Over the weekend of March 1-3, we attended the MIT Sloan Sports Analytics Conference in Boston, MA. The goal of the conference is to provide a forum to discuss the increasing role of analytics in the sports industry.  As MSBA students, it was exciting to have the opportunity to listen to industry professionals speak about how analytics are being applied to various functions of the sports industry.

The conference itself was a confluence of people from around the world representing many different sports at both the amatuer and professional level. The main reason for attending the conference was to learn about how analytics concepts that we have discussed in the program are being applied to a myriad of different sports in order to create more “beautiful” games, to better understand fan bases, to assist users in the world of sports betting, and to help facilitate decision making for coaches and management alike.

There was knowledge to be gained all around- from the panels, talks, and research paper presentations to casual chats with fellow attendees. We enjoyed meeting many different people involved in the world of sports analytics, from seasoned professionals to current students. By attending the Sloan Conference, we were able to network with various professional sports teams and league offices such as LA FC, FC Barcelona, the Buffalo Bills, and the Golden State Warriors. We even got to catch up with some MSBA alumni, including Drew Lieberman at the Miami Dolphins and Nick Fleder at the Indianapolis Pacers.

When considering what we learned of the sports industry and the exploding use of data and analytics in it, it’s clear that our MSBA curriculum is most certainly preparing us for such an industry and equipping us to adapt to the ever-evolving trends in analytics in general. Topics that arose at the conference include deep learning and neural networks, managing databases, social media analytics and ticket pricing/revenue management. This conference allowed us to see the importance of these analytics concepts as well as get a chance to see some of the practical applications in sports.

Additionally, the MSBA program prides itself on training “quantitatively capable storytellers” and preparing its students to be able to effectively communicate their findings to peers, stakeholders, and clients alike. The panels and presentations we saw all weekend were both inspirational and instructional in showing us how to present data insights to audiences with varying levels of fluency in statistics and machine learning concepts.

A few presentations in particular stood out. The first was “Beautyball: Using Analytics to Build Beautiful Games” by ESPN NBA Analyst (and current UT Professor Kirk Goldsberry). Using the framework of the NBA’s evolving shot distribution – fewer post-ups, more three pointers – Goldsberry asserted that sports leagues need to consider how they want their games to look and then use analytics-based methods to refine their rules to get there. One example would be identifying a reasonable proportion of shot attempts that should be three-pointers. The NBA could then move the three-point line back a distance where the new efficiency of the shot would incentivize teams to attempt them at the desired rate. Goldsberry is known for his beautiful visualizations, and he was a great example of how to effectively present data.

Among the more technical presentations, we were fascinated by “Going Beyond Tracking Data: Using Body-Pose for Next Generation Analytics” presented by Sujoy Ganguly, the head of Data Vision at STATS. Previously, STATS was only able to track player position in x-y coordinates if their SportVU cameras were installed in stadiums. Ganguly demonstrated how they can now use neural networks to analyze television broadcasts of sporting events to track each player’s position and body pose – including the position of their limbs. He even showed the famous footage of Michael Jordan’s game winning shot in the 1998 NBA Finals and overlaid the player positioning tracking data they were able to pull from the footage. This could be a revolutionary advancement, and it was great to see a new application of the neural network image recognition methods that we learned about in Professor Ghosh’s Advanced Predictive Modeling course.

Overall, the conference was a rewarding experience for this trio of MSBA students. It is certainly a must attend for students of future cohorts who have not only a passion for analytics but a love for sports and competition, and want to play their parts in the making of champions.

#TXMS Students attended the RE.WORK Deep Learning Summit – hear from them about the experience and what they’ve gained!

We had the amazing opportunity to attend the 5th Annual RE.WORK Deep Learning Summit in San Francisco held on 24-25th January this year. The conference had the most captivating sessions lined up by researchers and working professionals from organizations like Facebook, Google Brain, Uber AI Labs, Walmart Labs, GE, Samsung, AIRA working extensively to empower the blind and numerous more. The sessions also included workshops which were really constructive in getting us acquainted with alternative tools we can use for our ML oriented projects. This year conference was also special as in addition to Deep Learning and AI Assistant directed sessions, there were numerous other sessions revolving around Environment & Sustainability, Ethics & Social Responsibility, Futurescaping, Investors & Startups and Technical Labs, Education & AI, Industry Applications; 8 new stages that were never seen before for the Deep Learning Summit. (Pictured: MSBA- Serena Du, Avani Sharma, Apoorva Reddy, Atindra Bandi, Sagar Chadha, Akhilesh Narapareddy)

We collected our passes the night before 24th January in a welcoming event kicking off our networking early. The next day we started off from the Deep Learning stage where Anirudh Koul from Aira introduced the distinguished speakers of the subsequent sessions from divisions like Google Brain, OpenAI amongst many others. The most awaited session was in Anirudh’s words by a “smart fellow, humble fellow”, creator of Generative Adversarial Networks (GANs) and Research Scientist at Google Brain IAN GOODFELLOW. He presented his most recent research in Adversarial Machine Learning.

Ian shared how ‘machine learning algorithms are based on optimization: given a cost function, the algorithm adapts the parameters to reduce the cost. Adversarial machine learning is instead based on game theory: multiple “players” compete to each reduce their own cost, often at the expense of other players’.

He explained how we have come a long way in being able to generate completely new images from GANs. We can convert an image of ‘driving in the daytime’ to ‘driving in the night time’ without any labels or supervision. He also talked about the industrial applications of GANs like creating real world objects for use in dentistry (replica false teeth etc.). The representatives of different organizations like Twitter (Ashish Bansal), Walmart Labs amongst others talked about how AI disruption isn’t something that is concerning, due to the inability of machines to adapt and generalize. There were many other interesting sessions on Day 1 one of them being a session by Yixuan Li from Facebook AI. Their problem statement was understanding the mammoth visual content generated by Facebook users to help in connecting their users with things that matter to them the most. The algorithms they ran were so huge in scale that it took them 22 days with about 356 GPUs to process the entire data. Another interesting workshop we attended was a hands on session on PyTorch using a text classification application by Yannet Interian from University of San Francisco in the Connect stage sessions. The Lunch and coffee sessions were very informational as we got to know more about the experiences of working professional and researchers working in the Deep Learning Field. Other topics that we covered through the sessions were Applying ML & NLP in Google ads (Sugato Bose from Google), On- Device Neural Networks for Natural Language Processing (Zornitsa Kozareva from Google) amongst others.

On Day 2, we started off with a morning session by Anirudh Koul from Aira who hosted the deep learning stage the day before. The session revolved around how AI can empower the blind community, he talked about his motivation for the project coming from his late grandfather. He explained how an AI empowered goggle could help the blind users to understand their surroundings better. The App controlled by a camera (on mobile / goggle) would direct the user by describing the visuals that the algorithms could decipher. This session was a part of the Ethics and Responsibility stage. We also attended panel discussions on the Futurescaping Stage on ‘Human-Centric AI: Interpreting and Adjusting to Human Needs in Human-Machine Collaboration’ with panelists from different spaces like Dimitri Kanevsky (Research Scientist at Google), Vinod B.(Data Scientist at Coursera), Dorsa Sadigh ( Phd student at Stanford University). Another session we attended was “Brand is Beyond Logos – Understanding Visual Brand” by Robinson Piramuthu from e-bay where he explained how logos impacted the sensory perceptions of viewers and how that can be signaled using neural networks. Sessions like predicting the on setting of Alzheimers using neural networks made us aware of the breadth and depth to which we could utilize deep learning in different use cases.

The conference was really fruitful in helping us broaden our thought processes for utilization of deep leaning and ML techniques. We got to interact and network with professionals from the Data Science community who shared their experiences and helped us evolve our purview. We ended the memorable experience of attending the RE.WORK Summit by visiting the iconic Golden Gate Bridge and viewing the mesmerizing Pacific!

 Last week in a Fog City convention center, the word “networking” took a new meaning. Hundreds of business professionals and researchers gathered at the 2018 San Francisco Deep Learning Summit to share their advances in the hottest subdiscipline of Artificial Intelligence: deep neural networks.

Deep neural networks, also called deep learning, is a modeling approach loosely inspired by the way our brains work. Deep learning models have been wildly successful in a broad range of tasks such as image recognition, movie recommendations, drug discovery, and beating masters at games. And the pace of progress is exponential.

Many of the industry leaders driving the innovation graced the Summit stage.  Among them was Ian Goodfellow of Google Brain, who invented Generative Adversarial Networks (which train two competing neural nets to do crazy things like turn horses into zebras). We also had an opportunity to attend the hands-on workshop on Generative Adversarial Networks where we put input images of celebrities through generator and discriminator and outputs realistic celebrity-like images that you could manipulate (such as adding blonde hair or glasses or make them smile or frown, or turn woman into man). Deepgram’s Scott Stephenson spoke about creating a “Google for sound” that can understand how we naturally speak. We also heard from Coursera co-founder Daphne Koller on about the future of AI.

In traditional Silicon Valley style, over twenty AI startups were present to advertise and recruit. We met useAIble, an end-to-end machine learning startup that offers interpretable modeling solutions for business processes. Promising technologies from Luminoso, RiseML, objects.ai and others floored us in their live demos. We even learned our ABCs from an intelligent children’s education iPhone app.

Although the conference focused on neural networking, our favorite part was the more traditional kind. We connected with founders, venture capitalists, researchers and students of the highest caliber. They infected us with a passion for tech innovation we hope to share with the MS programs in the Silicon Valley of the South, so that one day we might share our own advances on the next Summit stage.

*Article contributed by #TXMS Business Analytics 2018 Chloe Kwon and Jake Schmidt

The Master of Science Programs at McCombs is hosting our annual Women’s Breakfast September 14, 2017, and we’re excited to showcase some of the amazing female talent across the fields of technology and business. Our staff had a chance to chat with Catherine King of BHP to get her insights on where she sees technology headed in the next 15 years, what it’s like to be a woman in technology right now, and her advice to women as they graduate from their MS Programs at McCombs.

Catherine King, BHP
Business Process Specialist

Tell us a little bit about what your average day is like:

I was hired into an analytics and data science team, and my first task was assessing demands across our business units globally. There was lots of building relationships and my networks at the beginning of my career. I needed to take advantage of my first 3-4 months in getting to know people around me and to understand what projects were going on and how my projects would play a part in BHP’s strategy. I needed to learn what each group of engineers or scientists were doing, the technology they were working on, and what they needed from our analytics and data science team in order to help them problem solve. This helped me and our team provide value to those groups, but it also helped me create real connections with the people I would be working with. Coming out of school, you only learn so much in theory, and getting to know these groups in the real world gave me a better sense of what I actually needed to do.

Once I got my rhythm down, I sat on a couple projects and had the chance to become manager for one particular project. Once that happens, your day-to-day becomes lots of meetings, project planning, and budget review. It was important to me on projects that we stick to the timeline. One thing I discovered in my daily interactions was that being new, young, and energetic goes a long way, and I wasn’t expecting that! In all my feedback, I would hear that the energy and the new ideas really helped me from the start. Taking risks (small ones maybe at first!) and being bold were seen as great initiatives. Also, the daily need to translate from business language to the technical folks became huge – until I was in this role, I didn’t understand how critical it was to be able to “speak” both languages. It became integral for me to serve as translator in order for our projects to expand in scope. 

Did you always know you wanted to go into technology? What really drew you to the field or helped you choose your path?

I interned at Shell for two summers doing a service and operations role and then as a technical and competitive IT role, so I got to see both sides early. I learned that I loved the energy industry – I felt like it was such a great work-life balance, and I got to see some culture shifts in the industry across the two summers, so I knew it was a field I wanted to go into. I majored in MIS and Business Honors at UT, so I ended up at BHP because they offered me the opportunity to work in IT. The people I interviewed with set me up for pretty much exactly what I wanted to do – not too technical but also embedded in the analytics and data science team. I also got to be a part of a team that was built from the ground up, so I was really given a role that I got to be a part of creating. I got to work on foundational projects and help with requirements, so it just felt like the perfect role for me. It was so important for me to take on a role that allowed for growth but that also had a vision for where I was going with my career. 

Can you tell us a little bit about how you think technology and business will interact in the next 10-20 years?

BHP’s technology strategy is that we aim to be fully integrated and highly automated by the year 2025. The idea is that we want all of our databases interconnected and all of our sensor data automated and streamed into a local, remote operations center so you can see what’s happening and make operational changes all from your desk in the office. If it’s time to turn off a valve, you can do so with a click of a button. All of this is founded in technology but still requires people like us to do this kind of work – building the technology, running the analytics, ensuring the tech is working the way it needs to. We’re ramping up our predictive analytics so we can get ahead of issues like a truck breaking down or a pipe leaking – these are the types of things we’ll be able to anticipate because of the data we collect and analytics we’ll be running. None of this can be done without our data and technology folks. It’s prevalent in my company, but I think this is really true of any company. In order for robots and automation to work, you can have to have people who can work with the data, create the algorithms, and develop the things we need to keep this all running. 

What are some of the challenges (or opportunities) facing women entering tech today?

I feel like this is a great time for women in tech –the industry has become so aware that we need more women in tech sothere’s really an opportunity to join the conversation and the work that’s being done. It’s no longer being brushed under the rug, so now is the time for women to really come in and showcase their talent. Everyone has been very conscious of talking about it – it is only to the benefit of the industry, whether it’s analytics or technology, that women are included. Rather than seeing it as a challenge, it’s an opportunity that is opening up for women to participate in all of the amazing developments going on. I’m proud of my company, too – BHP’s CTO is a woman, our Vice President of Petroleum Technology is a woman, and so the work I’ve been doing is in the midst of many incredibly accomplished women.

What advice would you give to women entering a career in technology?

It goes back to something I read about a few years ago- how people go about applying to job postings. The article stated that typically men will see only one thing they can do on the job posting and they will apply. Women might see 19 out of 20 things they can do and one they can’t do, and that one single skill or qualification that they don’t meet will discourage them from applying entirely.

One year into my career, I applied to a job that asked for 5 years of experience and I ended up securing that position, so if you’re excited about the opportunity – just go for it! Apply for the ones you don’t think you’re going to get, apply for the ones that excite you, apply for the ones that scare you (just a little bit!). I’d absolutely encourage women to take the job that scares you the most. Don’t only apply to the safe jobs that aren’t challenging or where you won’t learn anything. I found right out of school that I was applying to any job postings I thought I was qualified for …even if I wasn’t interested in the work. That’s not the way to go – I can’t imagine doing something day in day out that I wasn’t interested in because ultimately, that’s where I spend (and you’ll spend) most of your days. It’s not school, it doesn’t change every semester – you stick with your manager and your role, so you’ll get the most out of what you do if you are interested and challenged by the work.

I was so excited to start work, and I think I was expecting to come with a wrapped, bow-tied project of ‘here’s what you’re going to do, here’s who is going to help you, go do it’. Sometimes it isn’t like that – for me, it was a few months of figuring it out, but it’s a time I chose to capitalize on. I would encourage you to set up meetings to introduce yourself to new people and learn from others, sign up for training courses, and take advantage of that time to really prepare yourself for what’s coming. You also never know who you met with that might be helpful in projects you’re working on later on. Having those relationships and networking is very real, so take advantage! In some ways, that can be so much more important than those technical skills you learned in school.

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The Texas MS Programs would like to extend a thank you to all of the women attending our Women’s Breakfast this Thursday, September 14!