I have openings for PhD student or postdoc in the field of machine learning, data security and privacy. The detailed requirements are as follows:
Bachelor or Master degree in Computer Science, Software Engineering or Information Engineering in well-known universities. Preferences are given to applicants with some research experience.
Good programming skills in at least one mainstream language such as Java, Python and C++.
Good reading/writing skills in English. Preferences are given to applicants who score 80+ in TOEFL (iBT) or 6.5+ in IELTS.
Remuneration is highly competitive. If you are awarded Hong Kong PhD Fellowships (HKPFS), you can receive 25.8k HKD (equiv. to 3.2k USD) monthly and other matching benefits by the university/department.
For Postdocs, the monthly salary is 32k HKD (equiv. to 4k USD) or above depending on your qualifications.
Those interested please send your CV, publication, and/or transcripts to Dr. Haibo Hu at firstname.lastname@example.org
Our paper “Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning?” has received the Best Theory Paper Award in the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19), in conjunction with IJCAI’19.
Internet of Things (IoT) is renovating the way we monitor,
understand, and control the physical world. While there are many successful
stories on deploying IoT systems in various business sectors, we might
underestimate the long-term challenge when facing the explosive amount of IoT
and machine-to-machine (M2M) data. Almost all fields of data science need new
IoT-aware solutions, including but not limited to data acquisition, cleaning,
transformation, storage, integration, indexing, modeling, analysis, visualization,
The main focus of this special issue will be on the identification
of problems in adopting existing data science techniques for IoT/M2M data and
the renovation on them, with an emphasis on the retrieval and learning from
these data. We welcome papers on new problems, techniques, methodologies and
research directions for open problems in the context of IoT. Topics of interest
include, but are not limited to:
Data acquisition and cleaning techniques for IoT
Data storage and indexing techniques for IoT
Data modeling, representation, transformation,
and integration techniques for IoT
Streaming and query processing techniques for
Data mining and machine learning algorithms for
IoT Big Data
Data visualization and interpretation techniques
Security, privacy and trust in Internet of
Uncertainty and probabilistic IoT Big Data
Data-driven IoT system design, implementation,
Open issues for data management and analytics in
20th IEEE International Conference on Mobile Data Management (MDM 2019) invites submissions for Advanced Seminar (i.e., Tutorial) proposals on all topics listed in the Call for Papers of the conference. More information can be found in conference website: