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boundary="_000_CA80A4BE95014D1CAC74C2907E13AD84gatechedu_" MIME-Version: 1.0 X-OriginatorOrg: isye.gatech.edu X-MS-Exchange-CrossTenant-Network-Message-Id: ddbd7885-ddd9-460e-7d4a-08d73c9cb5dc X-MS-Exchange-CrossTenant-originalarrivaltime: 19 Sep 2019 01:00:05.6267 (UTC) X-MS-Exchange-CrossTenant-fromentityheader: Hosted X-MS-Exchange-CrossTenant-id: 482198bb-ae7b-4b25-8b7a-6d7f32faa083 X-MS-Exchange-CrossTenant-mailboxtype: HOSTED X-MS-Exchange-CrossTenant-userprincipalname: F6d40Go1PiHcqbVKdIVI7nDt81P4wKc21CrPWmD+gVXL0n57pdxHIaESdo5deDWtVaoz+BHDxvbDPldMr1LvPw== X-MS-Exchange-Transport-CrossTenantHeadersStamped: BN8PR07MB6241 X-Validation-by: fioretto@umich.edu Subject: [Caml-list] Two PhD Positions in Privacy-Preserving Distributed AI, Syracuse University Reply-To: "Fioretto, Ferdinando" X-Loop: caml-list@inria.fr X-Sequence: 17808 Errors-to: caml-list-owner@inria.fr Precedence: list Precedence: bulk Sender: caml-list-request@inria.fr X-no-archive: yes List-Id: List-Archive: List-Help: List-Owner: List-Post: List-Subscribe: List-Unsubscribe: --_000_CA80A4BE95014D1CAC74C2907E13AD84gatechedu_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable Apologies for cross-posting - Please forward to anybody who might be intere= sted ** PhD Positions in Privacy-Preserving Distributed Artificial Intelligence = ** Two funded PhD positions are available in the area of Privacy-preserving Di= stributed Machine Learning. The PhD candidate will work under the supervisi= on of Prof. Ferdinando Fioretto at the EECS Department, Syracuse University= . The position start date is flexible, with a start date as early as Januar= y 2020. The PhD candidate is committed to conduct independent and original research= , to report on this research in international publications and conference p= resentations, and to describe the results of the research in a PhD disserta= tion. ** Topic Description ** The recent surge in optimization and machine learning research, in particul= ar, deep learning, paved the way for a number of applications, many of whic= h use privacy-sensitive user data. The resulting models have been shown to = often reveal private user information, which may harm individual users. To = contrast these risks, a new line of research aims at developing variants of= optimization and ML algorithms that preserve the privacy of the individual= s contained in the used datasets. Additionally, there is an increasing inte= rest in leveraging distributed data shared across organizations to augment = AI-powered services. Examples include transportation services, sharing loca= tion-based data to improve on-demand capabilities, and hospitals, sharing d= ata to prevent epidemic outbreaks. The proliferation of these applications = lead to a transition from proprietary data acquisition and processing to da= ta ecosystems where different agents learn and make decisions using data ow= ned by different organizations, boosting the need for privacy-preserving te= chnologies. The project focuses broadly on protecting the privacy of individuals withou= t losing the benefits of large scale data analysis. Topics of interest incl= ude: - Privacy-preserving technology, such as Differential Privacy and sec= ure multi-party computation - Distributed Machine Learning - Privacy-preserving Multiagent Systems - Privacy-Preserving Adversarial Deep Learning Models The project will combine fundamental aspects of privacy, optimization and d= istributed computation to design algorithms that perform (distributed) mach= ine learning and decision making while guaranteeing they do not violate pri= vacy. The ideal candidate will have a strong background and interest in mac= hine learning, privacy-preserving technologies, and/or multi-agent systems.= Publications in leading international venues (such as AAAI, IJCAI, AAMAS, = ICML, NeurIPS) will be an advantage. ** To Apply ** Applications should be submitted at ffiorett@syr.edu and candidates should include their resume and transcript (if available)= . --_000_CA80A4BE95014D1CAC74C2907E13AD84gatechedu_ Content-Type: text/html; charset="us-ascii" Content-ID: <299F6325CF0C6541A517B7D249D57C95@namprd07.prod.outlook.com> Content-Transfer-Encoding: quoted-printable
Apologies for cross-posting - Please forward to anybody who might be interested


** PhD Positions in Privacy-Preserving Distributed Artificial Intelligence = **

Two funded PhD positions are available in the area of Privacy-preservi= ng Distributed Machine Learning. The PhD candidate will work under the= supervision of Prof. Ferdinando Fioretto at the EECS D= epartment, Syracuse University. The position start date is flexible, with a start date as early as January 2020.
The PhD candidate is committed to conduct independent and original res= earch, to report on this research in international publications and&nb= sp;conference presentations, and to describe the results of the resear= ch in a PhD dissertation.

** Topic Description **

The recent surge in optimization and machine learning research, in par= ticular, deep learning, paved the way for a number of applications, ma= ny of which use privacy-sensitive user data. The resulting models= have been shown to often reveal private user information, which may harm individual users. To contrast these risks, a new line of&nbs= p;research aims at developing variants of optimization and ML algorith= ms that preserve the privacy of the individuals contained in the used = datasets. Additionally, there is an increasing interest in leveraging distributed data shared across organizations to au= gment AI-powered services. Examples include transportation services, s= haring location-based data to improve on-demand capabilities, and= hospitals, sharing data to prevent epidemic outbreaks. The proli= feration of these applications lead to a transition from proprietary data acqu= isition and processing to data ecosystems where different agents learn and&= nbsp;make decisions using data owned by different organizations, boost= ing the need for privacy-preserving technologies.
The project focuses broadly on protecting the privacy of individuals w= ithout losing the benefits of large scale data analysis. Topics o= f interest include:
-       Privacy-preserving technology, such as Dif= ferential Privacy and secure multi-party computation
-       Distributed Machine Learning
-       Privacy-preserving Multiagent Systems
-       Privacy-Preserving Adversarial Deep Learni= ng Models
The project will combine fundamental aspects of privacy, optimization = and distributed computation to design algorithms that perform (di= stributed) machine learning and decision making while guaranteein= g they do not violate privacy. The ideal candidate will have a strong background and interest in machine learning, privacy-preserv= ing technologies, and/or multi-agent systems. Publications in lea= ding international venues (such as AAAI, IJCAI, AAMAS, ICML, NeurIPS) = will be an advantage.
 
** To Apply **

Applications should be submitted at ffiorett@syr.edu and candidates should include t= heir resume and transcript (if available). 

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