BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21270@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have questioned the robustness of longit udinal analyses based on statistical methods due to issues of temporal bia s and semantic shift. To what extent are changes in semantics over time af fecting the reliability of longitudinal analyses? We examine this question through a case study: understanding shifts in mental health during the co urse of the COVID-19 pandemic. We demonstrate that a recently-introduced m ethod for measuring semantic shift may be used to proactively identify fai lure points of language-based models and improve predictive generalization over time. Ultimately\, we find that these analyses are critical to produ cing accurate longitudinal studies of social media. DTSTART;TZID=America/New_York:20220207T120000 DTEND;TZID=America/New_York:20220207T131500 LOCATION:In Person or Virtual Option @ https://wse.zoom.us/j/96735183473 @ 234 Ames Hall\, 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health d uring the COVID-19 Pandemic” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media-a-case-s tudy-on-mental-health-during-the-covid-19-pandemic/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have questioned the robustness of longit udinal analyses based on statistical methods due to issues of temporal bia s and semantic shift. To what extent are changes in semantics over time af fecting the reliability of longitudinal analyses? We examine this question through a case study: understanding shifts in mental health during the co urse of the COVID-19 pandemic. We demonstrate that a recently-introduced m ethod for measuring semantic shift may be used to proactively identify fai lure points of language-based models and improve predictive generalization over time. Ultimately\, we find that these analyses are critical to produ cing accurate longitudinal studies of social media.
\n X-TAGS;LANGUAGE=en-US:2022\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-21616@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tu ning\, specifically in the presence of semantic shift\, can hinder robustn ess of the underlying methods. However\, little is known about the practic al effect this sensitivity may have on downstream longitudinal analyses. W e explore this gap in the literature through a timely case study: understa nding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable featur es can promote significant changes in longitudinal estimates of our target outcome. At the same time\, we demonstrate that a recently-introduced met hod for measuring semantic shift may be used to proactively identify failu re points of language-based models and\, in turn\, improve predictive gene ralization. DTSTART;TZID=America/New_York:20220318T120000 DTEND;TZID=America/New_York:20220318T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Keith Harrigian “The Problem of Semantic Shift in Longitudinal Monitoring of Social Media” URL:https://www.clsp.jhu.edu/events/student-seminar-keith-harrigian-the-pro blem-of-semantic-shift-in-longitudinal-monitoring-of-social-media/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nSocial media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of thes e tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tu ning\, specifically in the presence of semantic shift\, can hinder robustn ess of the underlying methods. However\, little is known about the practic al effect this sensitivity may have on downstream longitudinal analyses. W e explore this gap in the literature through a timely case study: understa nding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable featur es can promote significant changes in longitudinal estimates of our target outcome. At the same time\, we demonstrate that a recently-introduced met hod for measuring semantic shift may be used to proactively identify failu re points of language-based models and\, in turn\, improve predictive gene ralization.
\n X-TAGS;LANGUAGE=en-US:2022\,Harrigian\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23586@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23892@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.\nBiography\nChien-Ming Huang is the John C . Malone Assistant Professor in the Department of Computer Science at the Johns Hopkins University. His research focuses on designing interactive AI aimed to assist and collaborate with people. He publishes in top-tier ven ues in HRI\, HCI\, and robotics including Science Robotics\, HRI\, CHI\, a nd CSCW. His research has received media coverage from MIT Technology Revi ew\, Tech Insider\, and Science Nation. Huang completed his postdoctoral t raining at Yale University and received his Ph.D. in Computer Science at t he University of Wisconsin–Madison. He is a recipient of the NSF CAREER aw ard. https://www.cs.jhu.edu/~cmhuang/ DTSTART;TZID=America/New_York:20230915T120000 DTEND;TZID=America/New_York:20230915T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Chien-Ming Huang (Johns Hopkins University) “Becoming Teammates: De signing Assistive\, Collaborative Machines” URL:https://www.clsp.jhu.edu/events/chien-ming-huang-johns-hopkins-universi ty/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe growing power in computing and AI promises a near -term future of human-machine teamwork. In this talk\, I will present my r esearch group’s efforts in understanding the complex dynamics of human-mac hine interaction and designing intelligent machines aimed to assist and co llaborate with people. I will focus on 1) tools for onboarding machine tea mmates and authoring machine assistance\, 2) methods for detecting\, and b roadly managing\, errors in collaboration\, and 3) building blocks of know ledge needed to enable ad hoc human-machine teamwork. I will also highligh t our recent work on designing assistive\, collaborative machines to suppo rt older adults aging in place.
\nBiography
\nChien-Ming Huang is the John C. Malone Assistant Professor in the Departm ent of Computer Science at the Johns Hopkins University. His research focu ses on designing interactive AI aimed to assist and collaborate with peopl e. He publishes in top-tier venues in HRI\, HCI\, and robotics including S cience Robotics\, HRI\, CHI\, and CSCW. His research has received media co verage from MIT Technology Review\, Tech Insider\, and Science Nation. Hua ng completed his postdoctoral training at Yale University and received his Ph.D. in Computer Science at the University of Wisconsin–Madison. He is a recipient of the NSF CAREER award. https://www .cs.jhu.edu/~cmhuang/
\n X-TAGS;LANGUAGE=en-US:2023\,Huang\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24457@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing stigmatizing language in medical records. DTSTART;TZID=America/New_York:20240226T120000 DTEND;TZID=America/New_York:20240226T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Keith Harrigian (JHU) “Fighting Bias From Bias: Robust Natural Lang uage Processing Techniques to Promote Health Equity” URL:https://www.clsp.jhu.edu/events/keith-harrigian-jhu-fighting-bias-from- bias-robust-natural-language-processing-techniques-to-promote-health-equit y/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAs artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g.\, clinical decision support\, administrative tasks\, and public health surveillance – it is pe rhaps more important than ever to reflect on the broader purpose of such s ystems. While much focus has been on the potential for this technology to improve general health outcomes\, there also exists a significant\, but un derstated\, opportunity to use this technology to address health-related d isparities. Accomplishing the latter depends not only on our ability to ef fectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable\, but also our ability to measu re\, interpret\, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e.\, new populations\ , temporal dynamics). In this talk\, we will discuss progress made along b oth of these dimensions. We will begin by providing background on the stat e of AI for promoting health equity. Then\, we will present results from a recent clinical phenotyping project and discuss their implication on prev ailing views regarding language model robustness in clinical applications. Finally\, we will showcase ongoing efforts to proactively address systemi c inequality in healthcare by identifying and characterizing stigmatizing language in medical records.
\n X-TAGS;LANGUAGE=en-US:2024\,February\,Harrigian END:VEVENT BEGIN:VEVENT UID:ai1ec-24479@www.clsp.jhu.edu DTSTAMP:20240328T233800Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nThe speech field is evolving to solve more challengin g scenarios\, such as multi-channel recordings with multiple simultaneous talkers. Given the many types of microphone setups out there\, we present the UniX-Encoder. It’s a universal encoder designed for multiple tasks\, a nd worked with any microphone array\, in both solo and multi-talker enviro nments. Our research enhances previous multichannel speech processing effo rts in four key areas: 1) Adaptability: Contrasting traditional models con strained to certain microphone array configurations\, our encoder is unive rsally compatible. 2) MultiTask Capability: Beyond the single-task focus o f previous systems\, UniX-Encoder acts as a robust upstream model\, adeptl y extracting features for diverse tasks including ASR and speaker recognit ion. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integration: In contrast to mod els that first beamform then process single-channels\, our encoder offers an end-to-end solution\, bypassing explicit beamforming or separation. To validate its effectiveness\, we tested the UniXEncoder on a synthetic mult i-channel dataset from the LibriSpeech corpus. Across tasks like speech re cognition and speaker diarization\, our encoder consistently outperformed combinations like the WavLM model with the BeamformIt frontend. DTSTART;TZID=America/New_York:20240311T200500 DTEND;TZID=America/New_York:20240311T210500 SEQUENCE:0 SUMMARY:Zili Huang (JHU) “Unix-Encoder: A Universal X-Channel Speech Encode r for Ad-Hoc Microphone Array Speech Processing” URL:https://www.clsp.jhu.edu/events/zili-huang-jhu-unix-encoder-a-universal -x-channel-speech-encoder-for-ad-hoc-microphone-array-speech-processing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe speech field is evolving to solve more challenging scenarios\, such as multi-channel recordings wi th multiple simultaneous talkers. Given the many types of microphone setup s out there\, we present the UniX-Encoder. It’s a universal encoder design ed for multiple tasks\, and worked with any microphone array\, in both sol o and multi-talker environments. Our research enhances previous multichann el speech processing efforts in four key areas: 1) Adaptability: Contrasti ng traditional models constrained to certain microphone array configuratio ns\, our encoder is universally compatible. 2) MultiTask Capability: Beyon d the single-task focus of previous systems\, UniX-Encoder acts as a robus t upstream model\, adeptly extracting features for diverse tasks including ASR and speaker recognition. 3) Self-Supervised Training: The encoder is trained without requiring labeled multi-channel data. 4) End-to-End Integr ation: In contrast to models that first beamform then process single-chann els\, our encoder offers an end-to-end solution\, bypassing explicit beamf orming or separation. To validate its effectiveness\, we tested the UniXEn coder on a synthetic multi-channel dataset from the LibriSpeech corpus. Ac ross tasks like speech recognition and speaker diarization\, our encoder c onsistently outperformed combinations like the WavLM model with the Beamfo rmIt frontend.
\n X-TAGS;LANGUAGE=en-US:2024\,Huang\,March END:VEVENT END:VCALENDAR