Computers can crunch mind-boggling arrays of data. They can even win quiz shows. But are there more powerful applications of this analytical power yet to come? Claudia Pearce ’89 M.S., ’94 Ph.D., computer science, is the Senior Computer Science Authority at the National Security Agency (NSA). The winner of UMBC’s Alumna of the Year Award in Engineering and Information Technology in 2014, Pearce is diligently seeking the answer to that question.

Watson is IBM’s Deep Question Answering system. You might recall that when Watson was put to the test against human contestants on the television quiz show,Jeopardy!, the system successfully bested its competitors in providing questions to answers whose associated question was already known. (And won $1,000,000.)

But like any game, Jeopardy! has its rules – and its limits. Along with my colleagues and others in the field who study big data and predictive analytics, I’ve been wondering whether the techniques implemented in Watson could be used as a powerful knowledge discovery tool to find the questions to answers whose associated questions are unknown.

Subspecialties in the fields of computer science and statistics such as knowledge discovery, machine learning, data mining, and information retrieval are commonly applied in medicine and in the natural and physical sciences – and increasingly in the social sciences, advertising, and cybersecurity, too. (It’s often called “computational biology” or “computational advertising.”)

And as the scope of computational practices has increased, the resources needed to perform it have shrunken tremendously. Ten years ago, massive computation was primarily in the areas of physics, astronomy, and biology, where petabytes of data were collected and analyzed using massive high performance computing systems. The advent of Cloud computing technologies –and their increasing public availability – now allows institutions, companies, and users to rent time for large-scale computation without the enormous costs of creating and maintaining supercomputers.

Additionally, programming and data storage paradigms have evolved to make use of the inherent parallelism in many domain applications. This trend has created new applications for computer science that provide individuals and organizations access to a plethora of online information in real time.

Real-time data sources spur not only social media, but online commerce, video streaming, and geolocation. Wireless technologies and smartphones put that information in the palm of our hands.

The power and speed of these technologies have aided the machine learning and data mining techniques at the heart of analytics, from retrieval of simple facts to trends and predictions. Advertising applications, for instance, analyze your click stream and cookies so that ads tailored to your interests appear as you browse in real time.

Yet the process of developing and maintaining analytics has its costs. First, there is labor. It usually requires teams of people to identify and solve problems in various domains. Analysts (who are usually experts in their subject) develop a collection of research questions in their discipline. They are teamed with statisticians, computer scientists, and others to develop and write programs to put the data in a usable form and create machine learning applications, tools, and algorithms. This combination of data and programming combines into analytics designed to answer a question in a given line of inquiry.

Labor isn’t the only cost. Depending on the application, analytics can be reused – but eventually need to be refreshed with new data. This is particularly true when the analytic is designed to be predictive. Real-time financial industry data, for example, requires that credit card fraud models must be recreated at least annually. Computational advertising models must be refreshed weekly.

Outdated analytics become an artifact of the maintenance cycle. Reuse requires knowledge specific to the analytic, data, and inherent question, and is most likely unknown outside of a small development team. So how can we get beyond these limitations and make a simple easy-to-use system that helps people get answers to questions beyond Googling them?

Part of the answer may be found in a series of Beyond Watson workshops and other activities involving university, government, and industry partners, including one held at UMBC in February.

We’re looking at things like natural language, which is at the core of the ultimate computer human interface. Think back to Star Trek, when Spock would say something like: “Computer, what is the probability the Klingons will attack the Enterprise in this sector of the galaxy?” The computer would often engage in a back and forth interaction with Spock, asking for more information or clarification before offering one or more scenarios (and probabilities supporting those scenarios) to aid the crew of the Enterprise in their next move. When we can ask a computer a question and then engage in a dialogue with it in this way, we can freely use computers to their best advantage. This sort of exchange allows us to hone in on an answer (or answers) to our question, supported by knowing both how the information was derived and what level of confidence to place in it.

Envisioning such a model opens up new vistas. We may not be limited to retrieving existing answers from Wikipedia-like text, but use all available data to elicit answers to non-obvious questions, or to queries that have never been asked. We might also dispense with arcane interfaces that are poorly matched to both the task at hand and the needs of users and consumers. Database management systems, for instance, have historically been developed to ask a few specific questions of data, but imposing such a structure on the data makes it very difficult to anticipate (or answer) questions we weren’t thinking of when we built the system.

Pushing our thinking – and technology – to generalize on the automation of building big data analytics could permit us to leverage existing tools and build on them instead of shelving them. And pulling these tools together in yet another set of big data analytics may even allow for the entire enterprise of analytics (newly created systems as well as existing ones) to help us advance particular knowledge.

Your cell phone has a plethora of apps (collected in an “app store”) to help you make the best use of it. Creating a similar “analytics store” for our Question Answering systems will accelerate automation of these processes and encourage crowd sourcing in analytics development. It’s a vision of the future for which technology already exists. By specifying and clarifying what we want big data analytics to accomplish, we can start to build that future now.

Read more from the latest issue of UMBC Magazine.

Note: The views and opinions expressed are those of Claudia Pearce and do not reflect those of NSA/CSS.

Every so often, we’ll chat with an alum about what they do and how they got there. Today we’re talking with software engineer Akshay Java M.S. ‘03, computer science, Ph.D. ‘08, computer science, about his career and work for Google.

Name: Akshay Java
Job Title: Software Engineer for Google
Grad year: M.S. ‘03, computer science, Ph.D. ‘08, computer science

Q: Why was UMBC your university of choice?
UMBC has an excellent CSEE program that emphasizes on both research and core curriculum. During my undergraduate studies I had a keen interest in artificial intelligence and information retrieval. The opportunity to learn from the very best minds in the field is what made UMBC my top choice.

Q: What did you like most about UMBC during your time studying here?
During almost seven years at UMBC, I always felt a part of the close knit community. It is where I found close friendships that have lasted through the years and made strong connections both personally and professionally. The sense of community and the support system that UMBC provides to all its students, faculty, and staff is truly what makes it unique.

Q: Was there a professor or specific class that sticks out in your mind as particularly influential?
The classes that had been the most challenging are also the ones that I learned the most from. The very first semester at UMBC, I took artificial intelligence with Dr. Marie desJardins, operating systems with Dr. Anupam Joshi, and data mining with Dr. Hillol Kargupta. It was a combination that kept me busy juggling between assignments, mid-terms, and project deadlines. All three professors pushed us to really learn the core concepts and to this day I find myself going back to these fundamental topics. The first semester was really the foundation for the later courses and help me significantly.

My advisor, Dr. Tim Finin, has been the most influential figure in my education. Working with ebiquity research group at UMBC, I had the opportunity to focus on cutting-edge research topics and collaborated with several professors in the department. Dr. Finin always encouraged us to think out of the box and pursue challenging computer science problems with an eye for practical application.

Q: How did you create your startup?
I started Percept Labs with my co-founder Amir Padovitz, who was also my colleague at Microsoft. We started our company with the goal of building something that we felt is missing in our experience. Both of us are travel junkies and data enthusiasts, so we figured that we could combine the two and build something that would harness the power of the web and wisdom of the crowd to help us discover new travel content. Using large-scale data mining and machine learning techniques we built the technology that provides a personalized travel discovery engine.

Q: Did UMBC provide you with any tools that helped you pursue your own startup?
There were two key resources that UMBC provided and were the stepping stones to giving me the knowledge to pursue my own startup: First, I took an excellent class on entrepreneurship at the Alex. Brown Center for Entrepreneurship and I would highly recommend this class to anyone planning to create or work at a startup in future. It is a great course that covers every aspect of business and brought in guest speakers and CEOs who shared their knowledge and experiences.

The second important resource was the research lab I worked at while pursuing my Ph.D. While at ebiquity research group, faculty members encouraged us to think of practical application our research. We often collaborated with industry leaders like Microsoft, Google, IBM, and many others. Through various academic publications, grant applications, internships, fellowships, etc. we were given the skills necessary to learn how to present and communicate our work to others. This comes in handy in every phase of your professional life, be it in the industry or pitching to a venture capitalist or an angel investor.

Q: What led to your startup being acquired by Google?
Once we had launched a beta version of our product, we shared it with a close circle of friends for their feedback. In time, interest grew and we had more sign ups and our user base expanded. Throughout the process we iterated based on the feedback and suggestions we received from our users. We also actively engaged with industry experts and investors for advice and funding and were receiving interest from different companies on different aspects of the technology and experience we built. During this time, we were connected with Google and after a few discussions with them we strongly felt that this was a right fit.

Q: What is your favorite part about your job at Google?
I think the best part of my job is the opportunity to work with amazing people. The culture at Google is to think about how everything you do can make things 10 times better. From day one, everyone on your team and in the company supports you in working towards this goal. Collaborating with some of the best folks in the industry and working on hard technical problems is what makes every day fun and exciting.

Q: Are there any tips you might give to a person considering creating their own startup?
The most important factor is the team. Work with the best people you can find and the teamwork will keep you motivated when things are tough and help you face any challenge – be it technical or business related. The team also includes your advisors, lawyers, and business partners. The right team members will challenge you and support you in this incredible adventure.

With the right team in place, focus on the users with relentless dedication. Make sure your product addresses a real need and help people do something in a way that is better than anything else that exists out there. A differentiating product that provides value to a user is ultimately what will define your startup’s success.

Doing a startup will be a roller coaster ride – but remember to enjoy it! There will be days that will be exciting and others where you will feel like nothing is working out. No matter what remember to enjoy this incredible journey as it will be one that you will not forget for a long time to come.

Finally, a startup comes to life not just through your individual efforts but from a collective belief in your vision and passion. Your family, investors, advisors, and friends are all cheering for your success. Value and thank their contributions and support.

Q: Are there any words of caution you might give?
Don’t eat too many pizzas.