Monday, September 17, 2018

Steven Guggenheimer: From UC Davis to Microsoft and Everything in Between



Steve Guggenheimer, class of '87, is now Corporate Vice President
for AI Business at Microsoft.
When you think of the many ways that physics changes the world could you imagine also changing the world through software? In Spring of 2017 we heard from UC Davis physics graduate Steve Guggenheimer. He discussed his path from UC Davis to being a corporate vice president at Microsoft where he leads teams of technologists and business leaders from all over the world.


Here we provide a brief bio, key points from his presentation, and then a partial transcript of his remarks.


Guggenheimer’s career path in a nutshell:
  • Earned a bachelor’s degree in applied physics at UC Davis in 1987, with emphasis on lasers
  • Did hands-on research work in high energy physics during summers at UC Davis
  • Interned at Livermore National Lab
  • Did contract research for Livermore through ARACOR after graduation
  • Worked at Spectra-Physics Lasers
  • While at Spectra-Physics earned a Master’s degree in Engineering Management at Stanford University
  • Interviewed at Microsoft on a whim
  • Worked in lots of departments at Microsoft to gain different skills
  • Became Corporate Vice President of the OEM Business, then CVP and Chief Evangelist, and later Corporate Vice President for AI Business
Key points from the presentation:

  • Get work experience during your university time; it is crucial for post-degree employment.
  • Try jobs outside of your comfort zone to gain new skill sets.
    • For example, Guggenheimer went from working hands on with lasers, to coding software, to marketing the software he was working on, to marketing other software, to working with customers and partners, to managing large teams, and more.
  • Use your time at university to learn how to learn.
    • If you don’t know much about a field you want to go into, read books, do research, and talk to people in that field.
  • Learn how to communicate and use your connections with people effectively.
    • Help people around you -- it will usually pay off.
    • Take classes about writing, communication, and business during university.
  • Be self-aware: use your early work experience to figure out what you like and don't like.
    • Don’t expect to have it all figured out as soon as you graduate.
  • Keep a growth mindset, be careful not to fall into a stagnant routine.
    • Read ‘Mindset’ by Carol Dweck.
  • Keep up to date with relevant news and publications, as technical fields change rapidly.
    • If you work with technology, take time to understand how it works and why it’s useful to consumers.
  • If you’re looking to build a startup, many large companies have resources available, so don’t be afraid to reach out.
  • Everyone has different strengths and weaknesses: make sure to play to your own strengths and identify your weaknesses.

Partial Transcript:

Let me talk to you about my journey. When I was here, there were two things that became the norm: either go into the navy and work on a nuclear sub, or go get your PhD and become a professor. Neither of those was the right fit for me. And so it was a bit of a struggle to say, ‘Okay, what do you go and do?’ I’d earned this awesome degree, but when you go through the help wanted, at least in those days, there wasn’t any, ‘Hey: Looking for Someone with a Physics Degree.’

I’ve had the opportunity at Microsoft to work through all three generations of our CEOs: with Bill, with Steve, and with Satya, and I still work pretty closely with them today. And I would not have gotten there without the background I got here.

So how did I get to where I am today? It all starts with work: real world experience. I’ll tell you this: if you go out with a B.S. in applied physics or a B.S. in physics and you haven’t done any work, nobody will even talk to you, you’ll be flat out of luck. When I was here, sophomore year summer, I spent the summer working, helping make muon detectors and building circuit boards for the linear accelerator at Stanford. I did that for free, just to get experience.  I spent my sophomore summer here working in a lab, building even larger versions of the circuit boards. Junior year, internship at Livermore. By senior year I was specialized on lasers. I got my government clearance for Livermore, and some of the professors here were trying to do research at Los Alamos who didn’t have the clearance, so I actually could go run the experiments at Los Alamos National Labs for them.

After I graduated, I struggled, despite the work experience. I sent out at least 30 different resumes to different companies with no success.  I had some interest in going back to Livermore, but because I didn’t have my PhD, the best I could do was mech tech: a mechanical technician. I like working in the machine shop, but I aspired to a little more. So I found that there was a research company called ARACOR that did contract research back to Livermore, and I took that job. I was trying to get into lasers, because that was my focus here, all my applied physics classes were on lasers. There were some good professors here. Then, because I’d worked at Livermore, and because I’d actually spent some time building a laser lab there, I got an interview at Spectra-Physics. And because the guy who interviewed me had actually put that system together, I got a job there.

I spent five years at at Spectra-Physics with lasers, working in the lab, building things, doing marketing, etcetera, and I went back to grad school during that period of time. I got my Masters, and then it was like ‘hmmm.’ Actually, that was a funny point in time. The number one thing the Masters taught me was that I could actually work in other industries. Holy crud! I thought I’d never do anything but lasers. So I thought, ‘It would be nice to move to the northwest, I like it up there.’ Traffic here kind of sucked in those days, in the Bay Area.

I realized I wanted to work for a number one or two company, and I wanted to change industries before I got too far down the path in the laser industry. So I applied to Nike and Microsoft. Turns out physics, and my background, was closer to software than it was tennis shoes, so I went up to Microsoft. Didn’t know anything about it. I mean, I’d worked with computers; I was using an Apple at the time, we ran LabVIEW in the lab, but really, no clue what I was getting into. But that ability to translate technical to customer, which I’d done a lot with the laser side, that was what got me in. Over my career I’d worked in lots of different areas. But we’ll come back to that, that slow transition from being more technical and more hands-on to being essentially a translator between the technical side and what customers want to do, both in building products and then ultimately in building ecosystems. And that’s been the journey. Let me go through a couple other points, and it should come to life.

So my uber point on this is, and we can come back in q&a: work experience. Like, if anyone comes to me, I don’t care whether they’re out of college, or high school, or grad school. If you haven’t spent some time doing work, like building things, and it doesn’t have to be perfectly aligned to your major, you have to do that. And the world’s pretty good today about pushing people to internships, but in my era, the reason we ended up heading into grad school or heading into the navy was because it was not pushed as hard, getting that work experience. That allowed me to differentiate myself. I also got lucky: frankly, a chunk of it’s luck.

Another point is to take a chance and try new things. For me, that was moving to the northwest to go work for a software company.

In my career at Microsoft, I’ve gone back and forth from building products to actually working in the field organizations; living in the UK, trying different parts. I always coach people to stretch as far as you can away from what you’re doing today, as much as people will give you that latitude. And it’s hard. Take my initial starting position of doing technical marketing for Windows. Well, they might let me do partner marketing for Windows, or technical marketing for Office. They’re not going to let me go run the small business ecosystem. So you take these one step jumps, one degree of separation from what you’ve done. Sooner or later, somebody will let you take two degrees. Then three degrees. Then finally you get known for a skill set: hiring people, external speaking, leadership, the ability to translate technical to business, how to bridge between developers and customers. And then they’re willing to take chances: ‘Okay, go build the hardware ecosystem,’ ‘Go build the software ecosystem.’ I recently moved into the AI team to go help build a new business. That ability to do new things comes from stretchong yourself: you go as far away from what you know how to do and your comfort zone as people will let you.  They’re going to try and keep you somewhat close, so you’ve got to press them to led you stretch. Sometimes I took jobs that were more sideways, and people were moving up their career faster. But in the long run, that stretching has paid off, at least for me.

Learn how to learn. Physics doesn’t need to be the end goal. It can be; it could be an awesome career, but what I do today, what physics taught me, at least over my course, is how to be fairly confident to go learn anything. And that ability to learn, to know when you actually don’t know and to be comfortable understanding what you don’t know, and be willing to go ask and do the work necessary to learn -- physics is really good for that. And then the fundamentals, that ability to translate technology to customers. I don’t care whether it’s artificial intelligence,, how you build a game, or anything else. That core fundamental understanding that you get from your physics is incredibly valuable. That ability to use it, to translate it to something else, takes a little work from each one of you. So that comfort level, learning how to learn, and then wanting to stretch yourself and figure out what you have passion for, is the key.

The next thing is people matter. I live on computers and phones like everybody else, but face to face, in person, the network matters as you’re going through all those little work experiences along the way. The person that ultimately looked at my resume at Spectra-Physics had actually done some work at Livermore, in the place that I’d worked. The reason the person at Livermore hired me was because I was studying applied physics with lasers here at UC Davis, had that connection, and I’d worked in some of the labs here. So I had experience. Those little red threads that tie together, they matter. And people matter. So as you’re going through even this point in your life and the rest, what you do on social networks, what you say to people, where you piss people off, where you make friends, it has a long tail. So my advice to you as you’re younger: do less of what I did. I had a really sharp edge and I created challenges and hurdles for myself along the way, but fortunately I created some red threads as well.

And then there is self awareness. Do what you love and love what you do. That’s probably the hardest thing. You’ll learn more about what you don’t like in your career in the beginning than what you do like. It’s pretty lucky if you stumble upon it in college or in your first or second job, or like me fairly early in my career at Microsoft. By stumble on it I mean to feel that your current job is it, it's the one you want to do your whole life.

You have to be self aware enough to say ‘Ah, I like this part of what I’m doing, I don’t like this part, I’m going to go tune this way, or I like the way this person manages their leads, I don’t like this.’ And tune and tailor. Physics is about learning and teaching you how to learn. You have to have that self awareness to always be learning, and then that comfort level to figure it out. Figure out what you want to spend your energy and your time on. Because if you don’t love it, you won’t be great at it. And it may feel obvious right now, but ten years in, five years in, it may be less obvious. Take risks, go where you’re not comfortable at times to learn more. I’m fortunate that I get the opportunity to do what I want. I ran the ecosystems. I got the chance to go work on AI. I sit on some boards now and generally help startups when I can. But that’s through a long journey. Also, I know what I’m good at and I know what I’m not good at. And that makes a difference.

I am going to show you one video that’s related to physics and doing things that you love. One of the things about where I work is that we get to make a difference, good or bad. You can hate us, you can love us, you can hate Google, you can love Google, etcetera; the impact on society is pretty unique. That ability to affect society; you’re in a generation and an era where you can. This is a video from one of researchers that we get to work with that bring some of these things to life:



We live in a pretty incredible age. I spent the last 20 years watching computer storage and networking get more ubiquitous, better, more available. The ability to use tools now: artificial intelligence, IOT, big data, etcetera, to amplify human capability, that’s pretty impressive. It will continue to be, way past my time. It will be for your generations and the generations after too. But shoot high: don’t underestimate. Haiyan’s a researcher at our Cambridge lab, and we do a lot of fundamental research. You guys have a chance and a set of tools to work with that we didn’t, and we had a set of tools to work with that the generation before us didn’t. You can do amazing things, you can work places where you can sort of have real impact on society at any level. It’s heavy, but positive at the same time.

[applause]

Thursday, January 19, 2017

Tyler Otto on Finishing your PhD in Physics and Going into Data Science

Tyler Otto is a data scientist at Reddit with a PhD in experimental 
condensed matter physics completed at UC Davis in 2013. 
Tyler Otto completed his PhD in Physics at UC Davis in 2013 with experimental condensed matter physicist Professor Dong Yu. Formerly the head of data science at Hipmunk, Tyler is now a data scientist at Reddit. He lives in Davis, has spoken with our students in our alumni seminar series, and is generally interested in contributing to his alma mater. 

Recently Tyler, Professor Ethan Anderes (of UC Davis statistics), and I met with six of our current graduate students in physics to hear about their research projects as we looked for ways we could potentially help with the data analysis challenges they face. After the meeting, Tyler realized one way he would be happy to help out would be to offer his thoughts about how to transition from doing a PhD in physics to working in the field of Data Science. The rest of this post is the entirety of the resulting email from Tyler to the students. 


I want to start with some of the concerns that people have about freshly minted PhDs.

1.     Overly academic: As a PhD student, you worked on projects that took years to complete, and you were driven by trying to understand the most challenging phenomena. Contrast this with what most businesses need, quick insights that can be acted on immediately. Most companies are not at the level of sophistication that your respective field is (your field of science has likely been around centuries, far longer than most businesses). In physics terms, most businesses are still learning their kinematics equations, but many new candidates talk about Quantum Mechanics.
2.     Communication skills: You have been surrounded by people that speak your same language (science, I mean), and are generally deeply knowledgeable of the types of things you are working on. Similarly, when giving a talk, you are used to presenting to people that want to understand every detail of what you have done and why you have done it. As a result, academic researchers often develop a communication style that is very different than a typical person in industry. As a Data Scientist you will work closely with marketing teams, engineers, business development, VPs, etc. These people are all very smart, but require that you learn to adapt your communication style.
3.     Too specialized: This is the easiest problem to address. You spent the last 6ish years thinking about a particular set of problems, and likely addressing them with a handful of tools. For many of us, these tools did not include lots of programming, machine learning, statistics, etc. How the hell are those hundreds of hours in the clean room, or in front of an AFM going to make you a qualified data scientist!?!

Let’s address ways that you can convince potential employers that their concerns do not apply to you

1.     Focus on impact: Many inexperienced candidates tend to talk a lot about tools, and interesting problems. Instead, try to identify potential problems that the company may face, and explain what you can do to make an immediate impact on this problem (this also shows that you did your research and are somewhat knowledgeable of the company). I also suggest showing that you can scale up your complexity. “Here are some simple ways I would initially approach this problem for quick results. If we see the impact we are hoping for, investing time in a more complicated solution such as (insert buzzword)”
2.     Have an elevator pitch: You should be able to explain, in 30 seconds or less, what your PhD research was, why it was important, and what contribution you made to the field. This sounds hard...it’s not! Some people may ask you to go into some more details, this is not an excuse to to show off how smart you are. Spend a few minutes (or less) touching on some of the broad strokes of your research. If you did something cool, like work at CERN, feel free to mention that (not in a bragging sort of way). This is not a conversation with a fellow researcher, this is a cocktail conversation. I believe this is the hardest problem to address on this list. We have developed our communication style over years, it is going to take a lot of work to undo some of these habits. On top of being the hardest thing to change, it is the biggest reasons that I reject candidates. START PRACTICING NOW!!!
3.     Identify your core competencies: No one is hiring a recent PhD in physics because they are a machine learning expert (unless you are). They are hiring you because you are smart, have excellent critical thinking skills, have worked in collaborative environments, learned to ask hard questions (and answer them), and more. Convince yourself of this and be able to articulate it to a potential employer.
4.     Learn the tools (but don’t be a cliché): This is what most people focus on the most, but it is the most easily solved of the problems that new candidates face. Inexperienced candidates tend to throw around a lot of buzz words (neural network, machine learning, collaborative filtering, etc). This is the equivalent of interviewing a contractor based on their discussion of hammers, drills, saws, and levels. This should not be the focus of your discussion, but it is important to indicate that you have some experience with these tools. An exploding number of online resources has made it trivial to gain some basic facility with the tools you will be expected to use. There are tons of other sources sharing which skills you need as a data scientist. Do a bit of research and focus on the ones that come up repeatedly. Focus on the basics before getting more sophisticated. You will likely do more basic statistics than you will machine learning. You will certainly do more basic coding than anything else.

Finally, I would like to share my thoughts on how you should proceed from here and what you should expect:

1.     Start figuring out how to apply data science tools in your research. It will make for a better conversation during your application, and will likely improve your research.
2.     Start applying for jobs right away. It will give you an idea of what the application process looks like, and it will help you tailor what you focus on. It will also help you work on your communication skills. It will also help you optimize each of the hiring process.
3.     Work with your career center. They will help you with your interview skills, resume, etc...this is an excellent resource!
4.     Expect it to take 6 months or more to land your first job. Don’t get discouraged.

5.     Go to different meetups. They are boring, but they are a great way to meet people in the field you are interested in, and a good way to continue to develop your language.