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Kasparov In TED Talk: ‘Meeting Evil Halfway Is Still A Victory For Evil’ – Chess.com

April 16, 2022 | Author: admin

"Ukraine is now on the frontline of the global war of freedom against tyranny," was one of several powerful quotes from GM Garry Kasparov in a TED Talk published on Tuesdaythe day before his 59th birthday today. The pro-democracy activist and human rights advocate, who retired from chess as the world number-one player in2005, predicted Russia's war in Ukraine rather accurately in his 2015 bookWinter is Coming.

Kasparov's TED Talk on the war in Ukraine.

The central theme in Kasparov's TED Talk is good and evil. He notes that he identified evil at an early age, when as a young chess star he had the privilege of traveling outside of the Soviet Union and to the West, to the other side of the iron curtain. "It was obvious to me very quickly that they were the free world and we were not, despite what Soviet propaganda told us."

Kasparov mentions that he got into "good trouble" for his criticism of his own country and his praise for America in a famous interview he gave to Playboy magazine in 1989. The following quote, taken from chesshistory.com, must have been what he was talking about. Now 33 years ago, Kasparov answered the question why chess was so popular in the Soviet Union:

"Because most of the time, theres nothing else to do in our country! Chess fits the Soviet Union perfectly. Its the simplest of sports. You dont need a special field or court for it. Just a chess set, pieces, and a quiet place in the park. Its the easiest way for people to have a little bit of enjoyment. And if you become a strong player, chess is one of the best ways for a Soviet citizen to improve his life, to get a better position and maybe raise his standard of living above the averagewhich is not so good, by the way."

Kasparov's early activism included his demand to play under the Russian flag instead of under the Soviet hammer and sickle in his 1990 world championship with GM Anatoly Karpov. That was a year before the USSR disintegrated. Until the present day, the difference in ideology between the two adversaries on the chessboard continues: as a member of the State Duma, Karpov is supporting the Russian government while Kasparov is strongly opposing it.

Since the day Russia invaded Ukraine on February 24, Kasparov is being taken much more seriously by mainstream media than before. The former chess champion has been invited dozens of times in the past month by international media to share his views. As it turns out, Kasparov's Winter is Coming, with the subtitle "Why Vladimir Putin and the enemies of the free world must be stopped," was much closer to the truth than most people wished to believe.

Kasparov: "If I wrote a sequel, it would be called Winter is Here. And the subtitle would be: I [bleep]'ing told you so."

According to Kasparov, the warning signs from Putin came early, but the world failed to listen properly.

"When Putin said there is no such thing as a former KGB agent, I knew Russia's fragile democracy was in danger. When Putin said that the collapse of the Soviet Union was the greatest geopolitical catastrophe of the 20th century, I knew Russia's newly independent neighbors were at risk. And when Putin talked at the Munich security conference in 2007 about a return to spheres of influence, I knew he was ready to launch his plan."

Kasparov mentions the Second Chechen War, Russia's invasion of Georgia in 2008, and the invasion of Crimea in 2014, and notes: "It's a paradox, isn't it? Dictators lie about everything they have done but often they tell us exactly what they are going to do. Just listen!"

Having been told since 2005 that politics is "not black and white, not chess" and that politics requires compromise, Kasparov shows images of destroyed cities and corpses in the streets in Ukraine, and asks: "Compromise? Are you sure? Compromise with this? You cannot look at the images from Ukraine in recent weeks and say there is no pure evil."

Showing an image from The Lord of the Rings, Kasparov argues that pure evil is no longer reserved for fiction while noting the difference with pure good: "There is no pure good. If anyone says they know what pure good is, it's probably evil. (...) Good will disagree. Evil says: no more disagreements, ever. That was life in real Mordor: the Soviet Union. That's what Putin wants for Russia and the world."

As is also clear from his many tweets in the past month, Kasparov is not satisfied with the support from the western countries for Ukraine, which mostly consists of economic sanctions toward Russia and providing weapons and humanitarian help.

"The price of stopping a dictator goes up with every delay, every hesitation," says Kasparov. "Meeting evil halfway is still a victory for evil. Evil tempts us with our weakness, with our desire for comfort."

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Kasparov In TED Talk: 'Meeting Evil Halfway Is Still A Victory For Evil' - Chess.com

Formerly lost Iranian film Chess of the Wind lands at The Lilley – The Nevada Sagebrush

April 16, 2022 | Author: admin

Those who walked into the March 2 screening of the fabled 1976 Iranian film Chess of the Wind at the John & Geraldine Lilley Museum of Art were surprised to find the traditional seating replaced with an ornate arrangement of Persian carpets and pillows.

Rachel Jackson/Nevada Sagebrush.Students are ecstatic for the playing of Chess of the Wind at the Lilley Museum.

Ushered to the north wing of the museum, students slowly but resolutely huddled together atop the wool textile, in front of the temporary scaffolding where the laptop-connected projector operated above them. Some attendees were laid back, others leaned forward, and most simply sat as upright as comfortably as possible on the ground seating.

In many ways, the unconventional setup perfectly complemented the film they were about to watch: puzzling in its very existence, yet triumphant in its realization.

Chess of the Wind, which stars Fakhri Khorvash, Mohamad Ali Keshavarz and Shohreh Aghdashloo, is a passionate, golden-hour-soaked meditation on the material obsession and emotional claustrophobia inherent in royal aristocracy.

Despite its elaborate production values and lavish construction, the pre-revolutionary film has a history steeped in tumult and subverted expectations.

Weighed down by unfavorable press and a notoriously sabotaged screening at the 1976 Tehran International Festival, first-time director Mohammad Reza Aslani failed to mount the meagerest of theatrical runs for the picture. In the span of three years, there was a grand total of three public showings of the 100-minute domestic drama.

An outright ban of the film in his home country following the 1978-79 Revolution threatened to insulate Aslanis fearless political critique from the general public forever.

Following a chance discovery of a 35 mm print of the film in Tehran in 2014, a monumental 4K restoration under American director Martin Scorsese, and a heavily-publicized theatrical re-release in 2021, Chess of the Wind was exhibited.

The film is now being appreciated in more corners of the world than ever imagined beforeincluding now, in a college art museum in Reno, Nevada.

The free screening was organized by UNRs Department of English, but the success of the event can be attributed to English professor Pardis Dabashi.

Professor Dabashis efforts to bring the film to Reno began in late 2021. Preparing for the spring semester, she reached out to a representative from the esteemed art house cinema distributor, Janus Films, in a reluctant bid to gain a copy of the treasured masterwork to present to students in her Global Cinema course.

The single email would lead to something far more substantive. With Brian Belovarac at Janus Films, and the director and curator of the Lilley Museum of Arts approval, Vivian Zavataro, Dabashi secured herself and the university a place in the films ambitious comeback story.

On top of arranging and promoting the event for over two months, the professor also delivered a 10-minute introduction to the project. Standing before the snuggled pack of students, she balanced her detailed explanation of the films history and themes with lighthearted remarks regarding the occasion.

Im playing the film off my laptop. Ive tried to disable everything, but hopefully I dont get a FaceTime from my parents in the middle of the screening, Dabashi joked.

Nevertheless, in an evening destined for surprise, the ascendance and quality of the central attraction itself was a shock.

As an aesthetic object, Chess of the Wind is sectioned in its influences. The establishment of the royal setting in the beginning parts of the film evokes the solemn decadence of late Italian neorealism. Characters actions are framed through the mysterious and minimalistic power of directors like Robert Bresson. The films intense finale teems with the dark suspense of American noir.

Beyond copying his contemporaries, Aslani was hell-bent on conceptualizing a cinematic identity that meshed Irans sociopolitical upheaval with the broader context of disruption across several international film movements.

Students were riveted to spend time in this deeply deteriorative, yet quietly inspired regal wastelandwhere one line of a servants gossip lingers in the palace annals until eventually becoming the death sentence of a corrupt nobleman.

More importantly, it was clear that Aslanis mission to access deeper emotional and visual meaning rather than traditional politically-focused cinema transcended the boundaries of both time and language for the young audience.

Though there was not an official post-screening discussion, students immediately rose from the ground upon the films completion and congregated in groups inside the limited venue to exchange their thoughts.

Jefrin Jojan, Associated Students of the University of Nevada senate for the College of Engineering and self-proclaimed local movie enthusiast, described the film as a mix between two popular modern imports from South Korea: 2019s Parasite and 2016s The Handmaiden.

Carolyn Lemon, a sophomore at UNR, explained that, while she anticipated a more obvious takedown of the Shah government of Iran, the substitution of political themes for psychological travails made for a resonant viewing experience.

It was much more about the characters and how their motives played a part [in their actions], Lemon explained.

In an interview following the screening, Dabashi divulged her admiration for Chess of the Wind, which she described as a hidden gem and a beautiful and deeply smart film.

The fact that its been hiding for so long Dabashi trailed off, at a complete loss for words.

Dabashi, who is of Iranian descent, has extensively taught and written about films in the Persian cinematic canon. The films revelation visibly signaled a realigning moment in her years-long personal studies.

To that end, there was unanimous consensus among students for the continuation of international film screenings on-campus, especially after the high turnout of Chess of the Wind.

I think its interesting, and UNR should promote it more, Lemon stated before complimenting the culturally emblematic screening atmosphere. [The seating] definitely added something.

Dabashis enthusiasm for future events was evident in the immediate amount of thought she had concerning how to approach a global cinema screening series.

I would love to start with different sorts of national cinemas, she said, citing Iran and Japan as two countries of cinematic renown.

She explained that she would then branch out into showing movies revolving around specific themes while maintaining the international criteria.

Jojan expressed support for this prospect. He argued that the universitys English Department should push for more resources for larger venues and accommodate a discussion after the viewing period.

I enjoyed it, he explained, referring to the entire event at hand. It was a new experience for me.

In any case, the tapestry of global cinema felt palpably more luxurious and renewed in Reno with the arrival of this resurrected classic.

Wyatt Layland can be reached at jaedynyoung@sagebrush.unr.edu or on Twitter @NevadaSagebrush.

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Formerly lost Iranian film Chess of the Wind lands at The Lilley - The Nevada Sagebrush

Framing Data Science Problems the Right Way From the Start – MIT Sloan

April 14, 2022 | Author: admin

The failure rate of data science initiatives often estimated at over 80% is way too high. We have spent years researching the reasons contributing to companies low success rates and have identified one underappreciated issue: Too often, teams skip right to analyzing the data before agreeing on the problem to be solved. This lack of initial understanding guarantees that many projects are doomed to fail from the very beginning.

Of course, this issue is not a new one. Albert Einstein is often quoted as having said, If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute solving it.

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Consider how often data scientists need to clean up the data on data science projects, often as quickly and cheaply as possible. This may seem reasonable, but it ignores the critical why question: Why is there bad data in the first place? Where did it come from? Does it represent blunders, or are there legitimate data points that are just surprising? Will they occur in the future? How does the bad data impact this particular project and the business? In many cases, we find that a better problem statement is to find and eliminate the root causes of bad data.

Too often, we see examples where people either assume that they understand the problem and rush to define it, or they dont build the consensus needed to actually solve it. We argue that a key to successful data science projects is to recognize the importance of clearly defining the problem and adhere to proven principles in so doing. This problem is not relegated to technology teams; we find that many business, political, management, and media projects, at all levels, also suffer from poor problem definition.

Data science uses the scientific method to solve often complex (or multifaceted) and unstructured problems using data and analytics. In analytics, the term fishing expedition refers to a project that was never framed correctly to begin with and involves trolling the data for unexpected correlations. This type of data fishing does not meet the spirit of effective data science but is prevalent nonetheless. Consequently, defining the problem correctly needs to be step one. We previously proposed an organizational bridge between data science teams and business units, to be led by an innovation marshal someone who speaks the language of both the data and management teams and can report directly to the CEO. This marshal would be an ideal candidate to assume overall responsibility to ensure that the following proposed principles are utilized.

Get the right people involved. To ensure that your problem framing has the correct inputs, you have to involve all the key people whose contributions are needed to complete the project successfully from the beginning. After all, data science is an interdisciplinary, transdisciplinary team sport. This team should include those who own the problem, those who will provide data, those responsible for the analyses, and those responsible for all aspects of implementation. Think of the RACI matrix those responsible, accountable, to be consulted, and to be informed for each aspect of the project.

Recognize that rigorously defining the problem is hard work. We often find that the problem statement changes as people work to nail it down. Leaders of data science projects should encourage debate, allow plenty of time, and document the problem statement in detail as they go. This ensures broad agreement on the statement before moving forward.

Dont confuse the problem and its proposed solution. Consider a bank that is losing market share in consumer loans and whose leadership team believes that competitors are using more advanced models. It would be easy to jump to a problem statement that looks something like Build more sophisticated loan risk models. But that presupposes that a more sophisticated model is the solution to market share loss, without considering other possible options, such as increasing the number of loan officers, providing better training, or combating new entrants with more effective marketing. Confusing the problem and proposed solution all but ensures that the problem is not well understood, limits creativity, and keeps potential problem solvers in the dark. A better statement in this case would be Research root causes of market share loss in consumer loans, and propose viable solutions. This might lead to more sophisticated models, or it might not.

Understand the distinction between a proximate problem and a deeper root cause. In our first example, the unclean data is a proximate problem, whereas the root cause is whatever leads to the creation of bad data in the first place. Importantly, We dont know enough to fully articulate the root cause of the bad data problem is a legitimate state of affairs, demanding a small-scale subproject.

Do not move past problem definition until it meets the following criteria:

Taking the time needed to properly define the problem can feel uncomfortable. After all, we live and work in cultures that demand results and are eager to get on with it. But shortchanging this step is akin to putting the cart before the horse it simply doesnt work. There is no substitute for probing more deeply, getting the right people involved, and taking the time to understand the real problem. All of us data scientists, business leaders, and politicians alike need to get better at defining the right problem the right way.

Roger W. Hoerl (@rogerhoerl) teaches statistics at Union College in Schenectady, New York. Previously, he led the applied statistics lab at GE Global Research. Diego Kuonen (@diegokuonen) is head of Bern, Switzerland-based Statoo Consulting and a professor of data science at the Geneva School of Economics and Management at the University of Geneva. Thomas C. Redman (@thedatadoc1) is president of New Jersey-based consultancy Data Quality Solutions and coauthor of The Real Work of Data Science: Turning Data Into Information, Better Decisions, and Stronger Organizations (Wiley, 2019).

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Framing Data Science Problems the Right Way From the Start - MIT Sloan

Dr. Tina Hernandez-Boussard: Data Science as a Path to Inclusivity and Diversity in Medicine – Ms. Magazine

April 14, 2022 | Author: admin

Growing up in a rural community, Tina Hernandez-Boussard never thought she would go on to earn a Ph.D., much less be at the forefront of a new field intent on solving the inequities of our healthcare system through data science. However, with the support of a mentor who recognized her potential and encouraged her pursuits, Dr. Hernandez-Boussardnow a professor of medicine and biomedical data science at Stanford Universityleads efforts utilizing data in medicine to better serve people from all demographics, not only those who have traditionally been the focus of biomedical research.

For Hernandez-Boussard, solving the inequities within our healthcare system is only possible when we ensure that the people who collect, analyze and interpret data to make decisions, are as diverse as those who will be affected by those decisions. Not only does this make healthcare more equitable, it also creates more empathetic medicine. Through merging health and data science, Hernandez-Boussard is uniquely situated to understand both the challenges and the opportunities in biomedicine that she and other advocates for equity in health care confront. In the wake of a pandemic that drew attention to the numerous inequities in our healthcare system for minority and low-income populations, solving these problems is not only an academic venture, but a matter of life and death.

As Hernandez-Boussard observed at last months Women in Data Science Conference at Stanford University, one of the greatest challenges for data science in healthcare is also its greatest opportunity: creating datasets that include populations and perspectives traditionally excluded from medicine and medical research. Although data science can offer important insights into the problems we face, Hernandez-Boussard reminds us data analysis techniques, like natural language processing (an interdisciplinary approach to computer science that scrapes human language for data) and machine learning, only provide answers learned from the data we feed it. When that data is unbalanced, models perform poorly for different populations.

For example, the Boussard Lab has been working to identify depressive symptoms in cancer patients undergoing chemotherapy. While it is relatively straightforward to capture symptoms of severely depressed patients, intermediate symptoms are less easy to discern, especially among diverse populations who might express these symptoms or feelings differently and traditionally havent been researched. Diverse data scientists have the background to understand how people might communicate these symptoms across culture, gender, race, language and socioeconomic groups. To ask the right questions, data science needs to have diverse problem-solving teams who can better understand patients voices.

According to Hernandez-Boussard, one of the best ways to improve data-driven medicine is to ensure diverse teams of scientists and clinicians are thinking about the right questions to ask. For example, Hernandez-Boussard recalls the time a hospital asked for an algorithm to predict no-show appointments. Rather than simply creating such an algorithm, Hernandez-Boussards team challenged the hospital to think about why they wanted to predict no-shows instead of using data to find ways to reduce barriers that prevent patients from keeping their appointments. In this case, what worked best for the hospital perpetuated circumstances which restrict certain populations from accessing healthcare.

To ask the right questions, data science needs to have diverse problem-solving teams who can better understand patients voices.

Working with diverse populations allows scientists to challenge preconceived notions of symptoms, diseases and treatments, while also enabling practitioners and patients to work together to overcome histories of harm and misinformation. For data science to effectively rise to the challenge of unraveling bias in healthcare, the task requires an additional type of diversity. Not only must data scientists ensure diverse patient voices are better incorporated into healthcare systems, but data science as a field must also seek strategies for creating diverse team science approaches to problem solving.

In addition to ensuring diversity in gender, race, ethnicity and ability in biomedical data science, Hernandez-Boussard emphasizes the importance of diversity within backgrounds, professions and fields of study among teams of those studying problems in medicine. Collaboration across fields is critical, because the complexities of contemporary science and the problems confronting healthcare require multidisciplinary relationships; with computer scientists partnering with clinicians, engineers working with statisticians and social scientists bringing insights from qualitative research.

Data scientists can only rise to the challenge of healthcare inequality and become more collaborative and creative problem solvers by listening to diverse patient voices and engaging in conversations with those who push them outside their comfort zones. As lives continue to be lost as a result of incomplete data sets and single-minded solutions, Hernandez-Boussards efforts to diversify data in healthcare have the potential to save the lives of many people who have traditionally been left behind by medicine.

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Dr. Tina Hernandez-Boussard: Data Science as a Path to Inclusivity and Diversity in Medicine - Ms. Magazine

Interdisciplinary Cross-College Team Receives National Endowment for the Humanities Award – CU Boulder Today

April 14, 2022 | Author: admin

The ASSETT (Arts & Sciences Support of Education Through Technology) Innovation Incubator is thrilled to announce that a team of Arts and Sciences faculty has won a $150,000 Humanities Connections Implementation grant from the National Endowment for the Humanities. This project, titled Humanities Core Competencies as Data Acumen: Integrating Humanities and Data Science, aims to develop a curricular initiative at the University of Colorado Boulder that enhances both the humanities and data science by developing courses that are equally rooted in each discipline. The awarded team members are Project Director Jane Garrity (English), and Co-PIs Robin Burke (CMCI Lead), David Glimp (English), Nickoal Eichmann-Kalwara (CRDDS), Vilja Hulden (History), Thea Lindquist (CRDDS), Henry Lovejoy (History), Brett Melbourne (Evolutionary Biology), Nathan Pieplow (Program for Writing & Rhetoric), Rachael Deagman Simonetta (English), andEric Vance (Applied Math). In addition to the Innovation Incubator Inclusive Data Science team, this project will be supported by faculty from the College of Media, Communications & Information (CMCI) and the Center for Research Data & Digital Scholarship (CRDDS).

During the three-year period of the NEH award, team members will design eight courses, each of which will promote experiential learning and foster engagement with humanistic questions in the context of quantitative inquiry. Two additional key components of the project will be: a two-year course design and development workshop facilitated by CU Boulders Center for Teaching and Learning; and an ambitious plan for disseminating key findings in order to cultivate local and national conversations about the most effective ways of teaching data science and the humanities. The project aims to provide a model of cutting-edge pedagogical collaboration and an example of how the humanities can help equip twenty-first century learners with the intellectual resources they will need responsibly to inhabit a world being remade by data.

Prior to winning the NEH, the ASSETT Inclusive Data Science team members Garrity, Glimp, Hulden, Melbourne, Pieplow, and Vance launched a new introductory course, Interdisciplinary Data Science for All (AHUM 1825), that was team taught for the first time by Professors Glimp and Vance in Fall 2021. In this class students learned to analyze not just numbers, but to communicate the findings of data analysis effectively by highlighting human contexts and consequences. The course provides STEM majors with qualitative reasoning skills that are traditionally taught in the humanities, provides future humanities majors with an on-ramp to further study of data science, and provides all students with critical, statistical and computational skills they can apply in future courses and in the workforce. The Inclusive Data Science ASSETT team has also co-written an article, Integrating the Humanities into Data Science Education: Reimagining the Introductory Data Science Course that is forthcoming in the Statistics Education Research Journal. In addition, in 2021 the team won a three-year $300,000 National Science Foundation grant for their proposal, Integrating Content and Skills from the Humanities into Data Science Education. The animating insight of this and the NEH project is that essential data science competencies complementand benefit from being integrated withcore humanities competencies.

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Interdisciplinary Cross-College Team Receives National Endowment for the Humanities Award - CU Boulder Today

IBM vs Wipro: Explore Who is Leading the Data Science Race? – Analytics Insight

April 14, 2022 | Author: admin

Both IBM and Wipro are providing data science solutions, but who is leading the data science race?

According to a report released by Gartner, based on verified reviews from real users in the Data and Analytics Service Providers market, IBM has a rating of 4.4 stars with 45 reviews. Wipro has a rating of 4.7 stars with 90 reviews. Both IBM and Wipro go hand in hand with a few differences in their services.

IBMs data and analytics consulting services help organizations integrate enterprise data for operational, analytical, data science, and AI models to build an insights-driven organization. Also, the latest Gartner Magic Quadrant for Data Science and Machine Learning Platforms has just been released, and IBM is delighted to be recognized as a Leader in the space. Gartner acknowledges that IBM Watson Studio on IBM Cloud Pak for Data delivers a modern and comprehensive solution for organizations seeking to more efficiently run and manage AI models, simplify their AI lifecycle management, and empower their data scientists with technology that can help optimize their data-driven decision making.

Wipros data, analytics, and AI services enable organizations to deliver value across the customers journey by empowering users with more agile and intuitive processes. The companys services help organizations use data and analytics to create new business models and revenue streams all while ensuring security, quality, and regulatory compliance of data. Underpinned by technologies such as cloud, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics, its solutions help enhance decision making while enabling augmented intelligence and process automation. In addition, Wipros crowd-powered consulting helps secure innovation and scale programs to deliver tangible results.

IBM offers data strategy, consulting, architecture, transformation on the cloud, and management services to build a next-generation data platform. IBM analytics consulting helps you integrate and scale your business intelligence and automation efforts. Use data science, predictive analytics, and data visualization to gain meaningful insights that transform your business.

With Wipros Data Discovery Platform (DDP) you can extract deep insights from data and use sophisticated techniques such as visual sciences and storytelling to simplify interpretation and decision-making. The core of the platform brings together the Wipro HOLMES Artificial Intelligence PlatformTM and stream computing to deliver wide-ranging insights such as preventive action for customer attrition, predictive maintenance of assets to minimize downtime, and practices to reinforce online reputation.

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Mathematics Educator and Researcher Named CSUF’s 2022 Outstanding Professor | CSUF News – CSUF News

April 14, 2022 | Author: admin

I never had a professor who was better at explaining difficult topics than Dr. Behseta. He makes learning math enjoyable and I would recommend him to any of my peers looking to get the most out of their education.

Dr. Behseta demonstrated deep knowledge, contagious enthusiasm and encouragement. The course subject is difficult, and his great attitude and confidence helped us learn.

He is always willing to answer questions and make students feel comfortable asking them.

These are just a handful of student comments in support of Sam Behseta, professor of mathematics, this years recipient of Cal State Fullertons 2022 Outstanding Professor Award. Behseta was selected based on his exemplary contributions as an educator and scholar. CSUF President Fram Virjee announced Behsetas selection at todays (April 14) Academic Senate meeting.

In presenting the award, Virjee noted, If the goal of a professor is to ignite students passion for learning and hope they fall in love with the subject matter for life, Sam Behseta has achieved this. He has transformed classroom opportunities, changed his office hours and developed programs where students learn through participation. He offers real-world research experience to match what students will find in the field and to which they are entitled.

During my tenure at Cal State Fullerton, I have been fortunate to teach a large number of graduate and undergraduate classes in statistics and applied mathematics, Behseta said. Some of my personal favorites are introductory classes. In those classrooms, I have found some of the most outstanding research students, who in their own right, are now pathbreaking educators or visionary data scientists.

In data science, it is critical for students to develop a solid understanding of the basic concepts in theoretical and practical aspects of probability, statistics and computing, early on. Many students mistakenly believe this will be too hard for them. I tell them, If you fail, its because I failed as a teacher. Its my job to help you understand.

Behseta has supervised the work of more than 50 undergraduate researchers, many of whom are first-generation students.

Beyond being an approachable and beloved teacher, Behseta is also an accomplished researcher who has published extensively, presented at prestigious conferences and encouraged students to achieve far more than they thought possible. In fact, one of his former students is now an associate professor in the mathematics department.

When I look at my students and their success, it reinforces in me the idea of what we can accomplish at Cal State Fullerton, he said. It is quite important and makes a lifelong difference one classroom and one student at a time.

Among Behsetas accomplishments are:

Fellow of the American Statistical AssociationThe American Statistical Association recognizes fellows as those who have an established reputation and have made outstanding contributions to statistical science. The number of new fellows per year is limited to one third of 1% of the membership of ASA the second oldest organization in the U.S., formed in 1839. Behseta was selected to become a fellow in 2017.

Director of CSUFs Center for Computational and Applied MathematicsIn 2015, Behseta with a group of colleagues, initiated an effort to form an interdisciplinary scholarship with a computational component. In 2016, Behseta was appointed director of the Center for Computational and Applied Mathematics. Since its inception, CCAM has become a viable and highly active organization, supporting a significant number of data science research activities on campus.

High-Performance Computing ClusterIn 2021, CCAM secured two large grants: $600,000 from the U.S. Army for a new high-performance computing (HPC) clusterthat enabled science and mathematics faculty and students to engage in leading-edge research activities.The supercomputer, which is part of the Center for Computational and Applied Mathematics in the College of Natural Sciences and Mathematics, gives researchers the ability to process a large amount of data in a fraction of the time.

In 2022, CCAM secured a second supercomputer, ushering in an exciting era of advanced research for students, faculty and staff. The new high-performance computing clusters efficiency coupled with CCAM facultys diverse research topics, will not only pave the way for advancement in a multitude of fields: biology, biochemistry, statistics and applied mathematics, but it will also meet the demands of modern scientific research.

California Data Science Experience Transformation ProgramMore recently, in collaboration with researchers at UC Irvine and Cypress College, Behseta and colleague Jessica Jaynes, obtained a $1.5 million grant to provide opportunities for underrepresented and historically underserved students to receive training on the foundations and modern applications of data science, and to get involved in high-level research.

Statistical Modeling of COVID-19 DataSoon after the outbreak of the pandemic, Behseta and colleague Derdei Bichara, began building machine learning and mathematical models for predicting the spread of COVID-19. This model accounted for mobility among communities and human behavior and was showcased extensively in the media.

Serving Minorities in STEMBehseta was involved in securing National Science Foundation funding of $1.46 million to examine the effects of dual-language programs on increasing mathematics and science achievement among junior high students in the Anaheim School District.

He also helped secure $1 million for a project, Big Data Discovery and Diversity Through Research Education Advancement and Partnership, to inspire and train undergraduate students with diverse backgrounds about the approaches in big data analytics.

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Mathematics Educator and Researcher Named CSUF's 2022 Outstanding Professor | CSUF News - CSUF News

The role of data analytics in Fintech – Information Age

April 14, 2022 | Author: admin

Data analytics are crucial for companies leveraging Fintech in driving valuable insights.

This article will explore the role of data analytics in Fintech operations, as the disruptive innovation space continues to grow

Making use of data using a single view of all relevant assets, in real-time, has never been more vital, and for financial service institutions this is no different. Data-driven decisions, aided by analytics, are crucial in an ever-competitive landscape where consumers will look elsewhere if their needs arent met.

With this in mind, we explore the role that data analytics can play in Fintech operations.

Historically, financial service companies have dealt with data in their own respective departments, leading to disconnected pictures of business progress and customer behaviour. In todays business world though, organisations cant afford to continue with this approach, and need a unified view to gain true insights.

Current market research projects the global financial analytics market to grow to $25.38 billion by 2028. While the role of analytics in Fintech-powered operations is becoming more prominent, complexities remain when it comes to gaining insights from rising amounts of data, which can be attributed to skills gaps plaguing tech generally. This can be mitigated by establishing strong partnerships with vendors such as cloud service providers (CSPs) such as AWS and Azure, which are continuously adapting analytics capabilities.

To effectively process data in financial services, democratisation across the workforce is a must. No longer can assets afford to be kept solely with traditionally skilled IT personnel.

James Corcoran, senior vice-president of customer value at KX, explained: Whether its seeking Alpha, managing risk, ensuring compliance or identifying fraud, the financial services sector has always been at the forefront of data analytics.

The challenge facing financial services firms today is that there is simply too much data to process intuitively and without support. Insights are no longer visible; they must be mined and they must be mined quickly before either a problem occurs or an opportunity passes.

Throw in the disruption brought about by the pandemic, an ever more complex regulatory environment and the relentless impact of digital transformation on the sector and its clear that the focus on data, its management and its analysis, has never been greater. At KX, were hearing a lot from financial services customers on the need to democratise access to data across an organisation, and the need for data to be viewed as an enterprise asset rather than through the lens of individual teams or domain-specific requirements.

The consequences of not doing so are blatantly clear in too many organisations: the dreaded data silos that are costly to manage and hard to eliminate. The global datasphere is growing at an incredible rate, and much of that growth is coming from data created in real-time. Financial services firms must ensure their data analytics strategy can keep pace.

Analytics is proving particularly fruitful in the open banking segment of Fintech. With open banking being all about portability of personal financial data, to offer consumers more personalised services, data science and analytics have a fundamental role to play

Its difficult to overstate just how important data analytics is in Fintech. Not only can it be the basis for a huge range of different business offerings it also plays a critical role in optimising and informing how companies operate, said Alistair Dent, chief strategy officer at Profusion.

Startups can differentiate themselves and gain a competitive edge by offering more creative and useful services. The only way to do this is to have a strong and innovative data science capability that can support development. Put simply, you cannot have AI-driven financial advisors or financial aggregation platforms without data analytics.

Advances in data science and fintech are inextricably linked they are driving each other forward. More creative fintech solutions require more complex data science techniques.

Of course, no tech deployment project can drive that all important business value if board leadership isnt involved in the vision. Once this is achieved, financial service boardrooms can use analytics to make data-driven decisions that affect the company bottom line.

Once an IT issue, data should be at the heart of business models and strategies. Board level decisions need to be based on accurate insights rather than on approaches used in the past, which are not fit for purpose in the current environment, explained Anurag Bhatia, senior vice-president and head of Europe at Mphasis.

To harness innovation and monetise their data, the first step for leaders is to instil the right digital infrastructure to eliminate siloes and make quality data more accessible. Cloud is a strong enabler here, facilitating continuous innovation and opening the door for the use of advanced AI and machine learning capabilities for optimum data analytics.

Once leaders gain full visibility of their data, not only can they use it to arrive at meaningful insights, but they can also more easily comply with a changing regulatory landscape particularly when it comes to data protection, fraud prevention, and risk management.

Blockchain technology in financial services This article will explore the most valuable business use cases for blockchain technology in the financial services sector.

Fintech disruption of the banking industry: innovation vs tradition? Hasan Nawaz, CEO at HUBUC, explores how fintech disruption continues to challenge the traditional banking industry.

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The role of data analytics in Fintech - Information Age

Three Ways to Connect the Dots in a Decentralized Big Data World – Datanami

April 14, 2022 | Author: admin

Theres no shortage of data in this world. Neither is there a shortage of data-driven business plans. In fact, we are sitting on gluts of both. So why are companies still struggling to get the right data in front of the right people at the right time? One of the big challenges, sources say, is melding established data access and data management patterns with the new decentralized data paradigm. Here are three ways to do it.

That familiar urge to centralize data is falling by the wayside as the volumes of data continue to pile up. That represents a massive reversal of trends, according to Sean Knapp, the CEO and founder of Ascend.io.

Five to 10 years ago, there was a very strong push to consolidate data, consolidate it into your late, consolidate it into your warehouse, Knapp said during yesterdays Data Automation Summit, which continues today. And were starting to see those trends change. Were starting to see that organizations are embracing silos.embracing the fact that they cannot consolidate all of their data and there is no one platform at the data insurer layer to suit them all.

While were moving away from data centralization, that doesnt mean we can say goodbye to ETL. Ascend.io sells tools to automate the creation and management of data pipelines, which are proliferating at a furious clip at the moment, as data engineers seek to connect the various silos to enable data analysts and data scientists to get their data work done.

Knapp wants to improve the state of that art, and help automate the low-level muck that many data engineers are living with on a daily basis.

Automation of ETL/ELT pipelines is one way to tackle the growth of big decentralized data (Agor2012/Shutterstock)

The world of data has just grown too fast. It is like swimming upstream as we watched companies compete over the years, to try and pull all of their data into one spot, Knapp said. There will always be multiple data technologies.

While many companies want to use data in profitable ways, theyre having a hard time turning that desire into reality. Gerrit Katzmaeir, the vice president and general manager for database, data analytics, and Looker at Google Cloud, cited a recent study that found 68% of companies say theyre not getting lasting value out of their data investments.

Thats profoundly interesting, Katzmaeir said during last weeks rollout of BigLake, the companys first formal data lakehouse offering, which is slated to go up against lakehouses from Databricks and others.

Everyone recognizes that theyre going to compete with data, Katzmaeir said. And on the other side, we recognize that only a few companies are actually successful with it. So the question is, what is getting in the way of these companies to transform?

The answer, Katzmaeir said, lies somewhere in the jurisdiction of three paradigm changes that are currently taking place. First, the data is growing. The generation and storage of data is continuing to explode, and companies are grappling with storing a variety of data types and formats in multiple locations.

Second, the applications are expanding. Companies want to process this data with all sorts of engines and frameworks, and deliver a variety of data products and rich data experiences from it. Lastly, the users are everywhere. Data touches many personas today, including employees, customers, and partners, and the number of use cases for a given piece of data is growing.

The lakehouse concept melds data warehouses and data lakes into a unified whole (ramcreations/Shutterstock)

Even a company as large and technologically advanced as Google seems to realize that it cannot be the unifying force to bring all of its customers data back together. With BigLake, its melding the previously separate universes of the tried-and-true data warehouse, where structured data reigns supreme, and the looser-but-more-scalable data lake, where semi-structured data is stored.

In a way, the lakehouse architecture seeks to split the difference between the older approach (DWs) and the newer approach (data lakes) and delivering a semblance of data unification that will deliver some salvation from all those pesky data pipelines that keep popping up.

While Google Cloud is arguably the most open of the big three cloud providersindeed, Google Cloud says it extend into the data lakes of Microsoft Azure and Amazon Web Services and enable it to be accessed with BigLakenot everybody is convinced that a cloud-centric approach ultimately will solve customers modern data problems.

Data automation and lakehouses undoubtedly will help some organizations solve their data problems. But there are other big data challenges that wont be adequately addressed with either of those technologies.

Molly Presley, the senior vice president of marketing for Hammerspace, says some customers with large numbers of unstructured datasuch as what is found in science, media, and advertisingmay be best suited by adopting what she terms a global data environment.

Its the concept of I want to be able to make all my data globally available, no matter which storage silo or which storage system or which cloud region its sitting in, she says.

Being able to scale unstructured data storage broadly in a single name space with full high availability is important, Presley said. But distributed file systems and object systems can already do that. What is really moving the needle now is being able to simplify how users access and manage data, no matter where it sits, no matter what storage environment or protocol it uses, and meeting whatever performance requirements the customer needs.

Hammerspace offers what it calls a global data environment, but its mostly for unstructured data (Blue-Planet-Studio/Shutterstock)

Other environments are saying, Okay, I have NetApp, I have DDN, and I have some object store and I want to aggregate all of that data and make it available to my remote users who dont have connectivity to the data centers, dont have connectivity to the clusters, dont know how to interact with all those different technologies, Presley tells Datanami.

Hammerspace functions as that global data environment, which can function as a layer sitting atop other data stores, and smooth over the differences, while providing a common management and access layer to unstructured data. The key to Hammerspaces technology, Presley says, is the metadata.

So what well do is assimilate the metadataand now those remote users get local high-performance data access, she says. And they only have to interact with one thing, so IT doesnt have figure out how to make that user connected into all those different technologies.

While the cloud vendors are solving big data storage and processing challenges with infinitely scalable object storage systems that are completely separated from computenot to mention the data warehouses and lakehouses that offer a cornucopia of compute optionsthey still lack visibility into the legacy storage repositories that organization are still running on prem, Presley says. Thats the space that Hammerspace is attacking with its global data environment.

Its also why Microsoft is partnering with Hammerspace to help its Azure customers get access to large amounts of unstructured data that is still residing in on-prem data centers. Microsoft realizes that not all data and workloads are moving to the cloud, and it tapped Hammerspace to bring that into the cloud fold, Presley says.

What has changed is people are remote and data is distributed or decentralizedin a cloud data center, five data centers, whatever it isand the technologies that people are trying to use were designed for a single environment, she says. Theyre trying to say, Okay, I have all these technologies that were designed over the last 10 or 20 years for a single data center that were adapted a bit to use the cloud but werent adapted for multi-region simultaneously with remote users. And so theyre scratching their heads going Crud, what am I going to do? How do I put this together?

Weve mostly abandoned the idea that all data must live in a single place. The future of big data looks decidedly decentralized from this point forward. To keep data from becoming a distributed quagmire, there need to be some unifying themes. Theres a multitude of different methods to get there, including data automation, data lakehouses, and global data environment. Undoubtedly, there will be more.

Data Science and Machine-Learning Platforms Market Expected to Reach Highest CAGR 2028: The major players covered in Data Science and Machine-Learning…

April 14, 2022 | Author: admin

Predicting Growth Scope: Data Science and Machine-Learning Platforms Market Data Science and Machine-Learning Platforms Market , a new research report, presents an in-depth examination of the current market landscape as well as estimates through 2030. The Data Science and Machine-Learning Platforms critical market research methodology takes into account government policy, competitive context, historical data, competitive landscape, current market trends, impending technologies, technological developments, and thus the technological acceleration in similar industries, as well as market volatility, market barriers, opportunities, and challenges.

This market report looks at the worldwide and regional markets, giving you an in-depth look at the markets real growth prospects. It also sheds light on the large and pervasive territory of the market. A dashboard overview of influential organisations is also included in the report, which contains their effective marketing strategies, market contribution, and ongoing expansion in both historical and contemporary scenarios.

Competition Spectrum:The major players covered in Data Science and Machine-Learning Platforms are:SASDatabricksRapidMinerAlteryxDataikuIBMMathWorksMicrosoftKNIMETIBCO SoftwareDomino Data LabRapid InsightH20.aiAngossGoogleAnacondaLexalyticsSAP

Company profiles, product photos and specifications, production capacity, pricing, cost, profit, and contact information are all included in the Data Science and Machine-Learning Platforms Market study. Demand analyses for raw resources and instruments are provided both downstream and upstream. The Data Science and Machine-Learning Platforms market is being researched for more effective marketing methods. Finally, the feasibility of the existing investment projects is evaluated, and the overall analytical results are presented.

We Have Recent Updates of Data Science and Machine-Learning Platforms Market in Sample [emailprotected] https://www.orbisresearch.com/contacts/request-sample/5302872?utm_source=PoojaGIR4

The report highlights the nations that are growing in demand and also the nations where the demand for the Data Science and Machine-Learning Platforms market products and services is contracted. It highlights the worlds largest producers of the Data Science and Machine-Learning Platforms market products and the consumption of the products in million tons. The foreign and domestic demand for the products in mn tons is also given in the report. Moreover, the factors driving the increased demand in the selected nations are also studied. The challenges for the market participants including the cost competitiveness of the raw materials, competition from imports, and technology obsolescence are included in the report.

The market is roughly segregated into:

Analysis by Product Type:By Type, Data Science and Machine-Learning Platforms market has been segmented into:Open Source Data Integration ToolsCloud-based Data Integration Tools

Application Analysis:By Application, Data Science and Machine-Learning Platforms has been segmented into:Small-Sized EnterprisesMedium-Sized EnterpriseLarge Enterprises

Segmentation by Region with details about Country-specific developments North America (U.S., Canada, Mexico) Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS) Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific) Latin America (Brazil, Rest of L.A.) Middle East and Africa (Turkey, GCC, Rest of Middle East)

Table of Contents Chapter One: Report Overview 1.1 Study Scope1.2 Key Market Segments1.3 Players Covered: Ranking by Data Science and Machine-Learning Platforms Revenue1.4 Market Analysis by Type1.4.1 Data Science and Machine-Learning Platforms Market Size Growth Rate by Type: 2020 VS 20281.5 Market by Application1.5.1 Data Science and Machine-Learning Platforms Market Share by Application: 2020 VS 20281.6 Study Objectives1.7 Years Considered

Chapter Two: Growth Trends by Regions 2.1 Data Science and Machine-Learning Platforms Market Perspective (2015-2028)2.2 Data Science and Machine-Learning Platforms Growth Trends by Regions2.2.1 Data Science and Machine-Learning Platforms Market Size by Regions: 2015 VS 2020 VS 20282.2.2 Data Science and Machine-Learning Platforms Historic Market Share by Regions (2015-2020)2.2.3 Data Science and Machine-Learning Platforms Forecasted Market Size by Regions (2021-2028)2.3 Industry Trends and Growth Strategy2.3.1 Market Top Trends2.3.2 Market Drivers2.3.3 Market Challenges2.3.4 Porters Five Forces Analysis2.3.5 Data Science and Machine-Learning Platforms Market Growth Strategy2.3.6 Primary Interviews with Key Data Science and Machine-Learning Platforms Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players 3.1 Top Data Science and Machine-Learning Platforms Players by Market Size3.1.1 Top Data Science and Machine-Learning Platforms Players by Revenue (2015-2020)3.1.2 Data Science and Machine-Learning Platforms Revenue Market Share by Players (2015-2020)3.1.3 Data Science and Machine-Learning Platforms Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)3.2 Data Science and Machine-Learning Platforms Market Concentration Ratio3.2.1 Data Science and Machine-Learning Platforms Market Concentration Ratio (Chapter Five: and HHI)3.2.2 Top Chapter Ten: and Top 5 Companies by Data Science and Machine-Learning Platforms Revenue in 20203.3 Data Science and Machine-Learning Platforms Key Players Head office and Area Served3.4 Key Players Data Science and Machine-Learning Platforms Product Solution and Service3.5 Date of Enter into Data Science and Machine-Learning Platforms Market3.6 Mergers & Acquisitions, Expansion Plans

Do You Have Any Query or Specific Requirement? Ask Our Industry [emailprotected] https://www.orbisresearch.com/contacts/enquiry-before-buying/5302872?utm_source=PoojaGIR4

The high value-added products and services that the larger companies are focusing on and the low value-added products and services the smaller enterprises are focusing on are studied in the report. The report studies the raw materials scenario by outlining the proximity and availability of the raw materials that influence the location of the manufacturers in the selected countries and regions is given in the report. Additionally, the demand drivers and growth triggers that have enormous potential to drive the Data Science and Machine-Learning Platforms industry are studied in the report to provide a better understating of the actual influencers in the Data Science and Machine-Learning Platforms market. The report highlights the Data Science and Machine-Learning Platforms market segments that are slower capacity addition while detailing the segments that are witnessing a ramp-up in the market.

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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Data Science and Machine-Learning Platforms Market Expected to Reach Highest CAGR 2028: The major players covered in Data Science and Machine-Learning...

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Data Science and Machine-Learning Platforms Market Expected to Reach Highest CAGR 2028: The major players covered in Data Science and Machine-Learning…

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