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
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
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
2: Phong Nguyen, Senior Data Scientist, Expedia
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
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
2: Hugo Penedones, Research Engineer, Google DeepMind
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
