In a Proceedings of the IEEE focused on Electronic Design Automation (EDA), Carloni and three other editors bring together perspectives on the future and challenges of EDA.
After 47 years of teaching and research at Columbia, Jonathan Gross retired last semester, following a highly active career that allowed him to indulge his lifelong love of mathematics while doing pioneering work in graph theory, three-dimensional topology, shape modeling, and sociological modeling.
Professor Gross’s main specialty is topological graph theory, a math subdiscipline straddling combinatorics and geometry and marked by a strong visual component. In several of his 17 books and in over 100 papers and journal articles, Gross expanded topological graph theory by initiating new programs of investigation and by developing new methods for them, often collaborating with Thomas W. Tucker. Together Gross and Tucker authored the influential and comprehensive Topological Graph Theory, which at its release in 1987 represented the state-of-the-art in graph theory. Their objective in writing that book was to create a single source that would provide someone new to topological graph theory with sufficient background to move as quickly as possible into frontier research. It remains a standard reference today.
Gross invented the voltage graph construction in 1973, which is the basis for a concise algebraic specification of infinite families of large graphs and also of placements of such graphs on increasingly complicated surfaces. Gross’s joint work with Tucker on its generalization, published in 1977, includes some of the most frequently cited publications in topological graph theory. The name voltage graph plays on the fact that one of the key properties that sometimes occurs in the specification of placements in surfaces is an algebraic generalization of the Kirchhoff voltage law, which is a property of electrical circuits well known to electrical engineers and physicists. Another paper by Gross and Tucker explains how the voltage graph construction unifies dozens of special cases that occur in the solution of the Heawood map-coloring problem.
Topological graph theory has connections to many other areas of mathematics, including combinatorial and probabilistic models, as well as to knot theory. Since 2009, Gross has been working with Jianer Chen, one of his former Columbia PhD students, to apply topological graph theory to the computer graphics area called shape modeling. Another area that Gross tackled and examined for several years is behavioral and cultural rule systems, for which he developed information-theoretic models and measurement techniques. Working with the eminent British anthropologist Dame Mary Douglas, Gross demonstrated how such high-powered tools can be harnessed to better understand human social behavior. In his book, Measuring Culture, Gross and his co-author Steve Rayner describe how to measure information content in societal patterns, making it possible to obtain objective comparisons of different target populations.
For his research, Gross has earned multiple honors and awards: an Alfred P. Sloan Fellowship, an IBM Postdoctoral Fellowship, and numerous research grants from the Office of Naval Research, the National Science Foundation, the Russell Sage Foundation, and, most recently, from the Simons Foundation.
Gross began his formal mathematics education as an undergraduate at MIT, graduating in 1964. From MIT, he went to Dartmouth College where his PhD thesis on three-dimensional topology (1968) solved a published problem of Fields Medalist John Milnor. After graduate school, he joined the Mathematics Department at Princeton University, working with Ralph Fox, renowned for his work on knot theory and three-dimensional topology.
Though primarily a mathematician, Gross had an early interest in computers, and it was in computer science that he felt that his teaching would have greater impact. He has believed since his high school days that computing was for everybody, and his earliest books are concerned with computer programming. It was to set up a computer science curriculum for arts and science students that he was invited in 1969 to join the Statistics Department at Columbia. His first class in introductory computer programming at Columbia had eight students. Within a few years, 300 students in that same course filled the seats in the large lecture room in Havemeyer. The university expanded the computer science contingent that he headed within Statistics one by one, to five faculty members.
In the late 1960s and the 1970s, computer science was also taught by a small nucleus of professors of Electrical Engineering. In 1978-79, while Gross was Acting Chair of Statistics, Dean Peter Likins of SEAS committed funds from a substantial gift to SEAS to found a separate Computer Science Department, which both contingents agreed to join. Merging the computer science course offerings from Statistics and from Electrical Engineering was among the first initiatives that Gross orchestrated for the new department. He strongly encouraged faculty to balance their teaching assignments between undergraduate and graduate levels. His role in starting Columbia’s computer science department was fundamental; as the department grew over the years—it now numbers 44 professors and 5 lecturers—Gross was the organizer of department-wide efforts to keep the academic curriculum at the educational forefront. Over the years, he became the keeper of institutional memory.
“Not only did we have no cell-phones or personal computers when I was young, most families did not have a television before 1950. We would start being nice to the rich kid around Thursday, in the hope that he would invite us to watch television at his house over the weekend.”
Mathematician, researcher, author, and computer scientist, Gross was also an instructor to thousands of Columbia students. He taught discrete mathematics, graph theory, and combinatorial theory, lecturing with humor and with what he called “enhancement,” short historic anecdotes from science and mathematics as well as from his own mathematical career and personal history. “Enhancements” were as integral to his courses as his meticulously put-together notes, often giving students insight into a different time and place.
He proved popular with students, who variously described him as devoted to his work, brilliant, idiosyncratic, and highly quotable.
When I say a baby-level proof, that’s just how mathematicians talk.
I don’t actually know any babies who can do algebraic topology.
Negativebplusorminusthesquarerootofbsquaredminusfouracovertwoa.
You have to say it very quickly, or you’ll get it wrong.
I have no idea what liquid soap will make your dishes sparkle,
but I recommend liquid Joy for making high-quality knotted soap bubbles with interesting mathematical properties.
For his excellent teaching, Gross received two SEAS awards; in 1994 he received as well the career Great Teacher Award from the Society of Columbia Graduates.
In late career and retirement, Gross continues his research work with his co-authors around the world. Each year he produces numerous journal papers in topological graph theory, and he continues to travel to national and international mathematics meeting to give talks about his research and to chair sessions in his specialty. One math friend has joked, “Jonathan, you are in danger of flunking retirement.” To this, Gross responds that math is too much fun to stop and that he intends to flunk retirement for years to come.
His conclusion of active service at Columbia was marked in December with a dinner amidst remembrances by colleagues and family. Among those who shared their personal stories of Professor Gross, it was perhaps his daughter Rena who most closely articulated how much mathematics infused her father’s life when she recounted how, as a child and misbehaving, her father would threaten “Stop, or I’ll map you into the complex plane.”
The app will collect real-time personal data (hiding participants’ identities) while giving enough information to keep students engaged and willing to stick with the experiment.
Two professors in the Computer Science department at Columbia University have been elected 2015 Association for Computing Machinery (ACM) Fellows: Julia Hirschberg for “contributions to spoken language processing,” and David Blei, for “contributions to the theory and practice of probabilistic topic modeling and Bayesian machine learning.” The ACM fellowship grade recognizes the top 1% of ACM members for their outstanding accomplishments in computing and information technology or outstanding service to ACM and the larger computing community. This year, 42 have been named ACM Fellows.
Julia Hirschberg is the Percy K. and Vida L. W. Hudson Professor of Computer Science and Chair of the Computer Science Department. She is also a member of the Data Science Institute. Her main area of research is computational linguistics, with a focus on the relationship between intonation and discourse. Her current projects include deceptive speech; spoken dialogue systems; entrainment in dialogue; speech synthesis; speech search in low-resource languages; and hedging behaviors.
“I’m deeply honored to be joining this wonderful group of computer scientists,” says Hirschberg. “The ACM has done a wonderful job of supporting and promoting computer science for many years.”
Upon receiving her PhD in Computer and Information Science from the University of Pennsylvania, Hirschberg went to work at AT&T Bell Laboratories, where in the 1980s and 1990s she pioneered techniques in text analysis for prosody assignment in text-to-speech synthesis, developing corpus-based statistical models that incorporate syntactic and discourse information, models that are in general use today. She joined Columbia University faculty in 2002 as a Professor in the Department of Computer Science and has served as department chair since 2012.
As of November 2015, her publications have been cited 14,161 times, and she has an h-index of 60.
Hirschberg serves on numerous technical boards and editorial committees, including the IEEE Speech and Language Processing Technical Committee and the board of CRA-W. Previously she served as editor-in-chief of Computational Linguistics and co-editor-in-chief of Speech Communication and was on the Executive Board of the Association for Computational Linguistics (ACL); on the Executive Board of the North American ACL; on the CRA Board of Directors; on the AAAI Council; on the Permanent Council of International Conference on Spoken Language Processing (ICSLP); and on the board of the International Speech Communication Association (ISCA). She is also noted for her leadership in promoting diversity, both at AT&T and Columbia, and broadening participation in computing.
Among many honors, she is a fellow of the Association for Computational Linguistics (2011), of the International Speech Communication Association (2008), of the Association for the Advancement of Artificial Intelligence (1994); and she is a recipient of the IEEE James L. Flanagan Speech and Audio Processing Award (2011) and the ISCA Medal for Scientific Achievement (2011). In 2007, she received an Honorary Doctorate from the Royal Institute of Technology, Stockholm, and in 2014 was elected to the American Philosophical Society.
David Blei
David Blei is a Professor of Computer Science and Statistics and a member of the Data Science Institute. He is a leading researcher in the field of probabilistic statistical machine learning and topic models, having co-authored (with Michael I. Jordan and Andrew Y. Ng) the seminal paper on latent Dirichlet allocation (LDA), the standard algorithm for discovering the abstract “topics” that occur in a collection of documents. LDA has become an important statistical tool and is used to capture interpretable patterns in a range of applications, including document summarization, indexing, genomics, and image database analysis.
In addition to continuing work on topic models, Blei develops models of social networks, music and audio, images and computer vision, and neuroscience and brain activity. Recent work with students has resulted in efficient algorithms to fit a wide class of statistical models to massive data sets, enlarging the scale of data that can be analyzed using sophisticated methods.
“I am deeply honored to have been elected an ACM fellow,” says Blei. “The ACM is a wonderful organization—for many years it has nurtured the fantastic intellectual and community spirit of computer science.”
Blei’s research has earned him a Sloan Fellowship (2010), an Office of Naval Research Young Investigator Award (2011), the NSF Presidential Early Career Award for Scientists and Engineers (2011), the Blavatnik Faculty Award (2013), and the ACM-Infosys Foundation Award (2013). He is the author and co-author of over 80 research papers.
Before coming to Columbia in 2014, Blei was an Associate Professor of Computer Science at Princeton University. He received his PhD in Computer Science from UC Berkeley and his BSc in Computer Science and Mathematics from Brown University.
For his work on information privacy and anonymity in big data, Yunsung Kim (SEAS’16) was named one of the five male finalists from PhD-granting institutions.
Yunsung is an Egleston Scholar majoring in computer science with a minor in applied mathematics. His current area of research interest includes social and mobile computing, and information network modeling. Since his sophomore summer, he has worked in Augustin Chaintreau’s Mobile and Social Computing Laboratory, where he conducted a number of projects with a focus on how even seemingly irrelevant individual footprints in various domains can be cleverly leveraged to break anonymity and damage user privacy. Prior to joining Chaintreau’s lab and since his first year at Columbia, Yunsung was a member of Martha Kim’s ARCADE lab, where he investigated the design trade-offs of address translation for heterogeneous systems.
Yunsung is the co-author of multiple publications, including a paper presented at last year’s ACM Conference on Online Social Networks; another paper will be presented at the ACM International World Wide Web Conference next year. After he receives his Bachelor’s degree, Yunsung hopes to continue his studies and pursue a PhD in computer science.
Alison Chang
Alison Y. Chang (CC’16), who is majoring in Computer Science and concentrating in Psychology, received the Outstanding Undergraduate Female Research Award – honorable mention from the Computing Research Association (CRA). Chang has done 2.5 years of CS research in Columbia’s SpeechLab and co-authored several papers on her work, working with Julia Hirschberg and Erica Cooper on projects including code switching (the practice of switching interchangeably between languages), web scraping, and text-to-speech data selection. The papers include Overview for the First Shared Task on Language Identification in Code-Switched Data, which was presented in October at the Proceedings of The First Workshop on Computational Approaches to Code Switching.
Outside of Columbia, Chang spent a summer doing digital humanities research at Princeton University’s CS department and also interned twice at Google, on the Chromium team in Taipei, Taiwan and the research-based Machine Intelligence: Semantic Annotation For Text team in Mountain View, California. After graduating in 2016, she hopes to spend the summer teaching through Girls Who Code and then will return to Google as a software engineer, working under Machine Intelligence on Natural Language Processing-related topics.
Robert Ying
Robert Ying, who expects to receive his MS in Computer Science next spring, received a CRA honorable mention for his work on assistive robotics and brain-computer interfaces. This research was conducted in the robotics lab of Peter Allen, where Ying has been a research assistant since his first year at Columbia. While there, he helped create a brain-computer interface grasping system, co-authoring (with Allen and Jonathan Weisz) the paper Grasping with your brain: a brain-computer interface for fast grasp selection, which was published at the 17th International Symposium on Robotics Research. Other robotics-related work includes a semi-autonomous motorized wheelchair platform (based on a modified electric wheelchair) and a robotic arm control system capable of detecting facial expressions. During summer breaks, he has interned at D. E. Shaw, Dropbox, and Amazon Web Services. After he receives his MS, he plans to begin working full-time at Dropbox in San Francisco.
Halfway through their Ubiquitous Genomics class, 20 students were handed a MinION device, a mobile DNA sequencer the size of two matchboxes laid end to end. This $1000 device, still in development, is expected to play an important role in advancing the goal of real-time, on-site DNA sequencing, vastly increasing the applications for DNA sequencing and who can perform it. For their professor Dr. Yaniv Erlich, the device has a more immediate purpose: a teaching tool that gives students a direct experience with handling and analyzing DNA samples and a close-up and early look at the possibilities of mobile DNA sequencing. Plus he was curious. What happens when you give smart, ambitious students a new device not yet fully explored?
The parasites were a surprise. A food sample given out was pre-measured to contain 80% beef and 20% tomato, but the students sequencing the distributed sample identified three parasites (babesia bigemina, wuchereria bancrofti, onchocerca ochengi) by their DNA and duly noted it as part of their assignment. Identifying parasites in food hadn’t been the original intent, but when you give students a brand new tool not yet in general use, it’s never clear how they are going to use it or what they will find. That’s part of the fun, and the learning, too, and it shows the promise of onsite, immediate DNA sequencing.
But it was not all smooth sailing. While students found the accidental parasites, some also mis-identified the beef—purchased from a local New York City grocery store—as bighorn sheep. Not a huge leap (both animals are in the same family), but it does dampen the excitement about how soon real-time DNA sequencing can be used at airports to screen passengers.
Classroom encounters with DNA sequencing
Sequencing DNA from food samples was the first of two hackathons in the class Ubiquitous Genomics, offered for the first time at Columbia and developed by Dr. Yaniv Erlich, an assistant professor of computer science at Columbia who also is also faculty member of the New York Genome Center. The class teaches the basics of DNA sequencing with an eye on future sequencing technologies that promise to make DNA identification possible in real time at almost any location.
Taught in conjunction with Sophie Zaaijer, a postdoc in Erlich’s NY Genome Center lab, the class combines aspects of computer science, biology, electrical engineering, algorithms, and data science, particularly the special challenges of acquiring, storing, and analyzing huge amounts of genomic data. (The first reading assignment was “Big Data: Astronomical or Genomical?” by Stephens.
Slightly more than half the students were computer science majors.
The class, however, has a major DIY twist. Rather than sending out DNA samples to a lab equipped with $1M sequencing machines, Erlich would have students learn DNA sequencing by handling and sequencing DNA samples themselves.
What makes this scenario even imaginable let alone possible is a new, portable DNA sequencing device called a MinION. Inexpensive (approximately $1000), portable, and capable of sequencing DNA in almost real time, the MinION will vastly broaden the applications of DNA sequencing and who can accomplish it.
What makes this scenario even imaginable let alone possible is a new, portable DNA sequencing device called a MinION. Inexpensive (approximately $1000), portable, and capable of sequencing DNA in almost real time, the MinION will vastly broaden the applications of DNA sequencing and who can accomplish it.
The MinION is four inches long, weighs 4 ounces, and gets power from a computer’s USB port.
The MinION uses a sequencing method different from traditional (or sequential) DNA sequencing, which works by first breaking up the DNA into tiny snippets and painstakingly reassembling by mapping them against a template DNA—a process that can take days and requires a high level of expertise.
Instead, MinION relies on nanopore sequencing, where a single-stranded DNA molecule passes through a small biological pore, or nanopore, embedded in an electrical field. As the DNA molecule transits through the nanopore, the individual nucleotides (A, T, G, C) that construct a string of the DNA disrupt the ion current in characteristic ways, creating a profile (called a squiggle) that can be analyzed by software to “decode” the nucleotide sequence, almost in real time.
Erlich was able to procure for his class 5 MinIONs from the device’s manufacturer, Oxford Nanopore Technologies in the U.K. The devices, still under development, are being selectively distributed to researchers for feedback useful for refining and improving the device. (The class has generated interest among the community growing up around the MinION and was covered by a GenomeWeb article.)
Two hackathons count for half the grade
Half the grade would be determined by two hackathons, where the 20 graduate and undergraduate students, working in small groups, would be given the five MinIONs along with five PCs running MinION software. The first hackathon, “Snack to Sequence,” required student teams to identify ingredients of a food sample prepared by Zaaijer. In the second, CSI Columbia, students were given human DNA and asked to identify the specific individual who donated it. The first went much smoother than the second.
Before each hackathon, DNA samples have to first be prepared into what is called a DNA library, a step that was done by Zaaijer. Though generating DNA libraries for MinIONs is much simpler than for other sequencers, it is time-consuming, requires a lab setting (and is therefore not mobile yet), and takes some finesse and experience.
With the libraries prepared, the students take over. Using a pipette, they dispense a solution containing the prepared DNA into the MinION’s flow cell (which contains 512 channels containing nanopores). Care must be taken to not introduce air bubbles that render the pores inaccessible. It is tricky, and generally one person on each team learned how and performed the task each time.
As the solution seeps through the flow cell, individual modules transit the nanopores, and software on the PC powering the MinION starts detecting the ion current disruptions. This raw data (in HDF5 format) gets uploaded to the cloud where software analyzes the recorded events to identify the individual bases. Minutes later, students begin seeing preliminary sequencing data on their screens. (All reads—along with new code written—were posted to the class github site: (https://github.com/dspeyer/ubiq_genome).
Not all 512 channels contain a nanopore that produces reads, but those that do produce individual files for each sequenced read. It’s a lot of data in a very short time, both the promise of the MinION and the beginning of the difficulty for the students.
In a classroom at NY Genome Center, students observe MinION data during second hackathon. Screenshot shows stats on number and length of reads. Eventually students get base pairs needed for identifying samples.
A lot of data needing management
Right away, students were faced with the question of how to transfer thousands of individual files from the lab-supplied PCs to their own (mostly Mac) computers where they could carry out their analysis. The sizes of the files precluded using free cloud-based products such as Dropbox whose free accounts don’t support synchronizing such a large amount of data. The file-transfer issue, after some grappling, was finally solved by placing the data in a BitTorrent Sync folder that was then synched to students’ computers (maxing out the hard drive at least in one case).
Once sequenced data is downloaded, the students head out. Their task is now to compare their reads with existing DNA sequences already in public genetic databases to identify the sample DNA. This they do using existing alignment tools, many free, that compare two or more reads and produce a similarity score.
For the “Snack to Sequence” hackathon, students all used NBCI BLAST, a tool that makes it easy to run stand-alone searches for similar sequences, letting students know whether their read aligns more closely with tomato than zucchini, for instance. The concept is simple, but the difficulty level can ratchet up quickly depending on what two sequences are being compared. Discriminating between two species is one thing; differentiating between two humans who share many of the same traits is something else entirely.
CSI Columbia proved to be much more open ended. Here the aim was to test whether MinION sequencing could be used to identify one specific individual. Normally short tandem reads (STRs) are used to identify individuals (by the FBI for instance), not the long reads returned by nanpore sequencing. As yet, no scientific framework exists on how to identify an individual using the reads generated from the MinION nanopore sequencer. In trying to do so, students were venturing into new territory.
While there are existing alignment tools for comparing two or more human DNA sequences, almost all were developed for traditional sequencing methods, and the students on their own would need to figure out which alignment tool would work best.
Students in new territory
Choosing an alignment tool took time. With many different ones (many free), it was hard to know even when to begin. Among the first to see MinION data, students were operating without any clear guidelines on what tools would work best on nanopore sequencing data. Even downloading the tools took time, a step that often had to be repeated when students discovered their first tool choice didn’t work well. (Oxford Technologies is working on data analysis tools specialized for MinION data.)
File formats were another issue and consumed a significant amount of time for the teams. Different tools accept and output different file formats; many were incompatible, only some were standard.
For CSI Columbia, the difficulty level ratcheted up much more than even Erlich and Zaaijer had imagined. (In fact, CSI Columbia had initially been slated to occur first, ahead with the snack hackathon. However, preparing the DNA libraries for CSI Columbia took longer than planned, necessitating a switch in the order of hackathons.)
Students were not originally given any clues as to the identity of their suspect individuals, just told to take their human DNA sample and search public genetic databases to find the person. With students having difficulty finding the right tool and overcoming file incompatibilities, halfway through the assignment Erlich gave out the names of three possible subjects—Erlich himself, Craig Venter, James Watson, or someone in the 1000 Genomes Project. This extra information changed the scope considerably: rather than finding a single individual in a sea of others, the task became looking closely at three individuals, and ruling out two. Even then, only one of the five groups made the correct identification.
The main issue had to do with the number of reads remaining after students filtered out reads not meeting the quality requirements for nanopore sequencing, leaving a subset of reads to be aligned to the reference genome. The MinION reads covered the genome around 1%, which is extremely low coverage. In addition, nanopore sequencing is less accurate and has more errors (deletions, insertions, and substitutions) and more noise than traditional sequencing. This poses a challenge, since much information about ancestry or traits are tiny changes in the DNA (SNPs). Even so, students were able to learn aspects of an individual’s ancestry and traits (including susceptibility to diseases) but didn’t have enough data to differentiate one individual from others who shared some of the same characteristics.
(Erlich, who wants to offer the class again, is considering adding an intermediate, “where-you-are” report so students can help one another over particular humps.)
Fortunately for the students, the grade depended more on methodology and designing a workable pipeline for sequencing DNA and analyzing the results. In this regard, the students excelled, even with the severe computational challenges of constructing an integrated pipeline out of several distinct steps (acquisition, storage, distribution, and analysis), each with its own particular file incompatibilities and data storage problems. Without a clear route already mapped out by others, students responded by writing their own code to plug up the holes and seamlessly transition data from one step to another.
The fundamental structure was sound; it was the data that was lacking. But even then, students demonstrated they were able to properly interpret the data. If they couldn’t identify the exact donor with the data they had, they still were able to provide a list of traits that in the real world would help narrow the number of suspects.
Zaaijer points out also that students were dealing with a technology that is not yet mature. “Mobile sequencing is just now getting off the ground, and the error-rate in the reads is still relatively high compared to traditional DNA sequencing—though many scientific groups are working on improving this. It was good for the students to experience that not everything is an iPhone where you open the box and it works. Technology evolves by hard work of many people who see a future (and applications) for new types of devices and machines. The hackathons were a good learning experience. Even though there are obstacles to overcome, the students also saw the opportunities the technology has.”
Students not only demonstrated they absorbed the basics of DNA sequencing but added ideas and strategies of their own. One team had taken a throw-processing-power-at-the-problem approach, setting up a dedicated server for the sole purpose of downloading the entire genomes of Watson and Venter—enormous files (100 gigs for Watson, 80 gigs for Venter). It ran for over 24 hours before the team called a halt.
Interestingly the one group that did identify its suspect actually had the fewest reads, but compensated by using a statistical approach that assigned probabilities to different templates, thus narrowing choices to the most likely candidate. Even though their data was far from complete, the team made the correct identification. It was an impressive and highly workable solution that Erlich sees as the subject of a possible scientific paper.
Final project
The final project, good for 25% of the grade had students work in pairs to describe a new use for the MinION. Each group had different applications, from waste water management, to safe person identification at borders, to sequencing by zero gravity. Especially innovative was the idea for at-home sequencing to trace potential transplant rejection; another proposed using the sequencer when traveling to find edible food and clean water resources.
How soon before these applications or any others start appearing in the real world? The students themselves, after the hackathon experiences, may be more conservative than others who haven’t actually tried to do mobile DNA sequencing. Students were asked twice when mobile DNA sequencing replaces passport checks at national borders, once before the hackathons and once after. Their answers were more conservative and perhaps more realistic at the second asking. Students still see the breadth of new applications for mobile DNA sequencing, but know first hand that technical difficulties still need to be resolved.
Erlich and Zaaijer however focus on how much students new to genomics were able to accomplish.
Though there were hiccups, the problems had more to do with finding the proper tools and overcoming incompatible file formats. Erlich and Zaaijer had been pushing from the beginning to see how far the students could go; that some original assumptions didn’t work out was only to be expected. However, the main goal was clearly achieved: students new to DNA sequencing were able with a little training to successfully set up a sequencing pipeline and imagine new uses for the MinION. A sophisticated process once relegated to specialized labs worked in the classroom. It points to the huge possibilities of mobile, onsite DNA sequencing.
Says Erlich, “The future is here: we can place DNA sequencers in the hands of our students. No more theoretical explanation of how sequencers work, no more just data wrangling. We can let them feel the internal, promote critical thinking, and a sense of ownership. DNA is everywhere. In your food, on your clothes, everything you touch. By having these sequencers, we can let students get a glimpse for this rich data layer around them.”
Posted 2/23/16
Class photos by Tim Lee
– Linda Crane
Dean Boyce's statement on amicus brief filed by President Bollinger
President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”
This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.
I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.
Sincerely,
Mary C. Boyce
Dean of Engineering
Morris A. and Alma Schapiro Professor