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"Using the the toolbox from ICME’s core classes including CME305, I was able to develop algorithms to detect city centers and their movements over time..."
Everyday there are an uncountable number of changes that happen in the world. At Planet Labs we aim to image the entire surface of the earth everyday via satellites to track these changes. Our goal is to make the world a better place to live.
Using the toolbox from ICME’s core classes including CME305, I was able to develop algorithms to detect city centers and their movements over time, detect agriculture health, automatically find the signal-to-noise ratios of our images, and create a new high-speed image matching program, among others.
Each of the previous applications sit heavily on the mathematics that I learned in ICME. Without understanding numerical limitations these algorithms would not scale. Without understanding multivariate optimization they would not work. And without a foundational knowledge of discrete math they would not exists. All of these together allowed me to help Planet Labs take a tiny step towards making the world a more knowledgable and sustainable place.
Currently I’m heading a research department at a solar PV monitoring and analytics company. To get there, I had to switch gears from hydrodynamics to atmospheric sciences, from Matlab to R, and from C++ to Scala. With the solar PV market explosion, the amount of data my company is collecting is growing exponentially so I also had to become a part-time data scientist. Making sense of all this data to empower our customers is a very challenging, yet fascinating task.
Whenever I have time outside of work, I try to play tennis, which, in fact, I had picked up at Stanford in the final years of my Ph.D. trying to manage stress and enjoy California weather.
For my undergrad, I majored in Applied Mathematics but closer to the end started gravitating more towards Computer Science. ICME was a perfect match for me since it allowed me to work on both.
I’ve joined an alternative energy start-up, which I was consulting for in the last few years prior to graduation.
Quite a bit, indeed. I worked on Computer Graphics in the earlier years of my Stanford tenure. These days it’s helping me to process gigabytes of NOAA satellite imagery. My strong CFD background is empowering me to model the atmosphere. A parallel 3D Navier-Stokes solver, I’ve written from scratch for my Ph.D., allowed me to refine my coding skills, which are imperative in the industry.
My dear advisor, Professor Oliver Fringer. These days I genuinely miss our thoughtful weekly research meetings. Once in the real world, I’ve realized how important it was to have an advisor, who was able to see structure in this chaotic world.
I truly believe the future is bright for ICME students. As computers become increasingly ubiquitous, the need for modelling will arise in virtually any discipline with societal benefits.
Make sure you appreciate the present. It’s a blessing to be engaged in research at such an elite place as ICME. Also, make sure you get to know your classmates really well as they are the foundation of your lifelong network.
M.S. 2004 and Ph.D. 2013
Profession: Head of Science, Locus Energy- Palo Alto, California
Other Accomplishments of Note: Lieutenant of Tactical Ballistic Missile Forces
"I learned many programming and modeling skills at ICME, especially in statistical learning and optimization."
"I learned many programming and modeling skills at ICME, especially in statistical learning and optimization."
I’m working is GF Futures as a quantitative investment researcher, and my daily work is to analyze historical data to identify profitable trading signals.
Because my undergraduate education major is math, and I wanted to study a quantitative applied discipline for my graduate education.
I worked for a proprietary futures trading company.
I learned many programming and modelling skills at ICME, especially in statistical learning and optimization. They are very useful at my daily work.
The courses were very hard for me. I felt I couldn’t catch up with other classmates.
ICME students have very rigorous training in MATLAB/C++ programming and optimization/scientific computing, I think we are capable to do many technical work, such traditional developer role in Microsoft/Google/Amazon, or some quant jobs in finance.
For our major, a Ph.D. degree has much better chance than M.S. in job hunting. Nowadays Big Data/Statistical Learning is a hot area, but we need to compete with students in statistics and computer science. Look for some internships during summer, this is also very useful for future job hunting.
Profession: Quantitative Investment Researcher, GF Futures CO., LTD- Guangzhou, China
"My ICME coursework and research provided a great foundation in computational mathematics, numerical methods, and scientific computing."
"My ICME coursework and research provided a great foundation in computational mathematics, numerical methods, and scientific computing."
I have been a senior member of technical staff at Sandia national lab in Albuquerque, NM since October 2011. I am part of the computational mathematics department at Sandia. I have had the pleasure to work on a number of interesting projects, from model reduction to quantum computing to computational modeling of land ice for the prediction of sea level rise. I spend my free time hiking (New Mexico is a great place for that!) and traveling. I also enjoy photography, which these two hobbies enable.
When I was applying to graduate schools, I was really not sure what it was I wanted to study. Having come from a pure math background, I knew I wanted to apply my knowledge of mathematics to problems of interest in today’s world. Hence I looked at interdisciplinary programs like ICME that would enable me to explore various application areas. I knew ICME was the place for me when I visited as a prospective student during the process of choosing my graduate program. What impressed me the most – and what continues to impress me today – was the wide range of interdisciplinary research taking place at ICME, and the genuine enthusiasm that the students and faculty expressed towards their work, the program, and Stanford university.
I took some time off to travel to South America. Then I relocated to Albuquerque and started my current position at Sandia National Lab.
My ICME coursework and research provided a great foundation in computational mathematics, numerical methods, and scientific computing. I use the knowledge that I gained at ICME every day in my current position at Sandia.
Learning with and from my ICME classmates in the office we shared in Durand, back in the pre-engineering quad days.
As cutting-edge researchers and computational scientists in a growing set of fields that require mathematical modeling.
Take advantage of the variety of resources Stanford has to offer: talk to and get to know your classmates and faculty, take courses outside your comfort zone, attend seminars, and get involved!
Profession: Senior Member of Techinal Staff, Computational Mathematics Department at Sandia National Laboratories, Albuquerque, New Mexico
Recipient of NDSEG and NPSC fellowships (during Ph.D.)
Recipient of Robert J. Medal for the best student paper in finite element analysis (2008)
"I drew heavily upon the theory and tools gleaned through my rigorous graduate coursework taken through the Stanford ICME"
"I drew heavily upon the theory and tools gleaned through my rigorous graduate coursework taken through the Stanford ICME"
Every few minutes, a new customer orders a meal from a 4- or 5- star restaurant via tryCaviar.com, a new SF startup that is rapidly expanding to multiple cities. When an order arrives, the Caviar operations team finds a suitable courier to send to the restaurant and deliver food to the customer. I took on a consulting position at Caviar to automate this process, thereby enabling the operations team to more easily distribute hundreds of orders per meal period among their spatially distributed couriers. Working closely with the talented Caviar operations and engineering teams, I was able to develop and deploy a novel intelligent automation system that fits their existing workflow.
The problem is complex from a mathematical and algorithmic perspective, and I drew heavily upon the theory and tools gleaned through my rigorous graduate coursework taken through the Stanford ICME. For example, CME305, a challenging graduate course on Discrete Mathematics and Algorithms taught by Professor Reza Zadeh, provided a broad survey of relevant graph theory and methodology that was instrumental in my ability to rigorously formulate and solve the problem. Professor Percy Liang's excellent course on Artificial Intelligence provided me with a powerful toolkit for developing and evaluating intelligent systems. Together, these and other classes I have taken at the ICME gave me the ability to tackle this extremely complex and rewarding computational problem.
"The trainings that students receive at ICME can help them fit in many future careers, including education, finance, IT, as well as a recently emerged field named 'data science' which lies on the intersection of mathematics and computation."
"The trainings that students receive at ICME can help them fit in many future careers, including education, finance, IT, as well as a recently emerged field named 'data science' which lies on the intersection of mathematics and computation."
I am currently an Assistant Professor of Biostatistics at the University of Michigan. My research areas are Computational Statistics and Statistical Genomics. I enjoy playing sports and traveling as hobbies.
Having an undergraduate background in Computer Science and always being in love with Mathematics, ICME was a natural choice for me.
I did a postdoc in the Statistics Department at Stanford.
The courses that I took at ICME helped me substantially. Knowing exactly how scientific computation is done is a great plus for my current research in computational statistics.
I remember the fun times spent with my classmates working on homeworks, preparing for qualifying exams, playing games and sports.
The trainings that students receive at ICME can help them fit in many future careers, including education, finance, IT, as well as a recently emerged field named “data science” which lies on the intersection of mathematics and computation.
Enjoy your time at ICME and make friends with your classmates.
Profession: Assistant Professor of Biostatistics, University of Michigan-Ann Arbor, Michigan
"Fortunately, two courses that I took in Stanford’s ICME department were excellent preparation...
"Fortunately, two courses that I took in Stanford’s ICME department were excellent preparation..."
This summer, I worked as a software engineering intern at Factual, a SF- and LA-based company that has created an index of all the world’s places (the “Global Places” dataset) and is using this to provide context for other geographic data. Building an ontology of the world’s geographic data is a lofty aspiration, so not surprisingly there is a slew of graph clustering algorithms, statistical data structures, machine learning techniques, and monoidal combinators engineering this effort to vet and interpret our petabytes of raw data.
As an intern at Factual, I was responsible for building out a distributed statistics library in Clojure for interactive, aggregate analysis of our "Geopulse" product, which contains hundreds of gigabytes of unstructured data. Fortunately, two courses that I took in Stanford’s ICME department were excellent preparation for this math-intensive task. Reza Zadeh’s Discrete Math & Algorithms course, CME 305, introduced me to the space of graph theory, machine learning, and dynamic programming all of which I’ve relied on to some degree during the interview process and internship. More importantly, CME 305 teaches students how to approach abstract problems in intuitive ways that are productive and usefully structured. This type of thinking translates directly to scalable software design. Just as you can use a handful of powerful techniques -- proof by contradiction, pigeonhole principle, probabilistic method, build from small cases -- to understand most algorithms, you can use a handful of paradigms -- vectorization, map/filter/reduce, list comprehensions, composability & polymorphism -- to design most software solutions. (Particularly when designing in a high-level functional language that lends itself to abstractions.) Reza Zadeh and Ashish Goel’s Algorithms for Modern Data Models familiarized us with map-reduce contracts, streaming algorithms, and locality sensitive hashing, concepts that are fundamental to the work at Factual and enabled me to hit the ground running this summer.
"ICME students should be the pioneers of bringing computational and mathematical solutions to many unsolved problems and fields that haven't enjoyed the benefit of modern computational power."
"ICME students should be the pioneers of bringing computational and mathematical solutions to many unsolved problems and fields that haven't enjoyed the benefit of modern computational power."
I am a software engineer at Google; my work is to improve the overall Google experience by removing spam & abuse on Google’s websites. The projects I am involved in include design and implementation of spam & abuse detection and analysis tools for both engineers and analysts. Besides my job, I love to discuss with people about how to solve various real-world problems by innovative and data-driven approaches especially on topics about healthier food and better healthcare (those topics are also closely related to my Ph.D. research, which is about biomedical data mining).
I am a regular jogger and jogged almost everyday when I lived at Stanford. I still go back to Stanford for jogging very often during weekends. One of my new hobbies after graduation is playing programmable robots and drones (I bought an AR.Drone and a couple small ones during the 2013 Thanksgiving), and my goal is to make a drone that can be my jogging partner.
I love math. I already had degrees in math and computer science before coming to Stanford. Although design and implementation of information systems was a lot of fun for me, thinking and solving problems mathematically was what I really wanted to do. Fortunately, ICME provided the best for both fields. Doing math on computers was really attractive to me!
Math is a prerequisite of many data mining and machine learning techniques. ICME prepared me to be math-savvy for understanding those techniques, which are used everywhere in this data intensive era. However, another unique and important training I received at ICME is in how to solve numerical problems at a large scale and in a reliable way on computers. This is crucial but usually overlooked by people who do not realize doing math on computers can be very tricky.
Those really interdisciplinary courses and seminars and the variety of research ICME students were doing. Also the common mathematical insights shared with students even though their research topics are totally different.
ICME students should be the pioneers of bringing computational and mathematical solutions to many unsolved problems and fields that haven’t enjoyed the benefit of modern computational power.
Be brave enough to think how to solve problems in a computationally mathematical way, even for fields that mainly use computers as word processors and sending e-mails.
Profession: Software Engineer at Google- Mountain View, California
"The program provided the freedom to learn about and explore a variety of mathematics and application areas."
"The program provided the freedom to learn about and explore a variety of mathematics and application areas."
I work on modified theories of gravity primarily, but have also done research on nonlinear versions of E&M (Born-Infeld) and nonlinear couplings in quantum mechanics (Born-Infeld and gravity). I have also published expository articles on general relativity and E&M. I'm currently working on a new book in which each chapter consists of a single physical configuration, and as the chapter progresses, more and more physical elements are introduced, complicating the problem but making its solution more and more general. I play drums in a band that just put out a 7" EP (on vinyl, of course).
Most interesting physical problems require numerical solution of some sort, and I had no training in the methods one might reliably use. The SCCM provided an interdisciplinary environment in which to learn and apply numerical methods.
I was a visitor at the Pasteur Institute in Paris for a few months, and then went on to a post-doc in the physics department at MIT.
I teach a course on computational methods for physics at Reed College, that course has content that comes directly from my course work and research in the SCCM program
The program provided the freedom to learn about and explore a variety of mathematics and application areas. I took courses on stochastic differential equations, numerical linear algebra, string theory and algebra, and my overall education benefitted greatly from that breadth, both in terms of the coursework content, and from exposure to an array of teachers and researchers in disparate fields.
Almost anywhere - the SCCM degree was flexible, and I could easily have ended up in an applied mathematics department or industry instead of at a physics department at an academic institution.
Don't focus too much on what kind of career you will end up with after Stanford - instead, enjoy your work and use your time in graduate school to explore fields outside of the ones you are used to and comfortable with.
Profession: Associate Professor of Physics, Reed College- Portland, Oregon
Published Works: Books published by Cambridge University Press
- Advanced Mechanics and General Relativity (2010)
- Computational Methods for Physics (2013)
Other Honors:
- KITB Scholar at UCSB
- Visiting Associate Professor of Physics, Stanford University (2011)
"My favorite memories are of spending time working with the great professors and students at Stanford."
"My favorite memories are of spending time working with the great professors and students at Stanford."
Soon after graduating from ICME, I joined a self-funded DNA sequencing startup developing nanopore array microchips as their third employee. We obtained a series A funding round from Life Technologies in 2011, purchased an exclusive license for Dr. Ju's NanoTag chemistry for nanopore sequencing from Columbia University in 2012, and established a full time collaboration with Columbia and Harvard Medical School resulting in a 3-year $5.25M NHGRI grant in 2013. Recently, we have obtained our first proof of principle sequencing results using the NanoTag approach, and are on track to release a beta product next year.
In addition to working at Genia, I founded a self-funded startup in 2012. GAMEPREZ is a 3-sided marketplace serving game developers, gamers, and businesses. Our primary hypotheses are that 1) gamers want new types of games, 2) developers need better tools for deployment and distribution, and 3) that businesses want more tools for crowdsourcing and game-driven advertising. We're building a minimum viable product to test these hypotheses, providing tools for developers and cash prizes for high scores. Crowdsourcing the future of gaming: beyond entertainment.... Get paid to play!
To be completely honest, when I applied it was as a way of venting my frustration with the lack of scientific computing courses at Cornell while I was in a biophysics Ph.D. program there. I had no intention of actually going; when I got the acceptance letter I laughed at the irony and put it aside without thinking twice. Only later, after terminating my Ph.D. with a master's degree, and unable to find a challenging R&D job in NY, I moved back in with my parents. One night, I told one of my old high school friends about getting accepted to a Stanford graduate program and he went nuts on me, telling me I was a fool if I didn't at least make the attempt to go. Through friendster (still in use at the time) I got a hold of a friend (Sasha Kramer) at Stanford who could sublet her room while she worked on founding a nonprofit in Haiti (oursoil.org). I borrowed enough money from my parents to drive to California. Unwilling to take any student loans or go into debt for a second grad school attempt, I spent the first two weeks in the Palo Alto public library emailing professors in search of a research assistantship. I was ready to drive back to NY unless I found one, and I'm very glad that I did! Definitely one of the best decisions I've ever made.
Spent a summer doing medical image processing research at PAIRE and then joined a self-funded biotech startup.
Perhaps the most important thing I learned, in addition to countless useful theorems, proofs, and results from mathematics, was how to teach myself mathematics and algorithms. Having the power to devour books and research articles and rapidly absorb new theories and methods has been repeatedly useful in my research since graduating. On the other hand, one cannot overestimate the value of the systematic foundation in numerical techniques and the mathematical analysis of algorithms for stability and accuracy. Understanding when to expect difficulties due to ill-posedness or ill-conditioning of problems and how to effectively deal with them allows important decisions to be made with confidence and prevents wasted effort or inefficiency.
My favorite memories are of spending time working with the great professors and students at Stanford. My memories of Gene Golub are very dear and his influence and inspiration continues to motivate me. The lectures and discussions with Walter Murray, Michael Saunders, Peter Glynn, Eric Darve, and so many others have had a great impact on me. Taking their courses and learning about their research has shaped my direction and career goals towards higher standards of scientific and mathematical rigor. Of course, I couldn't have succeeded without the support of all the great students and TAs that I was in school with. The many hours doing homework together in groups was what enabled me to succeed in passing my courses, and without their generous help and knowledge I would not have graduated.
Big data is a growing challenge in almost every industry these days. In one form or another, ICME students will have to face huge challenges in scaling and large scale computing. Besides this, new computing technologies such as massively parallel, quantum, and bio machines will require new algorithms and programming techniques. Furthermore, the coming explosions of new biomedical, nanotechnological, and consumer devices will require new computational problems. Some of the most exciting developing hardware technologies like bioelectronics present enormous challenges in signal processing, statistical inference, and machine learning. ICME graduates will provide the theoretical and modelling tools that experimental scientists and engineers need to advance these technologies out from the lab and into the clinics and hands of real customers.
Read more - go beyond the course reading lists! Trace the history of the ideas and the academic genealogies of the original discoverers. Try to organize newly acquired knowledge into a single, unified framework as much as possible. Ask more questions. Don't be afraid to raise your hand or interrupt the lecture! As my father used to say: “The most difficult thing to know is when you've achieved complete understanding.” The answer is - when you can derive it yourself!
Profession: Computational Engineer at Genia Technologies, Inc.- Mountain View, California
At Genia, I am helping to develop new "NanoTag" sequencing technologies for single-molecule nanopore sequencing with Dr. Jingyue Ju's lab at Columbia University and Dr. George Church's lab at Harvard Medical School.
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