drivers of climate change in the Arctic. <br></div><div><br></div><div>Overall, these 
    predictive and inferential models have the potential to generalize for 
    multiple downstream tasks and can be extended to other domains beyond 
    Earth Science.</div><div><br><br></div></div>
]]>
  </Body>
  <Summary>We are excited to share that Sahara Ali will be defending her PhD  Dissertation on Thursday, June 6 at 2p (est).       PhD Candidate: Sahara Ali Dissertation Title: Spatiotemporal Forecasting and...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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</NewsItem>
  <NewsItem contentIssues="true" id="142182" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/142182">
  <Title>Tolulope Ale successfully defends his PhD Proposal</Title>
  <Tagline>Congratulations Tolulope!</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content">Tolulope Ale, iHARP Research Assistant, successfully defended his PhD Proposal on Thursday, May 23, 2024. Join iHARP in congratulating Tolulope on his
     successful PhD proposal defense. <div><br></div><div><span><strong>Title:</strong> </span>A Framework for Multivariate Explainable Anomaly Detection with Uncertainty Estimation  for Time Series Data.</div><div><br></div><div>Committee:</div><div>Dr. Vandana Janaja - Advisor and Committee Chair (UMBC)</div><div>Dr. Nicole-Jeanne Schlegel - Committee Member (NOAA)</div><div>Dr. Jianwu Wang - Committee Member (UMBC)</div><div>Dr. Patti Ordóñez - Committee Member (UMBC)</div><div>Dr. Sudip Chakraborty - Committee Member (UMBC)</div><div><span><br></span></div><div><span><strong>Abstract:</strong> </span><br></div><div>The
     multivariate time series analysis within climate data represents a 
    crucial yet less explored field. This is particularly relevant when 
    examining extreme climate events, such as snow melting in polar regions,
     which necessitate the consideration of multiple variables to capture 
    fluctuations accurately. Anomalies in climate data often result from the
     interplay of several variables, meaning that what appears anomalous 
    under univariate analysis may, in fact, align with expected patterns 
    once contextualized within a multivariate framework. This approach more 
    accurately reflects the interconnected nature of real-world phenomena, 
    where events seldom occur in isolation. The primary challenge in anomaly
     detection algorithms for multivariate climate time series lies in 
    effectively harnessing the intricate relationships among the 
    multivariate data. Despite the advancements in deep learning for anomaly
     detection, very few efforts have been directed toward climate data; 
    this could be due to the lack of labeled data and the complexity of 
    climate variables. Additionally, a significant limitation of existing 
    anomaly detection algorithms is their lack of explainability, especially
     in climate data, where it is crucial to pinpoint which variable most 
    significantly influences an anomaly score. Beyond merely identifying 
    anomalies, it is vital to ascertain the primary variable contributing to
     the anomaly, facilitating targeted strategies for mitigating such 
    occurrences in climate datasets.</div><br>In response, we propose a 
    novel framework incorporated with feature attribution for detecting 
    anomalies within multivariate time series data and then analyzing 
    climate change trends in polar regions. Our methodology employs a 
    Variational Autoencoder (VAE) framework, chosen for its stochastic 
    nature, which we enhanced by incorporating correlation-based feature 
    clustering and dynamic thresholding. These enhancements allow the VAE to
     focus on localized representations, thereby enriching the latent 
    representation quality and the accuracy of detected anomalies. Given the
     complex interdependencies among variables and over time within 
    multivariate climate data, we introduce the concepts of temporal overlap
     and proximity. These concepts allow us to identify how an anomaly in a 
    variable relates to an anomaly in other variables<span>.</span> Through
     extensive experimentation on three distinct datasets, our research 
    substantiates the efficacy of the proposed framework, marking a 
    significant advancement in anomaly detection within climate data 
    analysis<p><em>Image
     Note: In the left panel, Naomi Tack is defending her dissertation. In 
    the right panel, Naomi stands with her iHARP mentors, (left to right: 
    Dr. Becca Williams, Naomi Tack, and Dr. Don Engel).</em></p><p><em><br></em></p><p><em>Photo to the left: Pictured is Tolulope Ale defending his proposal. <br>Photo to the right: Pictured is Tolulope Ale standing with his committee. In the photo: Left of screen Left to Right: Dr. Vandana Janeja, Tolulope Ale <br>On Screen Top Right: Dr. Jianwu Wang, Bottom Left: Dr. Sudip Chakraborty, Bottom Right: Dr. Nicole-Jeanne Schegel <br>Right of Screen: Dr. Patti Ordóñez<br></em></p></div>
]]>
  </Body>
  <Summary>Tolulope Ale, iHARP Research Assistant, successfully defended his PhD Proposal on Thursday, May 23, 2024. Join iHARP in congratulating Tolulope on his  successful PhD proposal defense.     Title:...</Summary>
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  <PostedAt>Fri, 24 May 2024 15:15:45 -0400</PostedAt>
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  <NewsItem contentIssues="true" id="142157" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/142157">
  <Title>iHARP&#8217;s Tolulope Helps Plan UMBC&#8217;s Earth Day Symposium</Title>
  <Body>
    <![CDATA[
    <div class="html-content"><p>Tolulope Ale, an iHARP Research Assistant, was a planning committee member for the 8th Annual Earth Day Symposium and UMBC's first-ever Earth Month. UMBC collaborated with the Environmental Protection Agency (EPA) to organize 18 events throughout April.</p><p>The Earth Month activities concluded with the 8th Annual Earth Day Symposium on April 29th. The symposium covered various topics, including measuring the Earth system, theoretical studies, model forecasts and predictions, educational outreach, and community engagement. It featured presentations from esteemed speakers and included a poster session where graduate and undergraduate students shared their environmental research. </p><p><br>iHARP congratulates and thanks Tolulope for contributing to UMBC's Earth Day Symposium and Earth Month celebrations!</p><p><br>To learn more about UMBC Earth Day and Earth Month please visit: <br><a href="https://sustainability.umbc.edu/home/earth-month/" rel="nofollow external" class="bo">https://sustainability.umbc.edu/home/earth-month/</a></p><p><br>To read the UMBC article featuring UMBC Earth Month please visit:<br><a href="https://umbc.edu/stories/8th-annual-earth-day-symposium/" rel="nofollow external" class="bo">https://umbc.edu/stories/8th-annual-earth-day-symposium/</a></p><p><br>To learn more about the research being conducted at iHARP research center, or if you are interested in Polar and Data research, please visit: <br><a href="https://iharp.umbc.edu/" rel="nofollow external" class="bo">https://iharp.umbc.edu/</a></p><p><br></p><p><br></p><p><strong>Photo Credit: </strong>Planning committee members for the Earth Day Symposium and other Earth Day events with President Valerie Sheares Ashby. Left to right: Sharad Pandey, Adeleke Segun Ademakinwa, Kamal Aryal, Tony La Luna, Roshan Mishra, Rhonda Plofkin, Valerie Sheares Ashby, Tolulope Ale, Erin Hammer, and Maurice Roots. (Image by Zhibo Zhang)</p></div>
]]>
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  <Summary>Tolulope Ale, an iHARP Research Assistant, was a planning committee member for the 8th Annual Earth Day Symposium and UMBC's first-ever Earth Month. UMBC collaborated with the Environmental...</Summary>
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  <PostedAt>Thu, 23 May 2024 10:57:42 -0400</PostedAt>
</NewsItem>
  <NewsItem contentIssues="true" id="141852" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/141852">
  <Title>Dr.Vandana Janeja,iHARP Director, Receives Prestigious Award</Title>
  <Tagline>Join Us to Congratulate Dr. Janeja for her work and success</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><span><p><span>iHARP would like to congratulate Dr. Vandana Janeja on receiving the 2024 USM Board of Regents Faculty Award for Excellence in Scholarship or Research. These awards represent the highest honor bestowed on faculty by the University System of Maryland Board of Regents and recognize excellence in teaching; research, scholarship, creative activity; public service; mentoring; and innovation.</span></p></span><p><span><br></span></p><span><p><span>The award recognizes the hard work of Dr. Janeja's many students and collaborators, who helped make her research what it is today. </span></p><p><span><br></span></p></span><span><p><span>Please join us in congratulating Dr. Vandana Janeja!</span></p><div><span><br></span></div></span></div>
]]>
  </Body>
  <Summary>iHARP would like to congratulate Dr. Vandana Janeja on receiving the 2024 USM Board of Regents Faculty Award for Excellence in Scholarship or Research. These awards represent the highest honor...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <PostedAt>Thu, 09 May 2024 12:07:22 -0400</PostedAt>
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  <NewsItem contentIssues="true" id="141536" important="false" status="posted" url="https://dev.my.umbc.edu/groups/iharp/posts/141536">
  <Title>iHARP Research Assistants present at COEIT Research Day</Title>
  <Tagline>April 19, 2024</Tagline>
  <Body>
    <![CDATA[
    <div class="html-content"><span><span><span>UMBC <a href="https://www.linkedin.com/company/iharp/" rel="nofollow external" class="bo">iHARP</a>
     Research Assistants recently presented their work at UMBC College of 
    Engineering and Information Technology (COEIT) 2024 Research Day on 
    April 19, 2024.<br><br>Presenters include <a href="https://www.linkedin.com/in/ACoAABk5pekBnq_ohdgjCNAZ-hCvI5upbX87LHM" rel="nofollow external" class="bo">Tolulope Ale</a>, <a href="https://www.linkedin.com/in/ACoAAA_tdXwBVh3yZSndo9kL4gXFxB6iOx_UP4Q" rel="nofollow external" class="bo">Sahara Ali</a>, <a href="https://www.linkedin.com/in/ACoAAC97m_oBPdJe4XnRU5g0LQPOuco6c6Hfrrs" rel="nofollow external" class="bo">Atefeh Jebeli</a>, <a href="https://www.linkedin.com/in/ACoAACgz9LkBU6Ro2oJPd1OycONWZ2B7TME7HYM" rel="nofollow external" class="bo">Maloy Kumar Devnath</a>, <a href="https://www.linkedin.com/in/ACoAAB7H78ABswMHnARICyIKQ4JwwIaidVp0U-A" rel="nofollow external" class="bo">Emam Hossain</a>, <a href="https://www.linkedin.com/in/ACoAABCxlWkBOKR5apIzqIAg4Hsocy5RUn9-oQM" rel="nofollow external" class="bo">Chhaya Kulkarni</a>, <a href="https://www.linkedin.com/in/ACoAAEnN5m8B7Ek6BdxsA0TZrl9unf3D_VTlC6Y" rel="nofollow external" class="bo">Francis Ndikum Nji</a>, <a href="https://www.linkedin.com/in/ACoAADrDsfoBFDy_9zF0j_Wv_BnxToKyMbnEDRU" rel="nofollow external" class="bo">Akila Sampath</a>, <a href="https://www.linkedin.com/in/ACoAADuvEQEBffEvH3y83m7EsiNfHooOfHF6Erk" rel="nofollow external" class="bo">Naomi Tack</a>, and iHARP Postdoctoral Research Assistant Dr. <a href="https://www.linkedin.com/in/ACoAAAYG1zEBWu_zzUZ6kpg4mVvNjF5tivmmy3c" rel="nofollow external" class="bo">Bayu Adhi Tama</a>. .<br><br>To read poster abstracts, please visit: <a href="https://lnkd.in/e5waQsj3" rel="nofollow external" class="bo">https://lnkd.in/e5waQsj3</a>. <br><br>To learn more about iHARP and research being conducted, please visit: <a href="http://iharp.umbc.edu" rel="nofollow external" class="bo">iharp.umbc.edu</a><br><br>iHARP mentors involved in supporting research efforts include (but not limited to):<br><a href="https://www.linkedin.com/in/ACoAAA34MBkBmkHwZZ2R-u7iHz4MbqGrkypuxzA" rel="nofollow external" class="bo">Vandana P. Janeja</a> <a href="https://www.linkedin.com/in/ACoAAAAvJ-4B2AdyZbbKsEv8NgT8_zCFiPnOcyA" rel="nofollow external" class="bo">Aneesh Subramanian</a> <a href="https://www.linkedin.com/in/ACoAAAWKB8IBZqDpQ2sft0C2tsmCOkHGTA7cBVU" rel="nofollow external" class="bo">Nicole-Jeanne Schlegel</a><a href="https://www.linkedin.com/in/ACoAAAjHADwBWGnB842yXT6xPPEncNrL0-wo9Oo" rel="nofollow external" class="bo">Md Osman Gani</a> <a href="https://www.linkedin.com/in/ACoAAAMpbQMBCTl5v8RzXXzch-rOXIQcy9PIRjE" rel="nofollow external" class="bo">SUDIP CHAKRABORTY</a> <a href="https://www.linkedin.com/in/ACoAAAD2N_ABdSesaWWfXN2HeBArZozeUjN_zqU" rel="nofollow external" class="bo">Jianwu Wang</a> <a href="https://www.linkedin.com/in/ACoAABqbfIYBHer5mUD74Lozo6kU4pY459pUtGI" rel="nofollow external" class="bo">Devon Dunmire</a> <a href="https://www.linkedin.com/in/ACoAADSmINgBUsa3c0-W0Vd1swJHzbs7Nh3TvZA" rel="nofollow external" class="bo">Alison Banwell, Ph.D.</a> <a href="https://www.linkedin.com/in/ACoAAAYG1zEBWu_zzUZ6kpg4mVvNjF5tivmmy3c" rel="nofollow external" class="bo">Bayu Adhi Tama</a> <a href="https://www.linkedin.com/in/ACoAAAAAqegB2lMpPnuE-E7ISbv6APv_kOCxfaA" rel="nofollow external" class="bo">Don Engel</a> <a href="https://www.linkedin.com/in/ACoAAAn1-18BoGLomw1A2kJQbqZAea3PSUF5k4I" rel="nofollow external" class="bo">Rebecca Williams</a> <a href="https://www.linkedin.com/in/ACoAAA1RY9oB22MMvIUtTYFBYbySQIc9V0GB8S0" rel="nofollow external" class="bo">Nick Holschuh</a> <a href="https://www.linkedin.com/in/ACoAAAEt-5YB6WWtKbfIwSHVWdfEOt8lX-GhzZM" rel="nofollow external" class="bo">Sanjay Purushotham</a></span></span></span></div>
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  <Summary>UMBC iHARP  Research Assistants recently presented their work at UMBC College of  Engineering and Information Technology (COEIT) 2024 Research Day on  April 19, 2024.  Presenters include Tolulope...</Summary>
  <Website>http://iharp.umbc.edu</Website>
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  <PostedAt>Tue, 30 Apr 2024 14:11:12 -0400</PostedAt>
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