Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of September 15, 2023.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

Included in Toolbox is Anaconda Assistant, the recently released AI assistant designed specifically for Python users and data scientists, which can guide you in your first steps or supercharge your work, even if you have advanced experience.

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The Databricks Lakehouse unifies data, analytics and AI on a single platform so that customers can govern, manage and derive insights from enterprise data and build their own generative AI solutions faster. The support from Databricks financial and strategic partners comes on the heels of its Q2 momentum.

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This new product, driven by data semantics and real-world relevance, eliminates a major headache for data science teams preparing and deploying AI data. Powered by Generative AI, FeatureByte Copilot saves data science teams significant time, effort, and resources while moving AI projects from ideation to implementation faster, at scale, and with greater accuracy.

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Shared device mode is a device-level configuration that enables single sign-on (SSO) and device-wide sign-out for Microsoft Power BI and all other apps on the device that support this configuration.With shared device mode, frontline workers can securely share a single device throughout the day, signing in and out as needed.

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With Qlik Staige, customers can innovate and move faster by making secure and governed AI part of everything they can do with Qlik from experimenting with and implementing generative AI models to developing AI-powered predictions.

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Qrvey enables dashboard creators to build reports using different data sources to create customizable dashboards specific to their business needs. This means end users can have a single dashboard that combines data sourced from Snowflake and data sourced from Qrvey.

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The assistant, called Einstein Copilot, can summarize video calls, deliver personalized answers to customer questions and generate emails for marketing campaigns, among others, the company said ahead of its Dreamforce conference this week. AI copilots function like a virtual assistant which can set reminders, schedule meetings and also create content while a Generative Pre-trained Transformer (GPT) uses human language to answer questions and produce content requested by the user.

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SAS Viya Workbench is currently available under private preview, with general availability estimated for early 2024. For synthetic data generation, SAS is working with customers in the banking and health care industries. SAS is also extensively researching the application of large language models (LLMs) to industry problems with a primary focus on delivering trusted and secure results to customers.

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The investment has been led by World Trade Ventures with participation from new and existing investors. It takes the total capital raised by SQream to $135 million and comes at a time when data and analytics workloads are increasing at a breakneck pace.

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This long-running annual event provides attendees the opportunity to hear inspiring keynotes, learn from real-world success stories, and gain key insights on how to solve some of the biggest data challenges that companies face.

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Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the webs leading insights for enterprise technology practitioners.

With the next Spotlight event, the team at Solutions Review has partnered with leading developer tools provider Infragistics. The vendor will bring two of its biggest tools in the market together App Builder and Reveal to show you how to create end-to-end solutions with beautiful UX, interactions, theming, data binding, and self-service dashboards and embedded BI quickly.

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For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.

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Analytics and Data Science News for the Week of September 15 ... - Solutions Review

ATLANTA Joe Depa, a global leader in data operations, analytics and artificial intelligence (AI), has been named Emory Universitys inaugural chief data and analytics officer. He began his new position on Sept. 11.

In this inaugural role, Depa will use the power of data to enhance health outcomes by ensuring better patient care and reducing clinician burnout, expand Emorys academic impact through groundbreaking research and education, and create an environment where the Emory community can thrive by focusing on efficiency and culture. Depas new position will support both the Emory University and Emory Healthcare data infrastructure.

Joes expertise and experience is a perfect fit for Emory at this time, as we seek to leverage the power of data and AI to enhance our capabilities in academic, administrative and research areas and improve patient outcomes, says John Ellis, PhD, interim chief information officer and senior vice provost for Emory University.Joe is also passionate about using data for good and is committed to our mission of improving the health of individuals and communities at home and throughout the world. We welcome Joe warmly as he beginsthis pivotal work.

Depa comes to Emory from Accenture, a Fortune 50 technology provider, where he served as the senior managing director and global lead for data and AI for the companys strategy and consulting business. There he managed their award-winning team of global professionals specializing in data science and AI strategy, and served on the global leadership committee. He focused on helping clients in health, life sciences and across industries to leverage data to develop new clinical data products, improve the patient and employee experience and reduce operating expenses.

As health care pivots to address patient access, workforce shortages and ballooning expenses, AI, machine learning and large language models have the potential to help, but only if guided by the right expertise, says Alistair Erskine, MD, chief information and digital officer for Emory Healthcare and vice president of digital health for Emory University.Joes experience in and out of health care, combined with his purpose-driven mission to alleviate human suffering, makes him the ideal inaugural leader for this critical role.

I am excited to join Emory in this new role to help enrich the patient, clinician and researcher experience through AI and data science, says Depa. This position supports a purpose-driven mission, using the power of data, to help advance positive changes in the lives of patients being cared for at Emory, in our daily work on our campuses and in our society.

Depa received a bachelors degree in industrial and systems engineering and a masters degree in analytics from Georgia Institute of Technology (Georgia Tech). Outside of work, he is a board member for Cure Childhood Cancer and founder and supporter of other organizations focused on research and advancing precision medicine for childhood cancer.

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Joe Depa named inaugural chief data and analytics officer at Emory - Emory News Center

UNIVERSITY PARK, Pa. The U.S. Department of Energy awarded a $29 million grant to seven multi-institution teams across the country to explore applications of machine learning, artificial intelligence and data resources in fusion and plasma sciences. A Penn State faculty member is one of the 19 individual recipients recognized, with a share close to $400,000 to focus on the use of machine learning to help mitigate nuclear reactor disruptions.

Romit Maulik, assistant professor in the Penn State College of Information Sciences and Technology (IST) will collaborate with researchers from Los Alamos National Laboratory, the University of Florida and The University of Texas at Austin (UT Austin) over the next three years with this funding. The teams project is titled, DeepFusion Accelerator for Fusion Energy Sciences in Disruption Mitigation. The researchers will focus on using machine learning to better predict and prevent imminent failures in nuclear fusion reactors, which generate energy through the same process that powers the sun.

Artificial intelligence and scientific machine learning are transforming the way fusion and plasma research is conducted, said Jean Paul Allain, associate director for fusion energy sciences within the DOEs Office of Science, in a DOE press release. Allain is currently on leave from his role as the head of the Ken and Mary Alice Lindquist Department of Nuclear Engineering at Penn State. The U.S. is leveraging every tool in its pursuit of an aggressive program that will bring fusion energy to the grid on the most rapid timescale.

Before joining IST this year, Maulik who is also a Penn State Institute for Computational and Data Sciences co-hire had been collaborating on the Tokamak Disruption Mitigation project with Los Alamos National Laboratory to build machine learning algorithms to aid scientific discovery in nuclear fusion. He said this grant will support him as he takes a deeper dive into the machine learning side of things.

Nuclear fusion reactors are prone to catastrophic performance failures, Maulik said. This creates a safety hazard that prevents nuclear fusion from being commercialized or becoming a power source for the grid.

Maulik said one grand challenge is the inability to predict when a reactor will fail. Simulations provide insight, but they may be too slow and expensive to be used in real time to detect what might happen.

We want to use data science to accelerate these simulations dramatically, Maulik said. If we can rapidly predict an imminent failure, we can control the factors that affect our experiment so that this failure may be avoided.

Maulik said the project will develop machine learning models using previously run simulations as well as experimental data that is coming from actual reactor facilities.

Once were able to detect failures ahead of time, well be able to begin proposing mitigation strategies, he said.

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IST researcher among recipients of $29 million fusion energy ... - Pennsylvania State University

News Release

Tuesday, September 12, 2023

New research consortium will develop innovative strategies for clinical studies involving ancestrally diverse populations.

The National Institutes of Health is establishing the Multi-Omics for Health and Disease Consortium, with approximately $11 million awarded in the consortiums first year of funding. The new consortium aims to advance the generation and analysis of multi-omic data for human health research.

Multi-omics refers to a research approach that incorporates several omics data types derived from different research areas such as genomics, epigenomics, transcriptomics, proteomics and metabolomics. Each of these data types reveals distinct information about different aspects of a biological system, and leveraging all these data types at once is becoming increasingly possible with advances in high-throughput technologies and data science.

The integration of multiple types of data from an individual participants biological sample can provide a more holistic view of the molecular factors and cellular processes involved in human health and disease, including untangling genetic and non-genetic factors in health and disease. Such an approach offers great promise in areas such as defining disease subtypes, identifying biomarkers and discovering drug targets.

Beyond gaining insights into individual diseases, the primary goal of this consortium is to develop scalable and generalizable multi-omics research strategies as well as methods to analyze these large and complex datasets, said Joannella Morales, Ph.D., a National Human Genome Research Institute (NHGRI) program director involved in leading the consortium. We expect these strategies will ultimately be adopted by other research groups, ensuring the consortiums work will have broad and long-lasting impacts for clinical research.

Approximately half of the awarded funds will support the work of six disease study sites, which will examine conditions such as fatty liver diseases, hepatocellular carcinoma, asthma, chronic kidney disease and preeclampsia, among others. The sites will enroll research participants, at least 75% of whom will be from ancestral backgrounds underrepresented in genomics research. The sites will also collect data on participants environments and social determinants of health to be used in conjunction with the multi-omics data. Combining the multi-omic and environmental data can offer an even more comprehensive view of the factors that contribute to disease risk and outcomes.

Specimens provided by participants will be processed at the omics production center, which will use high-throughput molecular assays to generate genomic, epigenomic, transcriptomic, proteomic and metabolomic data that will be analyzed to generate molecular profiles of disease and non-disease states. The data analysis and coordination center will then incorporate all of these data into large, organized datasets that will be made available to the scientific community for further studies.

Multi-omics studies are at the forefront of biomedical research and promise to advance our understanding of disease onset and progression, said Erin Ramos, Ph.D., M.P.H., deputy director of NHGRIs Division of Genomic Medicine. All while potentially providing important clues for treatment design and drug-discovery efforts. This new consortium is an important step in making those advances a reality.

Approximately $50.3 million will be awarded to the consortium over five years, pending the availability of funds. The award is funded jointly by NHGRI, the National Cancer Institute (NCI) and the National Institute of Environmental Health Sciences (NIEHS).

Multi-Omics for Health and Disease Consortium

Disease study sites and principal investigators

Omics production center and principal investigator

Data analysis and coordinating center and principal investigator

The National Human Genome Research Institute (NHGRI) is one of the 27 institutes and centers at the NIH, an agency of the Department of Health and Human Services. The NHGRI Division of Intramural Research develops and implements technology to understand, diagnose and treat genomic and genetic diseases. Additional information about NHGRI can be found at: https://www.genome.gov/.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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NIH awards $50.3 million for multi-omics research on human ... - National Institutes of Health (.gov)

Not everyone you disagree with on social media is a bot, but various forms of social media manipulation are indeed used to spread false narratives, influence democratic processes and affect stock prices.

In 2019, theglobal costof bad actors on the internet was conservatively estimated at $78 billion. In the meantime, misinformation strategies have kept evolving: Detecting them has been so far a reactive affair, with malicious actors always being one step ahead.

AlexanderNwala, a William & Mary assistant professor of data science, aims to address these forms of abuse proactively. With colleagues at theIndiana University Observatory on Social Media, he has recently publishedan Open Access paperinEPJ Data Scienceto introduce BLOC, a universal language framework for modeling social media behaviors.

The main idea behind this framework is not to target a specific behavior, but instead provide a language that can describe behaviors, said Nwala.

Automated bots mimicking human actions have become more sophisticated over time. Inauthentic coordinated behavior represents another common deception, manifested through actions that may not look suspicious at the individual account level, but are actually part of a strategy involving multiple accounts.

However, not all automated or coordinated behavior is necessarily malicious. BLOC does not classify good or bad activities but gives researchers a language to describe social media behaviorsbased on which potentially malicious actions can be more easily identified.

A user-friendly tool to investigate suspicious account behavior is in the works at William & Mary. Ian MacDonald 25, technical director of the W&M undergraduate-ledDisinfoLab, is building a BLOC-based website that would be accessed by researchers, journalists and the general public.

The process, Nwala explained, starts with sampling posts from a given social media account within a specific timeframe and encoding information using specific alphabets.

BLOC, which stands for Behavioral Languages for Online Characterization, relies on action and content alphabets to represent user behavior in a way that can be easily adapted to different social media platforms.

For instance, a string like TpR indicates a sequence of four user actions: specifically, a published post, a reply to a non-friend and then to themselves and a repost of a friends message.

Using the content alphabet, the same set of actions can be characterized as (t)(EEH)(UM)(m) if the users posts respectively contain text, two images and a hashtag, a link and a mention to a friend and a mention of a non-friend.

The BLOC strings obtained are then tokenized into words which could represent different behaviors. Once we have these words, we build what we call vectors, mathematical representations of these words, said Nwala. So well have various BLOC words and then the number of times a user expressed the word or behavior.

Once vectors are obtained, data is run through a machine learning algorithm trained to identify patterns distinguishing between different classes of users (e.g., machines and humans).

Human and bot-like behaviors are at the opposite ends of a spectrum: In between, there are cyborg-like accounts oscillating between these two.

We create models which capture machine and human behavior, and then we find out whether unknown accounts are closer to humans, or to machines, said Nwala.

Using the BLOC framework does not merely facilitate bot detection, equaling or outperforming current detection methods; it also allows the identification of similarities between human-led accounts. Nwala pointed out that BLOC had also been applied to detect coordinated inauthentic accounts engaging in information operations from countries that attempted to influence elections in the U.S. and the West.

Similarity is a very useful metric, he said. If two accounts are doing almost the same thing, you can investigate their behaviors using BLOC to see if perhaps theyre controlled by the same person and then investigate their behavior further.

BLOC is so far unique in addressing different forms of manipulation and is well-poised to outlive platform changes that can make popular detection tools obsolete.

Also, if a new form of behavior arises that we want to study, we dont need to start from scratch, said Nwala. We can just use BLOC to study that behavior and possibly detect it.

As Nwala points out to students in his class onWeb Science the science of decentralized information structures studying web tools and technologies needs to take into account social, cultural and psychological dimensions.

As we interact with technologies, all of these forces come together, he said.

Nwala suggested potential future applications of BLOC in areas such as mental health, as the framework supports the study of behavioral shifts in social media actions.

Research work on social media, however, has been recentlylimitedby therestrictionsimposed by social media platforms on application programming interfaces.

Research like this was only possible because of the availability of APIs to collect large amounts of data, said Nwala. Manipulators will be able to afford whatever it takes to continue their behaviors, but researchers on the other side wont.

According to Nwala, such limitations do not only affect researchers, but also the society at large as these studies help raise awareness of social media manipulation and contribute to effective policymaking.

Just as theres been this steady shout about how the slow decline of local news media affects the democratic process, I think this rises up to that level, he said. The ability of good faith researchers to collect and analyze social media data at a large scale is a public good that needs not to be restricted.

Editors note:Dataanddemocracyare two of four cornerstone initiatives in W&Ms Vision 2026 strategic plan. Visit theVision 2026 websiteto learn more.

Antonella Di Marzio, Senior Research Writer

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Modeling social media behaviors to combat misinformation - William & Mary