Maxim's year-in-review letters
04 Jan 2018A list of the yearly updates I’ve sent out to close friends and mentors:
- January 2018: Maxim’s 2016 and 2017
- March 2016: Maxim’s 2015
- March 2015: Maxim’s 2014
- January 2014: Maxim’s 2013
A list of the yearly updates I’ve sent out to close friends and mentors:
This is a yearly update I send out to close friends and mentors. This installment is from January 2018. (Previous updates here.)
Over the last year and a half, I graduated from college, dove further into data science and biomedical research, and moved to New York for a gap year before I head to grad school. I’ll share stories from my work at a big New York City hospital, from my improvized travels on the cheap, and from my recent side projects. Links to photos and music from 2016 and 2017 are at the bottom.
In June, I completed my undergrad at Princeton. That’s right. This time around, I actually graduated. I don’t have the best track record here… Four years ago, I left high school early without a diploma (Princeton took a GED). Earlier, I skipped the last year of middle school. And I’m not positive I was in town for my elementary school graduation. So college graduation was a big deal, at least for my parents.
(And I finally got a high school diploma in the mail. Apparently, some of my Princeton classes fulfilled their requirements.)
My four years at Princeton were hands down the best of my life so far. Though the freedom of adult life is intoxicating, I miss Princeton madly — the daily routine of captivating seminars, encountering friends from all circles of life, the nightly political roundtable over dinner…
Besides solidifying my intimate circle of dear friends over the last year, I enjoyed some classes out of left field: the political history of health care reform (I was surprised by the ACA’s clever “hacks” to align incentives in the insurance market, though I suppose that’s irrelevant now); ecstatic dance (an opportunity, hidden deep in Princeton’s course catalog, to spend two hours lying on the floor one day, and slither around in the grass and mud imitating a worm the next — plus a fantastic instructor and a tabla player who enjoyed jamming with students); and health and science journalism.
The journalism class has actually been on my mind a lot recently. I’m trying to publish a story I wrote for that class about how software-designed cancer vaccines upset the established paradigms of medicine. To my knowledge, these drugs mark the first time artificial intelligence has played such a direct role in drug development. I’ll send an update when this story about the challenges of regulating A.I. in medicine is available. I didn’t expect I’d enjoy journalism, but I fell into my old practice of calling people whose work I found fascinating to ask how they got there, how they saw the world — with the extra step of putting their stories, and the complicated science behind them, to paper. Now that’s fun.
I’ve also pursued my growing interest in biomedicine. My Princeton senior thesis extended the research I worked on in summer 2016, which I spent in New York City at Mount Sinai’s school of medicine in Jeff Hammerbacher’s group. You could say Jeff has an atypical perspective on academia and biomedicine, having just taken his data science company public and being a professor without a PhD. I wanted to emulate his transition to biomedicine and to understand his way of thinking as a scientist and investor.
We focused on measuring the number of immune cells of different types in the “microenvironment” that surrounds a tumor — a problem that quickly got me into the weeds of both genetics and machine learning. Here’s the gist:
As part of the immune system’s response to cancer, immune cells often infiltrate the region around a tumor. Figuring out the contents of this area could give some important clues about the state of the cancer. Perhaps it could even explain why impressive new immunotherapies only work for a fraction of patients. If we knew what makes those patients unique, that would do a lot of good for targeting those treatments, maybe even developing better ones.
With Jeff and several colleagues who have taught me immensely, I’ve built a new method with the flexibility to characterize more cell types than possible with earlier manual measurement or computational approaches. My colleagues and I presented the work at a UPenn symposium in May, at the International Cancer Immunotherapy Conference in Germany in September, and at the Society for Immunotherapy of Cancer meeting in D.C. in November. Here’s the preprint — a complete manuscript is on the way.
In recent updates, I recounted my transition from software engineering to data science, shared my excitement of solving business challenges by translating them into statistics questions, then integrating the results back into the business, and told of my forays into building digital products in my spare time.
Recently, I committed to channeling these interests into biomedical research, which I’ve been diving into over the last year and a half. This isn’t a standard choice for a computer scientist these days. Let me explain why I decided research is the right next step.
My current focus is to apply machine learning to problems in biomedicine. I believe computer science can create a paradigm shift for a number of biomedical challenges. I’d like to gain a deeper mastery of machine learning methods while simultaneously contributing to immunology research. Long term, I’d like to build products and experiences at the intersection of science and technology and expand my impact beyond producing knowledge and writing papers.
Though cancer immunotherapy is a hot topic in immunology, I believe there’s more room within the field for data science-powered improvements. And when exposed to the constraints and traditions of medicine, AI needs to be developed and productionalized much more carefully than we are used to in the tech industry — as reinforced by what I learned when researching my forthcoming article on algorithmic cancer vaccines. This makes me highly interested in the systems side of machine learning, as well.
Pursuing biotech seriously requires far more education to make a meaningful contribution. A PhD program provides a protected time to learn the intricacies of immunology, find useful problems worth attacking, and make progress.
And I thrive when surrounded by fascinating people with similar interests. I feel a desire to keep learning. Why stop now?
Grad school will develop my craft as a researcher while I clarify how to apply it. In the long term, I can see myself extrapolating from my current interests and also tackling problems related to health care delivery systems and to bioethics. There’s a significant gap between the lab and the clinic that lends itself to innovation. My background in software engineering and product development makes me interested not only in exploring the fundamental scientific questions, but also in making practical changes.
But it will take significant immersion to get the knowledge and credentials to make an impact in biotech. So I’ve been proactive about studying the landscape of biomedicine and healthcare. One goal for the upcoming year is to consolidate my reading and conversations into some notes — indeed, a tech-VC friend suggested I write a biotech market map to build mental models for the landscape. I see the piece about the implications of bringing software to biomedicine as the beginning; digging into more stories like this will help me get the bearings of my newly adopted field.
First, I applied for the Rhodes scholarship to study statistics and health policy at Oxford. A friend of mine had just won the scholarship and convinced me to apply. It seemed like a lottery to join an incredible intellectual community for two years. Why not throw my name into the hat? And I believed I’d be equipped to contribute meaningfully at the intersection of fields that define biomedicine and healthcare when I returned to the U.S. to pursue a PhD.
Though writing the application was a ton of fun, the Rhodes didn’t pan out. Thanks to the extraordinary support of eight referees, I was endorsed by Princeton but didn’t make it to the final stage of the process.
I cast a narrow net in my U.S. grad school applications, searching for professors I’d fit well with and only applying to programs I couldn’t turn down. But that backfired! I faced a heart-wrenching choice among seven schools: Stanford, MIT, Columbia, Berkeley, UCSD, UW, and Princeton.
Visiting each school over the course of a month to breathe in its culture was exhausting, but exhilarating: a nonstop science party across the U.S. on someone else’s dime!
I settled on Stanford, where I meshed very well with professors and peers. I fell in love with the campus environment — though I confess, I’m still concerned about moving to a town populated almost exclusively by tech people. With three years of funding from NSF, I’ll have substantial freedom to continue my immunology and machine learning research.
I’m now back in NYC’s East Village, with a view right onto the river. Life here is vibrant. The density of interesting people in New York is unbeatable, and this is a jazz nerd’s mecca. My apartment complex (Stuy Town) is full of families and dogs; coming home feels like abruptly stepping out of the city and into some unusual country village. Finally, I’ve started using Citibike to get around town. There’s no feeling as freeing as being the fastest thing on the road during rush hour in Manhattan.
My Citibike “hazing” came just a week after I signed up when a mentor of mine who has biked in NYC for decades took me Citibiking from Times Square to Union Square at 5:30 pm. When you’re having a conversation with a fellow biker and there’s no dedicated bike lane, it turns out the move is to weave through traffic erratically, alone half the time, while shouting. It actually worked pretty well, though I decided to buy a helmet after.
When I moved here in August, I rejoined Jeff Hammerbacher’s group at Mount Sinai to complete my project and dive deeper into computational immunology. Mount Sinai seemed to be the right home base to explore the landscape of biotech in the Northeast. My hope was to validate and narrow down my interests in biomedicine while producing useful science and high-quality software. I love my immunology and genetics work, but know little else in the vast world of biotech and medicine. I wanted to find interesting problems to hit the ground running in grad school a year later, while also shadowing doctors at Mount Sinai and leveraging my proximity to the Boston’s biomedical ecosystem to understand what it is like to bring new therapies to market.
Unfortunately, Jeff’s NYC group just shut down due to some funding issues — a sad reality of academia. That said, it was a positive experience, affording me the opportunity to travel to several conferences and polish off my project (more details above, and here’s the preprint again). In a couple weeks, I’m starting a PM job at Butterfly Networks, a startup that has transformed ultrasound into a ridiculously affordable probe that looks small enough to fit in your pocket. I’ll be working on part of their go-to-market strategy — a fun way to see the inside of the medical devices world while building my PM and customer development skills before I return to research.
This year also comes right as I’ve turned 20 and am starting my life out of college, so it’s time to set and stick to good habits for the decade ahead. I’d like to continue to live intentionally, even when outside the structured cocoon of college. My “Mastermind” accountability group — the subject of a lot of airtime in the last few updates — lives on, though we’re now scattered across multiple continents, meaning updates comes less frequently and are more incisive. I’m hoping to piggyback off the structure of a startup gig to re- emphasize my reading, music, and fitness routines.
I’ve deeply enjoyed meeting new folks in the city — especially musicians and scientists. Still working on building a community and feeling at home here. Please don’t hesitate to suggest folks to meet in NYC or elsewhere in the Northeast, if anyone comes to mind.
Ready for some comparisons of budget bus services at unnecessary levels of detail?
Here are some recent travel highlights — with photos here (don’t miss Iceland on page 2!):
To top it off: the day after graduation, I hopped on a one-way flight to Europe with only one week planned out and no return date in mind.
Having never traveled solo for a month and a half before, I expected I’d either go crazy or love it. After the fact, I’m still not sure which it ended up being. What’s clear is that the summer of aggressive relaxation was a much- needed break after four long years.
Planning two days ahead at a time was thrilling. I lived with some Senegalese musicians outside Paris, who fed me delicious traditional food and showed me their unique take on traditional Senegalese folk music. Then Airbnb led me to a 3’ x 3’ attic room (that includes the space taken up by a shower) in the heart of Paris’ Latin Quarter. My buddies at Princeton connected me with their European friends and family, and I also reconnected with some former teachers who had moved abroad.
I did my best to breathe in the local culture, and especially focused on rekindling my French. The daily routine: hopping from cafe to bar to cafe, reading and talking to folks around. I can now present an authoritative ranking of schnitzel in Berlin, and I tried all the dark beer I could find in Prague and Amsterdam. Most importantly, I finally understand why Icelandic music has such an ethereal sound. It’s the only music appropriate for driving at the strict speed limit of 90 km/h along deserted two-lane roads through desolation.
A few years ago, two friends and I created ReCal, a course scheduler app that went viral on campus. At first, we ran the app off our own credit cards because the undergraduate student body’s hands were tied. They wanted to preserve their close relationships with administrators, who felt frustrated that students flocked to what appeared to be a competitor to infrastructure the university had just poured millions into.
Paying for ReCal ourselves quickly proved unsustainable. Meanwhile, the app had grown to 5,000 users (now we’re north of 6.5k). The engineering school had even started officially recommending ReCal to faculty advising freshmen. My solution: join the undergraduate student government and become an advocate for student developers on campus.
Together with an excellent team of partners in the undergraduate government (USG) and in the computer science department, we built a new TigerApps organization from the ground up in 2016+7. Our USG Labs program provides:
Launch assistance: Plenty of student software projects created for class projects never see the light of day, because launching a project on campus is arduous. We remove the roadblocks in students’ way, providing publicity, data access and APIs, and access to the right administrators.
Funding: we identified a critical funding gap and created a program to provide starter funding to help launch apps for students, by students. Unlike other funders on campus, with whom the conversation inevitably turns to who owns the IP, this money has no strings attached and is solely meant to help students build freely for their peers.
Sustainability: we ended the trend of unreliable student software disappearing as soon as its creators graduated. Instead, successful Labs projects “graduate” into full maintenance by TigerApps. Our containerized infrastructure makes reliability with a rotating cast of maintainers possible.
I quickly learned my strengths and weaknesses as a manager. Recruiting proved to be the biggest challenge — motivating students to work for free calls for some creativity. We found that granting members full ownership over their projects encouraged participation, though our limited budget couldn’t support paying our developers.
Then we ran a PR campaign to nix the image that USG TigerApps was full of deadbeat apps. Our sponsored hackathon prize was calculated to blow the other awards out of the water. This let us immediately incubate some promising apps: an innovative way to find clubs to join on campus, a search tool for study rooms based on the number of devices connected to WiFi in those rooms, a clever scanner app to make consumption of the event posters littered around campus easier, and a student directory browser embedded into the Gmail compose window. Training and positioning the new leader of TigerApps for success also took careful thought and made for a good learning experience for me.
Really, the TigerApps work boils down to the challenge of how to seed and incite a community. We wanted to unlock the latent student developer community and make prospective CS students come to Princeton because they see it’s a place where they will be supported in their creative endeavors. I’m glad to have gotten a hands-on education in building a community and organization in a low-stakes arena (10 engineers and 5,000 users) forgiving of mistakes.
At the same time, I put time into building another sustainable organization. From the sidelines, I’ve continued working on TigerTrek, Princeton’s annual student trip to Silicon Valley for a week of off-the-record Q&A with founders, VCs, and executives. When I co-led the trip in 2014, I saw TigerTrek as a way for students to discover alternative career paths — an antidote to Princeton’s tradition of sending 50% of grads into finance and consulting. In six years, the trip has transformed into a bustling community, thanks in large part to putting everyone under the same roof for a week. I count fellow Trekkers among my closest friends. So a big priority for me as I departed Princeton was to organize a TigerTrek board of directors with other former trip leaders, in an effort to preserve institutional memory and help advise and grow the TigerTrek program into the future. So far, so good.
I played piano in two groups coached by renowned saxophonist Rudresh Mahanthappa. Wednesday evening rehearsals and jams became the highlight of the week. Here’s a clip from one of our concerts:
Thank you for your support and guidance, and for being a part of this thrilling chapter of my life. Hope to keep in touch, and please don’t hesitate to let me know how I can be of help.
— Maxim
P.S. As always, I’ll close with pointers to recent readings and media I loved.
Music
Playlist link: http://maximz.com/playlist
Highlights to look for: J Dilla; A Tribe Called Quest; Return To Forever; Slum Village; The Bad Plus; Robert Glasper Experiment; Julio Jaramillo. (My convoluted path through the classical and jazz worlds have now brought me into Latin music and hip hop and old school rap. You can trace many artists on this playlist right back to the jazz musicians they were listening to.)
Reading
Shows
Photos
Two pages of photos at http://maximz.com/photos/2017
This is a yearly update I send out to close friends and mentors. This installment is from March 2016.
This year, I’ve refined my academic and career focus and gotten very interested in using data science to tackle problems in biology. I’ll share stories below from my summer at The New York Times, from my travel to Japan and elsewhere, and from my side project at school that is now being used by half the campus. Finally, I’d like to share links to some of the photos and music from 2015.
I’ve had another great year at Princeton. As a junior majoring in computer science, I’ve nearly completed my degree requirements. Now I have the chance to refocus my remaining class slots.
For the last five years, the theme behind my studies and work has been to apply computer science to other fields. I’ve narrowed my computer science interest to data science in particular. The popular definition of “data scientist” is someone who is better at statistics than any software engineer and better at software engineering than any statistician… so my software engineering experience has being quite helpful in this pursuit! And I’ve moved between several application fields – from neuroscience, to economics, to politics, and now to tackling problems in biology and healthcare using data science methods.
A combination of conversations, classes, and readings sparked my recent interest in biology and healthcare. I took a wonderful seminar taught by Professor Shirley Tilghman, a renowned biologist and the previous president of Princeton, that focused on genetics and public policy. We discussed fascinating topics ranging from eugenics, to the role of genetics in the criminal justice system, to direct-to-consumer testing (e.g. the 23andMe–FDA saga), to gene therapy and genetic editing. At the same time, I was having conversations with computational biologists on campus about their work, and reading books like The Emperor of All Maladies, which presents the history of cancer, cancer research, and cancer treatment in such an interesting way; I highly recommend it.
The current semester is a crash course, accompanied by many conversations from which I am trying to ascertain a zeitgeist of biotech:
(Other classes I took over the last year focused on machine learning and statistical theory, artificial intelligence, compilers, microeconomic theory, and linguistics.)
So far, this interest is still quite broad. But as I continue along this dive into biology and healthcare, I’m tracing the connections to technology, and specifically looking for the problems I can solve with a data science approach. This school year, I’ve been working on a research project with my good friend Andrew and with Professor Barbara Engelhardt, whose work lies in the intersection of computer science and biology. Specifically, we are focusing on improving experimental design for CRISPR experiments.
CRISPR is a new, revolutionary technique that makes gene editing straightforward. To edit DNA with CRISPR, all you have to do is design a guide sequence – made of RNA – that will guide CRISPR to the location in the genome that you want to edit. Basically, the guide RNA will bind with the piece of DNA that you want to edit, and then the system’s Cas9 protein will cut the existing genome at that location. If you also put some new DNA nearby, built-in DNA repair mechanisms will incorporate it to fill the gap. Biology researchers are jumping all over this simple and general gene editing technique. Democratizing gene editing will transform the next 50 years the same way democratizing technology and the Internet have transformed the past few decades. Of course, CRISPR is now the subject of a huge patent war between Stanford, Berkeley, MIT, Harvard, and the Broad…
The challenging part in using CRISPR is designing a guide sequence to match the DNA portion you would like to target but not make changes elsewhere in the genome (where similar sequences might appear, perhaps). Researchers have to meticulously tune their RNA sequence construction until the CRISPR system is able to bind precisely to a piece of the gene to be edited. This problem is very similar to tuning parameter settings in many other experimental fields of science (or, for that matter, in machine learning).
Today, biology labs try to do a “grid search” through the parameter space, meaning they slowly try all possible guide sequences (parameter settings) until they find one that works well. A more intelligent technique, named Bayesian optimization, has been developed for the analogous parameter tuning problem in machine learning. Instead of naively trying all parameters, this technique decides which next experiment to run – which parameter settings to try next – by estimating the expected improvement of a new set of parameters or by trying to gain as much information about the problem as possible through the next experiment. While Bayesian optimization works quite well when you have a single-digit number of parameters, it does not scale. CRISPR guide sequences are 20 bases long, and physical experiments often call for even more parameters. Thus, our goal is to scale this intelligent experimental design algorithm to work for higher-dimensional parameter spaces. Moreover, we’d like to produce something that researchers could use interactively in their lab to accelerate science.
I spent summer 2015 working on the data science team at The New York Times, run by Professor Chris Wiggins, a professor at Columbia and a long-term mentor of mine. The team embeds out into different business and newsroom divisions to improve the ways in which content reaches readers and to keep The Times in business. My understanding of our job was to be evangelists of data-informed decision making – an interesting and creative task at a 160-year old company like The Times – and to introduce some more skepticism into the product design process, i.e. to design and run experiments to evaluate hunches and inform business decisions.
I was offered several projects when I joined but instead forged my own path slightly after chatting with many newsroom and business people from across the company. I focused my summer on a particular customer retention challenge as well as on a long-term strategy experiment.
Working with Chris Wiggins and his team was a lot of fun, and being on the data team at NYT was truly a great learning environment, in many ways. I practiced how to reframe business problems as machine learning challenges and how to make the output of statistical analysis interpretable to business leaders – a task that involves careful evangelism of data science in an organization still adapting to the digital world. Chris Wiggins, whose research now focuses on biology, helped me understand how to do this reframing in the biology context as well, which I think will be helpful with my new interest. On a meta level, working cross-functionally / across the organization at NYT and chatting a lot with Chris about his philosophy in team structure and orientation gave me good mental models for how to organize data science teams and taught me about what powers team dynamics.
Besides all that, it was a thrill to spend 10 weeks at The Times. My hope was to immerse myself in the culture and understand the ethos of the people. It’s hard to convey the subtleties I noticed, but there were a few particularly memorable moments:
A colleague and I started a weekly group meeting to individually work through tutorials on technologies we wanted to learn, ranging from Git (which I had used for ages but never sat down and learned fully/correctly) to survival modeling. I’m now starting a similar “doing group” at Princeton to get my hands dirty and learn more about the following:
The highlight of the year was a trip to Japan in August-September. My girlfriend Shannon, who is in the same year at Princeton and studies environmental science and environment studies (a major she designed herself), speaks Japanese, so we were able to visit some fascinating places. After Tokyo and Kyoto, we traveled to some beautiful rural mountain villages like Takayama. I recently took a class on 20th century Japanese history, so it was particularly interesting to see Japanese lifestyles first-hand. We even witnessed an anti-rearmament protest – another cultural subtlety I would have missed without a translator like Shannon!
I also visited friends in Boston and Toronto over school breaks. Most recently, Shannon and I went on a beautiful road trip from the Bay Area to Ashland and Portland, Oregon over Intersession (aka ski week in late January, which exists because Princeton is still in the stone age and has finals after the winter holidays). I’m including pictures at the end!
With a couple of friends, I launched a webapp at Princeton this year that took off. It’s called ReCal, and it helps students pick their courses and design their schedule in a sleek and intuitive way. It so happened that the university decided to pay a vendor an ungodly amount of money to make its own version, called TigerHub, that, frankly, is incredibly confusing and annoying to use. In fact, leading with “Frustrated with TigerHub?” has gotten us to over 3,000 users making schedules in ReCal (and has made some administrators bitter!).
ReCal used to be a class project for Brian Kernighan’s COS 333. Then two of my friends from our class team isolated the class selection component and made that the new ReCal. I rejoined them to finish the project and especially to coordinate the launch. Having wanted to get some more operational experience, it was fun to handle PR and marketing and focus less on the technical aspects.
Now I’m broadening my involvement, for several reasons: I don’t want to paint student creations in a bad light for the administration, my friends are graduating soon, and I’d like to make ReCal less of a hack and more of a killer app. Some institution needs to be involved for ReCal not to suffer the fate of nearly every other Princeton app and fade after I graduate. As a new side project, I’m playing developer advocate by designing a sustainable, long-term hosting and maintenance solution and brokering it between the undergraduate student government and the computer science department. This will keep ReCal alive and will make it far easier for students to launch apps on campus. Since I’ve been playing with Docker a lot recently, I’m building the system on top of Docker containers. A Docker container is just like a virtual machine but without the overhead – you can run many Docker containers on the same machine and they will share resources nicely. That means we can containerize all student apps and standardize how we monitor, backup, and run maintenance on them all. Long story short, it’s a fun project for me to train my dev chops a bit further, and it will help keep my side project alive.
Finally, earlier in the year I worked on a side project with Professor Sam Wang, who does autism research by day and political forecasting by night. I found his work when I noticed a Twitter war between him and Nate Silver of FiveThirtyEight. We decided to look for ways to detect and quantify gerrymandering that are so simple a judge/the legal system could understand them. Though we did not publish our data analysis work, Prof. Wang has separately published an interesting law journal paper with statistical metrics of gerrymandering that is summarized in NYT pieces (1) and (2). Worth a read!
The excitement of being at Princeton has started to wear off, unfortunately. I’m focusing again on my routines to build a stable lifestyle and continue following my excitement. My Mastermind group with friends Andrew and Fiz is continuing – we meet weekly to help each other think through long-term goals and build habits, and especially to continue living deliberately despite the treadmill that is the Princeton experience. I’m currently doubling down on my fitness, piano, and reading habits.
I made good progress on my goals from 2014. First, after a maximum-entropy search process, I’ve found a new specialty: biology. If that ceases to interest me, I’ll continue following the path of things that seem most exciting, and may take a gap year (since I have two extra years working for me) to read broadly and travel. Second, I’ve continued to go deeper into machine learning and data science through classes, the summer, and by getting my hands dirty on my own. Finally, I’ve had more opportunities to hone my operational skills: it was nice to live on the business side at NYT and to handle product management and PR for ReCal.
I’m setting some new goals for 2016:
I’m so happy to be where I am now, and I’m very excited to see what this next year holds in store. I am very grateful to all my close friends and mentors for their kind support and advice. If you have any feedback, I would appreciate it if you could please send it my way!
Thank you for being a part of this chapter of my life, and all the best.
— Maxim
P.S. I always include some multimedia at the end. Here are some pictures from 2015 (click the info button to see descriptions on individual photos). And here is a curated Spotify playlist with some of the music I’ve been listening to.
Finally, here’s a selection of the books I read over the past year:
Having forgotten the root passwords to several of my Ubuntu virtual machines, I searched for ways to crack or reset the passwords. Here’s my solution.
First, you want to get access to a root shell so you can change passwords and other settings. The easiest way to do this is to boot into Ubuntu recovery mode from the GRUB bootloader screen. Select “root” from the options menu that appears. This drops you into a root shell!
Ubuntu now mounts the filesystem as read-only by default, so execute mount -o rw,remount / to remount it with read-write permissions.
If you don’t have a recovery mode option in your bootloader menu, boot up from a Linux live CD. Open a terminal window, then execute the following:
sudo su # Authenticate as root within your live-CD environment
fdisk -l # List the hard drives available on your system, and identify which one holds your Linux setup. In this example, /dev/sda1 is my Linux partition.
mkdir /mnt/internalhdd # Create a mounting point
mount /dev/sda1 /mnt/internalhdd # Mount your Linux partition
chroot /mnt/internalhdd # Change root into your Linux partition. Now you have root access!
Now that you have a root shell, run passwd root and passwd [your-username-here] to reset passwords for your accounts.
If you used the Linux live CD method, here are the commands you should execute to safely unmount your Linux partition:
exit # Exit to one level up, i.e. from /mnt/internalhdd root to root on your Linux live CD
umount /mnt/internalhdd # Unmount your Linux partition
mount # List mounted devices. Confirm that /dev/sda1 is not mounted anywhere else.
exit # Exit to one level up, i.e. from root on your Linux live CD to the default user account on your Linux live CD
exit # Exit to one level up, i.e. close the terminal window.
I was accidentally featured on the local nightly news on 05/31/13. A journalist called me up that morning to confirm the story, then arranged a 15-minute interview for the evening.
Here is the article the news bureau released the news morning: http://www.10news.com/news/teen-prodigy-headed-to-princeton-university-unable-to-walk-at-high-school-commencement06012013