Cognitive Science Alumni

Our Department isn't defined by the physical space we occupy on the campus of UCSD - it is defined, rather, by the remarkable individuals who make up our community. The lifeblood of any community is its people, and this is especially true of a community that relies on ideas and innovations. In recognition of this, the Department of Cognitive Science at UCSD presents our new "Alumnus/Alumna of the Month" feature.

The goal of the Alumnus of the Month program is to celebrate some of our outstanding Alumni, while giving all students - past, present, and future - an opportunity to meet some of our graduates, and to see some of the amazing things that people from our community have accomplished - in the field of Cognitive Science and beyond.

The hope is that it will both put a more 'human' face on Cognitive Science, as well as be a testimony to the wide range of interesting things that one can do with a Cognitive Science background. In addition, of course, it's a means for CogSci alumni to see what other alumni are up to.

Alumnus of the Month: Rob Liebscher, 2005

Rob Liebscher

Rob Liebscher is a Research Analyst at Adobe Systems, Inc. in San Jose, CA. Hailing from Philadelphia, he studied computer science, neuroscience, and psychology at the Pennsylvania State University before heading to San Diego in 1999 to pursue his PhD in Cognitive Science at UCSD. Intellectually curious to a fault, he studied too many different topics before his fourth advisor, Rik Belew, forced him to focus on his dissertation. He graduated in 2005.

He loves living and working in Silicon Valley. Particularly attractive to him are its inhabitants, a blend of creative geniuses, visionary workaholics, and intimidating tech geeks. "Walk into any coffee shop," he says, "and listen to the conversations. You'll find companies being formed and technologies being invented. There is a non-stop, forward looking aura to the region that keeps me awake and thinking, always thinking."

In his lack of spare time, Rob plays several sports and fights pseudoscience, a crusade that began with his 2003 lecture The Evolution of Creationism in the Cognitive Science WA lecture series at UCSD. He has won every slam dunk contest in which he has participated.

An Interview with Rob Liebscher

by Jenny Collins

What did you work on when you were at UCSD?

I can best answer this with a list of keywords. Here's what I studied in my first few years:

Complex and adaptive systems
Evolutionary theory
Behavioral ecology (thank you, John Batali)
Artificial life
Human-computer interaction (thank you, Jim Hollan)
Machine learning
Distributed cognition (thank you, Ed Hutchins)
Psycholinguistics (thank you, Jeff Elman)

A motley assortment of topics, and I found it difficult to concentrate my efforts on just a few of them. Through a few felicitous events, one being my decision to take Robert Kluender's excellent course on the evolution of language, I began to gain some focus and pick up more keywords:

Language evolution
Historical linguistics
Lexical semantics
Statistical natural language processing
Computational linguistics
Information theory

At this point, I knew my dissertation would involve computational aspects of language evolution. Then Rik Belew joined the department and became my advisor. He helped me to realize that there were practical applications waiting to be invented from the linguistic framework I had been developing. My dissertation eventually produced applications in:

Information retrieval
Text categorization
Document clustering
Keyword assignment
Link prediction
Citation analysis

If you'd like to learn more, or are strapped for cash, I put a $1 bill in the hardcopy version of my dissertation in the library.

What was the most valuable thing you learned while in the Cognitive Science department at UCSD?

Since you force me to choose a single most valuable thing, I'll say it was the process of doing science. Now I'm going to cheat by expanding this one thing into some of the many skills it encompasses:

Posing a problem
Doing background research to become familiar with the problem
Gathering evidence
Designing and performing experiments
Analyzing data
Forming conclusions
Communicating results

These are very valuable skills to have regardless of the career one seeks after graduate school.

What was your career path after graduate school?

I headed north to Silicon Valley, which until then had been a mythical land of enchantment for me. Turns out it's a real place. I joined the research group at a small company that specialized in Customer Relationship Management (CRM) software. A typical CRM system allows a company to author documents and publish them to the web or an internal site and provides a search engine for retrieving the documents. When you seek technical support or customer service for a product at the manufacturer's website, you're using a CRM system. Other bells and whistles include forums, document lifecycle management, various databases, and analytics to determine system performance. I focused primarily on search and a bit on analytics toward the end of my tenure.

I had a lot of fun there and worked with a great group of people to develop features that would appear in future products, but about a year after I arrived, we were acquired by a holding company. This is a very frequent occurrence in the Valley: in just over a decade of existence, Google and Yahoo! have each absorbed more than 50 smaller companies.

Since May of 2007, I've been with Adobe Systems, Inc.

What do you do at Adobe?

I am equal parts researcher, engineer, and analyst.

As a researcher, I devise metrics to make sense of massive amounts of data generated by the interactions between Adobe and its customers. Most of this data involves web search, but I also work with customer service and tech support phone data, surveys, sales data, etc.

As an engineer, I implement these metrics by developing code to convert raw data into useful information. Most of this is rapid prototyping work. When I have a hypothesis about a particular measure that might be of interest, I hack something together in a scripting language that can very quickly process and manipulate gigs of raw data, and generate output to answer questions such as: What percentage of users who purchased a product came to Adobe's technical support site within three days? How often do people who post to forum A also post to forum B? Which query terms engender the most frustration for users due to a lack of useful search results?

These are all hypothetical questions, of course... I can't reveal any real questions--nor why we would want them answered--without a non-disclosure agreement. Drop by sometime to sign that.

After transforming raw data into useful information, I shift into analyst mode. This means that I use the information to create actionable plans to improve the ways that Adobe and its customers communicate. Analysis of massive amounts of user behavior can suggest experiments such as presenting certain web search results in a particular format, routing calls to technical support through a new series of channels, or creating a new set of documents or online forums to meet user demand.

Are the skills you are using now things you learned in grad school, knew before, or have been trained to do at Adobe?


Much of what I learned at UCSD was in doing my dissertation work under the tutelage of my excellent advisor, Rik Belew. The knowledge of information retrieval, and statistical natural language processing in general, has been very useful.

Other skills, such as how to formulate a business case and evaluate a business decision, I've picked up since beginning work. I had to learn how to justify what I believe is relevant information and convince people of why a decision is a good one within the contexts of generating revenue or saving on costs. The process of drawing scientific conclusions and presenting scientific results is actually excellent preparation for this.

Another significant transition came in recognizing how much the people I work with come from very different backgrounds and possess very different skill sets. In the cognitive science department, if I wanted to start a conversation about the lateral geniculate nucleus, I wouldn't have to begin by establishing that the brain has four lobes. Now my coworkers and I have to first find common ground. It can take a few meetings on a project just to establish a common vocabulary. I remember having a conversation similar to the following with a machine translation expert:

MTE: So first we need to loak the XML documents--
Rob: You need to what?
MTE: We need to loak the documents.
Rob: You need to what the documents?
MTE: Loak.
Rob: Look?
MTE: Loak. [pause] Localize. It means to translate. Sort of.
Rob: Oh.

What is the most rewarding aspect of your job?

I'm privileged to work with an amazing echelon of people. The work itself is rewarding, but I get the most satisfaction out of having great colleagues with whom I get to think and analyze and laugh and rage and code. Most of them have interdisciplinary training, so they appreciate the necessity of looking at a problem with different techniques and tools.

And if I might sneak in another rewarding aspect, it's the data. In graduate school I struggled to obtain reliable data for building computational frameworks of human linguistic and information retrieval behaviors. Now I am completely awash in data--more than I can possibly analyze--and it offers me the opportunity to learn about the (sometimes very surprising) ways that people behave en masse.

What do you like to do when you are not working?

I do a lot of fiction writing (pseudonymously). I had always wanted to pursue this more in graduate school but had difficulty finding spare time. I never turned down an opportunity to play any intramural sport at UCSD, and I've found ways to continue this in recreational leagues. I'm also active in the skeptical movement and science education. And I protest things.

What advice would you give to current students?

Dude, chill. So your paper was rejected. So what? No scientist, living or dead, has gone through a scientific career without having at least one submission rejected. Persevere.

If you intend to enter industry: get to know anyone and everyone, and tell them about your skills and interests. Jobs in industry are often filled because Manager X knows Person Y is very smart and can quickly learn the skills necessary to do the job. Matching jobs to appropriate candidates is a very difficult task when the numbers of each are in the millions, so those who do the hiring often short-circuit the process and just ask the people around them if they happen to know any Persons Y or Z who have skills s, t, and v.

t is most important. You don't really need to know v.

To Nominate Someone

To nominate someone as an alumna/alumnus of the month, or if you would be interested in being featured yourself, please contact us at