500 hours. That is the cumulative number of hours that I have spent speaking in front of a group. That is three summers I taught courses with 40 lecture hours each; 8 quarters of graduate teaching assistantship; and 10 quarters of undergraduate group tutoring where I led lab lectures for an hour once a week before one-on-one tutoring. That is six conference presentations (and four practice talks), one in-house research presentation, an advancement proposal, and a commencement address. That is two guest lectures in a graduate class, and one in an all-girls' day school. That is at least one trite talk for general-education requirements (I lost count after one, because it matters so little).
I am not so nervous about public speaking anymore. It really does come with practice.
Friday, November 25, 2011
Clinical data management: My response to a new technology
Meanwhile in Austria, at the USAB 2011 conference on eHealth, hundreds of health and technology professionals gather to discuss topics relevant to information flow, patient empowerment, and clinical decision-making.
The first of four (four!) keynote presentations was given by Vimla Patel of Columbia University, whose interests lie in quality of eHealth data. In the talk, Cognitive Approaches to Clinical Data Management for Decision Support: Is It Old Wine In a New Bottle?, Dr. Patel argued that indeed it is new wine in a new bottle. This post chronicles my reactions to the talk. Dr. Patel, if you happen upon this post, please know that I am quite jet-lagged and had had three of the complimentary espressos, in rapid succession, shortly before your talk. I do not intend this as an apology, but as an explanation, and as a hope that you will not hate me for expressing my views so plainly. I don't buy it, and here, I explain why.
Patients are in danger!
The problem was outlined thus: Information technology impacts patient safety. There is simply not enough evidence that current information technology systems are good for patient safety -- in fact, they might be detrimental. One of the reasons is that there is no accountability for these systems. According to Dr. Patel, in many cases, systems are designed and deployed by engineers without consulting with clinicians or patients, and without proper responsibility for upkeep of the systems for new ideas and trends.
Federal regulations
A way to address this? Dr. Patel suggested: Technology should be monitored by an agency or government; there should be federal regulations on software released for eHealth purposes.
Pardon me while I gather my jaw from the floor. Right off the bat, I can think of at least two reasons this will never work.
First: The design-development cycle would be too cumbersome. Can you imagine being the poor programmer that has to succumb to federal regulations, to laws and restrictions, to government-imposed checks and balances? Can you imagine trying to add a new feature, a new decision flow, or a new interaction method? I thought the Apple Developer cycle was bad; this would be murder.
Second: Regulations on software are restricting. No, I don't have a citation for this, you overachiever. I know from my experience and from the experiences of all of my colleagues that regulations are inversely proportional to success of a software product. While it is true that some restrictions spark creativity, what we are talking about here is a severe impediment to the development process.
Here's a bonus: Third: Every hospital, every office, every provider has different requirements. How do you federally-regulate this difference and custom instances of the same product? It is a nightmare.
Why Electronic Health Records (EHRs) suck
Problems with current electronic health record (EHR) system include the following.
In EHRs, information is structured temporally, which is how clinicians come about gathering the data. As a clinician, you see a patient, you take notes; you see the patient again, you take more notes. Over time, this presents a time-oriented view of the patient's health. But the problem is that clinicians do not think about patient health in this way. They think in terms of symptoms and relationships between symptoms, tests, and diagnoses.
So the question is how to store the data in a way that is fundamentally useful to clinicians, and how to retrieve and display it in the same way that they think about the data. There is too much data, too much redundant data, and too many sources of related data. There is a mismatch between cognitive processes of clinicians and the way the data is stored and represented.
The result Dr. Patel drew is that there is poor usability study and requirements gathering for these EHR systems. By involving users (i.e., clinicians) in the process early and often, she argued, we can explicitly retain the relational structure in software that clinicians use in real life: a complex mental model of vital signs linked to symptoms linked to potential diagnoses. A directed graph of thoughts and decisions. Understand what people want, she said. Test iteratively with users, she said.
I thought: Don't be afraid to say participatory design.
User study is not enough
Dr. Patel never outright said it, but it is a question of tagging and metadata and, most certainly, provenance. In effect, the question is the same as in any large-scale file system (think peta-scale): how can you predict which data the user will want to retrieve? I refer the reader to work done by the Storage Systems Research Center which has been tackling the problem in full force.
Sure, representing the data is important. As with any file system (let's face it; that's what we are talking about here), we can know everything about what users want, but it may be fundamentally impossible to deliver this kind of system. Big data have an inherent bottleneck at retrieval; they have an inherent bottleneck at storage and archival.
Nobody likes to be wrong
In real life clinicians draw logical conclusions in a guess-and-check fashion: given a set of symptoms gathered from charts, nurses, attendings, and other sources of information, they make a mental model of the potential problems and solutions which can be confirmed or refuted. In the ideal scenario, the clinicians would chart these decisions and potential diagnoses. They would chart, in this system, anything that they considered potentially important in the future.
Oh god! So many problems!
First, think about the paperwork overhead. Electronic paperwork, whatever. Sure, in the ideal world with infinite time and infinite memory (as they say in computer science), doctors would save all of their thoughts.
Second, think of the liability. I am not even talking about not wanting to be wrong, which, of course, everyone feels. It is well-studied in elderly patients with dementia: they will not admit to forgetting appointments or missing meetings. People won't chart wrong guesses. Being wrong is bad. For a clinician, being wrong leads to liability. Mis-representing a symptom that can lead to a missed diagnosis leads to liability. How can you prove your motives were good, when the patient's health was compromised?
Third, is this another way to minimize patient interaction? Look, in labor and delivery in the US, the average doctor spends something like 2 hours, 41 minutes with her patient, total, throughout her entire average 10-month pregnancy and including the 24-hour birth. With such a system, will it mean that a doctor no longer needs to spend quality time with her patient, but instead spend this time mining data? I do not argue that in aggregate, data gathered over time in a particular facility can be powerful. But what happened to patient-centered care?
It's different on paper
Electronic health records have a different set of abstractions and information flow (and hence, a different set of mistakes one can make) than paper-based ones. For paper-based health records, it goes basic concepts (such as vital signs) : intermediate constructs (what to do with the vitals: e.g., compare to normal, compare to expected, compare over time) : heuristics (visualization and diagnosis). Concrete to abstract. But most experts do not bother writing down some basic concepts because it is inefficient, much in the same way you do math in your head or play chess without writing down the possible moves. For EHRs, the flow goes heuristics : intermediate constructs : basic concepts. Abstract to concrete. The overlap is at intermediate constructs, and the question is how to move them from the head to the computer.
I imagined WebMD, the website that spits out a list of things that could be killing you subtly or not-so-subtly, given an input of real or imagined symptoms. The output from WebMD is potentially useless. You have a stomach cramp and a head ache? It could be a brain tumor and pregnancy.
Disempowered
Of course, the tool Dr. Patel described would need to be understood by a doctor, or someone else medically trained. In fact, she said, in some cases, you do not want the patient to know at all. There are cases that the patient should not have access to these private thoughts of doctors. With the exception of one situation which I do not have training to understand, namely, adolescents seeking psychiatric care (if I were said patient, I would damn well like to know what the doctor thinks!), I thought that it was a huge oversight that the system would be unusable by anyone without proper training. Make it understandable, she said, for the doctor.
What about patient empowerment? What about patient information? In Germany, a doctor will sit alongside the patient to look through a clinical workflow, and they will decide together, collaboratively, on the proper treatment. Why is there not more of this worldwide? And why not just teach the patient?
Dr. Patel said the goal is to move towards patient-centered cognitive support for the clinician. I realize that this is the goal, but with this technology, I worry that we are removing real interactions between the clinician and the patient in favor of data collection. We are in a digital age where we teeter on worshipping data: in some ways, we hold data above all other things. We hold data collection, for example, above the real-life interactions, the real time that doctors and nurses used to spend with patients, that now they spend writing down things about their brief encounters.
Finally, and then I will stop ragging on this keynote, what about evidence-based medicine? Why has it never been mentioned, alone or in conjunction with "patient-centered" care? Why are we increasing the burden on care providers while decreasing the burden on the very people that are meant to do well -- by removing them from the patient and treating their thoughts, education, and logic, which makes them unique and valuable, as interchangeable with any other doctor, clinician, or robot?
Now, take this with a grain of salt because my triple caffeine buzz is wearing off. I was pretty excited about this talk when it began: the initial idea was that medical technology, and electronic health record systems in particular, are possibly doing harm to the patients they intend to serve. But near the end, it was clear that the only take-away, for me, is that more user study is needed for electronic health records, to determine what doctors need to make them disposable. As a patient and as a researcher, I feel disempowered.
But it is an interesting file systems problem.
The first of four (four!) keynote presentations was given by Vimla Patel of Columbia University, whose interests lie in quality of eHealth data. In the talk, Cognitive Approaches to Clinical Data Management for Decision Support: Is It Old Wine In a New Bottle?, Dr. Patel argued that indeed it is new wine in a new bottle. This post chronicles my reactions to the talk. Dr. Patel, if you happen upon this post, please know that I am quite jet-lagged and had had three of the complimentary espressos, in rapid succession, shortly before your talk. I do not intend this as an apology, but as an explanation, and as a hope that you will not hate me for expressing my views so plainly. I don't buy it, and here, I explain why.
Patients are in danger!
The problem was outlined thus: Information technology impacts patient safety. There is simply not enough evidence that current information technology systems are good for patient safety -- in fact, they might be detrimental. One of the reasons is that there is no accountability for these systems. According to Dr. Patel, in many cases, systems are designed and deployed by engineers without consulting with clinicians or patients, and without proper responsibility for upkeep of the systems for new ideas and trends.
Federal regulations
A way to address this? Dr. Patel suggested: Technology should be monitored by an agency or government; there should be federal regulations on software released for eHealth purposes.
Pardon me while I gather my jaw from the floor. Right off the bat, I can think of at least two reasons this will never work.
First: The design-development cycle would be too cumbersome. Can you imagine being the poor programmer that has to succumb to federal regulations, to laws and restrictions, to government-imposed checks and balances? Can you imagine trying to add a new feature, a new decision flow, or a new interaction method? I thought the Apple Developer cycle was bad; this would be murder.
Second: Regulations on software are restricting. No, I don't have a citation for this, you overachiever. I know from my experience and from the experiences of all of my colleagues that regulations are inversely proportional to success of a software product. While it is true that some restrictions spark creativity, what we are talking about here is a severe impediment to the development process.
Here's a bonus: Third: Every hospital, every office, every provider has different requirements. How do you federally-regulate this difference and custom instances of the same product? It is a nightmare.
Why Electronic Health Records (EHRs) suck
Problems with current electronic health record (EHR) system include the following.
In EHRs, information is structured temporally, which is how clinicians come about gathering the data. As a clinician, you see a patient, you take notes; you see the patient again, you take more notes. Over time, this presents a time-oriented view of the patient's health. But the problem is that clinicians do not think about patient health in this way. They think in terms of symptoms and relationships between symptoms, tests, and diagnoses.
So the question is how to store the data in a way that is fundamentally useful to clinicians, and how to retrieve and display it in the same way that they think about the data. There is too much data, too much redundant data, and too many sources of related data. There is a mismatch between cognitive processes of clinicians and the way the data is stored and represented.
The result Dr. Patel drew is that there is poor usability study and requirements gathering for these EHR systems. By involving users (i.e., clinicians) in the process early and often, she argued, we can explicitly retain the relational structure in software that clinicians use in real life: a complex mental model of vital signs linked to symptoms linked to potential diagnoses. A directed graph of thoughts and decisions. Understand what people want, she said. Test iteratively with users, she said.
I thought: Don't be afraid to say participatory design.
User study is not enough
Dr. Patel never outright said it, but it is a question of tagging and metadata and, most certainly, provenance. In effect, the question is the same as in any large-scale file system (think peta-scale): how can you predict which data the user will want to retrieve? I refer the reader to work done by the Storage Systems Research Center which has been tackling the problem in full force.
Sure, representing the data is important. As with any file system (let's face it; that's what we are talking about here), we can know everything about what users want, but it may be fundamentally impossible to deliver this kind of system. Big data have an inherent bottleneck at retrieval; they have an inherent bottleneck at storage and archival.
Nobody likes to be wrong
In real life clinicians draw logical conclusions in a guess-and-check fashion: given a set of symptoms gathered from charts, nurses, attendings, and other sources of information, they make a mental model of the potential problems and solutions which can be confirmed or refuted. In the ideal scenario, the clinicians would chart these decisions and potential diagnoses. They would chart, in this system, anything that they considered potentially important in the future.
Oh god! So many problems!
First, think about the paperwork overhead. Electronic paperwork, whatever. Sure, in the ideal world with infinite time and infinite memory (as they say in computer science), doctors would save all of their thoughts.
Second, think of the liability. I am not even talking about not wanting to be wrong, which, of course, everyone feels. It is well-studied in elderly patients with dementia: they will not admit to forgetting appointments or missing meetings. People won't chart wrong guesses. Being wrong is bad. For a clinician, being wrong leads to liability. Mis-representing a symptom that can lead to a missed diagnosis leads to liability. How can you prove your motives were good, when the patient's health was compromised?
Third, is this another way to minimize patient interaction? Look, in labor and delivery in the US, the average doctor spends something like 2 hours, 41 minutes with her patient, total, throughout her entire average 10-month pregnancy and including the 24-hour birth. With such a system, will it mean that a doctor no longer needs to spend quality time with her patient, but instead spend this time mining data? I do not argue that in aggregate, data gathered over time in a particular facility can be powerful. But what happened to patient-centered care?
It's different on paper
Electronic health records have a different set of abstractions and information flow (and hence, a different set of mistakes one can make) than paper-based ones. For paper-based health records, it goes basic concepts (such as vital signs) : intermediate constructs (what to do with the vitals: e.g., compare to normal, compare to expected, compare over time) : heuristics (visualization and diagnosis). Concrete to abstract. But most experts do not bother writing down some basic concepts because it is inefficient, much in the same way you do math in your head or play chess without writing down the possible moves. For EHRs, the flow goes heuristics : intermediate constructs : basic concepts. Abstract to concrete. The overlap is at intermediate constructs, and the question is how to move them from the head to the computer.
I imagined WebMD, the website that spits out a list of things that could be killing you subtly or not-so-subtly, given an input of real or imagined symptoms. The output from WebMD is potentially useless. You have a stomach cramp and a head ache? It could be a brain tumor and pregnancy.
Disempowered
Of course, the tool Dr. Patel described would need to be understood by a doctor, or someone else medically trained. In fact, she said, in some cases, you do not want the patient to know at all. There are cases that the patient should not have access to these private thoughts of doctors. With the exception of one situation which I do not have training to understand, namely, adolescents seeking psychiatric care (if I were said patient, I would damn well like to know what the doctor thinks!), I thought that it was a huge oversight that the system would be unusable by anyone without proper training. Make it understandable, she said, for the doctor.
What about patient empowerment? What about patient information? In Germany, a doctor will sit alongside the patient to look through a clinical workflow, and they will decide together, collaboratively, on the proper treatment. Why is there not more of this worldwide? And why not just teach the patient?
Dr. Patel said the goal is to move towards patient-centered cognitive support for the clinician. I realize that this is the goal, but with this technology, I worry that we are removing real interactions between the clinician and the patient in favor of data collection. We are in a digital age where we teeter on worshipping data: in some ways, we hold data above all other things. We hold data collection, for example, above the real-life interactions, the real time that doctors and nurses used to spend with patients, that now they spend writing down things about their brief encounters.
Finally, and then I will stop ragging on this keynote, what about evidence-based medicine? Why has it never been mentioned, alone or in conjunction with "patient-centered" care? Why are we increasing the burden on care providers while decreasing the burden on the very people that are meant to do well -- by removing them from the patient and treating their thoughts, education, and logic, which makes them unique and valuable, as interchangeable with any other doctor, clinician, or robot?
Now, take this with a grain of salt because my triple caffeine buzz is wearing off. I was pretty excited about this talk when it began: the initial idea was that medical technology, and electronic health record systems in particular, are possibly doing harm to the patients they intend to serve. But near the end, it was clear that the only take-away, for me, is that more user study is needed for electronic health records, to determine what doctors need to make them disposable. As a patient and as a researcher, I feel disempowered.
But it is an interesting file systems problem.
Tuesday, November 15, 2011
How to get my number at a tech conference
In technology, the male-to-female ratio strikingly favors the males. Of course, it depends on the specific field how rough it is: game design, for example, has more women than semiconductor research; human-computer interaction has more women than systems and security.
Having just had come from Grace Hopper Celebration of Women in Computing I was keenly aware that at Supercomputing 2011 the ratio was not 2:2900 (I think there were a few men at Grace Hopper) but more like 150:11. Yup, I counted, sitting in the back of the room where my workshop was being held. Interestingly, there were five of us students from the same university at this workshop, and four of us were women.
My talk was third in a block of three, and the latter two were similar in that they stemmed from the same set of interviews and touched on similar topics. After my talk, there was discussion in the audience, and when I rejoined my university's group of students, we began being approached by researchers interested in our work -- with comments, suggestions, and questions.
A young man, probably a few years younger than me, in a white and green graph-paper-patterned shirt, wearing dark-rimmed glasses on his long nose, and his hair cropped in the typical defense-industry fashion, approached me and my female colleague.
"I work on the very system you were studying," he said. "I'm the guy. I run everything, set the policy, and have tons of data on it."
"Hang on! Stay right there!" we exclaimed in unison. In a flash, my colleague and I ran off to retrieve our business cards. Tons of data! An expert in the field we are studying! This was very exciting. I ran back to my chair where I had left my laptop and bag, grabbed a stack of cards, and ran back, nearly knocking over chairs in the process. I saw my colleague also rushing and rummaging.
I made it back first. Presenting my card to him (American-style), I said, with a smirk, the first thing that came into my head:
"You just discovered the best way to get two women's numbers at the same time."
He looked at my card, and looked at me, and I could see that he was trying to determine whether what I had said was shocking or funny. I laughed.
Just a disclaimer that it was a joke.
But if you meet me at a tech conference, now you know how to get my card.
Friday, November 11, 2011
The Thin Line: Advising vs. Supervising
This is a post about the session The Thin Line: Advising vs. Supervising at Grace Hopper Celebration of Women in Computing.
The panelists: Laura Dillion has spent decades advising students at three different large universities, and spent time as department chair. Susanne Hambrusch has experience dealing with situations between students and advisors that went from bad to worse. She says that just knowing that a situation can occur is important. Lori Pollock has had experiences being the unbiased mediator between graduate students and their advisors.
Can I be co-advised by two professors? Can I switch advisors?
Sometimes switching advisors is controversial both in terms of your own work and in terms of the political climate of the department or school. Consider your own work, which may or may not move with you to the new advisor or department. It likely won't. Figure out if you will still have a project, and enough to do in order to graduate with a big dent in the new field. If there are concerns (and there should be), find someone that doesn't have a stake in the problem, and ask him or her for help. Someone unbiased can provide valuable advice. It could be a former instructor, a graduate advisor, or even more senior graduate students. Some students don't ask for help. In some places there is no help. That's where you should turn to other sources: e.g., friends' advisors, family, the Systers mailing list.
How and when do you ask about the author order and/or about presenting the paper?
Talk about it early. It can change, but know before you invest a ton into a paper how much credit you'll get for the work. Three possibilities are alphabetical order, percentage of writing done, and switching author orders if you expect more than one publication.
How do you know what your research contribution is on multi-authored work, and what you can present as your own work?
This is a good question in interviews, so make sure you have a well-reasoned answer: your research is your identity. The abstract of your dissertation, and your introduction, should make it very clear the different roles. Co-authored and multi-authored work can become "background" for a dissertation, and some papers never make it into anybody's dissertations. Think ahead: the part that's yours is the part that you will continue when you graduate. Be scrupulously honest.
My advisor keeps giving me more work, and I want to schedule my dissertation and graduate.
Ask. Sit down with your advisor(s), and have the conversation. Don't wait until after you've done the additional work to ask, but ask right then. Show your credentials: the number of papers you have, your advancement proposal which has been fulfilled, the chapters you've written, etc. It may the case that you aren't ready to graduate, but it may be that you are. It may be that you have differences in expectations (e.g., your advisor thinks you want to go to an R1 research institution, but you want to go into industry) which have serious differences in preparation for graduation. You won't know unless you have that conversation with the advisor.
What if my committee doesn't think I'm ready, but my advisor does?
Your advisor is your advocate. He or she needs to convince the committee that you're ready. There are no hard-and-fast rules to follow: you may want to look for a mentor for an outside, unbiased opinion.
What if my advisor is a total jerk? (This question was truncated and summarized.)
Find someone that can advocate for you. Go to the other faculty. But don't go straight to the dean, going over the head of the senior faculty and department chair, because this can cause bad feelings and really burn bridges. Learn about what's possible in your university and what resources you have. Sometimes it takes intervention for your advisor to do change; sometimes you have to switch departments and get your work to count toward your new affiliation. But get support from the senior people in the school that have influence.
What if my advisor lost his or her funding, and has no more money?
Understand that this could very well be true: the faculty member may have thought he had secured money but the money didn't come through; he or she could have overanalyzed the financial possibilities for the quarter or the year. Sometimes the money just disappears, such as with government contracts. It is embarrassing to the advisor, especially when the advisor had already made plans on the money (such as by promising you funding). Consider going to the chair or graduate director. Take up a TAship, teach a summer class, or find other sources of funding around your department or even in a different department.
How do you transition from being a student to being an advisor?
First you have to find a job in a supportive environment. Attend the CRA-W workshop for junior faculty in which they teach you how to be a good mentor and advisor. Have a mentor in the department that you join, that can help you along. Don't do it as trial an error, one student at a time. As a graduate student you can work with undergraduates in the summer on collaborative projects, and practice advising and mentoring.
Connecting the Disconnected: Improving Internet Access for the Other Four Billion
This post is about Connecting the Disconnected: Improving Internet Access for the Other Four Billion with Professor Margaret Martonosi from Department of Computer Science at Princeton University, at Grace Hopper Celebration of Women in Computing.
Information technology is an enabler. It is an enabler in education - for people to learn, through online courses, articles, and things in the public domain; in health care - getting an ultrasound when needed; in agriculture - the ability to find out why your crops aren't thriving; and in open and fair government - the ability to discuss politics openly with other people.
This presentation was about C-LINK, a type of delay-tolerant vehicular network. (Note: This vehicular network is an example of Sneakernet, though I don't think Margaret ever called it this.)
The current state of universal connectivity
Sure, the US is connected. Over 75% of the country has wired or wireless access. But there's the digital divide. The digital divide means that the places that need this technology the most are the places with the most impaired access to it.
The three factors influencing universal information technology access includes relevant and accessible software, using effective and affordable hardware, and have universal connectivity. C-LINK, Margaret's project, was influenced by all three.
The problem with affordable hardware is the "last mile" problem: it is easy to connect the majority of the people, but in the last mile, you have to extend connectivity to rural or hard-to-reach regions. This can be complicated, and costly. In rural Africa (for example), the last mile is of a whole different scale. So then we consider wireless technology, which is expensive, we need to think about how it still needs a wired backbone, requires ongoing maintenance, is subject to corruption or salvaging (because copper sells), and it needs political support. On the other hand, wireless technology is leapfrogging wired connectivity (especially in developing regions). Mobile and cellular is a big growth area and are increasingly penetrating the world. But even though there is so much cellular technology going on, that doesn't mean that it's cheap.
Let's look at effective and affordable hardware. Although there are efforts to build cheap hardware (such as the hundred-dollar laptop made by the One Laptop Per Child organization), it's not taking off. It's just not pervasive -- most of Africa and Asia have less than 10 computers per 100 people. That's even when you consider microcontrollers as "computers." So what is the world's computer? The world's computer is a cell phone. About half of the world's adults own one. There are more cell phones in India than credit cards -- and cell phones, even when they're not smartphones, are chock full of interactivity. (Note: Can you imagine an accessible Qwiki for developing nations that works over SMS? I can.)
Connectivity
Wired connectivity: availability is increasing and costs are dropping. For example, transit prices in Kenya have dropped to $120/Mbps -- which are similar to US prices in 2003.
Delay-tolerant network: Imagine a big city with a broadband connection, and a nearby village. Imagine that the village is on some rural bus or taxi route to the city. Now, imagine that the bus has a laptop inside, with a large hard drive and a wireless access point. People (and goats and chickens) board the bus in the village, send some requests on the laptop. Although the laptop isn't connected to the broadband, it can queue the request and send it when it reaches the city, and bring back the reply when it returns to the village. If the bus visits the village every 5 hours, that's a 5-hour latency -- but at least it is access to information.
The vehicular delay-tolerant network is very low-cost, easy to deploy, and has a very high data carrying capacity. Villagers can collaborate on their queries, and the results can be available to others in the village doing similar web searches (called collaborative caching). The system can also be improved by clever prefetching -- the computer on the bus can be thinking hard over the next several hours about what else can be proactively fetched based on the current queries. Clearly, prefetching all of the links on a page would be smart, but maybe so would translating a page from a local language to a more commonly-used language. This isn't a microcontroller with a tiny cache. This is a big hard drive. If the hard drive is huge, there is no worry about performance loss in prefetching things that will not be used, but the overhead of not having things that may be useful is huge.
"Come back tomorrow. Your data will be on the bus."
The authors designed, built, and tested C-LINK, the vehicular delay-tolerant network, over a week in Nicaragua. The city was Somotillo, Nicaragua -- no skyscrapers, but there was a school with a computer cluster and wired connectivity and a small, expensive Internet cafe with 2 computers. Participants were school children, invited to come in and browse the highly-delayed web. About 80% of the participants in the project had never used the Internet before. One of the cool things that the project looked at is when participants sent at the same query, or searched collaboratively. With each successive trip into the village, the bus brought back more data. As the village's cache filled up, users' miss rates plummeted. Data were on their computers or on the computers of their collaborators -- remember collaborative caching? The authors found that highly correlated access patterns provided strong incentive for collaboration. Oh, and we can evict old data or data that has not been accessed recently. Any cache eviction policy will work here.
Then Margaret and her team considered using laptops and other mobile computing devices and, despite the worry of data loss, found that going mobile wasn't so bad. If anything, it made for more kiosks -- little hubs for collaborative information. Then, they found that the requests for data were fairly small, so it was possible to exploit cellular connectivity, where available. They looked at data brought back by the once-a-day trip into town, by the 5-times-per-day bus ride, and a hybrid data retrieval mode by combining these with exploiting SMS.
Look around you
Other software that the authors found interesting and noteworthy included these.
Information technology is an enabler. It is an enabler in education - for people to learn, through online courses, articles, and things in the public domain; in health care - getting an ultrasound when needed; in agriculture - the ability to find out why your crops aren't thriving; and in open and fair government - the ability to discuss politics openly with other people.
This presentation was about C-LINK, a type of delay-tolerant vehicular network. (Note: This vehicular network is an example of Sneakernet, though I don't think Margaret ever called it this.)
The current state of universal connectivity
Sure, the US is connected. Over 75% of the country has wired or wireless access. But there's the digital divide. The digital divide means that the places that need this technology the most are the places with the most impaired access to it.
The three factors influencing universal information technology access includes relevant and accessible software, using effective and affordable hardware, and have universal connectivity. C-LINK, Margaret's project, was influenced by all three.
The problem with affordable hardware is the "last mile" problem: it is easy to connect the majority of the people, but in the last mile, you have to extend connectivity to rural or hard-to-reach regions. This can be complicated, and costly. In rural Africa (for example), the last mile is of a whole different scale. So then we consider wireless technology, which is expensive, we need to think about how it still needs a wired backbone, requires ongoing maintenance, is subject to corruption or salvaging (because copper sells), and it needs political support. On the other hand, wireless technology is leapfrogging wired connectivity (especially in developing regions). Mobile and cellular is a big growth area and are increasingly penetrating the world. But even though there is so much cellular technology going on, that doesn't mean that it's cheap.
Let's look at effective and affordable hardware. Although there are efforts to build cheap hardware (such as the hundred-dollar laptop made by the One Laptop Per Child organization), it's not taking off. It's just not pervasive -- most of Africa and Asia have less than 10 computers per 100 people. That's even when you consider microcontrollers as "computers." So what is the world's computer? The world's computer is a cell phone. About half of the world's adults own one. There are more cell phones in India than credit cards -- and cell phones, even when they're not smartphones, are chock full of interactivity. (Note: Can you imagine an accessible Qwiki for developing nations that works over SMS? I can.)
Connectivity
Wired connectivity: availability is increasing and costs are dropping. For example, transit prices in Kenya have dropped to $120/Mbps -- which are similar to US prices in 2003.
Delay-tolerant network: Imagine a big city with a broadband connection, and a nearby village. Imagine that the village is on some rural bus or taxi route to the city. Now, imagine that the bus has a laptop inside, with a large hard drive and a wireless access point. People (and goats and chickens) board the bus in the village, send some requests on the laptop. Although the laptop isn't connected to the broadband, it can queue the request and send it when it reaches the city, and bring back the reply when it returns to the village. If the bus visits the village every 5 hours, that's a 5-hour latency -- but at least it is access to information.
Never underestimate the bandwidth of a station wagon carrying tapes hurtling down the highway. -- S. Tanenbaum
The vehicular delay-tolerant network is very low-cost, easy to deploy, and has a very high data carrying capacity. Villagers can collaborate on their queries, and the results can be available to others in the village doing similar web searches (called collaborative caching). The system can also be improved by clever prefetching -- the computer on the bus can be thinking hard over the next several hours about what else can be proactively fetched based on the current queries. Clearly, prefetching all of the links on a page would be smart, but maybe so would translating a page from a local language to a more commonly-used language. This isn't a microcontroller with a tiny cache. This is a big hard drive. If the hard drive is huge, there is no worry about performance loss in prefetching things that will not be used, but the overhead of not having things that may be useful is huge.
"Come back tomorrow. Your data will be on the bus."
The authors designed, built, and tested C-LINK, the vehicular delay-tolerant network, over a week in Nicaragua. The city was Somotillo, Nicaragua -- no skyscrapers, but there was a school with a computer cluster and wired connectivity and a small, expensive Internet cafe with 2 computers. Participants were school children, invited to come in and browse the highly-delayed web. About 80% of the participants in the project had never used the Internet before. One of the cool things that the project looked at is when participants sent at the same query, or searched collaboratively. With each successive trip into the village, the bus brought back more data. As the village's cache filled up, users' miss rates plummeted. Data were on their computers or on the computers of their collaborators -- remember collaborative caching? The authors found that highly correlated access patterns provided strong incentive for collaboration. Oh, and we can evict old data or data that has not been accessed recently. Any cache eviction policy will work here.
Then Margaret and her team considered using laptops and other mobile computing devices and, despite the worry of data loss, found that going mobile wasn't so bad. If anything, it made for more kiosks -- little hubs for collaborative information. Then, they found that the requests for data were fairly small, so it was possible to exploit cellular connectivity, where available. They looked at data brought back by the once-a-day trip into town, by the 5-times-per-day bus ride, and a hybrid data retrieval mode by combining these with exploiting SMS.
Look around you
Other software that the authors found interesting and noteworthy included these.
- TEK is an e-mail-based web browser.
- M-Profesa helps Kenyan children prepare for the secondary school test through SMS.
- Ushahidi, also originally Kenyan, helps with crowd-sourcing of information.
Getting more involved
Imagine what you could do if you could alleviate teacher shortages by having better distance learning technologies. Imagine what you could do if you could have better information flow and expose corruption in the government. Get involved in Engineers Without Borders and attend Development conferences, such as ACM DEV 2012. Make a company. Build stuff!
Thursday, November 10, 2011
Community College Women in Computer Science: A Study's Preliminary Results
This is a post about Community College Women in Computer Science: A Study's Preliminary Results at Grace Hopper Celebration of Women in Computing.
The authors present their preliminary results from studies from community college students around California, aiming to answer the question what makes community college students transfer to 4-year university? That is: What factors determine whether community college students enrolled in a CS1-like course intend to study computer science at a 4-year university?
True or false? There are different motivations for males and females in pursuing computer science as a major, and women just don't value computing. The answer is false. Men were found to have great expectations for success with computing, but there were no gender differences in how much students value computing.
True or false? Family support is critical in choice of major and parental pressure is based on gender stereotypes. Also false. Family support was not critical, but peer encouragement was very important for both women and men in pursuing science.
True or false? Women's under-representation in computer science majors is due to lack of computer use an lack of computer game play. This one is true. Intention to pursue computer science was very important, and exposure to computing and computers (including gaming) was associated with intention.
The authors presented work collected over a true longitudinal study over two years, looking at demographics as well as social factors that may influence students' staying in computing after community college. They sampled the students three times: at time of enrollment, half-way through the program, and at the end of the two-year program.
Women were more likely to be older, have had a degree already, have a mother working in computing, and have had a programming mentor when enrolling in the introductory programming course, compared to male students. Whether or not a woman was comfortable talking to her professor did not affect her intention to pursue computing (and the other way around). Women thought computer programming was like thinking.
Men had a greater intention to pursue computer science at a four-year university, were more likely to play video games longer, and have mothers with no BS/BA, compared to female students. Men thought computer programming was creative.
How do we increase the number of women in computer science?
1) Men see computer science as creative, but women don't -- they see it as thinking. How can we bring creativity to women?
2) Men are influenced by computing experiences, including video games, in their intention to pursue compter science. How do we use games to help intention to pursue a bachelors (or higher) degree in computing? How do we get gals into games? How do we provide early programming opportunities to gals?
3) Men report that they get more support from their peers. How do we encourage peer support for women?
And this leads to the awesomest idea of the conference:
How do we use video games (especially competitive ones) to bring early interventions to women?
I'm envisioning a League of Legends clan for high school girls!
The authors present their preliminary results from studies from community college students around California, aiming to answer the question what makes community college students transfer to 4-year university? That is: What factors determine whether community college students enrolled in a CS1-like course intend to study computer science at a 4-year university?
True or false? There are different motivations for males and females in pursuing computer science as a major, and women just don't value computing. The answer is false. Men were found to have great expectations for success with computing, but there were no gender differences in how much students value computing.
True or false? Family support is critical in choice of major and parental pressure is based on gender stereotypes. Also false. Family support was not critical, but peer encouragement was very important for both women and men in pursuing science.
True or false? Women's under-representation in computer science majors is due to lack of computer use an lack of computer game play. This one is true. Intention to pursue computer science was very important, and exposure to computing and computers (including gaming) was associated with intention.
The authors presented work collected over a true longitudinal study over two years, looking at demographics as well as social factors that may influence students' staying in computing after community college. They sampled the students three times: at time of enrollment, half-way through the program, and at the end of the two-year program.
Women were more likely to be older, have had a degree already, have a mother working in computing, and have had a programming mentor when enrolling in the introductory programming course, compared to male students. Whether or not a woman was comfortable talking to her professor did not affect her intention to pursue computing (and the other way around). Women thought computer programming was like thinking.
Men had a greater intention to pursue computer science at a four-year university, were more likely to play video games longer, and have mothers with no BS/BA, compared to female students. Men thought computer programming was creative.
How do we increase the number of women in computer science?
1) Men see computer science as creative, but women don't -- they see it as thinking. How can we bring creativity to women?
2) Men are influenced by computing experiences, including video games, in their intention to pursue compter science. How do we use games to help intention to pursue a bachelors (or higher) degree in computing? How do we get gals into games? How do we provide early programming opportunities to gals?
3) Men report that they get more support from their peers. How do we encourage peer support for women?
And this leads to the awesomest idea of the conference:
How do we use video games (especially competitive ones) to bring early interventions to women?
I'm envisioning a League of Legends clan for high school girls!
What if... You Thrived on the Tenure Track?
This is a post about What if... You Thrived on the Tenure Track? at Grace Hopper Celebration of Women in Computing.
Ceclia Aragon took a 14-year leave from graduate school. In the time, she had two kids, was a stunt pilot (yup, in air shows), and worked for NASA. When she was a little tired with the life of a badass rockstar, she finished her PhD and got a tenure-track position (she is now tenured). She gives some advice about organic networking: It's easy to say that you should hang out with important people. But what do you do when you're shy? "I hang out with my friends, and now, guess what -- my friends are now important people." She says that the life of an academic suits her perfectly because it lets her pursue the things that are interesting and to truly find balance in her home. Jobs are more flexible, interesting, and fun at the top of the food chain, she says, and you work just as hard as in lower-level, less interesting jobs. "Life balance works well when you have that kind of autonomy."
Magdelena Balazinska has a lot of accomplishments, but also a lot of failures: grant rejections, paper rejections, proposal rejections. "Just don't list those," she says. Focus on the positive. She shows pictures of the house she and her husband designed and built, and her two children in various stages of infancy and toddlerhood. How does she manage? "I get help from anyone who's willing to help! I do what I have to do, and I ask for what I need." She flashes photographs of her husband with a baby during one business trip, and her mother with a baby during a conference in Greece where she had to present. NSF panel in Washington, DC? No problem -- a photograph of her and a baby outdoors with an explanation: "You can just call in, and then nobody sees how you nurse your baby!" Magdelena says, "I don't try to be perfect. I just do what I can."
Following a different path, Anne Condon was an assistant professor when she was barely 25 years old -- and now her daughter, whom she had when in her first faculty job, is attending Grace Hopper Celebration. "For me personally, the combination of the academic life and the family life is fantastic." Anne gives some advice:
Q: We all know that tenure-track positions are hard to get. Should we accept non-tenure-track positions (postdoc, industry), or hold out?
Ceclia Aragon took a 14-year leave from graduate school. In the time, she had two kids, was a stunt pilot (yup, in air shows), and worked for NASA. When she was a little tired with the life of a badass rockstar, she finished her PhD and got a tenure-track position (she is now tenured). She gives some advice about organic networking: It's easy to say that you should hang out with important people. But what do you do when you're shy? "I hang out with my friends, and now, guess what -- my friends are now important people." She says that the life of an academic suits her perfectly because it lets her pursue the things that are interesting and to truly find balance in her home. Jobs are more flexible, interesting, and fun at the top of the food chain, she says, and you work just as hard as in lower-level, less interesting jobs. "Life balance works well when you have that kind of autonomy."
Magdelena Balazinska has a lot of accomplishments, but also a lot of failures: grant rejections, paper rejections, proposal rejections. "Just don't list those," she says. Focus on the positive. She shows pictures of the house she and her husband designed and built, and her two children in various stages of infancy and toddlerhood. How does she manage? "I get help from anyone who's willing to help! I do what I have to do, and I ask for what I need." She flashes photographs of her husband with a baby during one business trip, and her mother with a baby during a conference in Greece where she had to present. NSF panel in Washington, DC? No problem -- a photograph of her and a baby outdoors with an explanation: "You can just call in, and then nobody sees how you nurse your baby!" Magdelena says, "I don't try to be perfect. I just do what I can."
Following a different path, Anne Condon was an assistant professor when she was barely 25 years old -- and now her daughter, whom she had when in her first faculty job, is attending Grace Hopper Celebration. "For me personally, the combination of the academic life and the family life is fantastic." Anne gives some advice:
- Work on important problems, because the unimportant ones aren't interesting and just aren't worth your time.
- Communicate effectively -- and if you need to bulk up your public speaking skills, it's never too late.
- Enjoy teaching others.
- Build strong research support networks. The research community is just not that supportive, she says. "You might give a talk at a conference and there might be 20 people in the room, and 19 of them are on their laptops." I think she looked at me, here, as I typed out that phrase. Oops.
- Persist in the face of challenges; and, of course, go for it!
Natalie Jerger just finished her 3rd year review on her way to tenure. She suggests that one of the most important things is to find a supportive partner. Next, set your priorities. For her, she and her husband always eat dinner together -- this is an important thing for her. Last priority? Cleaning the house. "Both me and my husband are professionals. We don't have time to clean the house. We don't have time to argue about whose turn it is to clean the house. Hire a cleaning lady." Professionally, seek out and work on problems that you find interesting -- problems that you are passionate about. Develop a support netwrok and find good mentors, those whose interests and priorities align with yours. Finally, practice saying "no" so that you aren't stuck in a situation that you don't enjoy.
Jodi Tims, when she was 4, taught her friends about arithmetic -- that's how early she knew that she would be a professor. Like Magdalena, Jodi took a decade to finish her PhD. Violating all her advisors' rules about what one should do while in grad school, she worked full time as an instructor, did her academic research, and had two children. "You just find a way to balance that together," she said. Her advice for aspiring academics:
- Accept advice of good mentors. This is as much about receiving good mentoring as it is about learning how to give good advice: "One day you wake up and realize, 'I'm the mentor!'"
- Don't underestimate your potential
- Focus on your students. Mentoring your students is a form of teaching, and this is a service that you need to provide to your students.
- Know your institution. You don't have to make it work if your expectations are not met.
- Get involved beyond your institution. This is where opportunities come from, to grow as a person and as a researcher.
- Appreciate life beyond work. Family and friends make life worthwhile.
- Enjoy the ride!
Questions were presented on index cards from the audience.
Q: How did you make the choice to go into academia?
Cecilia: In academia, you get to determine what is important. You choose to perform research that has impact.
Magdalena: Apply everywhere, and make the decision with an offer in hand from both industry and academia. The interviewing process is a lot of fun. The reason I went into academia because the interview in academia was more fun than in industry. One of the huge advantages in academia is not only that you get to pick what you work on, but also who you work with.
Q: We all know that tenure-track positions are hard to get. Should we accept non-tenure-track positions (postdoc, industry), or hold out?
Cecilia: In 2004 I wanted to be a faculty member, but I wasn't ready (by publication count and preparation). I took a job in an industry lab not really knowing if I would make the transition. But I worked on making my CV look like I was an academic while keeping up in my industry job. I wrote papers and attended academic conferences that came out of novel research in my job. Choose the industry position or the post-doc that will be most suited to your goals. Have a deliberate plan.
Jodi: There are lots of schools out there that are not the R1 institutions. If you really want to get into academia, consider going to lower-tier institutions. Maybe the pay isn't as good, maybe you start on a non-tenure-track position, but keep your mind open to other options.
Q: Do you really need a post-doc nowadays to get a tenure-track position?
Anne: As someone that went straight into such a position without a post-doc, I think it's a good thing to do. There is a maturity process that happens over that time; you meet different people and you investigate other institutions. I encourage you not to rush through things if there is no reason not to.
Cecilia: That's a great answer. I have seen people that have gone straight through. If you do it right from the very beginning of your PhD program -- you are publishing 2 papers in top conferences, you're networking with the top people in your field for 5 years -- then yes, you can get such a position. But if you're like everyone else, you don't have the pedigree in your publication record, then yes, take a post-doc. But make sure you choose your postdoctoral mentor carefully: they can make or break your career. A post-doc is an apprenticeship. You're getting paid less than you're worth. But on the flip-side, you're getting priceless mentoring from someone that's going to show you the ropes and make you very marketable.
Q: How do you make the choice between academic offers, or between a research lab versus a university?
Magda: If you have no other constraints (e.g., personal ones), go to the best university, because you will work with better students. The better the students, the easier it is for the faculty to do well.
Ioana: Go where the smarter people are and where there are more opportunities.
Cecilia: I put together a matrix of things that were important to me. Vacation locations, startup package, what my family liked.
Natalie: The people. Colleagues. Also, my husband was leaving his job so I wanted to go somewhere that he had a choice
You have to go home every night and still be happy.
Q: Cecilia, you are shy. How do you overcome this and how does it impact your career?
Cecilia: Yeah, I am shy. I miss important connections, and I just accept that. I know that when I started interviewing, I did not mention certain rare accomplishments that few academics achieve, and I didn't mention my highly technical background in mathematics and algorithms. The unsurprising feedback was that I wasn't technical enough, and I didn't get an offer from this institution. I told myself that for future interviews, I'm going to brag, even if I don't like doing that. I felt like I was acting kind of like a jerk, but I got offers. It works.
Q: How do you publish, write grants, mentor students, etc., in your first years?
Natalie: At first, it was terrifying. Teaching can suck up a lot of your time because it's the most urgent thing. The hardest thing is to make time for the thinking, to think about problems, solutions, and what you're going to do next. I think I messed up my first student. And have someone in your life that can give you practical advice when you're stuck.
Q: How do you deal with stress?
Jodi: It's actually a very important question. If you don't deal with it, it will impact everything that you do. I have to get out and do something: ride my bike, go to the gym, jump on the treadmill. And then I go do something else, make myself some space to think about something else. Then things fall into place and things don't look so bad as when you left them.
Magda: I talk -- to my husband, my family, my friends. But not colleagues. That's why it is so important to have family and friends.
Q: Family is my first priority. I want to be a professor, but the 7-year tenure time is prime baby time. I'm afraid that my male department won't like my priority of family dinners and baby-having.
Cecilia: Men, when they have to take care of a family obligation, they say, "I'm busy." Women say, "I'm busy, I have to go take care of my kid." So when you have to go take care of a family obligation, be more like a man, and say: "I'm busy."
Magda: I work and work and work, and at 5, I say, "I have to go." And I leave. And guess what, a lot of my male colleagues have to go too. And after the kids are in bed I work and work and work. Maybe I don't sleep always as much as I used to, but it works for me. And when I need to, I do sleep.
Ioana: Having family as a priority is not a problem. Being confident and admitting that is the way to go. I wouldn't want to be hired by a family-unfriendly institution. I was very open about my 2-body problem in my job search. The places that were not very accommodating, I did not consider.
Q: What advice do you have for someone applying for a tenure-track position with a partner? It's a 2-body problem with a similar area of computing.
Anne: I think it's good to bring it up reasonably early with the institution. If one of you has got an interview, that is a good time to bring it up. You may want to wait until you have an offer, but it is better to let them know sooner so that the university can work on this issue. Institutions need time for this. There are other options: a short-term position that can turn into something more permanent; maybe there is industry nearby. Be flexible but know what you really want. For the university, if you can attract two great people to your institution, it's amazing.
Q: What are you proud of that you have done outside your academic career that you wouldn't have been able to accomplish without an academic career?
Jodi: My academic career allows me to be flexible to do things like pursue mentoring with ACM-W and Grace Hopper Celebration
Anne: I got to take on many projects of national scope, such as the distributed mentor project and work through the Computing Research Association (CRA).
Tuesday, November 8, 2011
Grace Hopper '11 checklist: Five things to do at GHC11
Grace Hopper Celebration of Women in Computing is basically a giant lady-party in which all of the conference attendees are gorgeous, brilliant, and interesting. But you already know this, because I have blogged about Grace Hopper in my post about do-overs next time I go, and in my post from my attendance in 2009. That is not totally right -- it is not just a party. It is an opportunity to showcase your work, learn about other women's research, find out ways to bring more women into our field and make it more woman-friendly, meet new people, and build lasting relationships.
Here are the top five things that I wish to get out of attending Grace Hopper Celebration this year, in 2011.
5. Reconnect with old friends
It has been a year -- in some cases, more -- since I have seen old acquaintances, friends, and mentors. From my first room mate, to the professor that has changed how I see myself as a researcher, to the group of 200 women that have given me the gift of working with my rockstar undergraduate student, I look forward to seeing these ladies again.
4. Find a collaborator
Researching alone can be dismal. Would it not be fun to meet someone with similar interests, in which we can complement each others' strengths? I have this rosy dream about collaborating on a paper with someone I had met once (maybe twice) at Grace Hopper.
3. Meet a new mentor
My career will soon be in flux: next year, I will be dissertating while on the academic job market. (Note: Even though Blogger doesn't think "dissertating" is a word, it totally is.) I imagine I will be in need of new guidance -- of help finding appropriate job openings, navigating the complicated job-seeking and application system, and finding the best way to present myself to particular universities. What better place to meet someone that can potentially help me than at Grace Hopper?
2. Make a new mentee
(Note: Blogger doesn't think "mentee" is a word, but if it isn't, it should be. It's who mentors advise!) As a senior graduate student in her final throes, maybe -- maybe -- my experience can be valuable to someone. I have an undergraduate degree with two majors, I worked in industry for 3 years, I had a baby pre-advancement in graduate school, and I work in a highly interdisciplinary field doing research of my own invention. I write grants, I write blog posts, I invent eHealth learning methods, and I play video games. Surely I could be a resource to somebody!
1. Say thanks
I am pretty lucky that in four years of attending Grace Hopper, I have never paid for the visit myself. In three years, I have never paid for child care. In two years, I have contributed to the program at Grace Hopper, hosting sessions and panels. I feel privileged that I have helped shape the community of women that I am about to see again, meet, or just observe. So, at Grace Hopper this year, I plan to say thanks for accepting me as a part of the package of women in computing, of technical women.
If you will be at Grace Hopper Celebration of Women in Computing in Portland, please find me and say hello! I will be tweeting as @lexyholloway.
Here are the top five things that I wish to get out of attending Grace Hopper Celebration this year, in 2011.
5. Reconnect with old friends
It has been a year -- in some cases, more -- since I have seen old acquaintances, friends, and mentors. From my first room mate, to the professor that has changed how I see myself as a researcher, to the group of 200 women that have given me the gift of working with my rockstar undergraduate student, I look forward to seeing these ladies again.
4. Find a collaborator
Researching alone can be dismal. Would it not be fun to meet someone with similar interests, in which we can complement each others' strengths? I have this rosy dream about collaborating on a paper with someone I had met once (maybe twice) at Grace Hopper.
3. Meet a new mentor
My career will soon be in flux: next year, I will be dissertating while on the academic job market. (Note: Even though Blogger doesn't think "dissertating" is a word, it totally is.) I imagine I will be in need of new guidance -- of help finding appropriate job openings, navigating the complicated job-seeking and application system, and finding the best way to present myself to particular universities. What better place to meet someone that can potentially help me than at Grace Hopper?
2. Make a new mentee
(Note: Blogger doesn't think "mentee" is a word, but if it isn't, it should be. It's who mentors advise!) As a senior graduate student in her final throes, maybe -- maybe -- my experience can be valuable to someone. I have an undergraduate degree with two majors, I worked in industry for 3 years, I had a baby pre-advancement in graduate school, and I work in a highly interdisciplinary field doing research of my own invention. I write grants, I write blog posts, I invent eHealth learning methods, and I play video games. Surely I could be a resource to somebody!
1. Say thanks
I am pretty lucky that in four years of attending Grace Hopper, I have never paid for the visit myself. In three years, I have never paid for child care. In two years, I have contributed to the program at Grace Hopper, hosting sessions and panels. I feel privileged that I have helped shape the community of women that I am about to see again, meet, or just observe. So, at Grace Hopper this year, I plan to say thanks for accepting me as a part of the package of women in computing, of technical women.
If you will be at Grace Hopper Celebration of Women in Computing in Portland, please find me and say hello! I will be tweeting as @lexyholloway.
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