Podcasts about advanced computer studies

Dr. Rita Colwell is a pioneering scientist and professor at the University of Maryland and Johns Hopkins who has made groundbreaking contributions to microbiology and public health. She joins AMSEcast to discuss her experiences being the first woman to lead the National Science Foundation as well as her advanced research on Vibrio bacteria and cholera while founding CosmosID to improve rapid pathogen detection. During the 2001 anthrax attacks, she led a cross-agency effort to identify the spores, revolutionizing DNA sequencing techniques. Overcoming sexism early in her career, Dr. Colwell's achievements are detailed in A Lab of One's Own.     Guest Bio Rita Colwell is a Distinguished University Professor with an appointment in the University of Maryland Institute for Advanced Computer Studies. Colwell is one of the world's leading researchers of cholera—a waterborne disease estimated by the World Health Organization to strike three to five million people annually, many of them young children. Her efforts to track and predict cholera outbreaks are multi-faceted, combining bioinformatics with the pioneering use of satellite imaging. She was one of the first scientists to employ remote sensing for disease prediction, as well as recognize the impact of climate change on the waterborne microbial world.     Show Notes (0:35) About Dr. Rita Colwell (1:52) Dr. Colwell's irritation at people saying we need to interest more women in science (2:49) How Rita dealt with overt sexism and still found the determination to keep moving forward (3:56) What lead Dr. Colwell to marine biology and focus on Vibrio (6:20) How she ended up at the University of Maryland (9:31) Rita's groundbreaking work on cholera and obstacles in getting her findings accepted (15:03) How long it took the professional world for her findings to be accepted (18:49) Dr. Colwell's work as the director of the National Science Foundation (21:39) The role she played in the response to the 2001 anthrax attacks (26:03) The prospects for women in the business world (28:58) The cost of persistent sexism (30:41) Rita's thoughts on how to advance women in the science and business worlds (33:40) What's next for Dr. Colwell     Links Referenced A Lab of One's Own: One Woman's Personal Journey Through Sexism in Science: https://www.amazon.com/Lab-Ones-Own-Personal-Journey/dp/1501181270

Claire chatted to Pratap Tokekar from the University of Maryland about how teams of robots with different capabilities can work together. Pratap Tokekar is an Associate Professor in the Department of Computer Science and the Institute for Advanced Computer Studies at the University of Maryland, and an Amazon Scholar. Previously, he was a Postdoctoral Researcher at the GRASP lab of University of Pennsylvania and later, an Assistant Professor at Virginia Tech. He has a degree in Electronics and Telecommunication from the College of Engineering Pune in India and a Ph.D. in Computer Science from the University of Minnesota. He received the Amazon Research Award in 2022, and the NSF CAREER award in 2020. Join the Robot Talk community on Patreon: https://www.patreon.com/ClaireAsher  

This and all episodes at: https://aiandyou.net/ .   We continue talking about  human-centered AI design with the man who wrote the book on user interface design: Ben Shneiderman, Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director of the Human-Computer Interaction Laboratory and a member of the Institute for Advanced Computer Studies, all at the University of Maryland. His new book, Human-Centered AI, was just published, and in this conclusion we talk about what it's like to get into this field, and the role of standards and governance in human-centered AI. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

This and all episodes at: https://aiandyou.net/ .   Who better to answer the call for expertise in human-centered AI design than the man who wrote the book on user interface design? Ben Shneiderman, Emeritus Distinguished University Professor in the Department of Computer Science, Founding Director of the Human-Computer Interaction Laboratory and a member of the Institute for Advanced Computer Studies, all at the University of Maryland, received six honorary doctorates in human-computer interface design. His new book, Human-Centered AI, was just published, and in this interview we talk about rationalism and empiricism in human-computer interaction, and metaphors in HCI, including his four metaphors for AI that empowers people. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

Philip Resnik, PhD, returns to the Psychcast, this time with his research partner and wife, Rebecca Resnik, PsyD, to discuss the interface between language, psychiatry, psychology, and health. Dr. Philip Resnik appeared on the show previously to discuss artificial intelligence, natural language processing, and mental illness. He is a professor in the department of linguistics at the University of Maryland, College Park, and has a joint appointment with the university’s Institute for Advanced Computer Studies. Dr. Philip Resnik has disclosed being an adviser for Converseon, a social media analysis firm; FiscalNote, a government relationship management platform; and SoloSegment, which specializes in enterprise website optimization. Some of the work Dr. Philip Resnik discusses has been supported by an Amazon AWS Machine Learning Research Award. Dr. Rebecca Resnik is a licensed psychologist in private practice who specializes in neuropsychological assessment. In 2014, she served as cofounder of the Computational Linguistics and Clinical Psychology workshop at the North American Association for Computational Linguistics. She continues to serve as a workshop organizer and clinical consultant to the cross-disciplinary community. She has no disclosures. Dr. Norris disclosed having no conflicts of interest. Take-home points Dr. Rebecca Resnik and Dr. Philip Resnik are interested in finding measurable, observable features to apply to the assessment of psychological and psychiatric diagnoses. They point out that finding an objective measure is essential for scaling up mental health evaluations and treatment. Natural language processing (NLP) is focused on analyzing language content. NLP technology has generated tools such as Siri, Alexa, and Google Translate, and NLP allows computers to do things more intelligently with human language. Individuals are using machine learning and NLP to analyze language data sets to evaluate diagnostic criteria. The goal is to create or use language sets that can be analyzed outside of the clinic. Dr. Rebecca Resnik imagines a world where a patient gives a “language sample” to an app or an avatar that would be evaluated by NLP that would, in turn, offer some overarching hypotheses about the person. So much of evaluations is trying to home in on the correct signal, explicit and implicit, from the patient. In addition, neuropsychiatric tests/scales are standardized against a limited scope of the population, so NLP would be matched to the individual. Dr. Philip Resnik looks at signals in text and speech content, acoustics, microexpressions, and even biometric data. Machine learning can process and distill a huge amount of data with various signals more easily than any human. Dr. Rebecca Resnik revisits the idea of clinical white space, which is the “space” or the time between clinical encounters, and this is where decompensation and high-risk suicidal behaviors occur. She suggests that NLP software could be used to fill this white space by using apps to collect text samples from patients, and the software would analyze the samples and warn of patients who are at risk of decompensation or suicide. If clinicians were to use text or speech samples from people’s smart technology, we could assess an individual's risk in the moment and use nudge-type interventions to prevent suicide. Finally, Dr. Philip Resnik emphasizes that there are technologists who have the skills and technology that is on the verge of helping clinicians, but the key to progress is collaborating with clinicians. References Resnik P et al. J Analytical Psychol. 2020 Sep 10. doi: 10.111/sltb.12674. Coppersmith G et al. Biomed Inform Insights. 2018;10:1178222618792860. Zirikly A et al. CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology. 2019 Jun 16. Yoo DW et al. JMIR Mental Health. 2020;7(8):e16969. American Medical Informatics Association and Mental Health: https://www.amia.org/mental-health-informatics-working-group Selanikio J. The big-data revolution in health care.  TEDxAustin. 2013 Feb. CLPsych: Computational Linguistics and Clinical Psychology Workshop. 2019 Program. *  *  * Show notes by Jacqueline Posada, MD, associate producer of the Psychcast; assistant clinical professor in the department of psychiatry and behavioral sciences at George Washington University in Washington; and staff physician at George Washington Medical Faculty Associates, also in Washington. Dr. Posada has no conflicts of interest. *  *  * For more MDedge Podcasts, go to mdedge.com/podcasts Email the show: podcasts@mdedge.com

Philip Resnik, PhD, joins host Lorenzo Norris, MD, to discuss the use of AI and natural language processing to help clinicians identify patterns in the behaviors of patients with mental illness. Dr. Resnik is a professor in the department of linguistics at the University of Maryland, College Park. He also has a joint appointment with the university’s Institute for Advanced Computer Studies. Dr. Resnik has disclosed being an adviser for Converseon, a social media analysis firm; FiscalNote, a government relationship management platform; and SoloSegment, which specializes in enterprise website optimization. Some of the work Dr. Resnik discusses has been supported by an Amazon AWS Machine Learning Research Award. Dr. Norris disclosed having no conflicts of interest.  And don’t miss the “Dr. RK” segment, with Renee Kohanski, MD.  Take-home points  Artificial intelligence (AI) refers to the effort to get computers to develop capabilities that humans would consider intelligent when people do them. For example, a “smart” thermostat learns patterns of behaviors and changes the temperature accordingly. Natural language processing (NLP), an AI approach, focuses on the content of language from the words used and looks for cues within the content. NLP technology allows computers to do things more intelligently with human language, and NLP has generated technologies such as Siri, Alexa, and Google Translate. Much of clinical work is focused on language, and clinicians look for cues within the content. Dr. Resnik is a technologist who believes that NLP can help facilitate clinical progress, especially in the face of a shortage of mental health clinicians and the limited amount of time that clinicians are able to spend with their patients. Research aimed at using machine learning and NLP to analyze social media and other types of online presence to evaluate for suicide risk and the presence of mood disorders is underway. Dr. Resnik imagines an ecosystem in which computers and humans balance their efforts, with each “brain” doing what they are best at; he believes in technology’s ability to save us time so we can prioritize our efforts. Summary A common example of NLP is automatic dictation and transcription software embedded in medical records. Dr. Resnik thinks of technology as an enabler and augmentation strategy. Resnik and his wife, Rebecca Resnik, PsyD, completed a study using NLP to automatically detect clusters of language in the writing samples of college students. NLP software evaluated the natural patterns of language that might correlate with vegetative and somatic symptoms of depression and social isolation. His team was able to home in on language themes specific to college students that suggest specific symptoms of depression. Another example of NLP in mental health is using predictive modeling, taking in data, and then making a prediction about a pertinent variable to understand mental health outcomes. For example, Glen Coppersmith, PhD, and associates evaluated social media posts with NLP software and concluded that analysis of language in social media posts can accurately identify individuals at risk of suicide and facilitate earlier interventions. Resnik imagines a future in which speech and language samples are used to give a point-of-care evaluation of a patient’s mood and suicide risk. “Clinical white space” is all the “space” (for example, the time between clinical encounters) and this is where decompensation occurs. Resnik suggests that NLP software could be used to fill this white space by using apps to collect text samples from patients. Software would analyze the samples and warn of patients who are at risk of decompensation or suicide. Barriers to using this technology include engaging the technologists and clinicians, and accessing data samples because of privacy concerns, especially because HIPPA was written before the emergence of mega data. References Coppersmith G et al. Natural Language Processing of Social Media as Screening for Suicide Risk. Biomed Inform Insights. 2018 Aug 27. doi: 10.1177/1178222618792860. Zirikly A et al. CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology. 2019 Jun 6. 24-33. Lynn V et al. CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 2018.  37-46. Selanikio J. The big-data revolution in health care. TEDx talk. Graham S et al. Artificial Intelligence for Mental Health and Mental Illnesses: An Overview. Curr Psychiatry Rep. 2019 Nov 7;21(11):116. doi: 10.1007/s11920-019-1094-0. Show notes by Jacqueline Posada, MD, who is associate producer of the Psychcast and consultation-liaison psychiatry fellow with the Inova Fairfax Hospital/George Washington University program in Falls Church, Va.  Dr. Posada has no conflicts of interest. For more MDedge Podcasts, go to mdedge.com/podcasts Email the show: podcasts@mdedge.com  

Ben Shneiderman is a professor in the Department of Computer Science at the University of Maryland, where he is also the founding director of the Human-Computer Interaction Laboratory and a member of the Institute for Advanced Computer Studies as well as author or co-author of numerous influential books. On this episode of the PFF Podcast, Ben talks with Jeffrey about human-computer interaction, the balance between human and machine control and building machines that empower people - enhance, augment and amplify human abilities, not replace or mimic them.

Nesta décima nona edição do SegInfocast o nosso apresentador, Paulo Sant’anna, recebe o profissional Davidson Boccardo e conversa sobre análise forense computacional. O entrevistado explica as principais tarefas necessárias para exercer de forma assertiva a análise, assim como seus principais objetivos e finalidades. Foi abordado também o novo curso de Análise Forense Computacional pela academia Clavis, seus principais tópicos e ferramentas utilizadas. Davidson Rodrigo Boccardo é Doutor em Engenharia Elétrica pela Universidade Estadual Paulista (UNESP) com período sanduíche no Center for Advanced Computer Studies da University of Louisiana at Lafayette. Atualmente é pesquisador no Instituto Nacional de Metrologia, Qualidade e Tecnologia, e tem atuado nos seguintes temas: engenharia reversa, análise de software/malware, ofuscação de software, incorruptibilidade de software e marca d’água em software. Na Academia Clavis também é instrutor do curso: Análise de Malware em Forense Computacional e apresentou o Webinar #17 Análise de Malware em Forense Computacional.

The Patented SpecGram 5 Minute Interview: Philip Resnik — My guest today is Philip Resnik, a professor at the University of Maryland, with joint appointments in the Department of Linguistics and at the Institute for Advanced Computer Studies. Phillip is the director of the University of Maryland Computational Linguistics and Information Processing Laboratory, and a researcher and consultant with extensive experience in natural language processing and text analytics, specializing in combining knowledge based and corpus based statistical techniques. He is also a Strategic Technology Advisor to 3M Health Information Systems and the Founder of React Labs.

Creativity Support Tools is a research topic with high risk but potentially very high payoff. The goal is to develop improved software and user interfaces that empower diverse users in the sciences and arts to be more productive, and more innovative. Potential users include a combination of software and other engineers, diverse scientists, product and graphic designers, and architects, as well as writers, poets, musicians, new media artists, and many others. Ben Shneiderman is a Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory, and Member of the Institute for Advanced Computer Studies at the University of Maryland at College Park. He was elected as a Fellow of the Association for Computing (ACM) in 1997 and a Fellow of the American Association for the Advancement of Science (AAAS) in 2001. He received the ACM SIGCHI Lifetime Achievement Award in 2001. Ben is the author of Software Psychology: Human Factors in Computer and Information Systems (1980) and Designing the User Interface: Strategies for Effective Human-Computer Interaction (4th ed. 2004). His recent books include Leonardo's Laptop: Human Needs and the New Computing Technologies (MIT Press), which won the IEEE book award in 2004.

There is a growing awareness that new kinds of science are needed to cope with many contemporary problems. The idea of Science 2.0 shifts attention from the natural to the made world, where richly interdisciplinary problems are resistant to reductionist solutions. Science 2.0 includes topics such as environmental preservation, energy sustainability, conflict resolution, community building, and universal usability. The problems of universal usability have technical foundations, but its intensely human dimensions means that innovative solutions are needed to promote broad usage of the web, mobile technologies, and new media. The goals are to enable broad access to learning, democratic processes, health information, community services, etc. Challenging research problems emerge from addressing the needs of diverse users (novice/experts, young/old, abled/disabled, multiple languages, cross cultural) who use a wide range technologies (small/large displays, slow/fast networks, voice/text/video). In some cases, traditional controlled studies are successful (Science 1.0), but often novel case study ethnographic methods are more effective (Science 2.0). Ben Shneiderman is a Professor in the Department of Computer Science, Founding Director (1983-2000) of the Human-Computer Interaction Laboratory, and Member of the Institute for Advanced Computer Studies at the University of Maryland at College Park. He was elected as a Fellow of the Association for Computing (ACM) in 1997 and a Fellow of the American Association for the Advancement of Science (AAAS) in 2001. He received the ACM SIGCHI Lifetime Achievement Award in 2001. Ben is the author of Software Psychology: Human Factors in Computer and Information Systems (1980) and Designing the User Interface: Strategies for Effective Human-Computer Interaction (4th ed. 2004). His recent books include Leonardo's Laptop: Human Needs and the New Computing Technologies (MIT Press) which won the IEEE book award in 2004.

"n this talk, after briefly reviewing why we should build a good anomaly-based intrusion detection system, we will briefly present twIDS prototypes developed at the Politecnicdi Milanfor network and host based intrusion detection through unsupervised algorithms. We will then use them as a case study for presenting the difficulties in integrating anomaly based IDS systems (as if integrating usual misuse based IDS system was not complex enough...). We will then present our ideas, based on fuzzy aggregation and causality analysis, for extracting meaningful attack scenarios from alert streams, building the core of the first 360 anomaly based IDS. Also, we will introduce some brand new ideas for correlation based on statistical fitting tests." Andrew Walenstein is a Research Scientist at the Center for Advanced Computer Studies at the University of Louisiana at Lafayette. He is currently studying methods for malware analysis, and brings in experience from the area of reverse engineering and human-computer interaction. He received his Ph.D. from Simon Fraser University in 2002.