Computing Research Association honors two Khoury undergrads for their research
Wed 02.26.20 / Ysabelle Kempe
Computing Research Association honors two Khoury undergrads for their research
Wed 02.26.20 / Ysabelle Kempe
Wed 02.26.20 / Ysabelle Kempe
Wed 02.26.20 / Ysabelle Kempe
Computing Research Association honors two Khoury undergrads for their research
Wed 02.26.20 / Ysabelle Kempe
Computing Research Association honors two Khoury undergrads for their research
Wed 02.26.20 / Ysabelle Kempe
Wed 02.26.20 / Ysabelle Kempe
Wed 02.26.20 / Ysabelle Kempe
“It’s a big deal,” says Ben Hescott, Khoury College Associate Dean of Students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College Associate Dean of Students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College of Computer Sciences associate dean of students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce the risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College of Computer Sciences associate dean of students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce the risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College Associate Dean of Students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College Associate Dean of Students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College of Computer Sciences associate dean of students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce the risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
“It’s a big deal,” says Ben Hescott, Khoury College of Computer Sciences associate dean of students.
He is referring to the recent recognition of two Khoury College students as Outstanding Undergraduate Researchers by the Computing Research Association (CRA). Jeffrey Champion (BS, Math and Computer Science, ‘20) was named as a finalist, and Eric Lehman (BS, Computer Science ‘20) was awarded an honorable mention.
“The CRA is choosing students that are not only doing high-quality research, but clearly are making a contribution to that research area,” Hescott says. “These are creative, thoughtful, innovative ideas, and they’re producing work in a mentored, yet independent, way.”
Champion’s main research has been at the intersection of secure multiparty computation and differential privacy. He conducted it during his time as a research co-op with Northeastern University’s Cybersecurity and Privacy Institute. Champion says his work is best described with a concrete example.
Suppose you have two hospitals, Champion says, that want to share aggregate statistics, but can’t disclose private patient data to the other. The process can be completed with secure multiparty computation, a cryptographic protocol that allows parties to “compute a function such that no party learns more than their own input and the output of the function,” according to Champion. But there’s a catch with that solution.
“It’s been pretty well-studied that the output of some aggregate function can leak information about the individual people in the dataset,” Champion says. “What’s commonly used is differential privacy, which takes an original function and incorporates randomness in certain parts. If this randomness is generated correctly, your output will be so-called ‘private.’”
This randomness makes the function more difficult to compute for either participating party. The purpose of Champion’s research is to develop more efficient methods to securely run differentially private algorithms on large datasets. For this particular project, Champion improved the procedure of generating many samples of random noise in a secure computation. The result isn’t just applicable in the previous hospital example; it is relevant in any process involving large, private multi-party computations.
“For me, this was an introduction to how good research is conducted,” Champion says.
Lehman, the other Northeastern student honored, conducted research that, like Champion’s, can be directly applied to the medical field. His work, however, focuses on how to better equip doctors with the information needed to prescribe patients the best drug or intervention for their condition, especially in regards to cancer treatment.
His research uses a natural-language processing model to parse a medical trial report, in order to determine what it says about an intervention’s efficacy with respect to a given outcome. For example, does an article provide evidence supporting the use of aspirin to reduce the risk of stroke compared to a placebo?
Additionally, Lehman’s team hired doctors to mark important language in sample trials. This data allows the researchers to train machine-learning models to recognize where in the text the answer lies, not simply spit out a conclusion.
Lehman has also conducted research that uses machine learning to increase the accuracy of devices that detect heart arrhythmias, as well as worked with the nonprofit Cures Within Reach for Cancer, helping the organization better understand how certain interventions affect cancer survival and more.
“I like to do research in the medical field because these things actually have uses for people,” Lehman says. “I like the idea that what I’m working on will have a tangible impact.”
Both Champion and Lehman plan to pursue PhDs, a goal typical of those honored by the CRA, according to Hescott. He points to the recognition as a beacon of light pointing toward these students’ futures.
“Schools these students are applying to recognize the CRA,” Hescott says. “For Northeastern’s Khoury College to have two of our students in this space is fantastic.”
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