1. T HEORETICAL C OMPUTER S CIENCE Real World Problems Abstract Models Modeling Solutions Math Tools Inspire More Problems

2. C AREER D IRECTIONS Study Algorithms College ResearchInstitute Industry

3. Network Algorithm Team Hubert Chan Postgraduates: Li Ning 宁立 Fei Chen 陈飞 Mingfei Li 李明飞 Xiaowei Wu 吴晓伟

4. Natural Algorithm: to Achieve Consensus 5 1 2 5 3 2 3 5 2 4 1 Observation: An individual’s opinion will be influenced by its friends/neighbors. In some cases, people will achieve a consensus finally, even when their neighborhoods keep changing. Researcher: This kind of natural behavior is modeled by dynamic systems. By our results, it is very fast for people to achieve consensus even under dynamic networks. “Fast convergence for consensus in dynamic networks”, Hubert Chan, Li Ning, ICALP 2011. 3 3.3 2.6 3 3.3 2.7 3.4 3 3 4 2.6 3.1 2.9 3.1 3 3.3 3.1 2.9 3 3 3 3 3 3 3 3 3 3 3 3

5. Spanners: “Building the Roads” Government: We need roads to connect cities, and we want save the cost. Traveler: Travelling along the roads should save time. Researcher: This is a spanner problem. Use our methods, then you don’t need many roads, and travelling distance along the roads is almost the same with travelling directly. “Sparse fault-tolerant spanners for doubling metrics with bounded hop-diameter or degree”, Hubert Chan, Mingfei Li, Li Ning, ICALP 2012.

6. Observation: People will spread information, e.g., news, via talking to their friends. Moreover, the news talked by more friends attract more attention. Information Spreading in Social Networks News … Not a big dealAll my friends are talking about this news Not a big deal News … Advertiser: Persuade one person, then he will spread the advertisement… Researcher: To spread information efficiently, which person should be picked initially? … “Maximizing influence in information networks under the non-progressive linear threshold model”, Hubert Chan, Li Ning, ongoing project.

7. Bargaining Rent sharing: The total cost to rent a flat is 100. It can be shared by two persons. I’ll pay 50. I’ll pay 40. I’ll pay 30. I’ll pay 40. I’ll pay 45. I’ll pay more... 60. Good. I’ll pay less... 40. I have to pay more… 60. Sociologist: Human behavior is so complicated. What are the stable deals? Researcher: This is studied as the problem called bargaining game. “Optimizing Social Welfare for Network Bargaining Games in the Face of Unstability Greed and Spite”, Hubert Chan, Fei Chen, Li Ning, ESA 2012.

8. ONLINE ALGORITHMS: JOB SCHEDULING & POWER MANAGEMENT Scheduling research team: T.W. Lam 林德华 H.F. Ting 田慶豐 H.L. Chan 陳昊樑 L.K. Lee 李立基 S.H. Chan R. Li

9. O NLINE PROBLEMS ARE CHALLENGING. Robot motion Bin Packing Paging Load balancing Deadline Flow Trading Broadcasting Scheduling Response time Without knowing the future, one often makes the wrong decision.

10. “What matters most to the computer designers at Google is not speed, but power, low power, because data centers can consume as much electricity as a city.“ Eric Schmidt, CEO Google NY Times, 2002

11. R EPRESENTATIVE PUBLICATIONS Sleep Management on Multiple Machines for Energy and Flow Time. ICALP 2011: 219-231 Non-clairvoyant Speed Scaling for Weighted Flow Time. ESA 2010: 23-35 Sleep with Guilt and Work Faster to Minimize Flow Plus Energy. ICALP 2009: 665-676 Speed scaling with an arbitrary power function. SODA 2009: 693-701 Scheduling for Speed Bounded Processors. ICALP 2008: 409-420 Energy efficient online deadline scheduling. SODA 2007: 795-804

12. BIOINFORMATICS F. Chin T.W. Lam H.F. Ting S.M. Yiu

13. Bioinformatics involves the analysis of biological and genetic information, the results of which can then be applied to gene-based drug discovery and development to cure illnesses Our objective is to develop better software tools to aid bioinformatics research.

14. Next Generation Sequencing NGS is the new & cornerstone technology for today’s biological research and tomorrow’s medical care. Nowadays NGS equipment (Solexa, SOLiD, Illumina) can produce short fragments (read) of a DNA sequence (genome) efficiently and at a low cost. Our research work is to base on our algorithmic expertise to develop efficient and effective software tools for NGS data.

15. Research Publications and Funding Publications: Bioinformatics, J of Computational Biology, RECOMB, ISMB, ECCB GRF Grants Structural Alignment and prediction for non-coding RNAs with triple helix structure (2011-2014, HK$681,195) Algorithms for Inferring k-articulated Phylogenetic Network (2009-2012, HK$591,080) Combinatorial Phenotype Testing (2009-2012, HK$394,053) Finding Conserved Patterns In Biological Networks (2008-2012, HK$777,108) A New Motif Representation Based on Position Specific Patterns (2006-2009, HK$775,008) Compressed Indexes for Approximate String Matching, with Applications to Biological Sequences (2006-2008, HK$654,000) Design and Analysis of Algorithms for Constrained Structure Comparison (2006-2008, HK$612,816) Computationally Haplotyping Pedigree Data (2005-2007, HK$359,224) Algorithms for Uncovering Conserved Genes on Whole Genomes (2004-2007, HK$650,000) Finding Motifs for Sequences with Weak Binding Sites (2004-2006, HK$339,414)

16. Programming Languages Programming languages research team: B. C. d. S. Oliveira X. Bi H. Zhang Programming Languages

18. PROGRAMMING LANGUAGES ARE FUNDAMENTAL TO PROGRAMMER PRODUCTIVITY PROGRAMMING LANGUAGE RESEARCH AIMS AT: - ALLOWING FASTER DEVELOPMENT CYCLES - SUPPORTING LARGE-SCALE PROGRAMMING - PREVENTING MORE BUGS BY CREATING NEW PROGRAMMING LANGUAGES/ABSTRACTIONS

19. RESEARCH TOPICS - BETTER PROGRAMMING MODELS FOR MULTI-CORE COMPUTING, GPU PROGRAMMING - BETTER MODULARITY ABSTRACTIONS FOR LARGE-SCALE SOFTWARE - FUNCTIONAL PROGRAMMING (SCALA, HASKELL, OCAML, SCHEME …)