Detailed program

Information for poster sessions

Poster boards will be 180cm (vertical) x 95 cm (horizontal)

The display area will be located in the Foyer conference room

Monday June 4th

9.00

Gil McVean

Oxford University, UK

The genomic analysis of biomedical big data

10.00

10.00

Coffee break

10.30

10.30

Learning

Session Type: Lecture

Session Chair: Visa Koivunen

Time: Monday, June 4th, 10:30 – 12:30

Location: Auditorium C, STCC

1: SEMI-SUPERVISED TRANSFER LEARNING USING MARGINAL PREDICTORS

Aniket Deshmukh; University of Michigan, United States

Emil Laftchiev; Mitsubishi Electric Research Labs, United States

2: SEMI-BLIND INFERENCE OF TOPOLOGIES AND SIGNALS OVER GRAPHS

Vassilis N. Ioannidis; University of Minnesota, United States

Yanning Shen; University of Minnesota, United States

Georgios B. Giannakis; University of Minnesota, United States

3: DIVIDE-AND-CONQUER TOMOGRAPHY FOR LARGE-SCALE NETWORKS

Augusto Santos; École Polytechnique Fédérale de Lausanne (EPFL), Switzerland

Vincenzo Matta; University of Salerno, Italy

Ali H. Sayed; École Polytechnique Fédérale de Lausanne (EPFL), Switzerland 

4: COMPUTATIONAL STRATEGIES FOR STATISTICAL INFERENCE BASED ON EMPIRICAL OPTIMAL TRANSPORT

Carla Tameling; University Goettingen, Germany

Axel Munk; University Goettingen, Germany 

5: SPARSE SUBSPACE CLUSTERING WITH MISSING AND CORRUPTED DATA

Zachary Charles; University of Wisconsin-Madison, United States

Amin Jalali; University of Wisconsin-Madison, United States

Rebecca Willett; University of Wisconsin-Madison, United States

6: AN EXPONENTIALLY CONVERGENT ALGORITHM FOR LEARNING UNDER DISTRIBUTED FEATURES

Bicheng Ying; University of California, Los Angeles, Switzerland

Yuan Kun; University of California, Los Angeles, United States

Ali H. Sayed; École Polytechique Fédérale de Lausanne, Switzerland

12.30

12.30

Industry Outlook I

1: Srikrishna Chaitanya Konduru, Data Scientist, Bühler Group

 Opportunities for machine learning in the manufacturing Industry

2: Claudiu Musat, Research Director, Artificial Intelligence & Machine Learning Group, Swisscom

AutoML for Text Classification

13.00

13.00

Lunch break

14.00

14.00

Volker Markl

Technische Universität Berlin, Germany

Big Data Management and Apache Flink: Key Challenges and (Some) Solutions

15.00

15.00

Coffee break

15.30

15.30

 Poster 1

Session Type: Poster

Poster Time: Monday, June 4th, 15:30 – 16:30

Location: Foyer

1: SPECTRAL STATISTICS OF DIRECTED NETWORKS WITH RANDOM LINK MODEL TRANSPOSE-ASYMMETRY

Stephen Kruzick; Carnegie Mellon University, United States

Jose M. F. Moura; Carnegie Mellon University, United States

2: A NOVEL BACKBONE NETWORK ANOMALY DETECTOR VIA CLUSTERING IN SKETCH SPACE

Yating Liu; Tsinghua University, China

Yuantao Gu; Tsinghua University, China

3: UNCERTAINTY QUANTIFICATION IN SUNSPOT COUNTS

Sophie Mathieu; Université Catholique de Louvain, Belgium

Rainer von Sachs; Université Catholique de Louvain, Belgium

Véronique Delouille; Royal Observatory of Belgium, Belgium

Laure Lefèvre; Royal Observatory of Belgium, Belgium

4: OPTIMIZING THERMAL COMFORT AND ENERGY CONSUMPTION IN A LARGE BUILDING WITHOUT RENOVATION WORK

Sylvain Le Corff; CNRS, Université Paris-Sud, Université Paris Saclay, France

Alain Champagne; Oze-Energies, France

Maurice Charbit; Oze-Energies, France

Gilles Nozière; Oze-Energies, France

Eric Moulines; Centre de Mathématiques Appliquées, France

5: ROBUST AND CONSISTENT CLUSTERING RECOVERY VIA SDP APPROACHES

Chenxi Sun; The University of Hong Kong, China

Tongxin Li; California Institute of Technology, United States

Victor O.K. Li; The University of Hong Kong, China

6: LEARNING FROM SIGNALS DEFINED OVER SIMPLICIAL COMPLEXES

Sergio Barbarossa; University of Rome, Italy

Stefania Sardellitti; University of Rome, Italy

Elena Ceci; University of Rome, Italy

7: DISTRIBUTED NONPARAMETRIC DETECTION USING ONE-SAMPLE ANDERSON-DARLING TEST AND P-VALUE FUSION

Topi Halme; Aalto University, Finland

Visa Koivunen; Aalto University, Finland

8: LEARNING FLEXIBLE REPRESENTATIONS OF STOCHASTIC PROCESSES ON GRAPHS

Addison Bohannon; US Army Research Laboratory, United States

Brian Sadler; US Army Research Laboratory, United States

Radu Balan; University of Maryland, United States

9: PREDICTIVE MAINTENANCE OF PHOTOVOLTAIC PANELS VIA DEEP LEARNING

Timo Huuhtanen; Aalto University, Espoo, Finland, Finland

Alexander Jung; Aalto University, Espoo, Finland, Finland

10: ENDMEMBER EXTRACTION ON THE GRASSMANNIAN

Elin Farnell; Colorado State University, United States

Henry Kvinge; Colorado State University, United States

Michael Kirby; Colorado State University, United States

Chris Peterson; Colorado State University, United States

11: FALSE DISCOVERY RATE CONTROL WITH CONCAVE PENALTIES USING STABILITY SELECTION

Bhanukiran Vinzamuri; IBM Research, United States

Kush R. Varshney; IBM Research, United States

12: NEARLY OPTIMAL ROBUST SUBSPACE TRACKING: A UNIFIED APPROACH

Praneeth Narayanamurthy; Iowa State University, United States

Namrata Vaswani; Iowa State University, United States

13: RESTRICTED ISOMETRY PROPERTY FOR LOW-DIMENSIONAL SUBSPACES AND ITS APPLICATION IN COMPRESSED SUBSPACE CLUSTERING

Gen Li; Tsinghua University, China

Qinghua Liu; Tsinghua University, China

Yuantao Gu; Tsinghua University, China

LATE BREAKING RESULTS SESSION

14: FEATURE LEARNING OF VIRUS GENOME EVOLUTION WITH THE NUCLEOTIDE SKIP-GRAM NEURAL NETWORK

Hyunjin Shim; EPFL

15: DATA SCIENCE FOR ON-THE-GO PREDICTION OF STUDENT PERFORMANCE

Herman Dempere; Universidad de Barcelona 

Eloi Puertas; Universidad de Barcelona 

Laura Igual; Universidad de Barcelona

16: JOINT ESTIMATION OF LOW-RANK COMPONENTS AND GRAPH IN GROSSLY-CORRUPTED DATA

Rui Liu; Singapore Univ. Technology and Design

17: FROST — FAST ROW-STOCHASTIC OPTIMIZATION WITH UNCOORDINATED STEP-SIZES

Ran Xin, Tufts University

16.30

16.30

Scalability in Data Sciences Analysis

Session Type: Lecture

Session Chair: Axel Munk

Time: Monday, June 4th, 16:30 – 17:30

Location: Auditorium C, STCC

1: MULTI-SCALE ALGORITHMS FOR OPTIMAL TRANSPORT

Bernhard Schmitzer; WWU Münster, Germany

2: CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW

Nicolai Meinshausen; Seminar für Statistik, ETH Zurich, Switzerland

3: HIGH DIMENSIONAL CHANGE POINT ESTIMATION VIA SPARSE PROJECTION

Tengyao Wang and Richard J. Samworth; Statistical Laboratory, University of Cambridge

17.30

17.30

Welcome reception – SwissTech Convention Center

In partnership with Bühler Group

19.00

Tuesday June 5th

9.00

Surajit Chaudhuri

Microsoft Research, Redmond, USA

What Data Platforms can do to support Data Scientists?

10.00

10.00

Coffee break

10.30

10.30

Network Topology Inference

Session Type: Special Session Lecture

Session Chairs: Antonio G. Marques and Sundeep Chepuri

Time: Tuesday, June 5th, 10:30 – 12:30

Location: Auditorium C, STCC

1: ONLINE GRAPH LEARNING FROM SEQUENTIAL DATA

Stefan Vlaski; EPFL, Switzerland

Hermina Maretic; EPFL, Switzerland

Roula Nassif; EPFL, Switzerland

Pascal Frossard; EPFL, Switzerland

Ali Sayed; EPFL, Switzerland

2: ONLINE IDENTIFICATION OF DIRECTIONAL GRAPH TOPOLOGIES CAPTURING DYNAMIC AND NONLINEAR DEPENDENCIES

Yanning Shen; University of Minnesota, United States

Georgios B. Giannakis; University of Minnesota, United States

3: SPARSEST NETWORK SUPPORT ESTIMATION: A SUBMODULAR APPROACH

Mario Coutino; TU Delft, Netherlands

Sundeep Prabhakar Chepuri; TU Delft, Netherlands

Geert Leus; TU Delft, Netherlands

4: ON LEARNING LAPLACIANS OF TREE STRUCTURED GRAPHS

Keng-Shih Lu; University of Southern California, United States

Eduardo Pavez; University of Southern California, United States

Antonio Ortega; University of Southern California, United States

5: DIRECTED NETWORK TOPOLOGY INFERENCE VIA GRAPH FILTER IDENTIFICATION

Rasoul Shafipour; University of Rochester, United States

Santiago Segarra; Massachusetts Institute of Technology, United States

Antonio Garcia Marques; King Juan Carlos University, Spain

Gonzalo Mateos; University of Rochester, United States

6: LEARNING TO INFER POWER GRID TOPOLOGIES: PERFORMANCE AND SCALABILITY    

Yue Zhao; Stony Brook University, United States

Jianshu Chen; Tencent AI Lab, United States

H. Vincent Poor; Princeton University, United States

12.30

12.30

Industry Outlook II

1: Marios Anthimopoulos, Senior Researcher, Frontiers

Large-scale recommendations based on matrix factorization

2: Phong Nguyen, Senior Data Scientist, Expedia

Expedia Sort Algorithm

13.00

13.00

Lunch break

14.00

14.00

Andreas Krause

ETH Zurich, Switzerland

Towards Safe Reinforcement Learning

15.00

15.00

Coffee break

15.30

15.30

Poster 2

Session Type: Poster

Time: Tuesday, June 5th, 15:30 – 16:30

 Location: Foyer

1: SUBSAMPLING LEAST SQUARES AND ELEMENTAL ESTIMATION

Keith Knight; University of Toronto, Canada

2: DEEP CNN SPARSE CODING ANALYSIS: TOWARDS AVERAGE CASE

Michael Murray; The Alan Turing Institute and The University of Oxford, United Kingdom

Jared Tanner; The Alan Turing Institute and The University of Oxford, United Kingdom

3: NON-NEGATIVE SUPER-RESOLUTION IS STABLE

Armin Eftekhari; Alan Turing Institute, United Kingdom

Jared Tanner; University of Oxford, United Kingdom

Andrew Thompson; University of Oxford, United Kingdom

Bogdan Toader; University of Oxford, United Kingdom

Hemant Tyagi; University of Oxford, United Kingdom

4: SUBGRADIENT PROJECTION OVER DIRECTED GRAPHS USING SURPLUS CONSENSUS

Ran Xin; Tufts University, United States

Chenguang Xi; Tufts University, United States

Usman Khan; Tufts University, United States

5: VECTOR COMPRESSION FOR SIMILARITY SEARCH USING MULTI-LAYER SPARSE TERNARY CODES

Sohrab Ferdowsi; University of Geneva, Switzerland

Slava Voloshynovskiy; University of Geneva, Switzerland

Dimche Kostadinov; University of Geneva, Switzerland

6: SUBSPACE PRINCIPAL ANGLE PRESERVING PROPERTY OF GAUSSIAN RANDOM PROJECTION

Yuchen Jiao; Tsinghua University, China

Xinyue Shen; Tsinghua University, China

Gen Li; Tsinghua University, China

Yuantao Gu; Tsinghua University, China

7: THE MICHIGAN DATA SCIENCE TEAM: A DATA SCIENCE EDUCATION PROGRAM WITH SIGNIFICANT SOCIAL IMPACT

Arya Farahi; University of Michigan – Ann Arbor, United States

Jonathan Stroud; University of Michigan – Ann Arbor, United States

8: PROFIT MAXIMIZING LOGISTIC REGRESSION MODELING FOR CREDIT SCORING

Arnout Devos; University of Southern California, United States

Jakob Dhondt; Switch, Switzerland

Eugen Stripling; KU Leuven, Belgium

Bart Baesens; KU Leuven, Belgium

Seppe vanden Broucke; KU Leuven, Belgium

Gaurav Sukhatme; University of Southern California, United States

9: ALTERNATING AUTOENCODERS FOR MATRIX COMPLETION

Kiwon Lee; Korea Advanced Institute of Science and Technology (KAIST), Korea (South)

Yong H. Lee; Korea Advanced Institute of Science and Technology (KAIST), Korea (South)

Changho Suh; Korea Advanced Institute of Science and Technology (KAIST), Korea (South)

10: AN EFFICIENT RECOMMENDER SYSTEM BY INTEGRATING NON-NEGATIVE MATRIX FACTORIZATION WITH TRUST AND DISTRUST RELATIONSHIPS

Hashem Parvin; University of Kurdistan, Iran

Parham Moradi; University of Kurdistan, Iran

Shahrokh Esmaeili; University of Kurdistan, Iran

Mahdi Jalili; RMIT University, Australia 

11: SPARSE ANOMALY REPRESENTATIONS IN VERY HIGH-DIMENSIONAL BRAIN SIGNALS

Catherine Stamoulis; Harvard Medical School, United States 

12: PREDICTING ELECTRICITY OUTAGES CAUSED BY CONVECTIVE STORMS

Roope Tervo; Finnish Meteorological Institute, Finland

Joonas Karjalainen; Finnish Meteorological Institute, Finland

Alexander Jung; Aalto University, Finland

13: AIM: AN ABSTRACTION FOR IMPROVING MACHINE LEARNING PREDICTION

Victoria Stodden; University of Illinois Urbana-Champaign, United States

Xiaomian Wu; University of Illinois Urbana-Champaign, United States

Vanessa Sochat; Stanford University, United States 

14: NETWORK INFERENCE FROM COMPLEX SYSTEMS STEADY STATES OBSERVATIONS: THEORY AND METHODS

Hoi-To Wai; Arizona State University, United States

Anna Scaglione; Arizona State University, United States

Baruch Barzel; Bar-Ilan University, Israel

Amir Leshem; Bar-Ilan University, Israel

16.30

 

16.30

Data Science Theory

Session Type: Lecture

Session Chair: Waheed U. Bajwa

Time: Tuesday, June 5th, 16:30 – 17:30

Location: Auditorium C, STCC

1: SAVE – SPACE ALTERNATING VARIATIONAL ESTIMATION FOR SPARSE BAYESIAN LEARNING

Christo Kurisummoottil Thomas; Eurecom, France

Dirk Slock; Eurecom, France 

2: SUBSPACE DATA VISUALIZATION WITH DISSIMILARITY BASED ON PRINCIPAL ANGLE

Xinyue Shen; Tsinghua Univereity, China

Yuchen Jiao; Tsinghua Univereity, China

Yuantao Gu; Tsinghua Univereity, China 

3: BYRDIE: A BYZANTINE-RESILIENT DISTRIBUTED LEARNING ALGORITHM

Zhixiong Yang; Rutgers University, United States

Waheed Bajwa; Rutgers University, United States 

17.30

18.00

Olympic Museum Visit and Cocktail

 

22.00

Wednesday June 6th

9.00

Lisa Amini

Director, IBM Research, USA

Why AI needs even more Data Science, and vice versa

10.00

10.00

Coffee break

10.30

10.30

CNNs for Graph Data

Session Type: Special Session Lecture

Session Chairs: Antonio G. Marques, Geert Leus, and Alejandro Ribeiro

Time: Wednesday, June 6th, 10:30 – 12:30

Location: Auditorium C, STCC

1: CONVOLUTIONAL NEURAL NETWORKS VIA NODE-VARYING GRAPH FILTERS

Fernando Gama; University of Pennsylvania, United States

Geert Leus; Delft University of Technology, Netherlands

Antonio Marques; King Juan Carlos University, Spain

Alejandro Ribeiro; University of Pennsylvania, United States

2: MOTIFNET A MOTIF-BASED GRAPH CONVOLUTIONAL NETWORK FOR DIRECTED GRAPHS

Federico Monti; Università della Svizzera italiana, Italy

Karl Otness; Harvard University, United States

Michael Bronstein; Università della Svizzera italiana, Switzerland

3: REVISED NOTE ON LEARNING QUADRATIC ASSIGNMENT WITH GRAPH NEURAL NETWORKS

Alex Nowak; INRIA, Ecole Normale Superieure, France

Soledad Villar; New York University, United States

Afonso Bandeira; New York University, United States

Joan Bruna; New York University, United States

4: MATCHING CONVOLUTIONAL NEURAL NETWORKS WITHOUT PRIORS ABOUT DATA

Carlos Eduardo Rosar Kos Lassance; IMT Atlantique, France

Jean-Charles Vialatte; IMT Atlantique / Cityzen Data, France

Vincent Gripon; IMT Atlantique, France

5: ON GRAPH CONVOLUTION FOR GRAPH CNNS

Jian Du; Carnegie Mellon University, United States

John Shi; Carnegie Mellon University, United States

Soummya Kar; Carnegie Mellon University, United States

Jose Moura; Carnegie Mellon University, United States

6: TOWARDS A SPECTRUM OF GRAPH CONVOLUTIONAL NETWORKS

Mathias Niepert; NEC Labs Europe, Germany

Alberto Garcia-Duran; NEC Labs Europe, Germany

12.30

12.30

Industry Outlook III

1: Gregory Mermoud, Senior Technical Leader, Cisco

Building machine learning products: from the whiteboard to the field

 

2: Hugo Penedones, Research Engineer, Google DeepMind

Applied Reinforcement Learning: challenges and open problems

13.00

13.00

Lunch break

14.00

14.00

Victoria Stodden

UIUC, USA

Reproducibility and Generalizability in Data-enabled Discovery

15.00

15.00

Coffee break

15.30

15.30

Grand Challenge:

Investment Ranking Challenge

17.00