Category Archives: Data Science
Five UB faculty named SUNY Distinguished Professors – UB Now: News and views for UB faculty and staff – University at Buffalo Reporter
Campus News
The distinguished professor designation is the highest faculty rank in the SUNY system.
UBNOW STAFF
Published December 10, 2021
Five UB faculty members have been named SUNY Distinguished Professors, the highest rank in the SUNY system.
Quanxi Jia, Marianthi Markatou, Janet Morrow, Robert Shibley and Mark Swihart were appointed to the distinguished professor rank by the SUNY Board of Trustees at its meeting on Nov. 9. They were among 11 new Distinguished Professors appointed at the meeting.
The rank of distinguished professor is an order above full professorship and has three co-equal designations: distinguished professor, distinguished service professor and distinguished teaching professor.
The five were all named Distinguished Professors in recognition of their national and/or international prominence and distinguished reputations within their chosen fields. According to SUNY, this distinction is attained through extraordinary contributions to, and impact on, the candidates field of study, often evidenced by significant research and/or creative activity. The work must be of such character that it has the potential to elevate the standards of scholarship or creative activity of colleagues, both within and beyond their academic fields.
UBs nationally and internationally renowned faculty have a tremendously positive impact on our world through their excellent research, scholarship, teaching, creative activities and clinical contributions, says A. Scott Weber, provost and executive vice president for academic affairs. We are extremely proud that five of our most distinguished faculty members have been recognized for their leadership and groundbreaking contributions through appointment to SUNYs highest rank.
UBs newest SUNY Distinguished Professors:
Quanxi Jia, Empire Innovation Professor and National Grid Professor of Materials Research, is an internationally recognized leader in multifunctional and nanostructured materials for energy and electronic-device applications. He has made significant contributions to the development of high-performance superconducting coated conductors, or 2G wires, for electric-power applications. He has invented and pioneered the polymer-assisted deposition, a cost-effective coating technique to grow a vast number of electronic materials.
Jia has also designed and developed innovative approaches to integrate different materials for desired functionalities, making many original and high-impact contributions to nanostructured materials.
He has authored or co-authored more than 500 peer-reviewed journal articles, delivered more than 100 invited lectures and holds 50 U.S. patents.
Jia is an elected fellow of the American Association for the Advancement of Science (AAAS), National Academy of Inventors (NAI), American Ceramic Society (ACerS), the American Physical Society (APS), the Institute of Electrical and Electronics Engineers (IEEE), the Materials Research Society (MRS) and the Los Alamos National Laboratory.
Jia joined the UB faculty in 2016. In addition to his faculty appointment in the Department of Materials Design and Innovation (MDI), a joint program of the School of Engineering and Applied Sciences and the College of Arts and Sciences, he also serves as the scientific director of UBs New York State Center of Excellence in Materials Informatics (CMI), the founding co-editor-in-chief of Materials Research Letters and the principal editor of the Journal of Materials Research.
Marianthi Markatou, professor of biostatistics and associate chair of research and healthcare informatics in the Department of Biostatistics, is an internationally renowned expert in biostatistics, statistics and biomedical informatics. She has conducted seminal methodological research that has significantly advanced the fields of statistical robustness, mixture models, statistical distances, weighted likelihood methods and statistical machine learning.
Markatou has earned a distinguished reputation as an interdisciplinary scholar and has made pioneering contributions to both statistical sciences and domain sciences. Her interdisciplinary work has applied her rigorous statistical methodologies to advance pharmaco-epidemiological and emerging safety sciences research, biomedical informatics such as text mining to support patient safety, and computer science including big data analysis and data science.
Her work has been continuously supported by external funding agencies since 1990, and her influential statistical publications have appeared in highly regarded journals. Her current awards, including a $7 million grant from the Patient-Centered Outcomes Research Institute, the Food and Drug Administration, and the Kaleida Health Foundation, support Markatous pioneering work on the foundations of data science and application to biomedical and public health research.
Among her many honors and awards, Markatou has been named a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a member of the International Statistical Institute.
Janet R. Morrow, Larkin Chair and UB Distinguished Professor of Chemistry, is a highly regarded expert in the field of metal ion complexes in biology and in biomedical imaging. She is recognized for her invention of transition metal-based MRI contrast agents that have the potential to monitor disease states including bimodal imaging agents, paramagnetic liposomes and self-assembled cages as theranostic agents for imaging drug delivery to tumors.
Early in her career, she carried out highly cited studies on the mechanism for the recognition and sequence-specific cleavage of RNA by metal ion complexes, on the development of luminescence methods to study lanthanide complex catalysts, and the incorporation of lanthanide ions into modified DNA structures. To commercialize recent work on iron MRI contrast agents, she co-founded Ferric Contrast Inc., for which she serves as chief scientific officer.
Morrow has authored more than 120 publications, 12 book chapters and nine patents, and serves as associate editor of Inorganic Chemistry, the premier American Chemical Society journal in the discipline. She is the recipient of an Alfred P. Sloan Fellowship, a Special Award for Creativity from the NSF, and the Schoellkopf Medal from the ACS. She is a fellow of the AAAS.
Robert G. Shibley, professor and dean of the School of Architecture and Planning, is a globally renowned scholar, recognized for his work on the theory and practice of placemaking the way we transform the places we are into places we love.
He has authored more than 120 publications, including 17 books and 15 book chapters. His productivity is all the more impressive given the significant administrative roles he has held, first as chair of the Department of Architecture during the 1980s, and as dean for the past decade.
Shibley has worked as principal investigator with faculty, staff, students and collaborating partners on over 80 Buffalo-based projects totaling more than $25 million in sponsorships. The work has received global attention for its impact on the people and places of Buffalo, and its elevation of practice-based research in architecture and urban planning. The work has also led to top national awards in his disciplines, including an American Institute of Architects (AIA) Thomas Jefferson Award and induction into the College of Fellows in the AIA, and the American Institute of Certified Planners.
He is the recipient of the UB Presidents Medal, the New York State AIA Educator of the Year Award, and 45 other international, national and regional honors for outstanding design and planning projects, as well as additional lifetime achievement awards.
Mark Swihart is UB Distinguished Professor, chair of the Department of Chemical and Biological Engineering, and Empire Innovation Professor in the UB RENEW Institute. He is recognized globally for developing new nanoscale materials, engineering practical processes for producing unique materials, and generating fundamental understanding of those processes. Nanoscale materials exhibit size-dependent properties and functions that enable new high-impact applications from biomedical imaging to renewable energy, and Swihart has made key contributions to the synthesis and post-processing of these materials. His research group has developed methods and materials that have been adopted by researchers and industry worldwide.
He has published more than 285 manuscripts on these subjects, which have been cited roughly 20,000 times. He co-authored the two most recent editions of the best-selling chemical engineering undergraduate textbook of all time and has co-founded two startup companies. He is a fellow of AIChE and AAAS, and has been recognized with the Schoellkopf Medal from the American Chemical Society and the Whitby Award from the American Association for Aerosol Research.
Swihart has led multiple campus-wide initiatives, including the UB 2020 Strategic Strength in Integrated Nanostructured Systems and the New York State Center of Excellence in Materials Informatics, and has collaborated broadly within and beyond UB, promoting interdisciplinary and convergent research approaches.
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Athinia to Accelerate the Use of AI and Big Data to Solve Critical Semiconductor Challenges – PRNewswire
Athinia will bring semiconductor manufacturers and materials suppliers together to share, aggregate, and analyze data to unlock efficiencies. The platform will also enable industry actors to understand on a deeper level the interaction between materials and processes at semiconductor fabrication plants.
"The semiconductor industry is facing unprecedented disruption. This has created a critical need for a secure data collaboration platform that can provide the transparency and data intelligence companies need to solve challenges such as chip shortages and supply chain issues," said Kai Beckmann, Member of the Executive Board of Merck KGaA, Darmstadt, Germany, and CEO Electronics."Partnering with Palantir, we've combined our collective expertise in materials science, data analytics and security to increase our customers' efficiencies and time to innovation."
"We are excited to partner with market leaders in this space to create an ecosystem that will enable semiconductor companies and their suppliers to collaborate to make better decisions, combatting simultaneous demand and supply shocks," said Palantir COO Shyam Sankar. "Athinia will help companies across the value chain bring new products to market faster and accelerate their product differentiation and growth."
By harnessing Palantir's unique experience in building inter-organization ecosystems, Athinia will enable advanced data analytics, in turn limiting the costly impact of quality or performance excursions across the value chain, from supplier to semiconductor fabrication plants. It will also help fabs manage faster innovation in manufacturing processes in a single, secure platform that will support improved incoming material quality and increase supplier engagement. Suppliers will benefit from internal efficiency gains through smart data integration and can be a better partner for the fabs they serve. The partnership will help solve such challenges by creating a platform to analyze previously siloed data in a holistic way.
Merck KGaA, Darmstadt, Germany, and Palantir have already proven to be successful in using collaborative data analytics to help common customers minimize quality deviations and increase efficiencies. Athinia will build upon this experience and leverage Palantir's expertise to help customers improve their decision-making in optimizing semiconductor materials.
Merck KGaA, Darmstadt, Germany, has recently worked with leading semiconductor companies to leverage AI and data analytics for solving key challenges. "We worked with Merck KGaA, Darmstadt, Germany, to create a data sharing platform that enabled advanced predictive manufacturing for chemical mechanical polishing (CMP), a critical step in the semiconductor manufacturing process. Through this collaborative partnership, we implemented an AI-driven methodology to enable smart data collaboration that drove process and quality improvements. By extending this approach to the broader supply chain and enabling a data ecosystem, we believe advanced predictive manufacturing can be accelerated for the broader semiconductor industry,"said Raj Narasimhan, Corporate Vice President, Global Quality, Micron Technology, Inc.
The Athinia platform is powered by Palantir Foundry, which enables users to structure and analyze data from disparate sources, generate powerful insights and support operational decisions, all while helping to ensure that sensitive data is processed in accordance with applicable data privacy rules, regulations, and norms. Palantir Foundry is designed to provide world-class security, access controls, partitioning, auditing, and accountability functions to support responsible data use. Athinia acts independent from the Electronics business sector of Merck KGaA, Darmstadt, Germany, and enables data sharing only on codified and anonymized data and customers will retain full control of their data, including intelligent purpose-based access control management. The secure data collaboration environment will provide continuous feedback through a holistic view and a common operating picture of in-fab performance that can help solve quality deviations.
Merck KGaA, Darmstadt, Germany, and Palantir already started collaborating in 2017. Through the partnership "Syntropy"both companies are determined to unleash the power of biomedical data and revolutionize cancer therapy and accelerate research. Syntropy's aim is to provide researchers with intuitive analytics techniques to enable them to aggregate, analyze and then also share data from disparate sources.
For more information about Athinia, visit the website or social media channels: LinkedInYouTubeTwitter
All Merck KGaA, Darmstadt, Germany, press releases are distributed by e-mail at the same time they become available on the EMD Group Website. In case you are a resident of the USA or Canada please go to http://www.emdgroup.com/subscribe to register for your online subscription of this service as our geo-targeting requires new links in the email. You may later change your selection or discontinue this service.
About Merck KGaA, Darmstadt, Germany
Merck KGaA, Darmstadt, Germany, a leading science and technology company, operates across healthcare, life science and electronics. Around 58,000 employees work to make a positive difference to millions of people's lives every day by creating more joyful and sustainable ways to live. From advancing gene editing technologies and discovering unique ways to treat the most challenging diseases to enabling the intelligence of devices the company is everywhere. In 2020, Merck KGaA, Darmstadt, Germany, generated sales of 17.5 billion in 66 countries.
The company holds the global rights to the name and trademark "Merck" internationally. The only exceptions are the United States and Canada, where the business sectors of Merck KGaA, Darmstadt, Germany operate as EMD Serono in healthcare, MilliporeSigma in life science, and EMD Electronics. Since its founding in 1668, scientific exploration and responsible entrepreneurship have been key to the company's technological and scientific advances. To this day, the founding family remains the majority owner of the publicly listed company.
About Palantir Technologies
Palantir Technologies Inc. builds and deploys operating systems for the modern enterprise. Additional information is available at http://www.palantir.com.
Who dares, wins.
Forward-Looking Statements
This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantir's expectations regarding the terms and the expected benefits of the strategic partnership. Forward-looking statements are inherently subject to risks and uncertainties, some of which cannot be predicted or quantified. Forward-looking statements are based on information available at the time those statements are made and were based on current expectations as well as the beliefs and assumptions of management as of that time with respect to future events. These statements are subject to risks and uncertainties, many of which involve factors or circumstances that are beyond our control. These risks and uncertainties include our ability to meet the unique needs of our customers; the failure of our platforms to satisfy our customers or perform as desired; the frequency or severity of any software and implementation errors; our platforms' reliability; and our customers' ability to modify or terminate their contracts. Additional information regarding these and other risks and uncertainties is included in the filings we make with the Securities and Exchange Commission from time to time. Except as required by law, we do not undertake any obligation to publicly update or revise any forward-looking statement, whether as a result of new information, future developments, or otherwise.
SOURCE Athinia
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Aktify Announces New Chief Product Officer and New Chief Technology Officer to Expand Team and Product Offering – Yahoo Finance
SaaS veterans Chase Rigby and Dave Barney will advance Aktify's suite of revenue-building products
LEHI, Utah, Dec. 10, 2021 /PRNewswire/ -- Aktify, a conversational collective intelligence (CI) platform for enterprise businesses, recently announced the appointment of SaaS-industry product powerhouse Chase Rigby as Chief Product Officer and tech talent Dave Barney as Chief Technology Officer.
Working in tandem, Barney will guide engineering and data science teams at Aktify, while Rigby will spearhead the product development and design. Their leadership will further develop Aktifys science-first approach to cutting-edge conversational AI.
Barney and Rigby's leadership will further develop Aktify's science-first approach to cutting-edge conversational AI.
Rigby has vast experience creating and enhancing enterprise SaaS software. In his six years at Google, he led various teams at Niantic Labs, Search & Assistant, and ML reengagement for ads across YouTube, and Chrome. He also led Google's Social Good team, processing payments for mining public data to predict public health epidemics that aided federal and NGO relief efforts.
Barney spent more than a decade at Google developing machine learning and digital attribution models, teaching ML classes to fellow Google engineers, and leading fulfillment efforts in its global support organization. Prior to Google, Barney spent a decade working at various ML/AI start-ups, where he came to know and understand the intricacies of building strong ML teams in a start-up environment.
Most recently, Rigby and Barney rebuilt the technology, product, stack, and culture at Kanopy, an enterprise video streaming platform. Their efforts resulted in the acquisition of the company by KKR and OverDrive in June 2021.
Working in tandem, Barney will guide engineering and data science teams at Aktify, while Rigby will spearhead the product development and design. Their leadership will further develop Aktify's science-first approach to cutting-edge conversational AI.
"Aktify has experienced extraordinary growth in the last year," said Rigby. "When you consider its technology, people, and market opportunity, that progress makes sense. There's much more room to grow, and I'm excited to be a part of it."
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Barney and Rigby's appointment comes at a pivotal time for Aktify. The company continues to generate previously unseen revenue for its clients. It is onboarding an ever-increasing number of new customers. The dynamic duo will help innovate products and develop the technology to meet future customers' needs.
Aktify's product, data science, and engineering teams develop conversational AI technology. The AI has increased sales and meetings across various industries.
"Chase and Dave's expertise will allow us to take not only Aktify, but the entire conversational AI space to the next level," said Aktify CEO Kreg Peeler. "They join the other powerhouses in our company to create a product and a long-overdue solution for sales needs. We expect big things to come shortly."
About Aktify
Aktify is a conversational intelligence company. It uses robust data science and machine learning to delight customers with thoughtful conversations and well-curated gestures. Aktify's solutions are invisible, integrating with CRMs, and marketing automation platforms. It creates millions of weekly conversations through SMS and phone calls, delivering a 10x ROI to clients. Headquartered in Menlo Park, California, and Lehi, Utah. To learn more, visit aktify.com.
Cision
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Implementing AI: Bridging the Gap | ARC Advisory – ARC Advisory Group
Summary
Implementing Artificial Intelligence (AI) has become a key challenge for organizations looking to create a competitive advantage through their data. Introducing a new technology (and associated process changes) is always a demanding task. According to ARC research, around 50 percent of respondents consider themselves to be in the piloting phase of an AI implementation. End users also expect 40-50 percent of all industrial applications to leverage AI by the year 2030. To reach this lofty goal, they need technology partners to help them address the main challenges that come with AI implementation: bridging the gap between multiple stakeholders and dealing with data. This ARC View will show how the use of RapidMiner as a partner for AI projects can help end users address these two key challenges.
Implementing an AI application is still a relatively new concept for many end users. As with any new technology or strategy, every step from proof-of-concept to lifecycle management can seem daunting. ARCs continuous research on artificial intelligence in manufacturing has uncovered the most common challenges that organizations face in their AI projectsyou can see the results in the chart below.
The blue bars represent challenges that are not just unique to end users, but to their organizations as a whole. In the case of AI, these challenges are amplified by the fact that it is such a cross-functional technology and has to address the needs of plant floor workers, data scientists, business executives, and many other groups. At times, the interests and goals of these groups can vary strongly, leaving many gaps to bridge.
The orange bar represents a common blocker thats preventing many organizations from even getting started with AI. Anyone who works with data knows that you must clean and prepare it to be usable for analysis. For those who arent lucky enough to have processes in place for data prep, this process can consume a lot of time and resources while having significant implications for the success of a project.
Still, as mentioned above, most of the respondents in our survey expect AI to become an integral part of production through 2030. Research also shows that early adopters of AI can typically expect a faster ROI and greater impact to their bottom line. So, what does it take to be part of the early adopter group?
The gap in question is characterized by different groups, their goals, and varying levels of knowledge in data science and manufacturing, respectively. The knowledge of these groups is often tribal knowledge that is only passed on within the group. As Sarma Malladi from the Swiss engineering and manufacturing company SWM International puts it, all manufacturers face the problem of tribal knowledge.
To bridge the gap, strong management, leadership, and technology are needed. In ARCs view, these fundamental internal requirements must be supported with a suitable tool that fulfills the following crucial demands:
RapidMiners tools specifically address the skills gap issue and support the need to bridge that gap.
The tailored user interfaces enable both beginners and experts to work with the same data. For example, beginners can use the AutoML solution RapidMiner Go to create basic models in just a few clicks, while experts can custom-code their own functions and share them with teammates using RapidMiner Notebooks.
This is all done on a single version of truth, e.g. a common database. This helps to bridge the aforementioned knowledge gap, as OT people from the plant floor can work with data quickly and create their own insights. Typically, the first aim is to re-create existing views and test the system against tribal knowledge. Then, after trust is built up, new insights and productivity gains follow.
In the following case study from the electronics industry, data from customer support was used by the data science teamgiven the size of the organization, the data represented millions of customers around the world.
As is the goal with any data science project, customers arent aware of whats happening behind the scenestheyre simply served better as a result of the right model being implemented. This case study below also shows how AI impacted the post-sales department as well as the production of spare parts. All parties involved rely on and trust the predictions of their developed machine learning models.
The organization that implemented this use case is a well-known, leading electronics manufacturer for the consumer and professional markets. Its diversified business includes consumer and professional electronics, gaming, entertainment, and financial services. The company needed to reduce overall customer support costs and tasked the data science team in their post-sales organization with achieving that goal.
The main project owner and their data science team understood the basic customer support statistics -- how many people called, how long people stayed on the phone, how many people visited the support website etc., but it was more difficult for the team to determine why people were calling. The reason for their lack of understanding was that the vast quantities of unstructured data that could help them had not previously been used.
A first step towards deeper analytical insight and greater business value was to focus on classification analyses -- why people are calling and document it in as much detail as possible (reasons and multiple layers of sub-reasons). To do this, the team first had to automate many of their existing business processes. An example of this was the translation process. With RapidMiner, the team could create workflows that allowed unstructured data in 26 different languages to be routinely translated for easier interaction and analyses.
This electronics manufacturer first used RapidMiner for web and text mining to support their classification analysis, which allowed them to identify trends and the reasons behind customer service calls. Today, the team is moving on to do more powerful analysis with RapidMiner, such as:
In a recent panel discussion at the ARC Europe Forum, it was stated that 80 percent of the work on AI is not about AI itself. One of the major challenges is lack of data and poor data quality. Imagine having to connect 40 years of equipment usage in a brownfield plant to gain access to the necessary data. Even if youre successful, the resulting data will likely vary in completeness, collection frequency, units, accuracy, availability, etc. Its also likely that most of the data has not been labeled correctly, which creates even more work.
Over the years, ARC has done a lot of research into the way organizations typically approach the problem of unlabeled data. Most rely on internal experts, often supported by some sort of tool, which can range from excel templates to more sophisticated software. This time-consuming process often increases the true cost of a project.
To support the data preparation and labeling, RapidMiner offers their data preparation tool Turbo Prep, which helps address the issue of inconsistent data. Its supporting functions are divided into five broad categories:
In this case study, the electronics manufacturer first experienced benefits by simply using the translation function. It created insights and tangible benefits without even getting to the core of AI.
The end user also mentioned that previously, the data science team operated in an 80/20 environment 80 percent of its time was spent on collecting and managing data, and only 20 percent on analyzing it. Now that all the tedious tasks of data cleansing and collection are automated with RapidMiner, the company flipped this ratio -- 80 percent of their time is spent on in-depth data analysis, and only 20 percent on collecting and managing data.
ARC has experienced it often in the past: End users and machine builders do not implement any new technology unless they understand it fully and are convinced it can help them achieve their goals. This has often resulted in the development of proprietary, in-house solutions, which can be effective but is extremely time and resource intensive. After solutions of this nature are implemented, they often stay in operation for sometimes up to 40 years, resulting in huge lifecycle costs.
While it is certainly true that an end user or machine builder needs to understand the technology fully as they are responsible for the safe operation of plants and equipment the right platform (such as RapidMiner) combined with industry expertise can help to kickstart the process, accelerate implementation, lower lifecycle costs, and even create opportunities that go well beyond the initial scope that your organization envisions.
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Keywords: Artificial Intelligence, AI, Machine Learning, AI Implementation, RapidMiner, Data Conversion, Anomaly Detection, ARC Advisory Group.
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Implementing AI: Bridging the Gap | ARC Advisory - ARC Advisory Group
Data Science Program | The George Washington University
EXPERT PROBLEM SOLVERS Meeting the worlds demand for data-driven solutions
Nearly every profession relies on data to succeed. And with the huge quantities of digital information being collected and exchanged in todays marketplace, the demand for trained data scientists is higher than ever.
The STEM-designated Data Science Program at GW's Columbian College of Arts and Sciences prepares students to meet that need and enter competitive careers in government, technology, private industry and much more.
Onward GW: COVID-19 Updates
"The data community is huge here in Washington, D.C. From politics to health to technology, a lot of companies are just so interested in data science."
What We Do
Data science experts learn how to make sense of massive data sets, and they use that information to improve the way we live, work and communicate. Whether forecasting stock market trends, constructing a social media profile for a marketing client or capturing GIS locations for disaster relief, Data Science Program students become adept at meeting todays most pressing challenges.
Alongside classwork, students strengthen their rsums with the practical knowledge required for data-intensive jobs. The program offers access to partnerships with numerous startups, companies and agencies, connectingstudents with internships and careers at employers like Amazon, Booz Allen Hamilton, Capitol One, D.C. Government, the National Institutes of Health, Oracle, the U.S. Department of Defense and more.
Degree Programs
Tailai Jin (MS '17) led a team of five data science students to participate in the Virginia Datathon.The team analyzed text mining on job descriptions in Virginia cities, and their final product was a job recommendation system based onuser-input preferences and keywords.
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Data Science
Data science is everywhere
Incredible amounts of data are generated every day, from apps on your phone to medical devices. And, every part of society connects with data: agriculture, engineering, finance and more.
Make a difference.Use data to solve global hunger or drive better business decisions. Maybe one day you could shape a hurricane disaster response plan, improve self-driving cars or define a military defense strategy.
Challenge yourself.Engage your quantitativeandcreative side. Youll learn to apply the technical fundamentals of data science to data analysis pipelines and develop the knowledge and skills to transform data into insights. Learn about data visualization and how to communicate your findings to inform actions.
Create your future.Data scientists have the skills for a rewarding career in almost any industry!
Were a place where you can ask new questions and chase big ideas. Conduct undergraduate research with award-winning faculty. Intern in Silicon Valley, or explore a new culture through study abroad. Compete in a case competition with the Data Science Club or do outreach with K-12 students.
At Iowa State, it all adds up to your innovative adventure.
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South African crowd-solving startup Zindi building a community of data scientists and using AI to solve real-world problems – TechCrunch
Zindi is all about using AI to solve real-world problems for companies and individuals. And the South Africa-based crowd-solving startup has done that over the last three years they have been in existence.
Just last year a team of data scientists under Zindi used machine learning to improve air quality monitoring in Kampala as another group helped Zimnat, an insurance company in Zimbabwe, predict customer behavior especially on who was likely to leave and the possible interventions that would make them stay. Zimnat was able to retain its customers by offering custom-made services to those who would have otherwise discontinued.
These are some of the solutions that have been realized to counter the data-centered challenges that companies, NGOs and government institutions submit to Zindi.
Zindi announces these challenges and invites its community of data scientists to take part in solution-finding competitions. Participating data scientists submit their solutions and the winner gets a cash prize. The hosts of the competitions get to use the best results to overcome the challenge they had like in an air quality monitoring project by AirQo, which sought solutions for forecasting air pollution across Uganda, and in helping Zimnat cut its losses.
So AirQo now has a dashboard that allows the public to check air quality and air quality forecasts. One of the exciting things about this project is that AirQo hired two of the winners from the challenge to help with the implementation of the project, said Zindi co-founder and CEO, Celina Lee. South African Megan Yates and Ghanaian Ekow Duker are the platforms other co-founders.
AirQo also raised funding from Google, based on the solution that they built, and theyll now be replicating it in other African countries, said Lee about the competition that was organised in partnership with the Digital Air Quality East Africa (DAQ EA) project of the University of Birmingham and the AirQo project from Makerere University, Kampala.
Zindi is a database of data scientists across Africa. The crowd-solving startup recently secured $1 million in seed funding. Photo Credits: Zindi
Among other notable private and public organizations that have tapped Zindi include Microsoft, IBM, Liquid Telecom and UNICEF, and the government of South Africa.
So far, Lee is excited about what Zindi has achieved and is enthusiastic about the communitys future, given how the crowd-solving startup has grown since launch. The platform is now providing alternatives and stepping up competition against traditional consulting firms operating across Africa, which are often expensive.
Zindis users have grown three-fold from the start of last year, to 33,000 data scientists from 45 countries across the continent. It has also paid data scientists $300,000 in prize money.
This number is set to grow as it hosts the third inter-university Umoja Hack Africa challenge in March next year, where college students will compete against one another for different solutions.
Zindi is using the inter-university competition to expose students to practical data science experiences and to solve real-life challenges using AI. During last years event the platform attracted about 2,000 students during the event that took place virtually because of the pandemic.
Students get to build their first machine learning models, and from there, it opens up all kinds of doors for their careers and education, said Lee, who is originally from San Francisco.
Zindi currently has a jobs portal to shorten the path from learning to earning. The talent placement portal allows organizations to tap from its pool of talent by posting openings.
The crowd-solving platform is also planning to introduce a learning component that provides training material to budding data scientists; this is after it realized a knowledge gap and need for training. Besides, Lee said that most of Zindis users are university students in need of learning experience, and who require enhanced skills to solve world problems.
The new plans will be made possible by a $1 million seed funding the platform recently secured.
Image Credits: Zindi
Lee said, For us, its really about scaling the community and creating more value for all of our data scientists.
So were going to be using the funding to introduce much more learning content, because one of the things we understand is that, especially in Africa, data science is such a new field. And a lot of our data scientists are still university students or very early in their careers. And theyre just looking for a chance to learn and build their skills.
The seed round was led by San-Francisco based VC firm Shakti, with participation from Launch Africa, Founders Factory Africa and FIVE35.
All these plans are toward building a strong data science community in Africa and for the continent, according to Lee, who said that they want to grow their users to reach one million in the near future. This, she said, will be achieved by opening up training opportunities to early career data scientists and by forming a strong community that encourages collaboration and mentorship.
Lee said, And so where we want to eventually reach a million data scientists in Africa we want to make data science something that any young person whos interested in pursuing this career has access to the tools, the connections and the experience that they need to make a successful career in this field.
Our vision is to make AI accessible to everyone.
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Equity Data Science Appoints Theresa Elamparo as Head of Marketing – Business Wire
NEW YORK--(BUSINESS WIRE)--Equity Data Science (EDS), a cloud-based analytics platform provider that delivers decision support tools for the investment process to hedge funds and asset managers, has named Theresa Elamparo as Head of Marketing to accelerate the companys business strategy and growth plan. Elamparo will lead all brand, marketing and communications strategies to continue to build the brand and to focus on the expansion of the business. She will be based in New York.
Elamparo brings 23 years of marketing communications experience, serving the fintech community for 16 years, most recently as Chief Marketing Officer at Tier1 Financial Solutions where she was the recipient of the 2019 Markets Media Women in Finance award for Excellence in Marketing and Communications for her work leading Tier1s rebrand and building the firms marketing organization. Prior to that, she held marketing leadership roles at fintech firms including Ipreo, Investment Technology Group and Tradeweb Markets.
We are thrilled to have Theresa join our leadership team, said Greg McCall, President and Co-founder at EDS. Her wealth of experience within the financial services industry and strategic marketing expertise will be integral to expanding our global footprint as we deliver on growing demand for data aggregation, analytics, workflow and scalable decision support for the fundamental investment process.
Im delighted to join EDS at such an exciting time, Elamparo said. The fundamental investment community is faced with fragmentation, underutilized data and technical inefficiencies. EDS provides a modular, decision-support workflow platform for the full investment lifecycle, helping clients better manage their process to maximize returns.
Throughout 2021, EDS invested heavily in building out solid leadership across key functions, including the appointment of Jen Vermeulen, CFA as Head of Sales, and Erin Greenfield as Head of Customer Success. These recent appointments strengthen EDSs ability to expand its position in fundamental investing. At the start of the year, Northern Trust announced a strategic investment in EDS, allowing for integration of EDSs decision-support tools with Northern Trusts core technology platforms to provide highly specialized and innovative solutions to the most sophisticated institutional investors across the globe.
ABOUT EQUITY DATA SCIENCEEquity Data Science (EDS) empowers fundamental investors to build, operate and sustain a modernized, repeatable investment process by aggregating data sources and refining workflows to govern investment decisions. EDS provides a fully configurable, measurable, and scalable platform with purpose-built analytics to support idea generation, research management, portfolio construction and risk management. Visit us at http://www.equitydatascience.com.
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Equity Data Science Appoints Theresa Elamparo as Head of Marketing - Business Wire
Doma’s Chief Data Science Officer Andy Mahdavi Named 2021 HousingWire Tech Trendsetter – Business Wire
SAN FRANCISCO--(BUSINESS WIRE)--Doma Holdings, Inc. (NYSE: DOMA), a leading force for disruptive change in the real estate industry, announced today that Andy Mahdavi, Chief Data Science Officer at Doma, was recognized as a 2021 Tech Trendsetter by HousingWire. The third annual list identifies the most impactful and innovative technology leaders serving the housing economy.
HousingWires 2021 Tech Trendsetters are made up of the top product and technology leaders who have been essential in bringing innovative tech solutions to market for housing industry clients, and the award recognizes the people who drive innovation for their mortgage and real estate clients.
Recognized as an interdisciplinary leader with over 20 years of experience bringing large datasets and scientific rigor to fields ranging from financial services to astrophysics, Mahdavi and his team are leveraging machine intelligence to build uniquely differentiated, technology-first solutions to all those involved in the closing of a real estate transaction including lenders, real estate professionals, and ultimately the homeowner. At Doma, Mahdavi led the deployment of the first title insurance model of its kind, which determines risk for title defects using proprietary algorithms inspired by his past experience in these diverse applications of statistics.
Through its machine intelligence driven-approach, Doma has been able to return the majority of title commitments through its Doma Intelligence platform in about 1 minute instead of the industry standard 3-5 days and in some cases is able to take a closing from over 45 days to less than one week. As a result, Doma counts Chase, PennyMac, Wells Fargo, among other large mortgage originators and lenders, as its customers.
Its an exciting opportunity to build technology that drives such profound transformation of an industry long overdue for change, said Andy Mahdavi, Chief Data Science Officer at Doma. Homeowners across the U.S. are closing their mortgages faster and more reliably through technology enabled by our machine learning algorithms, and I am honored to be recognized for this achievement among other talented technology leaders.
This years list of Tech Trendsetters have, yet again, proven to be the driving force behind the digital transformation in housing, HousingWire Editor and Chief Sarah Wheeler said. This impressive list of honorees are finding solutions to some of the industry's toughest challenges from improving the borrowers journey to streamlining every step of the real estate transaction process.
The 2021 Tech Trendsetters were carefully selected by HousingWires editorial selection committee based on their vital and dynamic contributions to their organizations and to the industry as a whole. Profiles of the 2021 HW Tech Trendsetters honorees can be found here.
About Doma Holdings, Inc.
Doma (NYSE: DOMA) is architecting the future of real estate transactions. The company uses machine intelligence and its proprietary technology solutions to transform residential real estate, making closings instant and affordable. Doma and its family of brands States Title, North American Title Company (NATC) and North American Title Insurance Company (NATIC) offer solutions for current and prospective homeowners, lenders, title agents, and real estate professionals that make closings vastly more simple and efficient, reducing cost and increasing customer satisfaction. Domas clients include some of the largest bank and non-bank lenders in the United States. To learn more visit doma.com.
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Why is Blockchain the Next Big Thing for Data Science? – Analytics Insight
Blockchain is the next big thing for data science
Innovative technologies such as big data and blockchain are being hailed as the next big things that will change the way businesses operate. Majority of us believe that these technologies are totally exclusive, with each having its own route of being employed independently. While data science focuses on using data for efficient administration, blockchains distributed ledger protects the data protection. These technologies offer a lot of untapped promise in terms of improving efficiency and productivity.
There hasnt been much work into the connection between blockchain and data science, if there is one. To put it another way, data is at the heart of each of these technologies. Data science focuses on generating relevant insights from the data for problem-solving, whereas blockchain certifies and stores data. Algorithms are used in each of these technologies to regulate interactions with various data segments. In a nutshell, data science is used to predict and blockchain is used to validate data.
Peer-to-peer partnerships are made easier using blockchain. If a published account, for example, fails to adequately explain any approach, any peer can analyse the entire process and determine how the results were reached.
Anyone can learn whether data is accurate to use, how to preserve it, how to update it, where it originates from and how to utilise it properly thanks to the ledgers open channels. To conclude, blockchain technology will allow users to track data from start to finish.
Real-time data analysis is extremely tough. The most effective approach of detecting fraudsters is to be capable of monitoring developments in real time. For a long period of time, though, real-time analysis wasnt really possible. Companies can now detect any irregularities in the dataset from the outset, thanks to blockchains decentralized nature.
Spreadsheets have a feature that allows you to see modifications in data in real time. Similarly, blockchain allows two or more individuals to collaborate on the same data and information.
The data in blockchains digital log is kept in a variety of nodes, both private and public. The data is cross-checked and examined at the entrance point before being added to further blocks. This procedure in and of itself is a means of data verification.
When data flows smoothly and easily, there are numerous benefits for organisations. With paper records, its quite tough. This problem is exacerbated when the information contained within it is needed elsewhere. True, these data will get the other division, but it may take a long time and there is a possibility that they will be lost in the process.
Several data scientists are interested in blockchain today because it allows two or more individuals to view data simultaneously and in real time.
As a result, when data moves freely, the administrative process becomes more efficient.
Biases are common when there is a central body, as you are well aware. Putting too much faith in one person might be dangerous. Because of trust difficulties, many businesses refuse to provide other parties access to their data. Sharing information becomes virtually impossible as a result of this. The trust issue does not stand in the way of exchange of information with the block chain. By sharing the knowledge, they have at their availability, businesses can interact efficiently.
The primary focus of corporations in the past decade was on increasing data storage capacity. That was fixed by the end of 2018. The new issue for most businesses is securing and validating the datas authenticity.
The fundamental reason for this is that organisations collect data from various sources. Even data gathered from government institutions or generated domestically can be prone to inaccuracies. Furthermore, data from other sources, such as social networks, may be erroneous.
Data scientists are now employing blockchain technology to assure data validity and trace it across the chain. Its unchangeable security is among the reasons for its widespread acceptance. Data is safeguarded at every step by multiple signatures on blockchains decentralised record. The precise signatures must be provided in order for anyone to obtain access to the data. As a result, occurrences of data hacking and breaches are greatly reduced.
The following are some of blockchains security aspects that are beneficial to data science:
Every transaction that occurs in Blockchains record is encrypted using complicated mathematical techniques. These transactions are recorded as digital contracts that are both immutable and irrevocable.
Data scientists frequently keep track of their companys information in data lakes. When using blockchain to monitor the provenance of data, it is recorded in a distinct block with a unique cryptographic key. This ensures that everybody who uses the data has the correct key from the person who created it, indicating that the data is correct, of excellent quality and authentic.
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Why is Blockchain the Next Big Thing for Data Science? - Analytics Insight