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Bo Zhao

I have multiple open PhD/Postdoc/Intern positions. Please find details in vacancies.

I am a tenure-track Assistant Professor in the Department of Computer Science at Aalto University. I am also affiliated with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). My research focuses on efficient data-intensive systems that translate data into value for decision making. The scope of my research spans across multiple layers of the data-intensive systems, from scalable machine learning systems to distributed data management systems, as well as code optimization techniques. That is to answer the question “how to co-design multiple layers of the software stack to improve scalability, performance, and energy efficiency of machine learning systems”. My long-term goal is to explore and understand the fundamental connections between data management and modern machine learning systems to make decision-making more transparent, robust and efficient. Please find more details in my research statement and the press coverage.

Before joining Aalto, I was an Assistant Professor at Queen Mary University of London, after working as a post-doctoral researcher at Imperial College London in the Large-Scale Data & Systems (LSDS) group with Prof. Peter Pietzuch. Before Imperial, I was a research assistant and obtained my PhD in the Databases and Information Systems Group at Humboldt-Universität zu Berlin (HU), supervised by Prof. Matthias Weidlich. Before HU, I worked as a student assistant at the Parallel Programming group with Prof. Felix Wolf in RWTH-Aachen University and Technical University of Darmstadt.



Assistant Professor
Aalto University, Finland


Assistant Professor
Queen Mary University of London, UK


Post-doctoral researcher
Imperial College London, UK


Software Development Engineer
Amazon Web Services, Redshift Team



PhD in Computer Science
Humboldt-Universität zu Berlin, Germany


Visiting PhD student in Computer Science
University of Queensland, Australia


Visiting master student in Computer Science
RWTH-Aachen University, Germany


Master of Science in Computer Science
Xi'an Jiaotong University, China


Bachelor of Science in Computer Science
Wuhan Istitute of Technology, China

Publications DBLP Google Scholar

Marcel Wagenländer, Guo Li, Bo Zhao, Luo Mai, Peter Pietzuch
TENPLEX: Changing Resources of Deep Learning Jobs using Parallelizable Tensor Collections
perprint in arXiv, 2023

Huanzhou Zhu*, Bo Zhao*, Gang Chen, Weifeng Chen, Yijie Chen, Liang Shi, Yaodong Yang, Peter Pietzuch, Lei Chen (*equal contribution)
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
In the USENIX Annual Technical Conference (USENIX ATC), Boston, MA, USA, 2023.

Song Liu, Xinhe Wan, Zengyuan Zhang, Bo Zhao, Weiguo Wu
TurboStencil: You Only Compute Once for Stencil Computation
In (Future Generation Computer Systems), 2023.

Gururaghav Raman, Bo Zhao, Jimmy Chih-Hsien Peng, Matthias Weidlich
Adaptive incentive-based demand response with distributed non-compliance assessment
In (Applied Energy), Volume 326, 2022.

Bo Zhao
State Management for Efficient Event Pattern Detection
(Dissertation), Humboldt-Universität zu Berlin, 2022.

Bo Zhao, Han van der Aa, Nguyen Thanh Tam, Nguyen Quoc Viet Hung, Matthias Weidlich
EIRES: Efficient Integration of Remote Data in Event Stream Processing
In Proc. of the 47th ACM SIGMOD International Conference on Management of Data (SIGMOD), Xi'an, China, ACM, June 2021.

Bo Zhao, Nguyen Quoc Viet Hung, Matthias Weidlich
Load Shedding for Complex Event Processing: Input-based and State-based Techniques
In Proc. of the 36th IEEE International Conference on Data Engineering (ICDE), Dallas, TX, USA, IEEE, April 2020.

Gururaghav Raman, Jimmy Chih-Hsien Peng, Bo Zhao, Matthias Weidlich
Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring
In Proc. of 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA. IEEE, August 2019.

Bo Zhao
Complex Event Processing under Constrained Resources by State-based Load Shedding
In Proc. of the 34th IEEE International Conference on Data Engineering (ICDE), Paris, France, IEEE, April 2018.

Before PhD

Song Liu, Bo Zhao, Qing Jiang, Weiguo Wu
A Semi-Automatic Coarse-Grained Parallelization Approach for Loop Optimization And Irregular Code Sections
In Chinese Journal of Computers, 2017, 40(9): 2127-2147.

Bo Zhao, Zhen Li, Ali Jannesari, Felix Wolf, Weiguo Wu
Dependence-Based Code Transformation for Coarse-Grained Parallelism
In Proc. of the International Workshop on Code Optimisation for Multi and Many Cores (COSMIC) held in conjunction with CGO, San Francisco, CA, USA, ACM, February 2015.

Zhen Li, Bo Zhao, Ali Jannesari, Felix Wolf
Beyond Data Parallelism: Identifying Parallel Tasks in Sequential Programs
In Proc. of 15th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP), Zhangjiajie, China, Lecture Notes in Computer Science, Springer, November 2015.

Song Liu, Weiguo Wu, Bo Zhao, Qing Jiang
Loop Tiling for Optimization of Locality and Parallelism
In Journal of Computer Research and Development, 2015, 52(5): 1160-1176.

Tel. : +358 503227953

Mail Address:
Tietotekniikan laitos, P.O.Box 15400, FI-00076 Aalto

Visiting Address:
B254, Department of Computer Science, Aalto University
Konemiehentie 2, 02150 Espoo, Finland

Cong Yu (Doctoral researcher, Dec.2023-)

Songlin Jiang (Incoming doctoral researcher, Summer 2024)

Mustapha Abdullahi (Incoming doctoral researcher, Spring 2024)

Alireza Samar (Part-time doctoral researcher, Spring 2024)

Programme Committee: CIKM'21,22,23,    CoNEXT'24,    VLDB'25

Availability Committee: SIGMOD'22,23

Demonstration Track Committee: ICDE'23,24

Journal Reviewer: TPDS'23

The Data-Intensive System group at Aalto University is seeking full-time doctoral researchers (PhD candidates) in data-intensive computing systems. Due to the large volume of applications, I will only be able to contact again those candidates who are being most actively considered for the position.

We have Research Fellow and Postdoctoral Fellow Positions funded by The Helsinki Institute for Information Technology (HIIT). Please find details here.

We have summer internship positions. Aalto students see here. International students see here.

We conduct research on efficient data-intensive systems that translate data into value for decision making. The scope of our research spans across multiple subfields, from scalable data-centric machine learning systems to distributed data stream management systems, as well as code optimization techniques. That is to answer the question “how to co-design multiple layers of the software stack to improve scalability, performance, and energy efficiency of ML systems”. Our long-term goal is to explore and understand the fundamental connections between data management and modern ML systems to make decision-making more transparent, robust and efficient. Please find more details in our research statement.

Successful applicants will conduct impactful research in the field of data-intensive systems and their applications, publish research results in top-tier conferences, and collaborate with other researchers and visit world-leading research groups and industry labs within our international network (e.g., Imperial College, TUM, MPI-SWS, HU Berlin, NUS, Uni Edinburgh, AWS, Huawei, etc).Previously, we had the pleasure to work with following students Marcel Wagenländer, Alessandro Fogli, Jinnan Guo, and Mustapha Abdullahi.

What we offer

  • An outstanding research environment at one of top universities in Computer Science field in Europe
  • A full-time position with a competitive salary starting from more than 2600 EUR per month and increasing with dissertation progress
  • The chance to conduct foundational and impactful research, with the goal of a doctorate qualification
  • Manifold collaboration opportunities, within the group's international network and funded travels
  • World-class computing resources including the group-exclusive GPU cluster, Aalto HPC cluster Triton and the access to Europe's fastest (world’s 3rd fastest) supercomputer LUMI (located in Finland) equipped with quantum computing capacity
  • A wide range of staff benefits, such as occupational health care, flexible working hours, excellent sports facilities and several restaurants and cafés on campus with staff discounts. PhD candidates also enjoy discounted rents for student housing

Applicants should have

  • An excellent MSc degree in computer science, data science, or related fields
  • Very good programming skills
  • Strong analytical thinking skills
  • Comprehensive interest in scientific problems and the ability to work independently and within a larger team
  • Excellent scientific communication and writing skills (knowledge of the Finnish language is not required)

The position is available from September 2023 with a flexible starting date as mutually agreed. To apply and for further information, please contact Prof. Bo Zhao. Applications shall include at least the following information in a single PDF document:

  • Cover letter
  • Academic CV including the publication list and links to personal Git repositories (e.g., github, gitlab, bitbucket)
  • Transcripts for both BSc and MSc degrees
  • Academic writing sample (peer-reviewed publications, technical reports or MSc thesis)
  • Contact details of at least two referees to provide recommendation letters

About Aalto University, the CS Department, and Finland

Aalto University is a community of bold thinkers where science and art meet technology and business. It is the largest technology-oriented university in Finland (1st in Finland according to QS ranking 2024). We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto has six schools with nearly 11,000 students and a staff of more than 4000, of which 400 are professors. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness in the future as well. This is why we warmly encourage qualified candidates from all backgrounds to join our community.

The Department of Computer Science is the largest department at Aalto and one of the leading computer science research units in northern Europe. It is routinely ranked among the top 10 in Europe and in the top 100 globally (Shanghai ranking 51-75, USNews 71, Times 73, QS 84). The CS Department is located at the Otaniemi campus in Espoo – one of the most important north-european technology hubs, with a high concentration of companies, startups and research institutes from the high-tech sector, and with a thriving culture of entrepreneurship. It is less than a 15 minutes metro ride away from the center of Helsinki, capital of Finland. The campus is designed by the renowned architect and designer, Alvar Aalto. Please check out our virtual campus experience.

Aalto University is located in Finland which is among the best countries in the world according to many quality of life indicators. For the sixth year in a row (including 2023), Finland is listed as the world's happiest country, according to the World Happiness Report. Please find more information about living in Finland and the Aalto inforamtion package. Want to know more about us and your future colleagues? You can watch these videos: Aalto University – Towards a better world, Aalto People, Shaping a Sustainable Future. Read more about working at Aalto.

TempoRL: Efficient Deep Reinforcement Learning with Recurrent Tensors

Pedro Silvestre, Imperial College London, 19.Feb.2024, 13:00 EET, Online

[Abstract] Reinforcement Learning (RL) is an increasingly relevant area of algorithmic research. Though RL differs substantially from Supervised Learning (SL), today's RL frameworks are often simple wrappers over SL systems. In this talk, we first analyse the differences between SL and RL from the system designer's point-of-view, then discuss the issues and inefficiencies of RL frameworks arising from those differences. In particular, we discuss how the existence of cyclic and dynamic data dependencies in RL forces the decomposition of algorithms into disjoint dataflow graphs, preventing holistic analysis and optimisation.

We then propose TempoRL, a system designed to efficiently capture these cyclic and dynamic data dependencies in a single graph by instead viewing RL algorithms as Systems of Recurrence Equations (SREs). TempoRL is then able to holistically analyse and optimise this graph, applying both classic and novel transformations like automatic vectorisation (when memory allows) or incrementalisation (when memory is scarce). Because SREs impose no control-flow, TempoRL is free to choose any execution schedule that respects the data dependencies. Luckily, by designing around SREs, we are able to leverage the powerful polyhedral analysis framework to find efficient and parallel execution schedules, as well as, compute a memory management plan through dataflow analysis. The remainder of the talk discusses the surprising advantages that this novel computational model brings, and the applications it may have outside of RL.

[About the speaker] Pedro Silvestre is a PhD student in the Large-Scale Data & Systems Group at Imperial College London, under the supervision of Prof. Peter Pietzuch, working on Dataflow Systems for Deep Reinforcement Learning. Before Imperial, Pedro was a Research Engineer at the TU Delft’s Web Information Systems Group working on Consistent Fault-tolerance for Distributed Stream Processing. Pedro completed both his MSc and BSc from the NOVA School of Science and Technology.