October is National Medical Library Month and this year the UCSF Library is celebrating with a series called Library Conversations. Communications Manager, Kemi Amin, sat down with four Library staff members to discuss everything from books to new technology to childhood aspirations, and everything in between.
This week Kemi chats with Data Science Initiative Scientific Lead Karla Lindquist. We discuss art and science, women in science, and touch on Frankenstein.
Karla works with researchers, postdocs, and students at UCSF with their bioinformatics or statistical challenges and teaches a collection of programming classes through the Library’s Data Science Initiative.
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Kemi: [00:00:03] Hi, Karla. Thanks for meeting with me today. How are you doing?
Karla: [00:00:05] Yes sure. I’m good. Thank you.
Kemi: [00:00:08] Okay, so I think we’re just gonna jump into it.
Karla: [00:00:14] Okay. Sounds good.
Kemi: [00:00:14] What did you want to be when you grew up…as a kid? Did you have a journey of different things, or…
Karla: [00:00:23] Yes, I did. I started out wanting to be a musician, like my mom or an artist. In particular, I liked photography. And then when I started to like science I thought I wanted to be a doctor like my uncle. And yes, so I still love all those things science and music and photography but science seemed like a better career choice [laughs] for a number of reasons.
Kemi: [00:00:55] So, at what point did you make that decision?
Karla: [00:00:55] In college actually. Yeah, yeah. So, I started out as a music major, realized that, yeah, the career choices were limited unless you know you were in the upper echelons of the talent spectrum which I thought I may not have been, so decided that to choose something more practical.
Kemi: [00:01:17] Interesting. When you mention that your mom was a musician, what kind of musician?
Karla: [00:01:20] Pianist and organist. Yeah. So, I learned to play from her from probably about the time I was able to sit up I was playing the piano so…
Kemi: [00:01:33] Interesting. Starting here at UCSF, I am learning that there is an interesting connection between scientists and the arts. So, I think there’s something.
Karla: [00:01:45] It makes sense. Absolutely yes. Yeah.
Kemi: [00:01:49] Cool. So, can you talk a little bit about your career journey, so from college you decided to go from music to science?
Karla: [00:01:56] Biology, yeah.
[00:01:59] After I finished college I came out of the Bay Area not knowing exactly what I would do. Not having a job but thinking I would be here for about a year. The biotech industry was booming then. It was 1992. So, I actually found a job immediately. Luckily [chuckle]. In a lab. So, I did that for a couple of years I ended up working at the Human Genome Project, also continuing to do wet lab work and that’s really where I became interested in analytical sciences and someone suggested that I study statistics. So, I took some courses in statistics at Berkeley, ended up loving it going to UCLA for a master’s degree in biostatistics.
Kemi: [00:02:48] So, what is it about statistics that you love? Because I remember taking statistics and not loving it [laughs].
Karla: [00:02:55] Right right. Most people don’t [laughs] love it.
[00:03:01] I don’t know. I think it’s… You know some of it is that math came sort of naturally to me. And I think that’s they have something to do with the musical background. I think it is a very mathematical art form. And… I don’t know, I liked the fact that it was applied to something that I was interested in biology. So pure math didn’t interest me so much because it felt isolated from, you know, from actual practical applications, so that’s why I chose statistics.
[00:03:45] And I also thought that it would be interesting to continue studying genomics which I was doing at the time when that was suggested to me.
[00:03:58] And the reason that I thought that would be interesting is because we were just sort of getting towards the end of finishing the first human genome, the sequencing and you know we knew that there would be more sequencing happening once the first genome was done and that produces obviously a lot of data. And so, I was really interested in applying it to that particular kind of data.
Kemi: [00:04:27] That’s exciting.
Karla: [00:04:28] Yeah, very exciting time, yeah.
Kemi: [00:04:30] I don’t think I knew that you were involved in that work.
Karla: [00:04:33] Yeah and well so after I did my master’s degree I came back to San Francisco and that’s when I joined UCSF in 2001. And I did not end up doing genomics work but I did continue doing statistics. And I worked in that division of geriatrics for about eight years as a statistician. So, I did a lot of clinical and epidemiological statistics.
Kemi: [00:05:13] What did you learn while working in that department?
Karla: [00:05:15] I learned why people say that statistics is an art form.
[00:05:22] And so that really did help me maintain my interest in it. And I also just continued to learn more and more about statistics. I also learned a lot about research and in general. How to design a good research study, kind of what the challenges of it are, what some of the biases are embedded in it. And just in general about working on teams and you know, following a passion and discipline. All of that stuff [laughs].
[00:06:08] I also learned that I was I still had an interest in genomics. And so that’s why I went back to school to get my PhD in bioinformatics, which kind of combines statistics applied to genomics, along with computer science.
Kemi: [00:06:25] Wow, that’s a dynamic collection of expertise there [laughs].
Kemi: [00:06:25] And so, what is your title here at the Library?
Karla: [00:06:35] So my title is Scientific Lead for the Data Science Initiative.
Kemi: [00:06:42] What does that all entail, what does that look like? Actually, let’s go back a bit, how do you find out about that position and what interested you about the Library?
Karla: [00:06:50] I actually found out about the position on accident by running and to the person who held my position previously.
Kemi: [00:06:59] Nice!
Karla: [00:06:59] I knew her a little bit from having been at UCSF for a while. And yeah I just ran into her at a conference and found out that she had moved on and that the position was open. So I talked to her a little bit about it.
[00:07:20] I’ve always loved libraries. First of all, my grandmother was a librarian and I also thought it was a unique opportunity to be able to use all of the skills that I’ve been developing over the last 20 plus years.
[00:07:36] And at the same time be able to help people with their research and continue to learn about different areas of research and to have sort of an immediate impact by helping them find resources to do that.
Kemi: [00:07:54] This kind of goes into the describing your title or your position here at the Library. Do you feel like you’re using all of the skills and things that you’ve learned?
Karla: [00:08:04] A lot of them.
[00:08:07] Yeah, definitely, especially when I’m doing consultations which is one aspect of my job that I really like because people could ask me, you know, a statistics question or a genetics question or you know, any kind of bioinformatics related question or like a computer programming questions so it’s really a wide range of issues that people bring to me. So yeah that definitely keeps me on top of things [laughs] and so does teaching and just interacting with other departments and institutes at UCSF that are also here to support researchers.
Kemi: [00:08:59] Does your job have a typical day?
Karla: [00:09:02] Not really [laughs].
[00:09:06] So, I would say a typical week might be… half of it might be meetings and the other half, or maybe 25 percent of it is related to teaching or consulting and then another 25 percent might be doing a little bit of research. Whether it be to help somebody that I’m consulting with or for my own, you know, projects that I’m trying to get going.
Kemi: [00:09:38] And yeah, that sounds about right. I think everyone in the Library has all of these different aspects. And speaking of that, do you work with any departments closely or have you within the Library?
Karla: [00:09:46] Within the Library? Yeah. The Ed Research, formerly known as the Ed Ref team, I think, has recently joined with the Data Science Initiative in the group meetings, weekly meetings.
[00:10:07] And so part of the reason for that is that they also do a lot of consulting and teaching. And there’s obviously some content overlap there as well. They help researchers find resources to do that research so do we. And yeah, I mean I’ve been trying to work with other people from different parts of the Library as well. Definitely some of the people who do like data science-y stuff.
[00:10:37] You know, some of the people who are doing are doing digital archiving and or programming and that sort of thing.
Kemi: [00:10:46] Yeah, definitely overlap there for sure. Do you see any opportunities? Particularly with Education & Research, are there any opportunities there that you can see down the pipeline?
Karla: [00:11:00] Yeah, yeah. Well, I think, you know, everybody should and will probably have to know some data science-related skills. So yeah, I think doing more of our classes for just for library staff.
[00:11:19] Like recently we did the machine learning class for Library staff.
Kemi: [00:11:24] That was a lot of fun actually, I enjoyed learning about that.
Karla: [00:11:25] Good, good.
[00:11:27] But I would also like to learn from other people the Library more about what they do. I’ve been trying to do that kind of here and there…
Kemi: [00:11:34] I’ve seen you do a shift in the Makers Lab.
Karla: [00:11:40] Yeah, yeah, no that’s been really fun. I do those once in a while. So yeah, I think for sure it’s.
[00:11:50] It seems like people are working together more. I mean I’ve only been here nine months. I know that’s always been the case to some extent. I think that’s a new emphasis that I think is really exciting.
Kemi: [00:12:06] And what about outside, like, within UCSF what are the departments that you work with closely?
Karla: [00:12:07] I work closely with the Institute for Computational Health Sciences, CTSI, various people from different grad division programs, because of my research history people from the Cancer Center, I’m still in touch with in terms of just what they’re doing and go to seminars now and then to stay on top of it. And I work, well, through my consultations and teaching primarily with students and postdocs, so they can be from any department both clinical and grad division.
Kemi: [00:12:44] Yeah, that’s interesting because I think thing when the Data Science Initiative was first established and I think right now maybe a bulk of your audience, maybe, that you serve are maybe postdocs and grad students?
Karla: [00:12:57] Yeah.
Kemi: [00:12:58] But there is a real need and benefit for professional students working with you. Can you talk a little bit about in what ways?
Karla: [00:13:06] Yeah, I’d like to work with them more, I think through my consultations I have realized that people who are going through clinical training are often required to do research projects which I wasn’t aware of before I started this job. And, yeah, they do need a lot of help. I think they’re kind of expected to do this in between their rotations and clinical training and often they have you know a couple of months to try to write a paper or whatever. So, you know, that doesn’t really afford them necessarily the time to take a full course and how to do research or gain the technical skills that they need for that. So yeah, I mean I would like to find a way to help those students more, you know, aside from just the one-on-one consultations that I do. And so, one promising direction that some pharmacy students have taken is to…they took it upon themselves to form a small group of data science-interested students who were now in the process of teaching a short series of courses on data science to them.
[00:14:22] And so they’re clinical you know pharmacy students who are just interested in it. So, I think doing more mini course serious like that would be really helpful.
Kemi: [00:14:36] So shifting gears a little bit, what books are you reading right now?
Karla: [00:14:41] I tend to do a lot of reading articles and either you know scientific articles are magazines.
[00:14:50] But let’s see, in terms of books…the last one I read was a completely trashy spy novel, that like in January when I was on vacation. But I do have two books loaded up into my iPad now that I’m gonna start reading soon. One is Dreams from My Father by Obama. I’ve been wanting to read that for a long time. And then recent Pulitzer Prize-winning novel Less. I think it’s supposed to be very good.
Kemi: [00:15:23] Yeah, that’s what I hear, I haven’t read it yet.
Karla: [00:15:24] Okay, yeah, I got those cued. Hopefully, soon I’ll start those, one of those.
Kemi: [00:15:30] Nice. Can you mention the trashy spy novel?
Karla: [00:15:31] I don’t actually even remember [laughs] that’s how trashy it was.
Kemi: [00:15:36] That’s so great!
Karla: [00:15:36] Yeah It wasn’t worth remembering [laughs].
Kemi: [00:15:41] Was it something that was a guilty pleasure or by accident?
Karla: [00:15:41] Yeah. Totally guilty pleasure but also by accident. You know I happened to be on vacation with my in-laws and my mother-in-law had just finished it. I’d forgotten to bring a book with me so it was there so I read it [laughs].
Kemi: [00:16:04] There’s a whole, like, population of people who love romantic novels or mystery novels, so yeah, I totally get that.
Karla: [00:16:08] I do as well. I love the trashy mystery novel.
Kemi: [00:16:12] Yeah, yeah. Nice.
Kemi: [00:16:14] As far as your top three favorite books of all time…
Karla: [00:16:20] Of all time, definitely Frankenstein Mary Shelley, Les Miserables is by Victor Hugo, probably Orlando by Virginia Woolf.
Kemi: [00:16:30] Yeah, okay. And can you talk briefly about why each book is so important to you?
Karla: [00:16:35] Oh boy. Well I read Frankenstein first when I was very young and it just made a really strong impression on me because I think it was I identified with Frankenstein just the character Frankenstein. Just feeling… I guess you know, I was very shy was very much of a loner and sometimes felt isolated because of that. And so, I identified with, you know, him sort of feeling like an outsider and, you know, kind of looked at by other people you know, look looked… well, I guess he was tall, so look down upon [laughs]. Criticized or with fear or misunderstanding.
[00:17:20] So I identified with that at a very young age.
Kemi: [00:17:24] Yeah, I totally get that [laughs].
Kemi: [00:17:25] Yeah. And actually, I mean I think that theme is also in Les Miserables is just misunderstood like feeling a little bit outside of the norm. And actually, I think there’s also some of that in Orlando as well. Not to say that I you know feel like a persecuted here I think, but I guess I’ve also always identified with the characters who feel a little bit outside of whatever the norm is.
Kemi: [00:18:02] Totally and that’s the beauty of books in their specificity they speak to universality.
Karla: [00:18:02] Right, right, yeah.
[00:18:06] Well and on another level, I just appreciate the writing of all these authors are just almost poetic writers and so, just the beauty of their art is like yeah, it’s also what captured me with all these.
Kemi: [00:18:27] What initially attracted you…I’m getting back to work now [laughs]…what initially attracted you to your specific line of work?
[00:18:28] And do you have any…[pause] because I think there’s maybe… with all of the activity around the place of women and where we are and where we stand in everything, right. Do you… what attracted you to the work and after getting into the sciences, what was your journey being a woman in the sciences? Did you have an experience that was gendered?
Karla: [00:19:11] Yes absolutely. That’s an interesting question. I think what attracted me to the work. Well. I guess some that, I was, I think was a kid I was always interested in math and science especially biology. And like I remember in my spare time doing math problems [laughs].
[00:19:37] That sounds crazy.
[00:19:41] And then I was always also like just doing surgery and my dolls you know bandaging them up and so I was interested in my anatomy and physiology also from an early age.
Kemi: [00:19:54] How forward thinking of you. I was abusing my dolls and pulling their hair, but anyway… [laughs]
Karla: [00:20:02] Well I was cutting them open so it’s not that much different and not doing a very good job of sewing them back up either, so [laughs].
[00:20:11] But yeah it was a very gendered thing because my brother is a scientist and had the same interests growing up. And I think that he was definitely much more encouraged to go in that direction.
[00:20:30] And you know, and I was definitely… my interested in art and music was probably encouraged a little bit more than my interest in science and math. But you know, and part of that is because not to go into too much of my childhood and family history but you know, my parents were children of the depression. They were married before the 60s happened so you know, they were kind of old school you know, my dad was an engineer and my mom was a musician and so girls were supposed to be doing artist stuff and boys were supposed to be doing science stuff. And so that’s not to say that they didn’t encourage me at all in my interests in math and science but it was definitely much more, oh you like music, okay you should definitely, you know, play music and do art stuff. And so, I think that’s part of the reason that I didn’t realize until college that I could actually choose science as a possible career.
[00:21:34] And yeah, I think I’ve just sort of throughout my life even after doing science professionally for a while, you know, it took a lot to encourage me to believe in myself even to go and get a master’s degree in biostatistics.
[00:21:54] You know, I was filled with doubt about whether or not I could do it. And you know I didn’t start my Ph.D. until I was 40 and dealt with a lot of just sort of internal questioning like am I really smart enough for this? Can I really, you know, can I really accomplish this? Whereas you know, yeah, my brother went straight from college to Ph.D. And you know, I mean I’m okay with that now because I’m okay with where I’m at right now. But I do a lot of volunteering to help younger girls who are in their years of formative years of kind of deciding what, you know, what they can and want to do.
[00:22:40] So like junior high school girls even high school girls, I try to help them like understand that they could have a career in science if they wanted it. And that you don’t necessarily have to, you know, like be a genius or be the number one in your class or go to the best schools or whatever, but if he if you have an interest in it then you, that they should follow that. So, I like being able to try to inspire younger people younger girls especially, to believe in themselves that way.
Kemi: [00:23:18] Yeah. Yeah. That’s so important. I remember being encouraged by my parents or African immigrants so all they saw were sciences and that was the only option for us. So, we were encouraged to get into that space. But the self-talk, right, and whatever the societal expectations were or impressions that you internalize: “Well I’m not doing good here so I should not do this. I think it’s probably a bit of a different experience for boys.
Karla: [00:23:56] I think so. Even today.
Kemi: [00:24:02] Speaking of women in sciences, what do you see as some of the challenges in your field?
Karla: [00:24:08] Well, in general, I mean it is a kind of a service field even before I joined the library and being a statistician is really a service, it’s a support field.
[00:24:24] I mean unless you’re really into this theoretical stuff that, you know, you’re developing new methods. Most statisticians are helping other people research.
[00:24:44] It’s a challenge because you don’t get to be, you know, I’m fine with this but you don’t necessarily get to be there front and center.
[00:24:52] You know that getting the credit for that work but I actually like that.
Karla: [00:25:00] Sorry, I think I’m straying from…
Kemi: [00:25:06] No, no, you’re totally on it.
[00:25:06] So it’s, you know, it’s a service that’s in very high demand and requires a lot of expertise but also flexibility. Being able to follow whatever the lead researcher is doing whether or not you think it’s a good question and not you’re there to provide the support to them, to be able to answer the question appropriately. So, it’s, you know, you really have to stay on top of things so it’s a challenge to do that and at the same time to stay very flexible and understanding and giving [laughs].
Kemi: [00:25:44] Yeah, yeah, interesting. So, it sounds like it takes a lot of patience. What are some of the best aspects of your work?
Karla: [00:25:50] Well actually those are some of the best aspects [laughs], at least for me because I like working with different people on different projects and I get to learn a little bit about a lot of different fields just by being peripherally involved. So, I find that really interesting to just know a little bit about you know, say, you know neurology or you know, urology [laughs] or any of the ology’s like immunology.
[00:26:23] So yeah, I work on a lot of different kinds of projects and meet a lot of different kinds of people. And I just find that really interesting it keeps me on top of my skillset because you use slightly different tools for different fields, different projects. So, it keeps me sort of pretty well-informed. Maybe not a, you know, an expert in anything but, you know, I have a breadth of experience with applications and it’s just interesting.
[00:26:57] I never get bored. [Laughs].
Kemi: [00:27:01] Yeah, I’m sure. Well, I’m going to throw this question at you, how would you describe your position to an eighth grader?
Karla: [00:27:07] My position now? I Think I would probably say that I do teaching. I teach people skills in computer science and math. And I also help them when they have questions about how to design an experiment or how to analyze the data from an experiment and just generally help people find resources because that’s what, you know, people who work in libraries do, most of them.
Kemi: [00:27:41] Totally, yeah, that’s great. And with the Library, I think the final question I had for you or sent to you was about, you know, what brought you to the Library and you talked about that a little bit, so what keeps you here?
Karla: [00:28:00] Well I mean I love what I’m doing. It’s really diverse. And there’s a lot of room for growth both for myself and for the Data Science Initiative. I think one aspect of my job that I find really exciting is that you know, I’m kind of here to help the Data Science Initiative. Make sure that we’re providing relevant services and training to the research and clinical community. So, I just like my job. And I also like the people who work here. My colleagues, including you, in the Library all seem to be very passionate or interested in what they’re doing and care a lot about it and also just happen to be very nice people.
Kemi: [00:28:57] So, that brings us close to the end of our conversation. I want to ask you a couple a rapid-fire, well not a couple, but a few rapid-fire questions. That shouldn’t take too long, it’s either/or, you can only choose one.
Karla: [00:29:05] Okay. Can’t be indecisive. [Laughs] No rambling.
Kemi: [00:29:21] Ready?
Karla: [00:29:22] Yes.
Kemi: [00:29:22] Okay, science or research?
Karla: [00:29:25] Research.
Kemi: [00:29:27] Okay, Mission Bay or Parnassus?
Karla: [00:29:33] Parnassus.
Kemi: [00:29:37] Python or R?
Karla: [00:29:37] R.
Kemi: [00:29:37] Why?
Karla: [00:29:37] It’s a statistical programming language.
Kemi: [00:29:40] Got it! Bike or motorcycle? I remember when I first met you, you said that you rode a motorcycle and I was like, yay! Because I had a scooter at the time.
Karla: [00:29:49] Yeah, I do. Gosh, bicycle.
Kemi: [00:29:53] Bake or cook?
Karla: [00:29:55] Bake.
Kemi: [00:29:56] By the way, Karla’s a great baker, I’ve had some of your treats. Really good!
Karla: [00:30:00] Thank you.
Kemi: [00:30:02] Well that’s it, Karla. Thank you, that was great.
Karla: [00:30:04] Thank you.