`

Timezone: »

 
Poster Session
Ahana Ghosh · Javad Shafiee · Akhilan Boopathy · Alex Tamkin · Theodoros Vasiloudis · Vedant Nanda · Ali Baheri · Paul Fieguth · Andrew Bennett · Guanya Shi · Hao Liu · Arushi Jain · Jacob Tyo · Benjie Wang · Boxiao Chen · Carroll Wainwright · Chandramouli Shama Sastry · Chao Tang · Daniel S. Brown · David Inouye · David Venuto · Dhruv Ramani · Dimitrios Diochnos · Divyam Madaan · Dmitrii Krashenikov · Joel Oren · Doyup Lee · Eleanor Quint · elmira amirloo · Matteo Pirotta · Gavin Hartnett · Geoffroy Dubourg-Felonneau · Gokul Swamy · Pin-Yu Chen · Ilija Bogunovic · Jason Carter · Javier Garcia-Barcos · Jeet Mohapatra · Jesse Zhang · Jian Qian · John Martin · Oliver Richter · Federico Zaiter · Tsui-Wei Weng · Karthik Abinav Sankararaman · Kyriakos Polymenakos · Lan Hoang · mahdieh abbasi · Marco Gallieri · Mathieu Seurin · Matteo Papini · Matteo Turchetta · Matthew Sotoudeh · Mehrdad Hosseinzadeh · Nathan Fulton · Masatoshi Uehara · Niranjani Prasad · Oana-Maria Camburu · Patrik Kolaric · Philipp Renz · Prateek Jaiswal · Reazul Hasan Russel · Riashat Islam · Rishabh Agarwal · Alexander Aldrick · Sachin Vernekar · Sahin Lale · Sai Kiran Narayanaswami · Samuel Daulton · Sanjam Garg · Sebastian East · Shun Zhang · Soheil Dsidbari · Justin Goodwin · Victoria Krakovna · Wenhao Luo · Wesley Chung · Yuanyuan Shi · Yuh-Shyang Wang · Hongwei Jin · Ziping Xu

Fri Dec 13 09:35 AM -- 10:30 AM (PST) @ None

Author Information

Ahana Ghosh (MPI-SWS)
Javad Shafiee (DarwinAI & University of Waterloo)
Akhilan Boopathy (Massachusetts Institute of Technology)
Alex Tamkin (Stanford University)
Theodoros Vasiloudis (Research Institutes of Sweden)

My research deals with large scale learning for decision trees and graphs, including the development of novel algorithms and contributions to data analytics frameworks. The projects that I have been involved in included developing algorithms that make use of streaming frameworks such as Apache Flink and Apache Samoa, and contributing to the design and implementa- tion of learning systems capable of handling massive datasets, such as the machine learning library for Apache Flink. A research area that I am currently exploring is online decision trees, and more specifically quantifying the uncertainty in the predictions of random forests, with the aim of bringing the techniques to online learning. I’ve also recently become involved in the development of XGBoost, focusing on improving its distributed gradient boosted tree implementation.

Vedant Nanda (MPI-SWS & University of Maryland)
Ali Baheri (NA)
Paul Fieguth (NA)
Andrew Bennett (Cornell University)
Guanya Shi (Caltech)

PhD student in machine learning and robotics

Hao Liu (Caltech)
Arushi Jain (Mila/ McGill University)
Jacob Tyo (NA)
Benjie Wang (University of Oxford)
Boxiao Chen (NA)
Carroll Wainwright (NA)
Chandramouli Shama Sastry (Vector Institute/Dalhousie University)
Chao Tang (Georgia Institute of Technology)
Daniel S. Brown (NA)
David Inouye (NA)
David Venuto (NA)
Dhruv Ramani (National Institute of Technology Warangal)

I am a senior undergraduate, who has been fascinated by machine learning since high-school. After exploring different areas of ML application, I persuaded my interest in Deep RL and research surrounding that. I am specifically interested in Exploration, Hierarchical RL and AI Safety. Other than that, I love exploring new areas within ML and learning in general!

Dimitrios Diochnos (NA)
Divyam Madaan (KAIST)
Dmitrii Krashenikov (NA)
Joel Oren (Bosch Center for Artificial Intelligence)
Doyup Lee (NA)
Eleanor Quint (University of Nebraska-Lincoln)
elmira amirloo (Huawei Technologies)
Matteo Pirotta (Facebook AI Research)
Gavin Hartnett (NA)
Geoffroy Dubourg-Felonneau (Cambridge Cancer Genomics)
Gokul Swamy (UC Berkeley)
Pin-Yu Chen (IBM Research AI)
Ilija Bogunovic (ETH Zurich)
Jason Carter (University of New Hampshire)
Javier Garcia-Barcos (NA)
Jeet Mohapatra (NA)
Jesse Zhang (UC Berkeley)
Jian Qian (NA)
John Martin (NA)
Oliver Richter (ETH Zurich)
Federico Zaiter (NA)
Tsui-Wei Weng (NA)
Karthik Abinav Sankararaman (University of Maryland)
Kyriakos Polymenakos (NA)
Lan Hoang (IBM Research UK)

My research interests are Deep Reinforcement Learning, GIS, decision support systems, interdependencies of complex systems, agent-based modelling and uncertainty analysis. My focus is to create applied research outputs that can address industry's needs. I have a background in Physical Geography and Environmental Sciences, in particular decision making under climate change impacts, hydrology, water management and GIS applications for environmental management.

mahdieh abbasi (laval universite)
Marco Gallieri (NNAISENSE)

Marco Gallieri is a Research Scientist at NNAISENSE, in Lugano. He received a PhD in Engineering from Sidney Sussex College, the University of Cambridge, in 2014. His PhD Thesis was on LASSO-MPC and is published by Springer.  In 2009 he received an MSc in automation engineering from the Universita’ Politecnica delle Marche, in Italy. He wrote his MSc thesis during a visiting term at the National University of Ireland, Maynooth.  In 2010 he was a Marie Curie early stage researcher at the Instituto Superior Tecnico in Lisbon working on non-linear control of autonomous underwater vehicles. Before joining NNAISENSE, he spent three years with the McLaren group, where he developed a model based Li-Ion battery management system for the F1 power unit and a prototype for next generation F1 driver-in-the-loop simulator. He then worked as a data scientist in the R&D branch of the group. He’s currently leading the control theory R&D efforts of NNAISENSE. His research interests are at the intersection between control and machine learning and include the study of stability of deep and recurrent neural networks as well as their use in control systems for safety-critical applications.

Mathieu Seurin (NA)
Matteo Papini (Politecnico di Milano)

Matteo Papini was born in Sondrio, Italy, on 5th July 1993. In 2015 he obtained the Bachelor Degree in Ingegneria Informatica (Computer Engineering) cum laude at Politecnico di Milano. In 2017 he obtained the Master Degree in Computer Science and Engineering - Ingegneria Informatica cum laude at Politecnico di Milano. From November 2017 he is a Ph.D. student at Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) at Politecnico di Milano. His research interests include artificial intelligence, robotics, and machine learning, with a focus on reinforcement learning.

Matteo Turchetta (NA)
Matthew Sotoudeh (University of California, Davis)
Mehrdad Hosseinzadeh (University of Manitoba)

I'm a PhD Student of Computer Science, doing Machine Learning research at the intersection of Vision and Language domains using Deep Learning.

Nathan Fulton (NA)
Masatoshi Uehara (Harvard University)
Niranjani Prasad (Princeton University)
Oana-Maria Camburu (NA)
Patrik Kolaric (NA)
Philipp Renz (NA)
Prateek Jaiswal (Purdue University)
Reazul Hasan Russel (University of New Hampshire)

I'm a PhD student at the computer science department at University of New Hampshire. I am interested about applying Reinforcement Learning into real world problems with safety and robustness guarantees.

Riashat Islam (MILA/McGill)
Rishabh Agarwal (Google)

I am a researcher in the Google Brain team in Montréal. My research interests mainly revolve around Deep Reinforcement Learning (RL), often with the goal of making RL methods suitable for real-world problems.

Alexander Aldrick (University of Cambridge)

PhD student specialising in machine learning for physics, and next generation materials for renewable technologies.

Sachin Vernekar (NA)

Theoretically motivated machine learning research student. My current research interests include the safety of deep learning models in computer vision, generative modeling in an autonomous driving context. My interests, in general, include Classical Machine Learning, Deep Learning, Theory, Uncertainty, Computer Vision, Bayesian Deep Learning, Reinforcement Learning, NLP, Autonomous Driving.

Sahin Lale (California Institute of Technology)
Sai Kiran Narayanaswami (The University of Texas at Austin)
Samuel Daulton (NA)
Sanjam Garg (NA)
Sebastian East (NA)
Shun Zhang (University of Michigan)
Soheil Dsidbari (NA)
Justin Goodwin (MIT Lincoln Laboratory)
Victoria Krakovna (NA)
Wenhao Luo (NA)
Wesley Chung (NA)
Yuanyuan Shi (University of Washington)
Yuh-Shyang Wang (GE Global Research)
Hongwei Jin (University of Illinois at Chicago)
Ziping Xu (University of Michigan)

My name is Ziping Xu. I am a second-year Ph.D. student in Statistics at the University of Michigan. My research interests are on sample efficient reinforcement learning and transfer learning for reinforcement learning. I am looking for a research-based internship in Summer 2020.

More from the Same Authors