Bo Zhao

Publications DBLP Google Scholar

Before PhD

Email: bo.zhao@aalto.fi
Tel. : +358 503227953

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

Visiting Address:
A108, Department of Computer Science, Aalto University
Konemiehentie 2, 02150 Espoo, Finland

Our group is funded by Research Council of Finland, Business Finland, the Finnish Doctoral Program Network in Artificial Intelligence (AI-DOC), and the Quantum Doctoral Pilot Programme (QDOC).

Postdoctoral researchers

Dr. Tuo Shi (Fall 2024)

Doctoral researchers

Cong Yu (Dec.2023-)

Mustapha Abdullahi (Apr.2024-)

Jiaxin Guo (Jun.2024-)

Songlin Jiang (Spet.2024-)

Alireza Samar (Oct.2024)

Zheyue Tan (.Jan.2025-)

MSc student researchers

Zhongxuan Xie ( Feb.2024-)

Linus Jern ( Fall 2024-)

Summer intern researchers

Wenxuan Li (Summer 2024. Home university: University of Cambridge, now intern at Microsoft Research Asia)

Visiting PhD students

Shilong Deng (Spring 2024. Home university: University of Liverpool)

CV Download (updated in Aug.2024)

Organizing Committee: UbiComp'25 Local Arrangement Chair

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

Availability Committee: SIGMOD'22,23

Demonstration Track Committee: ICDE'23,24

Journal Reviewer: TPDS'23    JMLR'24

Journal Editor: Proceedings of the ACM on Networking (PACMNET)

Co-Chair: The great talk series, CS Department, Aalto University

Aalto Data-Intensive System group is seeking full-time doctoral and postdoctoral researchers in scalable machine learning systems and distribute data-intensive systems. Due to the large volume of applications, I am not able to reply all candidates. But I promise to carefully go over each application. Thanks!

We have several current openings
• (1) 1-2 Postdoc positions in scalable ML systems
• (2) 1-2 PhD positions in efficient ML systems
• (3) HIIT research fellowship in data-intensive systems and ML systems
• (4) TA/RA positions at Aalto CS

[About the group] 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.

(1) Postdoc positions in scalable ML systems

Requirements

• An excellent PhD degree in computer science
• Strong publication record in top ML or system venues -- at least one first-authored paper in SIGMOD/VLDB/SOSP/OSDI/EuroSys/SIGCOMM/NSDI/ICML/NeurIPS/ICLR, etc
• Experience in distributed ML training / inference, distributed/networked systems, distributed databases and code optimization
• Very good programming skills (Python + system programming languages e.g., C++/Rust)
• Strong analytical thinking skills
• The ability to work independently and within a larger team
• Excellent scientific communication and writing skills in English

What we offer

• An outstanding research environment at one of top universities in Computer Science field in Europe
• A competitive salary from 4,021 EUR to 4,201 EUR per month
• World-class computing resources including the group-exclusive GPU cluster and the access to Europe's fastest supercomputer LUMI (11,912 GPUs) and quantum computer
• Manifold international collaboration opportunities with both academia and industry, and funded travels to conferences
• A wide range of staff benefits, flexible working hours, world-leading health care, family and social security support

How to apply

• Send Prof. Bo Zhao a single PDF file that compacts (i) a cover letter (2 pages), (ii) an academic CV, (iii) a research statement (3-5 pages), (iv) transcripts for BSc, MSc, PhD (v) PhD certificate, and (vi) contact details of three referees
 
• Online portal, link

(2) PhD positions in scalable ML systems

Requirements

• An excellent MSc degree in computer science
• Knowledge in distributed ML training / inference, distributed/networked systems, distributed databases and code optimization
• Very good programming skills (Python + system programming languages e.g., C++/Rust)
• Strong analytical thinking skills
• The ability to work independently and within a larger team
• Excellent scientific communication and writing skills in English
• Publications in major ML or system conferences

What we offer

• An outstanding research environment at one of top universities in Computer Science field in Europe
• A competitive salary from 2,747 EUR to 3,424 EUR per month
• World-class computing resources including the group-exclusive GPU cluster and the access to Europe's fastest supercomputer LUMI (11,912 GPUs) and quantum computer
• Manifold international collaboration opportunities with both academia and industry, and funded travels to conferences
• A wide range of staff benefits, flexible working hours, world-leading health care, family and social security support

How to apply

• Send Prof. Bo Zhao a single PDF file that compacts (i) a cover letter (2 pages), (ii) an academic CV, (iii) a research statement (3-5 pages), (iv) transcripts for BSc, MSc and (vi) contact details of two referees
 

(3) HIIT research fellowship

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

(4) TA/RA positions at Aalto CS

Current TA/RA positions are for Aalto students. We are looking for MSc students with strong CS background.

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.

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.

The ADIS Seminars provide a forum to discuss the state of the art in systems research with experts from leading academic institutes and industry labs. All events are free to join, please reach out to our group members for accesses.

Window-based Learned Index

Liang Liang, Imperial College London, 20.Jun.2024, 14:00 EEST, Online

[Abstract] Learned indices have shown substantial improvements in lookup perfor- mance. With the advancement of updatable learned indices, they are partic- ularly effective in dynamic environments like stream processing. Data stream processing systems, which allow querying over sliding windows of data streams, require efficient index structures for operations such as aggregation and joins. We introduce FLIRT, a learned index specifically designed for managing in- coming streams of sequential data, such as time-series data, over a window. FLIRT utilizes a piece-wise linear model to reduce lookup times and employs a queue-assembly structure to efficiently support updates. While FLIRT is optimized for scenarios where the search keys arrive in a spe- cific order, this requirement can be restrictive in diverse streaming environments. The need for sequential key arrivals limits FLIRT’s applicability, for instance, in settings where the order of data arrival (processing time) does not reflect the inherent order of the data events (event-time). To overcome these limita- tions and broaden the use cases, we developed SWIX, a versatile learned index designed for more generic window processing. Unlike FLIRT, SWIX accommo- dates out-of-order key insertions, thereby enhancing its utility in environments where data arrival order is unpredictable. To support its versatility, SWIX em- ploys sophisticated buffering techniques and lightweight structures that enables efficient query execution at a low cost and makes SWIX memory efficient. Our experiments demonstrate that both FLIRT and SWIX achieve excellent per- formance in their respective applications and pave the way for incorporating streaming learned indices into other operations, such as streaming joins.

[About the speaker] Liang is currently a PhD student at Imperial College London, where he is supervised by Prof. Thomas Heinis and specializes in database research. Before embarking on his PhD, Liang earned a Master’s degree in High Performance Computing with Data Science from the University of Edinburgh with distinction in 2020 and a Master’s degree in Data Science from Monash University with high distinction in 2019. He completed his undergraduate studies in Law at TianGong University in 2016. Liang’s research focuses on data-oriented streaming workflow systems and streaming learned indices. He has published in to prestigious journals and conferences venues and continues to explore the integration of learned indices into streaming operations, such as joins and aggregations. Additionally, Liang is very interested in combining machine learning methods to enhance streaming tasks, join cardinality estimation, predictive state representations, and hybrid auto-scaling approaches.

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SQL2Circuits: Estimating Metrics for SQL Queries with A Quantum Natural Language Processing Method

Valter Uotila, University of Helsinki, 26.Mar.2024, 14:00 EET, CS Building B322

[Abstract] Quantum computing has developed significantly in recent years. Developing algorithms to estimate various metrics for SQL queries has been an important research question in database research since the estimations affect query optimization and database performance. This work represents a quantum natural language processing (QNLP) -inspired approach for constructing a quantum machine learning model that can classify SQL queries with respect to their execution times, costs and cardinalities. From the quantum machine learning perspective, we compare our model and results to the previous research in QNLP and conclude that our model reaches similar accuracy as the QNLP model in the classification tasks. This indicates that the QNLP model is a promising method even when applied to problems that are not in QNLP. We study the developed quantum machine learning model by calculating its expressibility and entangling capability histograms. The results show that the model has favorable properties to be expressible but also not too complex to be executed on quantum hardware, for example, on the current 20-qubit quantum computer in Finland.

[About the speaker] Valter Uotila is a second-year doctoral researcher at the University of Helsinki researching quantum computing applications for databases and data management. His research interests are in the intersection of quantum computing, databases and category theory.

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Advances, challenges, and opportunities in Table Representation Learning

Madelon Hulsebos, UC Berkeley, 22.Mar.2024, 17:00 EET, Online

[Abstract] The impressive capabilities of transformers have been explored for applications over language, code, images, but the millions of tables have long been overlooked while tables dominate the organizational data landscape and give rise to peculiar challenges. Unlike natural language, tables come with structure, heterogeneous and messy data, relations across tables, contextual interactions, and metadata. Accurately and robustly embedding tables is, however, key to many real-world applications from data exploration and preparation to question answering and tabular ML. In this talk, I will discuss the general approaches taken towards adapting the transformer architecture towards tables and give an overview of the tasks already explored in this space. I will zoom in on some of the shortcomings of these approaches and close with the open challenges and opportunities, and some ongoing work.

[About the speaker] Madelon Hulsebos is a postdoctoral fellow at UC Berkeley. She obtained her PhD from the Informatics Institute at the University of Amsterdam, for which she did research at the MIT Media Lab and Sigma Computing. She was awarded the BIDS-Accenture fellowship for her postdoctoral research on retrieval systems for structured data. Madelon her general research interest is on the intersection of data management and machine learning, with recent contributions in methods, tools and resources for Table Representation Learning.

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Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models

Wenqi Jiang, ETH Zürich, 7.Mar.2024, 14:00 EET, Online

[Abstract] The recent advances in generative large language models (LLMs) are attributable to the surging number of model parameters trained on massive datasets. However, improving LLM quality by scaling up models leads to several major problems including high computational costs. Instead of scaling up the models, a promising direction, which OpenAI has recently adopted, is known as Retrieval-Augmented Language Model (RALM), which augments a large language model (LLM) by retrieving context-specific knowledge from an external database via vector search. This strategy facilitates impressive text generation quality even with smaller models, thus reducing computational demands by orders of magnitude.

However, RALMs introduce unique system design challenges due to (a) the diverse workload characteristics between LLM inference and retrieval and (b) the various system requirements and bottlenecks for different RALM configurations including model sizes, database sizes, and retrieval frequencies. In this talk, I will present Chameleon, a heterogeneous accelerator system integrating both LLM and retrieval accelerators in a disaggregated architecture. The heterogeneity ensures efficient serving for both LLM inference and retrieval, while the disaggregation allows independent scaling of LLM and retrieval of accelerators to fulfill diverse RALM requirements. Our Chameleon prototype implements retrieval accelerators on FPGAs and assigns LLM inference to GPUs, with a CPU server orchestrating these accelerators over the network. Evaluated on various RALMs, Chameleon exhibits up to 2.16× reduction in latency and 3.18× speedup in throughput compared to the hybrid CPU-GPU architecture.

[About the speaker] Wenqi Jiang is a fourth-year Ph.D. student at ETH Zurich, where he is affiliated with the systems group advised by Gustavo Alonso and Torsten Hoefler. Wenqi's research interests span data management, computer architecture, and computer systems. His work primarily focuses on designing post-Moore data systems, which involve cross-stack solutions including algorithm, system, and architecture innovations. Some examples of his work include large language models, vector search, recommender systems, and spatial data processing.

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DeltaZip: Multi-tenant Language Models Serving via Delta Compression

Xiaozhe Yao, ETH Zürich, 26.Feb.2024, 14:00 EET, Online

[Abstract] Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model. We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of 6× to 8× while maintaining high model quality. DeltaZip increases serving throughput by 1.5× to 3× and improves SLO attainment compared to a vanilla HuggingFace serving system.

[About the speaker] Xiaozhe Yao is a second-year doctoral student at Systems Group, Department of Computer Science, ETH Zürich advised by Prof. Dr. Ana Klimović. Working on a wide spectrum of machine learning and systems, his research direction is to build systems that support large-scale machine learning and democratize machine learning. Prior to ETH, Xiaozhe Yao gained his Master’s degree at the University of Zurich in Data Science, advised by Prof. Dr. Michael Böhlen and Qing Chen. Before that, he completed his Bachelor’s study at Shenzhen University in Computer Science, advised by Prof. Dr. Shiqi Yu. He interned at Shenzhen Institute of Advanced Technology in 2016 as a data scientist.

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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.