Guiding a New Generation of Talent

0:00 in terms of not just how prepared they are but simply the breadth so our the

0:06 diversity in our classes especially undergraduate classes is substantially higher than it was 20

0:13 years ago when I started teaching for example Hopkins last year was almost 50 50 in Engineering in between men and

0:21 women so it was I think 49.4 percent women and so so this is an example which was

0:29 is quite different from when I was and it's a change for the better

0:34 [Music] hello everyone and welcome to another

0:42 episode of the b-ball podcast remember to like And subscribe to our

0:48 podcast on Spotify Apple music and other major platforms

0:53 in this episode Our Guest is Sunil Kumar currently Provost and Senior vice president for academic Affairs at John

1:00 Hopkins University and in July he will become the next president of Tufts University

1:07 Sunil has a very distinguished academic career starting with Stanford University as a professor in The Graduate School of

1:15 Business after spending about a dozen years there he was named the dean of the University

1:21 of Chicago Booth School of Business most recently in 2016 he became the

1:27 Provost and Senior vice president at John Hopkins Sunil really loves what he does

1:35 during his recent appointment to become the next president of Tufts he said I never lost that kind of awe and wonder

1:43 about the academic Enterprise and I'm happiest in it

1:49 in today's conversation we will learn about sunil's Journey from his early days in India to eventually making his

1:56 way to the United States where he received a PhD in electrical engineering at the University of Illinois ravana

2:02 champion he'll also ask about his work with today's young people and how he can

2:08 how we all can Inspire more of them to pursue career in stem fields

2:15 hi Sunil it's an absolute pleasure to have you join us today you and I went to school together and

2:22 have known each of the four 30 plus years I still remember both of us starting on

2:28 the semester project two weeks before it's due in our final year if I remember it correctly not our

2:35 brightest moment eh but from what I can tell you have not done too shovel in your career Sunil neither have you and

2:43 uh for the congratulations on your position at Juniper and thank you for

2:49 inviting me and funnily enough that project if you remember was uh trying to

2:55 do make the PC act as a kind of dynamic

3:01 controller in the Dos days it was impossible to do multitasking and we

3:06 managed to get our PC to do it us and it and funnily enough I've stayed in

3:12 control theory and controls the rest of my life that is I still work on problems similar

3:18 to the one we did in our final project it's delightful to hear that I'm gonna you know pick up on that a little bit

3:25 later from now but let's just get the audience to kind of familiarize with you and your academic Journey you know what

3:32 kept you motivated to continue a trajectory and eventually earn a PhD

3:38 right I think that would be great for the audience to hear that so let's just start there yeah so

3:44 um so for me uh I didn't have a particular view that I would get a PhD I

3:51 actually had a very good job coming out of the National Institute of Technology that you and I went to uh I was I had a

3:59 job with the tatas in Pune and which is a large Indian conglomerate and and I I

4:08 didn't want to do that job because they said I had to start as a night shift

4:13 supervisor at a truck manufacturing plant and I didn't want to be night shift

4:18 supervisor and and so I wrote The Graduate exam and and got into the

4:26 Indian Institute of science and uh and um and and that's where I realized

4:34 um I understood for the first time what high-end academic research looks like

4:39 and realized that's what I wanted to do so so the year my two years at iic were

4:45 the ones that kind of transformed my aspiration yeah it's great to hear that if I remember

4:51 correctly in your school days itself you were a ham Enthusiast you were into things which were engineering oriented

4:58 and then you went to ISC you know you mentioned you know your

5:04 uh your career in iic in in actually spending time there as well as

5:09 eventually you know graduating with a computer science and automation degree in ISC if I'm not mistaken

5:16 all through this like you know what drove your interest and passion about Engineering in engineering right from

5:22 the start yeah so I I you know the the basic hubris if you will of engineering is

5:29 that that will simply fix it uh that we

5:34 Engineers would simply find solutions to problems and that is always appeal to me

5:42 um I'm not particularly handy with my hands so even when I was a ham radio

5:48 Enthusiast somebody else would build my equipment for me I I didn't build them myself and uh and so but it that's why

5:56 when I realized that I could use math to find solutions to problems that was

6:02 the kind of uh point where I realized finally I have something which I'm both good at and like otherwise things I

6:09 liked I was not necessarily good at and things I was good at I didn't necessarily like so this was the one thing that check both boxes

6:17 great we talked about math you know one of the things India is especially known

6:23 for is it's producing so much talent specializing in stem computer science and engineering as an educator yourself

6:30 and an academic leader now what can the world and specifically the United States

6:35 of America learn from India about inspiring young people to pursue careers in stem

6:40 yeah so I think one of the things that's uh really

6:46 um great about Indian Society is at the in the family you know studying is

6:52 emphasized and and and stem

6:58 is emphasized in as much as the government had invested in very high-end

7:05 institutes like the ones we went to in the stem fields which was seen as necessary for the country's progress and

7:12 indeed has contributed to the country's progress and so uh so I think a

7:17 combination of commitment by local and federal governments to education and and

7:25 investment in it combined with a kind of culture shift or

7:31 at least a a a a an emphasis on education in general so I don't

7:37 particularly see stem as somehow Superior or diff better than

7:43 social sciences or the humanities but education in general I think is uh you

7:50 know is a lesson that is worth learning I think makes a lot of sense I think it's

7:55 emphasized to the Grassroots level much more than in the US and that's something which we can actually learn in this

8:01 country also let's fast forward a little bit right you know you come come over to the United States in the 90s you join

8:07 your doctoral program at up in a champagne you receive a PhD and eventually you start a teaching position

8:14 at Graduate School of Business and right in the heart of Silicon Valley if I'm not really stable

8:20 so you could have easily joined a leading technology company in those days you know what kept you in Academia what

8:27 is the burning passion and desire that kept you in Academia so uh well I I'd say it's three things

8:33 first and foremost um uh you know I you get you get the

8:41 freedom to follow your nose in Academia in a way that it's very hard

8:47 to duplicate elsewhere it would be very efficient for Juniper as a company if

8:52 everybody wanted to do their own thing um universities are not designed for efficiency so the you know people do

8:59 their own thing and that's seen as a and for me that is extremely appealing the second is and I think this is the

9:06 part that appears to be the most about a university is that almost nobody else is like you

9:12 almost nobody else is working on a problem similar to what you're working on

9:17 um there are team-based uh you know projects of course especially in the Sciences large teams and they're

9:23 essential for making progress on those problems but their members of the team bring very different skills to the team

9:30 and so on so this kind of having people who are very different from you doing very cool things that are

9:37 very different from what you're doing is another very appealing feature of Academia and so those two things so

9:45 um yeah makes a lot of sense you know the breadth the diversity and it's not like

9:52 a homogeneity in like in one particular area so that that is very appealing in the Academia but I'm going to kind of

9:58 drill down a little bit more here right because for most folks in Academia being a professor an associate Dean at

10:04 Stanford is kind of the Pinnacle of their carriers right now kind of wow this is this is the achievement I want it to be you did not stop there next

10:12 thing I know you're a Dean at school you know Chicago School boot School of Business

10:17 what made you pursue a different Journey you know going into this administrative world something broader than actually

10:22 just teaching yeah so you know as an academic I was doing okay uh you know I my work was

10:30 getting published my students were getting very good jobs uh I was editing it you know a journal

10:37 top journal in my field so I could have kept doing what I was doing uh but but

10:43 it was clear that um and this again like most things in my life

10:48 happened partly by Serendipity I happened to serve on a committee at Stanford

10:54 which looked at the MBA Core Curriculum and proposed some radical changes some of which have even stuck

11:01 and uh and um and while in that process I realized that I was actually

11:08 good at helping people who were way better than me get even better

11:15 um and so that's what drove me to this that I could have a much greater impact

11:20 on institutions and by making people who were significantly

11:25 more talented than me uh be even better at whatever it is that

11:31 they wanted to do so I see myself as a as a taxi driver almost uh I don't necessarily want to tell

11:38 people where to go but I do want to help them get there and so for me therefore the academic

11:45 Administration leadership is something that appeals to them yeah so that's what made them made me

11:52 take that opportunity and of course great school and great opportunity

11:58 relatively early in my career so that was the other kicker okay

12:03 I totally get the appeal of the force multiplier right because you're not just doing one thing based on your 24 7

12:10 you're able to influence a broader set of people to move in a direction which is good for the you know institutional

12:16 as well as the country so I do get the appeal completely makes a lot of sense

12:21 specifically let's just jump into teaching and kids you know kids to students in general

12:27 it all starts with you know who you're teaching at the end of the day right as you progress in your career work with

12:32 students all along the way what has struck you about how students have evolved over a period of time a student

12:39 in the early 2000s is probably very different from a student today uh has you know academic institutions

12:46 really evolved to better cultivate talent in this changing you know Dynamics

12:51 um and has your own approach to teaching changed over the years teaching or Administration in General changed over

12:57 the years yeah I um I'll answer that in two parts

13:04 um I think the institutions have changed um in many respects and and have changed

13:11 Less in other respects so uh we have changed a great deal

13:18 um in who we teach uh in terms of not just how prepared they are or but simply

13:26 the breadth so our the diversity in our classes especially undergraduate classes

13:31 is substantially higher than it was 20 years ago when I started teaching for example Hopkins last year was almost 50

13:38 50 in Engineering in between men and women so it was I think 49.4 women

13:46 and so so this is an example which was is quite different from when I was and

13:52 it's a change for the better the other way in which especially the so-called need blind or the institutions that need

13:59 meet full Financial need do is they've improve the economic diversity of

14:07 uh the classes significantly as well so our student bodies have evolved uh and

14:13 they've evolved by becoming just more interesting for me as a professor they come from much more different backgrounds they bring much more

14:20 different perspectives so in many many ways they're just it just feels better uh as a student body the professor yet

14:28 evolved slower it has changed less so but it is changing it is getting better

14:34 what has not changed as much is what we teach and some of it shouldn't change I don't

14:41 think uh you know thermodynamics is completely different because the students are different

14:47 but I think we over emphasize uh a lot of information transfer which in today's

14:55 world is perhaps less important than teaching people to think critically

15:00 do analysis to synthesize create

15:05 to dream and I think that's the part that in a curricular getting better at

15:11 it but it's the change is slow and if you ask me as a professor

15:16 um you know if I taught a class today versus my first year as

15:22 a assistant professor at Stanford my first year I would be worried about

15:29 um making sure that you know

15:36 everybody learned the stuff I had to teach them that they could do is a problem set

15:42 um today I'd be more worried about whether I have taught them to learn all the things that will change in my field the

15:49 stuff I taught you know 25 years ago a lot of it has changed completely you

15:56 you wouldn't be doing those things the way I used to teach you should do them right so

16:03 yeah what I'm you know if I synthesize what you say it said just now I think the most important thing is you know

16:09 really teaching the young minds to think and evolve as

16:15 as things around them evolve right the ability to think and shape rather than like think you know focus on a

16:21 particular thing the ability to think and create a frame and improve upon what they're doing uh is very well said

16:28 especially in this current changing environment where almost everything is changing from geopolitical stuff to you

16:34 know you know everything else so it's very good to hear that actually I also wanted

16:40 to touch a point right since you were very clear about what you wanted to do

16:45 when you wanted to start when you started teaching and then over a period of time you said okay things have

16:50 changed the goal post has changed a little bit in terms of what I need to emphasize were there any mentors you had

16:56 in your career who kind of really helped you you know sharing some you know inside

17:03 support say like you know maybe you can share some insights about special mentors you had along the way and have you know has there been some of

17:11 these mentors along the way and can you share some of the sage advice you've got from those yeah I I've had several

17:18 um you know I've been very lucky to have several very good mentors uh the person who stands out is a guy

17:26 called Mike Harrison at Stanford who was my senior colleague for almost 15 years

17:32 and and if you think about what's the hardest thing for a person to pick up on

17:37 their own it is uh the ability to place their work

17:44 in the context of others and to figure out what it is that they are capable of doing

17:49 because you do something like I'll think of our final project right we told our

17:54 advisor that we'd get this to work and we got it to work everybody was happy I think we got good

18:00 grades I'm not I can't remember my grade from that project uh but

18:06 we didn't ask ourselves why did we stop here what else could we have done

18:15 um and so this kind of getting calibrated on aspiration and to kind of

18:21 learn to push to say this is not where we stop we got to do more that you

18:27 learned from a mentor and especially in Academia where since you have so much Freedom you

18:35 can stop on a project whenever you want and say this is as far as I'm willing to go

18:40 because I can get three quarters of the credit for two-thirds of the work and yet you if you haven't fully

18:47 understood what you did in some sense you're not being true to your academic roots and so then you're going to spend

18:54 the remaining two one third of the time to just understand the remaining one quarter of the problem that you didn't

19:00 understand when it began but it's worth it and that's something that a mentor needs to teach you by both by example

19:07 but also pointing out what other people are doing and so this notion of when not to stop

19:13 and how to decide what to Aspire for is what I think I picked up the most from my mentors

19:19 very well said you know it really is about broadening The Horizon right you're not stopping at any point you're

19:25 the ability to look at you know something beyond that and you know kind of aspire to you know even goals you

19:31 wouldn't have thought of otherwise totally makes sense flipping into a mentee mentor role and you as a mentor

19:38 right you know I'm sure you've had honored to be a mentor to many students many aspiring students like you said the

19:44 word is aspiring uh what are some of the common questions you know you typically get from students and how do you help

19:50 them along the way yeah so some are tactical um they for example very common question

19:57 is writing is hard because in our world eventually you have to communicate what you've done in a written in a written

20:04 way even if it's just a conference proceeding so communication by writing is hard is a very common thing and there

20:11 the advice you give is simply you know like my favorite cliche is uh the key to

20:18 writing is writing which is uh you just keep writing it and

20:23 keep getting told that well you can do better and you revise the draft and you start over

20:29 and sumathi my wife is fought she laughs every time I write something because she

20:35 says so when are you going to throw it out and start over because you know you know that the first

20:42 draft you can do better than your first draft so you toss it and start over and and so

20:48 um so that's something so tactically you teach them that look it's the iteration

20:54 is fine making mistakes is fine the fact that it doesn't work is actually a good thing in research it means you've

20:59 stumbled onto something otherwise it's pedestrian if everything worked and what you set out to do you did

21:05 a work that it's pedestrian so if it doesn't work that's actually a good thing it means it's the problems more

21:11 interesting than you thought it was and uh and so those things I teach but the

21:18 one that I struggle with and this is the hardest thing for a mentor to do

21:23 is to not tell them to tell them look if you just did these

21:30 six steps you'll get a thesis and you'll be out of here and you can tell them that and in fact they will probably

21:36 finish a year earlier but you wouldn't have served them well you have to let them stumble around a

21:43 little and keep your mouth shut while they're doing it uh so that they they

21:48 just get better at it and it's not cruelty on my part it's because I don't want my intellect to limit their

21:55 achievement by stumbling around they may stumble onto something that I haven't seen in the problem at all

22:01 right and so you don't wanna you don't want that to not happen so that's that's uh that's the hard part of it

22:10 um it's it's funny you said that because when my personal experience too I think you know I always see it as a two-way

22:15 street you know because I learn a lot from my mentees so it's a totally

22:20 two-way street so there are some aspects of that you just touched now

22:25 um let me just take the conversation to a different area

22:31 um you know I just read the story about your appointment for the being the next president of Tufts University there's a

22:38 there's a topic which talks about you know seeing the big picture as part of your research in operations

22:45 management you built mathematical models of systems you know that guarantee the

22:50 best solution you know or the almost best solution to many problems and I know you started off in you know

22:56 champagne and then in Stanford can you tell me a little bit about your work and how you've applied that in your

23:02 career these mathematical models and systems and how they guarantee the

23:07 solutions you're looking for yeah so um the the problems I work on tend to be

23:15 systems that intersect people and and automated systems in some sense from the

23:23 perspective of trying to find the best answer to something and so a the current problem I'm working

23:30 on is worth talking about which is asking the following question suppose uh

23:36 a city were to provide a uber like app for all the taxi cabs

23:43 how did price and manage the cabs different than Uber would

23:49 and first of all why would it be different because the city doesn't care about maximizing profit it cares about

23:55 maximizing access so suppose you were interested in maximizing access to cabs how would

24:01 things be different so that becomes very quickly a hairy

24:07 problem because you know just figuring out what

24:12 the pricing involves for example realizing that each taxi is driven by a

24:18 driver who's trying to maximize her return on her medallion

24:23 so she is not interested necessarily in Access she's interested in maximizing

24:29 her return and yet the city has to manage it in such a way that access is maximized and so this becomes so you

24:37 gotta it's big becomes very hairy and complex very fast and so you got to cut

24:42 through the details and ask what is important in this problem and what's not and so that is the and in second is you

24:50 have to ask yourself I clearly can't find the very best answer here what's the closest I can get to best and how

24:57 can I guarantee that I'm close enough to the best answer so so the second one is about

25:02 approximating the solution the first one is modeling which is figuring out what goes into the math and what just gets

25:09 left out so for example my current paper which is as yet still under review

25:16 argues that it actually matters a great deal how you relocate the empty cabs and

25:21 how you incentivize drivers to relocate their empty caps it matters more than people have given it credit for and so

25:29 so that's something where it's gotten partly by a fudge but partly thinking of the big picture and arguing okay so the

25:37 empty caps matter more than we are giving it credit for in some sense so it's it's fun

25:44 yeah I think the way you explain it it became a lot more fun because then when I look at you know mathematical modeling

25:50 you know it wasn't exactly fun when I first thought about it but I think the way you explain it you made it very lucid very easy to understand for you

25:57 know folks so thank you for doing that Sunil okay a completely different area and an area

26:03 which is very near and dear to me as well as you know most people around Silicon Valley most leaders

26:09 um what what do you think our industry you know especially Silicon Valley can learn from universities as it relates to

26:15 diversity efforts you know how can we embolden more people from DuBois background you know to pursue carriers

26:22 in fields like computer science yeah so uh you know this is uh I it's a

26:28 it's given that you know half our graduating students here will be uh

26:35 women in engineering at Hopkins I will um you know this is a is something that I

26:43 care deeply about and it's not just women but of course underrepresented groups uh Health students you know

26:50 students from who have greater economic need Etc and

26:56 the key there is how do you evaluate talent so one question that the valley in

27:03 particular but Employers in general can ask themselves is the following which is why did Hopkins admit this kid

27:11 and or whichever institution it was I admit this kid uh and in particular one of the things

27:20 as a consequence of the pandemic but something that we are probably going to keep for at least forcible future at

27:27 Hopkins is making the SATs optional so many of our files come in without the

27:34 SATs and so what we do is to holistically review the file look at all

27:40 aspects of the file look at given the opportunities this kid had

27:45 how have they achieved against those opportunities rather than looking at some absolute standard

27:51 and and so the way I characterize those is there are two ways to assess Talent

27:57 one is minimize downside risk so you're hiring a coder make them code if they

28:02 can already code the downside risk is low Ohio

28:07 right and the better they can code faster they can code the more likely you give them a job your downside risk is

28:14 minimized we could do the same thing we could make basically give them the first year exams and say whoever did well on

28:21 the exams is the only people will admit into the first year because we know they won't

28:26 fail the exams they've already passed them but that's not the point of college education right so so what you have to

28:35 do is to actually ask the other question ignore the downside

28:40 what's the upside here and if you manage to the downside you

28:46 will need a homogenous bunch of people because that is in fact if you did the mathematical modeling if you want to

28:52 minimize downside you want to minimize variance which means you're going to actually get

28:57 people who look like each other at least on you know in terms of what they can do and in many cases that

29:03 translates to they look like each other and who they are and that's not a great thing I think

29:10 whereas if you say I want to maximize upside then you want to have high variance

29:16 you want to have people who look don't look like who you would hide hire the court

29:21 for example and then so so the key thing is to figuring out how to assess Talent

29:26 and how to assess Talent whether we upside potential and in a in a time when

29:33 uh you know how can we have a one hour or 30 minute conversation without saying chat GPT so

29:39 I'm going to say it now and so um when there are assistants automated

29:47 assistance for coding and more routinized tasks you have to ask the things that are

29:54 harder to recognize like thinking critical thinking synthesis design

30:00 and so so those are I think that's really what

30:05 um would be of value I think to most employers and particularly to the valley

30:11 right so I think it caught me thinking in many ways I think especially the parallels you're drawn in from you know

30:17 the mathematical modeling too how you look at Talent right and the second part is you know

30:23 we tend to pigeonhole ourselves if you look at the downside risk and the upside

30:28 is not something which um I think Industries in general you know technology technology folks General

30:35 hasn't done enough uh I think you bring up a very good point there and also

30:40 there is parallels to in a fixed mindset with the growth mindset too here right you know you're looking at downside

30:46 downside risk you're getting to more of a fixed mindset which is not good for expanding your horizon so and the upside

30:53 growth growth mindset very similar so I really get that point I think some

30:58 something is good Sage advice for all of us in the in the valley and the it and Industry in general

31:04 so it's and the other thing is uh you know you'd be surprised if you looked at mid-carrier people from carriers outside

31:11 uh lots of talented people who are far from Tech who potentially could be

31:16 useful in things so as we close out the conversation you know you're taking on a new challenge as

31:23 the president of Tufts University how do you prepare for a job like that and what are you looking forward most

31:28 part of this journey well um the thing I'm looking for most is uh to

31:36 learn about an institution that I'm getting familiar with but not as familiar as I am with other institutions

31:42 so the learning part is always very kind of rewarding for me and to serving that

31:49 institution like I said there are lots of very talented people at Tufts who I hope I can help them achieve more uh

31:58 through whatever little I can do so that's that's really what I'm looking forward to so

32:04 it's uh it's delightful to be with you Sunil thank you for spending the time with us

32:09 um happy to have you back anytime thank you well thanks for the opportunity I'm very grateful thank you