I am a Ph.D. student in Computer Science at the NLP Lab of the University of British Columbia (UBC) who likes to solve challenging tasks using data-driven approaches. My research focuses on designing and implementing innovative computational models that enable automated systems to better understand natural language. In addition to my primary research on data-driven discourse parsing, I am interested in new and creative applications of modern machine learning methods and algorithms.
In my free time I strive to explore new destinations around the world, learn about new culures and history. I like working on fun little side projects, mostly related to game development and graphic design and participate in Hackathons. I love to explore the many small coffee shops and unique craft breweries around the Pacific Northwest. In the winter, I like to go snowboarding on local mountains around Vancouver or in Whistler.
Sep 2018 - present
Discourse Parsing, Question Answering, Summarization, Machine Translation, Natural Language Processing, Natural Language Generation, Computational Linguistics, Machine Learning, Artificial Intelligence
Research Assistantship (Industry Grant)
Prof. Dr. Giuseppe Carenini
Oct 2014 - Dec 2017
Cognitive Systems, Software Engineering
A Hierarchical Approach to Neural Context-Aware Modeling
Oct 2011 - Sep 2014
Software Engineering, Electronical Engineering
Multidimensional Indoor Object Tracking with active RFID
Sep 2018 - present
Methods and applications of discourse parsing, focused on deep learning techniques and distant supervision
more_horizSupervisor: Giuseppe Carenini
Jan 2017 - Jun 2017
Developed four web applications on SAP Cloud Platform frequently used by over 1000 customers worldwide
more_horizManaged repository transition into a new landscape with strict time constraints (hot swap) to enhance system stability
more_horizImplemented customer-driven development projects, reducing manual efforts by 75% and preventing media breaks
May 2016 - Dec 2016
Full-stack web development with SAPUI5 and SAP Cloud Platform
more_horizLed a team of four international interns and implemented cross-topic communication to ensure coding conventions and quality
more_horizDesigned and implemented three process-driven applications to enhance customer projects, reduced managing efforts by 60%
more_horizSet up an autonomous development infrastructure which improved code review processes and enhanced overall code quality in the productive system
Oct 2011 - Apr 2016
Designed and implemented company internal native mobile applications on BlackBerry and Android
more_horizDeveloped a proof-of-concept real-time emergency control center using the push enabled Java web framework Vaadin
Patrick Huber and Giuseppe Carenini
Department of Computer Science, University of British Columbia (UBC)
Patrick Huber and Giuseppe Carenini
Department of Computer Science, University of British Columbia (UBC)
Discourse parsing could not yet take full advantage of the neural NLP revolution, mostly due to the lack of annotated datasets. We propose a novel approach that uses distant supervision on an auxiliary task (sentiment classification), to generate abundant data for RST-style discourse structure prediction. Our approach combines a neural variant of multiple-instance learning, using document-level supervision, with an optimal CKY-style tree generation algorithm. In a series of experiments, we train a discourse parser (for only structure prediction) on our automatically generated dataset and compare it with parsers trained on human-annotated corpora (news domain RST-DT and Instructional domain). Results indicate that while our parser does not yet match the performance of a parser trained and tested on the same dataset (intra-domain), it does perform remarkably well on the much more difficult and arguably more useful task of inter-domain discourse structure prediction, where the parser is trained on one domain and tested/applied on another one.
Patrick Huber, Jan Niehues, Alex Waibel
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT)
Patrick Huber, Jan Niehues, Alex Waibel
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT)
We present a new approach to evaluate computational models for the task of text understanding by the means of out-of-context error detection. Through the novel design of our automated modification process, existing large-scale data sources can be adopted for a vast number of text understanding tasks. The data is thereby altered on a semantic level, allowing models to be tested against a challenging set of modified text passages that require to comprise a broader narrative discourse. Our newly introduced task targets actual real-world problems of transcription and translation systems by inserting authentic out-of-context errors. The automated modification process is applied to the 2016 TEDTalk corpus. Entirely automating the process allows the adoption of complete datasets at low cost, facilitating supervised learning procedures and deeper networks to be trained and tested. To evaluate the quality of the modification algorithm a language model and a supervised binary classification model are trained and tested on the altered dataset. A human baseline evaluation is examined to compare the results with human intuition. The performance on the evaluation task indicates the difficulty to detect semantic errors for machine-learning algorithms and human intuition, showing that the errors cannot be identified when limited to a single sentence.
Patrick Huber, Jan Niehues, Alex Waibel
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT)
Patrick Huber, Jan Niehues, Alex Waibel
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology (KIT)
We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the developed system captures the narrative on word-level, sentence-level, and context-level. Through the hierarchical set-up, our proposed model summarizes the most salient information on each level and creates an abstract representation of the extended context. We subsequently use this representation to enhance neural language processing systems on the task of semantic error detection. To show the potential of the newly introduced topology, we compare the approach against a context-agnostic set-up including a standard neural language model and a supervised binary classification network. The performance measures on the error detection task show the advantage of the hierarchical context-aware topologies, improving the baseline by 12.75% relative for unsupervised models and 20.37% relative for supervised models.
3+ years of professional Java experience in a business context using Java Servlets, the Vaadin Java framework, JMS and Hibernate.
2+ year of professional experience with Python for Data Science and Machine Learning applications. Basic knowledge of the Flask Python backend.
2+ year of professional JavaScript experience as a full-stack web developer using the JavaScript frameworks like SAPUI5, XSJS, AngularJS and NodeJS.
2+ years of partly professional Android experience as a native Android developer implementing business applications and through personal and university projects.
1+ year of professional experience with Google's Tensorflow framework for Machine Learning and Numeric Computation, implementing artificial neural networks.
2+ year of professional experience with the PyTorch neural network API building low-level convolutional and recurrent neural network topologies.
1+ year of professional SAPUI5 experience as a full stack Software Developer at SAP Canada & Germany.
1+ year of professional experience with the SAP HANA database and server systems as a full stack Software Developer.
Under a year of AngularJS experience on personal web development projects.
Under a year of experience with NodeJS creating REST webservices for personal projects.
2+ years of professional experience with MySQL for multiple business applications. Also used in combination with Hibernate.
Under a year of experience with MongoDB creating multiple databases for personal and university projects.
Used the D3 library for a graduate-level course project dynamically visualizing tree structure and attribute alignment
1+ year of professional experience with the Git version control system used for the majority of projects.
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University project for a Software-Design course.
We developed a Client-Server application to enable people to set up a soccer prediction game and compare their knowledge of the German Bundesliga. The project was part of a second year university course at the DHBW and has been developed as a native Android application with a Java backend and a MySQL database.
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As part of the Cognitive Systems course at the KIT, we build and implemented an autonomous Lego Mindstorms robot to solve a number of challenging tasks, like a previously unknown maze, multiple bridges and other difficulties.
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As part of a six-month seminar on humanoid robots, we designed a real-time interface for Aldebaran's Nao robot, which natively only supports commands at compile time. Our added functionalities enabled the robot to be controlled at runtime.
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As part of a graduate-level course on Information- and Social Networks, I created a natural language based Question Answering system, which can answer factoid questions based on a natural input question. The system is implemented using PyTorch and a sequence-to-sequence recurrent neural network.
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For a three-month seminar during the second year Master's degree at the KIT, I analysed and enhanced current gesture detection algorithms using hollistic body models.
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While experimenting with Unity I created this one-touch controlled action game. The objective is to bring the hotel guests to their desired floors before they lose their cool and commplain about you!
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As a personal project, I implemented a web-based version of the social card game "Mafia", also known as "Werewolfe". The implemented prototype takes advantage of the benefits provided by the technological framework and combines these with the classical elements of the card game
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Within the 24 hours of the 2018 nwHacks hackathon, I and my team created a proof-of-concept, showing the possiblities of the new features introduced with iOS 11 and how they, in combination with Apple's privilege-concept, can be maliciously used to intrude the privacy of iPhone users. As a proof-of-concept, we used the facial information provided by Apple's framework and trained our own artificial neural network with the data. With only a minimal amount of training data, we were able to accurately predict the user's emotions, which we limited to {Happy,Sad,Angry and Surprised}. To expose the impact of our findings, we created a Facebook-like newsfeed, which uses the phone-camera in the background - without any indication to the user - and show new feed-content depending on the users emotions.
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To solidify my understanding of artificial neural networks, I created an object-oriented feed-forward neural network from scratch and trained the model on the task of solving the game "connect four". Through the nature of the task, a reinforcement-learning approach utilizing an evolutionary algorithm has been used to train the model. The final results indicated clear evidence that the network reached nearly-human performance on the task.
Awarded to exceptional students in a thesis-based graduate program
received in September 2019
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In the area of "Summarization"
Fall 2019
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In the area of "Summarization and Generation"
Summer 2019
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Top 10% of Graduating Class 2018
received in March 2018
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Talent program for the top 10% interns at SAP
received in May 2017
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12 month company scholarship awarded with €250 per month and company events
received in April 2015
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Best graduate student of the class of 2014
received in September 2014
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Mother Tongue
Professional Level, IELTS Class C1
Basic Level
One day workshop for highschool girls to get interested in techinical jobs. I held a keynote presentation on "International Opportunities i nTech Companies" and was co-josting a 60 minutes workshop on the coding language "Alice"
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I volunteered at the bicycle valet, providing secure bicycle parking in and around Metro Vancouver to support environmentally friendly ways to get to major events in Vancouver
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I volunteered as a grad buddy for incoming international graduate students at UBC
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Snowboarding
Surfing
Soccer
Football
Volleyball
Boardgames
Reading