learning

Xilinx to Showcase Innovations for Industrial IoT Solutions at SPS IPC Drives 2017

By IoT – Internet of Things

Xilinx, Inc. the leader in scalable and comprehensive All Programmable Industrial IoT (IIoT) platforms from Edge to Cloud, will showcase the latest solutions for Factory Automation, Smart Grid and Robotics applications at SPS IPC Drives 2017. Highlights include advancements in Time-Sensitive Networking (TSN) for Industrie 4.0, Machine Learning, Cybersecurity, Functional Safety, and Motor Control on […]

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Xilinx to Showcase Innovations for Industrial IoT Solutions at SPS IPC Drives 2017

By News Aggregator

By IoT – Internet of Things

Xilinx, Inc. the leader in scalable and comprehensive All Programmable Industrial IoT (IIoT) platforms from Edge to Cloud, will showcase the latest solutions for Factory Automation, Smart Grid and Robotics applications at SPS IPC Drives 2017. Highlights include advancements in Time-Sensitive Networking (TSN) for Industrie 4.0, Machine Learning, Cybersecurity, Functional Safety, and Motor Control on […]

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What’s Keeping Deep Learning In Academia From Reaching Its Full Potential?

By Scott Clark

Deep learning is gaining a foothold in the enterprise as a way to improve the development and performance of critical business applications. It started to gain traction in companies optimizing advertising and recommendation systems, like Google, Yelp, and Baidu. But the space has seen a huge level of innovation over the past few years due to tools like open-source deep learning frameworks–like TensorFlow, MXNet, or Caffe 2–that democratize access to powerful deep learning techniques for companies of all sizes. Additionally, the rise of GPU-enabled cloud infrastructure on platforms like AWS and Azure has made it easier than ever for firms to build and scale these pipelines faster and cheaper than ever before.

Now, its use is extending to fields like financial services, oil and gas, and many other industries. Tractica, a market intelligence firm, predicts that deep learning enterprise software spending will surpass $40 billion worldwide by 2024. Companies that handle large amounts of data are tapping into deep learning to strengthen areas like machine perception, big data analytics, and the Internet of Things.

In the academic world outside of computer science from physics to public policy, though, where deep learning is rapidly being adopted and could be hugely beneficial, it’s often used in a way that leaves performance on the table.

Where academia falls short

Getting the most out of machine learning or deep learning frameworks requires optimization of the configuration parameters that govern these systems. These are the tunable parameters that need to be set before any learning actually takes place. Finding the right configurations can provide many orders of magnitude improvements in accuracy, performance or efficiency. Yet, the majority of professors and students who use deep learning outside of computer science, where these techniques are developed, are often using one of three traditional, suboptimal methods to tune, or optimize, the configuration parameters of these systems. They may use manual search–trying to optimize high-dimensional problems by hand or intuition via trial-and-error; grid search–building an exhaustive set of possible parameters and testing each one individually at great cost; or randomized search–the most effective in practice, but unfortunately the equivalent of trying to climb a mountain by jumping out of an airplane hoping you eventually land on the peak.

(gor kisselev/Shutterstock)

While these methods are easy to implement, they often fall short of the best possible solution and waste precious computational resources that are often scarce in academic settings. Experts often do not apply more advanced techniques because they are so orthogonal to the core research they are doing and the need to find, administer, and optimize more sophisticated optimization methods often wastes expert time. This challenge can also cause experts to rely on less powerful but easier to tune methods, and not even attempt deep learning. While researchers have used these methods for years, it’s not always the most effective way to conduct research.

The need for Bayesian Optimization

Bayesian optimization automatically fine tunes the parameters of these algorithms and machine learning models without accessing the underlying data or model itself. The process probes the underlying system to observe various outputs. It detects how previous configurations have performed to determine the best, most intelligent thing to try next. This helps researchers and domain experts arrive at the best possible model and frees up time to focus on more pressing parts of their research.

Bayesian optimization has already been applied outside of deep learning to other problems in academia from gravitational lensing to polymer synthesis to materials design and beyond. Additionally, a number of professors and students are already using this method at universities like MIT, University of Waterloo and Carnegie Mellon to optimize their deep learning models and conduct life-changing research. George Chen, assistant professor at Carnegie Mellon’s Heinz College of Public Policy and Information Systems, uses Bayesian Optimization to fine tune the machine learning models he uses in his experiments. His research consists of medical imaging analysis that automates the process of locating a specific organ in the human body. The implications of his research could help prevent unnecessary procedures in patients with congenital heart defects and others. Before applying Bayesian Optimization to his research, Chen had to guess and check the best parameters for his data models. Now, he’s able to automate the process and receive updates on his mobile phone so he can spend time completing other necessary parts of the research process.

Unfortunately, the vast majority of researchers leveraging deep learning outside of academia are not using these powerful techniques. This costs them time and resources or even completely prevents them from achieving their research goals via deep learning. When those experts are forced to do multidimensional, guess-and-check equations in their head, they usually have to spend valuable computational resources on modeling and work with sub-optimal results. Deploying Bayesian Optimization can accelerate the research process, free up time to focus on other important tasks and unlock better outcomes.

Scott Clark is the co-founder and CEO of SigOpt, which provides its services for free to academics around the world.. He has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.

Related Items:

Getting Hyped for Deep Learning Configs

Dealing with Deep Learning’s Big Black Box Problem

Machine Learning, Deep Learning, and AI: What’s the Difference?

The post What’s Keeping Deep Learning In Academia From Reaching Its Full Potential? appeared first on Datanami.

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What’s Keeping Deep Learning In Academia From Reaching Its Full Potential?

By News Aggregator

By Scott Clark

Deep learning is gaining a foothold in the enterprise as a way to improve the development and performance of critical business applications. It started to gain traction in companies optimizing advertising and recommendation systems, like Google, Yelp, and Baidu. But the space has seen a huge level of innovation over the past few years due to tools like open-source deep learning frameworks–like TensorFlow, MXNet, or Caffe 2–that democratize access to powerful deep learning techniques for companies of all sizes. Additionally, the rise of GPU-enabled cloud infrastructure on platforms like AWS and Azure has made it easier than ever for firms to build and scale these pipelines faster and cheaper than ever before.

Now, its use is extending to fields like financial services, oil and gas, and many other industries. Tractica, a market intelligence firm, predicts that deep learning enterprise software spending will surpass $40 billion worldwide by 2024. Companies that handle large amounts of data are tapping into deep learning to strengthen areas like machine perception, big data analytics, and the Internet of Things.

In the academic world outside of computer science from physics to public policy, though, where deep learning is rapidly being adopted and could be hugely beneficial, it’s often used in a way that leaves performance on the table.

Where academia falls short

Getting the most out of machine learning or deep learning frameworks requires optimization of the configuration parameters that govern these systems. These are the tunable parameters that need to be set before any learning actually takes place. Finding the right configurations can provide many orders of magnitude improvements in accuracy, performance or efficiency. Yet, the majority of professors and students who use deep learning outside of computer science, where these techniques are developed, are often using one of three traditional, suboptimal methods to tune, or optimize, the configuration parameters of these systems. They may use manual search–trying to optimize high-dimensional problems by hand or intuition via trial-and-error; grid search–building an exhaustive set of possible parameters and testing each one individually at great cost; or randomized search–the most effective in practice, but unfortunately the equivalent of trying to climb a mountain by jumping out of an airplane hoping you eventually land on the peak.

(gor kisselev/Shutterstock)

While these methods are easy to implement, they often fall short of the best possible solution and waste precious computational resources that are often scarce in academic settings. Experts often do not apply more advanced techniques because they are so orthogonal to the core research they are doing and the need to find, administer, and optimize more sophisticated optimization methods often wastes expert time. This challenge can also cause experts to rely on less powerful but easier to tune methods, and not even attempt deep learning. While researchers have used these methods for years, it’s not always the most effective way to conduct research.

The need for Bayesian Optimization

Bayesian optimization automatically fine tunes the parameters of these algorithms and machine learning models without accessing the underlying data or model itself. The process probes the underlying system to observe various outputs. It detects how previous configurations have performed to determine the best, most intelligent thing to try next. This helps researchers and domain experts arrive at the best possible model and frees up time to focus on more pressing parts of their research.

Bayesian optimization has already been applied outside of deep learning to other problems in academia from gravitational lensing to polymer synthesis to materials design and beyond. Additionally, a number of professors and students are already using this method at universities like MIT, University of Waterloo and Carnegie Mellon to optimize their deep learning models and conduct life-changing research. George Chen, assistant professor at Carnegie Mellon’s Heinz College of Public Policy and Information Systems, uses Bayesian Optimization to fine tune the machine learning models he uses in his experiments. His research consists of medical imaging analysis that automates the process of locating a specific organ in the human body. The implications of his research could help prevent unnecessary procedures in patients with congenital heart defects and others. Before applying Bayesian Optimization to his research, Chen had to guess and check the best parameters for his data models. Now, he’s able to automate the process and receive updates on his mobile phone so he can spend time completing other necessary parts of the research process.

Unfortunately, the vast majority of researchers leveraging deep learning outside of academia are not using these powerful techniques. This costs them time and resources or even completely prevents them from achieving their research goals via deep learning. When those experts are forced to do multidimensional, guess-and-check equations in their head, they usually have to spend valuable computational resources on modeling and work with sub-optimal results. Deploying Bayesian Optimization can accelerate the research process, free up time to focus on other important tasks and unlock better outcomes.

Scott Clark is the co-founder and CEO of SigOpt, which provides its services for free to academics around the world.. He has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.

Related Items:

Getting Hyped for Deep Learning Configs

Dealing with Deep Learning’s Big Black Box Problem

Machine Learning, Deep Learning, and AI: What’s the Difference?

The post What’s Keeping Deep Learning In Academia From Reaching Its Full Potential? appeared first on Datanami.

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The post What’s Keeping Deep Learning In Academia From Reaching Its Full Potential? appeared on IPv6.net.

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Swiss Company PlantCare Makes Breakthrough in Digital Farming

By IoT – Internet of Things

Can you imagine being able to determine the fertilizer content in the soil without taking samples and then having to have them analyzed in a chemical laboratory? PlantCare Ltd., a company based in Switzerland in the field of soil sensors and intelligent self-learning irrigation controls, has succeeded to enable exactly this. Agriculture has been waiting […]

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Swiss Company PlantCare Makes Breakthrough in Digital Farming

By News Aggregator

By IoT – Internet of Things

Can you imagine being able to determine the fertilizer content in the soil without taking samples and then having to have them analyzed in a chemical laboratory? PlantCare Ltd., a company based in Switzerland in the field of soil sensors and intelligent self-learning irrigation controls, has succeeded to enable exactly this. Agriculture has been waiting […]

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The post Swiss Company PlantCare Makes Breakthrough in Digital Farming appeared on IPv6.net.

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CFP for @CloudExpo and @ExpoDX Opens | #SmartCities #DigitalTransformation

The 22nd International Cloud Expo | 1st DXWorld Expo has announced that its Call for Papers is open. Cloud Expo | DXWorld Expo, to be held June 5-7, 2018, at the Javits Center in New York, NY, brings together Cloud Computing, Digital Transformation, Big Data, Internet of Things, DevOps, Machine Learning and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding business opportunity. Submit your speaking proposal today!

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CFP for @CloudExpo and @ExpoDX Opens | #SmartCities #DigitalTransformation

By News Aggregator

The 22nd International Cloud Expo | 1st DXWorld Expo has announced that its Call for Papers is open. Cloud Expo | DXWorld Expo, to be held June 5-7, 2018, at the Javits Center in New York, NY, brings together Cloud Computing, Digital Transformation, Big Data, Internet of Things, DevOps, Machine Learning and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding business opportunity. Submit your speaking proposal today!

read more

Read more here:: iot.sys-con.com/index.rss

The post CFP for @CloudExpo and @ExpoDX Opens | #SmartCities #DigitalTransformation appeared on IPv6.net.

Read more here:: IPv6 News Aggregator

Glasswing Ventures announces Connect Council

By Byron Reese

When I first heard about the Connect Council, I was intrigued. I knew Rudina Seseri and Glasswing Ventures, and knew they didn’t have a reputation for doing things half way. When I heard about the mission of the Connect Council and the people involved, I was more than intrigued, I was impressed. What follows is a quick Q&A with Rudina about the council.

What is the Connect Council?

It is the first of three advisory councils to support and extend Glasswing Ventures’ investment strategy. Collectively, these councils bring together 40 renowned entrepreneurs and technologists, AI visionaries, and world-leading executives to exclusively advise and support the firm and its portfolio companies. The Connect Council is a critical part of the Glasswing Ventures’ DNA, extending our strength in providing AI expertise and advice exponentially amplifying the firm’s and our portfolio companies’ competitive edge. The Connect Council is comprised of two working groups: the AI & Academic Group, and the Business Leadership Group. Today, we are announcing the AI & Academic Group.

Who is on it?

A group of extraordinary individuals who have been lending their support to us since the founding of Glasswing over 18 months ago – we are grateful to them and very happy to announce that the members of the Glasswing’s Connect Council – the AI & Academic Group include:

  • Sir Tim Berners-Lee, inventor of the World Wide Web, Professor at MIT and Oxford University and winner of the ACM A.M. Turing Prize
  • Dr. Brad Berens, Chief ‫Strategy Officer at the Center for the Digital Future at USC Annenberg and Principal at Big Digital Idea Consulting
  • Dr. Cynthia Breazeal, Associate Professor of Media Arts and Sciences at MIT, Founder and Chief Scientist of Jibo, Inc.
  • Dr. Thomas R. Eisenmann, Howard H. Stevenson Professor of Business Administration at the Harvard Business School, Faculty Co-Chair of the HBS Rock Center for Entrepreneurship
  • Dr. Alex ‘Sandy’ Pentland, MIT Professor and Media Lab Entrepreneurship Program Director
  • Dr. Manuela Veloso, Herbert A. Simon University Professor and Head of Machine Learning Department at Carnegie Mellon University
  • Dr. Peter Weinstock, Executive Director and Anesthesia Endowed Chair of the Boston Children’s Hospital Simulator Program and Associate Professor of Anesthesia at Harvard Medical School

Why did you start it?

We started the Councils as we know that they can bring tremendous scale to the firm as we help harness the positive potential of AI across industries and markets. Because the Connect Council is a collaborative and vibrant body composed of the most influential thought leaders and innovators in academia and AI technology today — our team, our founders and portfolio companies, gain access to a brilliant collective of luminaries at the forefront of AI and innovation, who are committed to fueling its success and growth. These visionaries have extensive experience across AI, academia, startups and Fortune 500 companies. They are the catalysts in extending our reach, supporting our portfolio companies and advising us, and helping Glasswing become a cornerstone of the AI ecosystem. They also play a pivotal role in helping bring AI to its full potential in the broader ecosystem and society at large.

What do you hope to accomplish with it?

Our council members are a resource for candid views and discussions about new technology trends, opportunities and talent in AI – they aren’t just big names and faces on a website. We won’t agree all of the time — and that’s exactly what we hope for. In fact, it’s beautiful when we brainstorm together, as that is when the best outcomes emerge. Our portfolio startups, and many more in the ecosystem, will be able to benefit first-hand from these brainstorms and the brilliance and experience of our advisors.

We have a symbiotic relationship with our advisory council members. They enhance the value we add to founders and companies, well beyond smart capital. At the same time, through their affiliation with Glasswing, they are part of a platform that is developing and shaping the next generation of AI leaders and technology companies. It is because of this mutually beneficial dynamic that our advisors work with us on an exclusive basis.

How will you know if it is working? Any metrics you are tracking?

Our Connect Council members are catalysts in extending our reach, supporting our portfolio companies and advising us, and helping Glasswing become a cornerstone of the AI ecosystem. They also play a pivotal role in helping bring AI to its full potential in the broader ecosystem and society at large. Being as exclusive and engaged as they are, their inbounds — whether it is bringing in a unique deal flow or helping with diligence or key talent are part of the tremendous value they bring to us.

Is AI really as big as the hype suggests?

Artificial Intelligence has been at the forefront of tech innovation for some time, but 2017 has been the year in which it has truly taken center stage. In a world of pervasive connectivity, AI is essential to harnessing the power of data. Companies have to create an AI advantage to survive — Google, Facebook, Amazon and countless startups know this and are betting their businesses on it – in fact, startups are becoming major value creators.

AI is already changing many aspects of our daily lives both at home and at work. However, this is just the start. AI is steadily and pervasively redefining our relationship with technology, enhancing human capacity and, fundamentally, how we live. It is big – and it’s going to be bigger than we imagined it.


Rudina Seseri is founder and managing partner at Glasswing Ventures. With over 15 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in the fields of robotics, Internet of Things (IoT), SaaS marketing technologies and digital media. Rudina’s portfolio investments include Talla, Celtra, CrowdTwist, Jibo and SocialFlow. Rudina has been appointed by the Dean of the Harvard Business School (HBS) for a fourth consecutive year to serve as Entrepreneur-In-Residence for the Business School and as Executive-In-Residence for Harvard University’s innovation-Lab. She is also a Member of the Business Leadership Council of Wellesley College. Rudina also serves as Advisor for L’Oreal USA Women in Digital, as Director on the Board of the Massachusetts Innovation and Technology Exchange (MITX) and on the Board of Overseers for Boston Children’s Hospital. She has been named a 2017 Boston Business Journal Power 50: Newsmaker, a 2014 Women to Watch honoree by Mass High Tech and a 2011 Boston Business Journal 40-under-40 honoree for her professional accomplishments and community involvement. She graduated magna cum laude from Wellesley College with a BA in Economics and International Relations and with an MBA from the Harvard Business School (HBS). She is a member of Phi Beta Kappa and Omicron Delta Epsilon honor societies.

Read more here:: gigaom.com/feed/

Glasswing Ventures announces Connect Council

By News Aggregator

By Byron Reese

When I first heard about the Connect Council, I was intrigued. I knew Rudina Seseri and Glasswing Ventures, and knew they didn’t have a reputation for doing things half way. When I heard about the mission of the Connect Council and the people involved, I was more than intrigued, I was impressed. What follows is a quick Q&A with Rudina about the council.

What is the Connect Council?

It is the first of three advisory councils to support and extend Glasswing Ventures’ investment strategy. Collectively, these councils bring together 40 renowned entrepreneurs and technologists, AI visionaries, and world-leading executives to exclusively advise and support the firm and its portfolio companies. The Connect Council is a critical part of the Glasswing Ventures’ DNA, extending our strength in providing AI expertise and advice exponentially amplifying the firm’s and our portfolio companies’ competitive edge. The Connect Council is comprised of two working groups: the AI & Academic Group, and the Business Leadership Group. Today, we are announcing the AI & Academic Group.

Who is on it?

A group of extraordinary individuals who have been lending their support to us since the founding of Glasswing over 18 months ago – we are grateful to them and very happy to announce that the members of the Glasswing’s Connect Council – the AI & Academic Group include:

  • Sir Tim Berners-Lee, inventor of the World Wide Web, Professor at MIT and Oxford University and winner of the ACM A.M. Turing Prize
  • Dr. Brad Berens, Chief ‫Strategy Officer at the Center for the Digital Future at USC Annenberg and Principal at Big Digital Idea Consulting
  • Dr. Cynthia Breazeal, Associate Professor of Media Arts and Sciences at MIT, Founder and Chief Scientist of Jibo, Inc.
  • Dr. Thomas R. Eisenmann, Howard H. Stevenson Professor of Business Administration at the Harvard Business School, Faculty Co-Chair of the HBS Rock Center for Entrepreneurship
  • Dr. Alex ‘Sandy’ Pentland, MIT Professor and Media Lab Entrepreneurship Program Director
  • Dr. Manuela Veloso, Herbert A. Simon University Professor and Head of Machine Learning Department at Carnegie Mellon University
  • Dr. Peter Weinstock, Executive Director and Anesthesia Endowed Chair of the Boston Children’s Hospital Simulator Program and Associate Professor of Anesthesia at Harvard Medical School

Why did you start it?

We started the Councils as we know that they can bring tremendous scale to the firm as we help harness the positive potential of AI across industries and markets. Because the Connect Council is a collaborative and vibrant body composed of the most influential thought leaders and innovators in academia and AI technology today — our team, our founders and portfolio companies, gain access to a brilliant collective of luminaries at the forefront of AI and innovation, who are committed to fueling its success and growth. These visionaries have extensive experience across AI, academia, startups and Fortune 500 companies. They are the catalysts in extending our reach, supporting our portfolio companies and advising us, and helping Glasswing become a cornerstone of the AI ecosystem. They also play a pivotal role in helping bring AI to its full potential in the broader ecosystem and society at large.

What do you hope to accomplish with it?

Our council members are a resource for candid views and discussions about new technology trends, opportunities and talent in AI – they aren’t just big names and faces on a website. We won’t agree all of the time — and that’s exactly what we hope for. In fact, it’s beautiful when we brainstorm together, as that is when the best outcomes emerge. Our portfolio startups, and many more in the ecosystem, will be able to benefit first-hand from these brainstorms and the brilliance and experience of our advisors.

We have a symbiotic relationship with our advisory council members. They enhance the value we add to founders and companies, well beyond smart capital. At the same time, through their affiliation with Glasswing, they are part of a platform that is developing and shaping the next generation of AI leaders and technology companies. It is because of this mutually beneficial dynamic that our advisors work with us on an exclusive basis.

How will you know if it is working? Any metrics you are tracking?

Our Connect Council members are catalysts in extending our reach, supporting our portfolio companies and advising us, and helping Glasswing become a cornerstone of the AI ecosystem. They also play a pivotal role in helping bring AI to its full potential in the broader ecosystem and society at large. Being as exclusive and engaged as they are, their inbounds — whether it is bringing in a unique deal flow or helping with diligence or key talent are part of the tremendous value they bring to us.

Is AI really as big as the hype suggests?

Artificial Intelligence has been at the forefront of tech innovation for some time, but 2017 has been the year in which it has truly taken center stage. In a world of pervasive connectivity, AI is essential to harnessing the power of data. Companies have to create an AI advantage to survive — Google, Facebook, Amazon and countless startups know this and are betting their businesses on it – in fact, startups are becoming major value creators.

AI is already changing many aspects of our daily lives both at home and at work. However, this is just the start. AI is steadily and pervasively redefining our relationship with technology, enhancing human capacity and, fundamentally, how we live. It is big – and it’s going to be bigger than we imagined it.


Rudina Seseri is founder and managing partner at Glasswing Ventures. With over 15 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in the fields of robotics, Internet of Things (IoT), SaaS marketing technologies and digital media. Rudina’s portfolio investments include Talla, Celtra, CrowdTwist, Jibo and SocialFlow. Rudina has been appointed by the Dean of the Harvard Business School (HBS) for a fourth consecutive year to serve as Entrepreneur-In-Residence for the Business School and as Executive-In-Residence for Harvard University’s innovation-Lab. She is also a Member of the Business Leadership Council of Wellesley College. Rudina also serves as Advisor for L’Oreal USA Women in Digital, as Director on the Board of the Massachusetts Innovation and Technology Exchange (MITX) and on the Board of Overseers for Boston Children’s Hospital. She has been named a 2017 Boston Business Journal Power 50: Newsmaker, a 2014 Women to Watch honoree by Mass High Tech and a 2011 Boston Business Journal 40-under-40 honoree for her professional accomplishments and community involvement. She graduated magna cum laude from Wellesley College with a BA in Economics and International Relations and with an MBA from the Harvard Business School (HBS). She is a member of Phi Beta Kappa and Omicron Delta Epsilon honor societies.

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