Obviously, the schools are incredibly different, Brown is Ivy, UCLA is not... but I was just wondering (academics wise) how incredibly different the schools are. Would you say they are about equal in terms of the same technology and level of education being provided to students to be able to be adaquetely prepared later in life. And again, Brown is an Ivy Legue University and UCLA is not, but do they recieve around the same amount of recognition in terms of being able to get a job? What are the similarities? I'm just trying to get a general idea of both.

Hey everyone,

I’ll be starting at UCLA in about five months, and I wanted to get ahead and ask for any advice before I get there. Anything you wish you knew before your first quarter—academics, making friends, dorm life, time management, or just general college life tips.

What should I definitely do, and what should I avoid?

Appreciate any help 🙏

I just think we need to get through these few days together. Anyone else excited? I can't wait any longer. Everyone share their acceptances and expectations!!!!!!!

Computational Biology Major: Overview Guide and Thoughts

I graduated Spring 2025 with Computational Biology (CB) and wanted to share my insights for anyone in or considering the major. The post is long but I try to cover everything you’d need to know from someone who’s been through it and I was inspired by this super helpful post years ago that could use an update + provide a fresh perspective. For context, I’m on the PhD route so that’s where I’ll have the most input on.

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1. What is Computational Biology?

Basically, biology generates a lot of data these days so there’s value in people who specialize in analyzing it and building models. CB prepares you to do that.

Some notes:

• It’s a very research-focused major because a capstone thesis is required to graduate

• I’ll use comp bio/bioinformatics interchangeably for simplicity, but there’s technically nuances between them

• The name used to include “Systems Biology” but this half wasn’t emphasized well (tldr: you study biological components together).

2. Comp Bio Timeline

1: Complete pre-major You need a >2.7 GPA in pre-major courses to declare it

2: Get into major You declare a concentration (Biological Data Sci, Bioinformatics, Dynamic Modeling) in your application but really it doesn’t matter which you choose b/c it doesn’t matter for career prospects. Choose whichever overlaps with classes you’re already interested in. No one interviewing me has ever asked about my concentration.

3: Complete “core” and “concentration” classes You don’t necessarily need to be in the major in order to start these classes, but the only bottleneck would be enrollment restrictions for certain majors. One workaround is declaring another major/minor that gets first-pass priority in your desired course, then switch to CB later.

4: Complete 2 “capstone” research courses Highly recommend finding a UCLA lab to join before your senior year since it’s lowkey an unwritten requirement for these courses.

5: Graduate

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3. Pre-Major Courses

Classes I took but aren’t required are marked with (parentheses)

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Lower Divs

LS7A, 7B, 7C (+7L)

The fundamentals you learn in 7AB (central dogma, genetics) show up often in the biology upper divs so make sure you understand the material. 7L is an easy A: complete the pre-labs, attend lab, submit the writeup

PHYS 5A, 5B, 5C

Take the 5 series over the 1 series unless you’re genuinely interested in physics. The 1 series is the engineering version and demands more time. The 5 series was still hard for me though, especially 5C. Try to start by Spring of freshman year since seats are competitive and the professor matters.

CHEM 14A, 14B (+14C, 14D)

I took the entire 14 series (except labs) because I wanted to take biochem. 14AB are manageable if you took AP Chem. 14CD were rough, but OChem isn’t necessary for bioinformatics, it just adds extra context especially if you do mol bio-related research. CS31, 32

Take these offseason from the CS majors b/c they lower the difficulty for non-CS backgrounds. I took CS31 w/ Stahl and CS32 w/ Smallberg in winter and spring, respectively. I can’t speak on what the courses look like now since I took both before ChatGPT. Don’t rely on AI to get through the projects because you’ll struggle in the long term. I had no prior coding experience and still use fundamentals I learned like polymorphism, data structures, classes. Both teach C++, which I haven’t used since but it’s a strict language, which forces you to have good coding fundamentals.

LS 30A, 30B, MATH 33A, 33B, STATS 10 (+61)

• I took LS30AB because I thought it was interesting as a freshman but in hindsight it wasn’t necessary. The series covers mathematical modeling in the context of biological systems so it’s a decent intro to systems biology and dynamic modeling if you’re into that. Grading is generous and the coding labs aren’t very useful imo since you’re mostly running single lines of pre-written code, but they give you a basic foundation if you’ve never coded before.

• MATH 33AB covers linear algebra and differential equations. Pay attention because topics like matrices, eigenvectors, linear equations come up in upper divs and are fundamental to ML and DS. Difficulty is professor-dependent, and I had Wang for 33B who’s notoriously easy.

• MATH 61 was the hardest lower div for me, but also one of the most useful. It’s proof-based math, which most students haven’t done before. It’s challenging but essential if you want to do anything CS-related. Focus on set theory, combinatorics, and graph theory.

• I substituted AP Stats for STATS 10, but this isn’t allowed anymore. STATS 10 is supposedly an easy class though.

C&S BIO 10

The most immediately useful lower div. You learn basic Python, R, and Unix. Super low-pressure class and the lectures cover current bioinformatics research, which gives you a good idea of the current field. Nadel teaches it and he’s great.

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Upper Divs

Probability + Statistics: MATH 170E + BIOSTATS 100A

• MATH 170E is useful because probability distributions come up constantly in bioinformatics. STATS 100A is essentially the same so just take whichever is more convenient.

• BIOSTATS 100A was an easy A but didn't teach me much; it's mostly applying basic statistics in public health contexts.

Gateway Courses: C&S BIO M184, 185

• M184 is a seminar where you attend a weekly talk and write a paragraph about what you learned.
• 185 is project-based and is dependent on what you put into it. Wollman teaches it and guarantees you an A so you could half-ass it, but if you actually put in the effort, it gives you amazing practical experience. You form a group with 4 other CB majors, find a paper you like, and do a follow-up coding project based on the paper. It’s a good opportunity to meet other CB majors and I met some of my closest friends in the major here.

Biological Modeling: C&S BIO M150

The class was a fever dream to me imo. Make friends because the take-home midterm and final took my five-person group 10+ hours within the 24-hour window. I skipped most lectures and mostly just reviewed slides weekly, but I was fortunate enough to have friends I could ask if I needed help. The content got super confusing halfway through the quarter.

Capstone: C&S BIO M187, 199

• I recommend starting SRP 199 as early as possible because it counts towards your GPA (up to 8 quarters). My PI was chill and would give me an A regardless, so I stacked like 6 quarters of it.

• M187 is a cool class. I took it as a sophomore so I met a lot of CB upperclassmen. You spend the quarter developing a research poster and 10-minute presentation based on your UCLA research to show at a symposium (and you get a free poster).

LS 107

Required to access most life science electives, including MCDB and MIMG courses. Without it, you're mostly limited to EEB offerings. The format mirrors the 7 series and it's not a hard class.

MCDB 138, 165A, 144, 187AL

I took these while planning to double major w/ MCDB (but ultimately didn’t). The core MCDB courses (138, 165A, 144) will prepare you well for connecting bioinformatics data to real biological mechanisms.

• 165A (Cell Bio) was my favorite because it teaches experimental design like how to identify good controls, which is useful even if you’re focused on bioinformatics.

• 144 (Molecular Bio) is valuable if you’re interested in genomics, mol bio, or biophysics

• 138 (Developmental Bio) is dense and complex, but I ended up in a neurodevelopmental lab postgrad so I still reference my notes from the class. Not necessary imo unless you’re interested in development or STEM cell work.

I had Rigeur for 138 and 144, and Coller for 165A - both are amazing lecturers. I recommend taking 144→165A→138.

• 187AL is a genomics dry lab and a free A. You annotate a plant genome all quarter and finish with a writeup and presentation. I took it just to have something to do my last quarter, but I’m not really interested in plant work. Pellegrini teaches it but he’s a dry lecturer.

CHEM 153A

I learned a lot, but wish I could’ve taken more biochem. Biochem is another course that’s useful to take if you wanna have a deep biology background. 153A covers macromolecule structures and metabolism. If you wanna do research in cancer or immunology, metabolism is essential to know. Lannan is a funny and engaging prof and his tests are extremely fair.

Comp Bio Electives: C&S BIO M178, M130

• M178 (taught by Hoffmann and Deeds) covers biological circuitry and a bit of immunology. Most of the work is coding homeworks in Jupyter notebooks, which is just filling in Python code

• M130 (taught by Shah) is about image processing in biology with a similar homework format, but uses MATLAB. MATLAB is annoying but not hard to learn.

CS Electives: CS 180, CM121, M146

• CS180 is useful for SWE since it covers algorithms you’ll need for LeetCode and tech interviews. I took it over the summer w/ Bautista, which made it manageable.

• M146 introduces machine learning, but be aware that the concepts taught are like 20 years old at this point (but not useless). We only got through basic neural nets by the end. If you want to get into AI (or whatever’s trendy), you’ll need to take additional courses or self-learn. It’s proof-heavy and essentially a math class with applications. If you want applied data science, take M148 instead. I recommend taking w/ Sankararaman.

• CM121 is “Introduction to Bioinformatics” but is really an introduction to genomics/sequencing (a subset of bioinformatics). We covered DNA/RNA-seq, pseudoalignment, graph theory, dimensionality reduction, probability. There’s a bias towards covering DNA/RNA and not much on proteomics or evolutionary biology. Pimentel was engaging when I had him.

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4. Career Outlooks

PhD in Comp Bio/Bioinformatics

Even if a comp bio PhD is your goal, I’d still recommend building a strong foundation first. For the majority, you’re better off majoring in:

• MCDB, Biochemistry, MIMG, or BioE with a Bioinformatics/Data Science Eng Minor, or

• CS, Math, or Statistics, while taking upper-level biology electives

The problem with CB’s curriculum is that:

• It doesn’t give you enough biological intuition to ask good questions

• It doesn’t give you enough math to build new models

I’m not saying it’s impossible with a CB degree, but you’ll need extra effort through your lab research since classes alone won’t prepare you. Many hardworking people in my graduating class are now in computational biology PhD programs. Looking back, I would major in MCDB for the biology and done computational work in my research lab.

Tech/SWE

It’s possible, but an uphill. The people I knew who landed FAANG/unicorn jobs followed a pattern:

1. SWE/DS/Analytics internship at biotech company
2. 2nd internship at traditional tech company
3. FT offer in tech

If tech is your goal and biology doesn't interest you, switch to something like CS or Math of Comp - don't waste time on biology courses you won't need.

Biotech/Pharma Industry

Be warned: most entry-level bioinformatics and comp bio roles require an MS or PhD. BS-level openings are mainly research associate or bench-heavy technician roles. Since CB has no required wet lab courses, you'll lack hands-on experience (PCR, cell culture, etc.) unless you gained wet lab experience elsewhere.

Pre-Med

The math and CS courses make it harder to maintain your GPA, and the required courses don't cover all MCAT topics. Easier majors like psychobio or human bio are better if you want to maximize GPA.

Healthcare/Business/Consulting

I know some CB majors pursue these, but I don't personally know any, so I can't say much here.

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5. Other Notes

Research at UCLA

If you’re looking for bioinformatics research, just cold email professors, there’s plenty of advice out there on how to do it. I recommend emphasizing CS/Stats/Math courses you’ve taken at UCLA if you’re looking to join a computational lab here.

There’s big names in the department like Alex Hoffmann, Paul Boutros, Eleazar Eskin, Matteo Pellegrini, but you’re also not limited to CB-affiliated faculty.

Gripes

Counseling is essentially nonexistent - advising appointments were nearly impossible to get, though I'm not sure if that's changed. You're largely left on your own, relying on upperclassmen for course planning advice. If you're more biology-focused, I'd recommend seeing Maggie from the MCDB department instead.

While you can take a diverse set of classes, most are hard to get into because they’re locked to other majors and CB is usually an afterthought. For example, CB isn’t guaranteed first-pass priority for MIMG, MCDB, PHYSCI, COMSCI, EC ENGR classes. You’ll have to rely on department PTEs.

Departmental Scholars Program

You stay an extra year for a BS+MS in 5 years. It sounded impressive as a freshman but I wouldn’t recommend it now. From what I’ve heard, you retain undergrad status in the master’s portion so you miss out on the benefits of being a full graduate student, and the program isn’t that rigorous overall. My own PI in the CB department also discouraged the +1 MS. This is just what I heard so take this with a grain of salt. If you want to pivot to tech, a MS in CS/ML/DS is better.

Clubs

There are comp bio focused clubs but I wasn’t too involved with them. I do like Biomedical Engineering Society though - they run great technical projects that look good on resumes. I recommend Undergraduate Science Journal for those on the PhD route. There are also data science/CS clubs, but I don't have experience with those.

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6. My Own Journey

The major worked well for me because I wanted to do both bench and computational work and get a PhD. I graduated in 3 years, but I didn't use department resources much. What carried me was being self-driven and building a good network. The CS majors, bio majors, chem majors, etc. you meet throughout are more useful than any department resource.

The most important thing I did at UCLA was joining a faculty lab my freshman year. I started on the bench, moved to a computational project, then went back to bench work. Having both skills is rare at the undergrad level - bench experience sets you apart from pure computationalists, and coding skills set you apart from pure experimentalists. It shows in interviews, and it opened doors for me: an R&D internship at a large pharma company and a molecular biology PhD. I understand the majority of CB majors don’t want to do bench work, but don’t be scared to try it.

CB worked for me because I used its flexibility intentionally. If you're willing to do the same, it can work for you too.

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7.Takeaways

Comp Bio is designed to be a PhD pipeline so it’s only worth choosing if you’re interested in research. That said, it’s a jack of all trades, master of none.

One underrated upside is that the major has weight on a resume. Computational Bio isn’t offered at most universities so it tends to turn heads in interviews in a way a generic CS or Bio degree won't. That said, resume flash only gets you so far.

If you don’t want a PhD, you risk graduating without enough depth for biotech roles or tech roles. The major has requirements from different departments, but it consequently spreads you too thin. Without actively tailoring your courses, you’ll likely have only surface-level knowledge on both the biology and computational sides. A CB bachelor’s as a terminal degree just won’t get you as far as you’d think.

For those seeking a PhD: you need to figure out how you learn best. If you learn better from structured coursework than hands-on research, I’d point you toward a different major. Most people shouldn’t specialize in bioinformatics at the undergrad level anyway. Instead, build a strong foundation in either biology or math/CS/stats first, then supplement with the other as needed.

Ultimately, the major as a whole is as fulfilling or as mickey as you make it.

Why has our Bruin bash lowkey sucked these past few years. Feel like all other UC’s have better performers and come to find out UCSB is going to have the Two Lips perform

There really is no point to doing much outside of classes here except Wooden Center maybe. I love and admire the talent and effort of our sports teams but I mean like, fucking....no time to brew some coffee and had to buy one from the filthy vending machine with coffee grinds fossilized inside them from way...I just felt like a sardine on my way to class. One person after another passed by. Some I knew from a course before. One guy had his zipper open and I reminded him out loud to zip it up and he frowned like he was giving me a middle finger with his face!

I see all the exam pressures building up like it does in waves and then back at it again and again. There's got to be more to all of this but there really isn't. Just classes, exams, and the gym to be sexy. I go to the gym because I am the sexiest Bruin alive and it's nobody else's business.

There's just a lack of uniqueness here. I see the clubs full of like-minded individuals who create community out of a sense of uniqueness. That's why I want a good wrestling club so we can each be unique and still take each other down tournament style. I think nobody likes wrestling anymore because of how WWE writing is so lazy and the shows underperform and the tickets cost way too much. Or they think that everyone who likes wrestling is like the former owner of WWE who is speculated to have taken a dump on someone's chest!

I do that kind of shit, literally. I'm in class right now and I feel like drawing a picture of mark tramo having a dump taken on his chest by a TA who I have a crush on. Maybe if I study hard in this class enough to get 100%, I can get her attention finally.

Anyone taking this class rn? How cooked do you feel?? And anyone who took it before -- how cooked did you feel 😭😭

yo any bruins out there who had the chance to choose between berk or ucla for stem/tech related majors, what made you choose ucla?? what was the factor that made u choose ucla over berkeley? im kind of in a pickle and need motivation to choose ucla over berk guys

Title

Should I dorm at UCLA or find some other option? I had a -1500 SAI but I’m scared that with the amount of classes I’m taking (4) financial aid won’t cover enough.

I’ve calculated estimated financial aid and I’d have to pay $8700 out of pocket but is there a chance I could get more aid too?

Hi everyone, I was admitted for Computer Engineering and really hoping I can attend here in the fall. I'm OOS from the East Coast but had some questions about the engineering program, and UCLA in general. Would love to get some answers and tips!

• How difficult is it to get the classes you need each quarter? Has enrollment ever delayed your progress or forced bad schedules?
• How competitive does it actually feel (classes, research, clubs)? Is it manageable if you’re proactive?
• How easy is it to get involved in research or meaningful engineering projects early (freshman/sophomore year)?
• Do you feel like UCLA gives you strong access to internships (especially in California), or is it mostly on you to figure that out?
• Does the large size of the school ever make it harder to stand out or get support?
• For out-of-state students: how was the transition and overall experience?

go bruins!

There are some posts floating around the internet, and which AI has run with, that state that ~2.8% of UCLA's and 4% of UC Berkeley's baccalaureate grads become MDs. The source for both is from a forum in which B is perpetually hailed and UCLA is perpetually degraded. This info was from a couple of posts that were, if I recall correctly about the date of these posts as to when I was researching this recently, from about seven years ago. There are posts from this forum which bubble to the top of search queues from > decade ago in order to supposedly answer questions relative to this current date and time. But, regardless, I don't think there are effectively any years in which B has outperformed UCLA with respect to producing MDs according to the aamc.org website which lists applicants by college, which I will reference in my spreadsheet below which I'll pdf dating back to the 2013-14 academic year.  

Additionally, there are various advisory sites that claim to help premeds to become more admissible in the med-school admissions process. There's one site that is fairly prevalent which props B's and, again, degrades UCLA's premed process.  This site claims that ~1% UCLA grads become MDs, and again it props B's which it reports as producing ~2% grads. This site, with initials CT, in another graphic, claims that B's premeds attend more prestigious m-schools than UCLA's. It mentions UCB's dominant landing spots as UCLA and UCSF, and UCLA's as being UCSD and UCLA. Being that UCLA grads have many more applicants, I would venture to guess that there are more UCLA grads at UCSF and certainly more at its naive UCLA Geffen. This notion of prestige is also bs as there are Ivy grads who predominantly attend med schools which are perceived as lower in standing than their ug schools. UCLA's grads also attend m-school literally all across the nation by casting an extremely wide net. If I get a chance, I'll list some of them from a quick look at the employment platforms. I also noted that there are some grads who'll have a platform database but not upkeep it because they've gotten into med school.

Here's my spreadsheet of yearly applicants taken from the aamc.org FACTS menu selection, and I've included the other top-producing UCs with respect to applicants to allopathic med schools:

https://preview.redd.it/vs0mkbku11wg1.png?width=395&format=png&auto=webp&s=b7f87e9a15afe057a48d2fb7190b8e9d18d36d0e

Some YT videos claim that B has a 57-58% acceptance rate into med school -- that a person accepted with obtain ≥ 1 acceptance. UCLA in the last of its SAIRO numbers in 2019 claimed to have a 52-53%. You'll see these numbers perpetuated in AI.

With than in mind, UCLA & B on their common data set from 2025-26 state that there are 10,000 baccalaureates given out in that year. SD is in the 9,000 range even though it has more ugs than the other two now. Both UCLA and B have > 90% who graduate in two and four years for the respective transfers and those who enter from high school.

So here's the math of how many from UCLA and B who attend allopathic med schools/year and what percentage of grads would these be:

UCLA: 1,200 * .52 = 600+; 600+/10,000 = 6-7%. This percentage is massively under what has been presented in the internet. Another AI query stated that UCLA produces '180-300' med students/year, which severly undershoots reality.

B: 689 * .58% = 394; 394/10,000 = 4%. This percentage matches the percentage I cite at the top of the post.

Some other points:

It doesn't matter that the national average for those who reapply if the number of acceptances/year is steady, which UCLA's is. The national average for this is ~25%, besides the fact that UCLA and B's acceptance rates are 10%+ over the national average of acceptance which is in the low 40%, 42-43%.

It appears that UCSD's acceptance rate is ~ the national average 43% or so. I have no idea of the other UCs.

A lot of Ivy colleges will claim an extremely high percentage of acceptance, but they also cull their presented numbers by who've been advised. And the aamc numbers are those who apply with or without advisement.

UCLA's advisement is apparently more student-based which would seem to be as good as faculty-based. E.g., as an example, UCLA has an IB workshop through the Undergraduate Business Society, which places and tracks students into cohorts of tiers IB placement standing.

I’m graduating in the Spring and taking the last classes I need to graduate. Unfortunately I got sick with a brutal sinus infection and missed a week and a half of some classes that are confusing enough without any absences. I’m set to graduate summa if I get an A in all of these classes. However our midterm is next week and I’m less than confident about those prospects. Trying to get into a pretty prestigious MFA program after a gap year. If this midterm goes to shit is it better to take p/np to save my chances at summa or to let the GPA tank a bit? I know neither matter too much, just wondering which would be better for admissions (the class isn’t in my major if that matters)

hope you’re okay girl that was a brutal collision

I’ve been feeling down too much lately, crying my heart out all of a sudden for no apparent reason or just thinking about the future and all I need to do within this year. ive been overeating and my brothers wedding is coming up in 2 months and I’m the heaviest ive ever been and I don’t want my family to see me this way. theres no one at the end of the day I can talk to about my struggles exceot my mom but I don’t wanna burden her. I’ve lost the motivation to continue studying or pursue my dream career even though I should be grateful for this opportunity to be here cuz for the longest time transferring here was a dream I never thought would happen. I thought about going to therapy but I’m not sure if I’m at that point or if it’ll help. any suggestions

I am quite glad to have gotten into both these colleges, however I am having quite a hard time deciding between the two. I have gotten in for Data Science into both, however I will try to switch to engineering (EE or MechE) or am planning to at least pursue a double major in something business-related. CMU is top 3 for Data Science I believe whereas UCLA is top 12.

I have visited both the campuses however I am still having a hard time deciding. I liked the campus of UCLA more and I've heard CMU receives a lot of rain (I'm okay with cold weather but I don't like rain). I can be picky with making close friends/it can be hard for me. I did feel like I may get bored really quickly at CMU, however at UCLA there seemed to be a lot more to do and the surrounding area has a lot as well. Only downside about UCLA vibes is it feels similar to San Diego (where I'm from), however that's not a big concern.

The career outcomes are quite important to me though. I've heard that at CMU I am more likely to get a higher-paying job, which is the main reason I am considering it. I was wondering how much the CMU vs UCLA degree would actually matter though after the first job? And realistically what would the difference in salary be in the jobs with CMU vs UCLA?

I'm just trying to get an idea of how much of a difference there really would be career-wise between the two so any information/opinions would be helpful. Also which would be closer to the startup/entrepreneurship scene if I were to get into it?

Ignoring the cost of attendance, which would you guys recommend? Any insight, any opinions, or any experiences would be highly appreciated. Thank you!!

Title. Broooo im still on the fence between this school (which I adore) and USC, but it seems like USC is better for who I am. I'm just hearing alot about the fact that it will be hard for me to change majors later on. Other than that will I be able to explore lots of different fields at UCLA and see what appeals to me? My goal in college is to figure out what I wanna do in life I feel so lost 🫠