# Colloquia and Seminars

To join the email distribution list of the cs colloquia, please visit the list subscription page.Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

- Bioinformatics Forum
- BizTEC Forum
- ceClub
- CGGC Weekly Seminar
- Coding Theory Seminar
- Colloquia
- Haifux, Haifa Linux Club
- Pixel Club
- Theory Seminar

## Upcoming Colloquia & Seminars

### Inverse Problems and Unsupervised Learning with applications to Cryo-Electron Microscopy (cryo-EM)

- Speaker:
- Roy Lederman - CS-Lecture
- Date:
- Sunday, 26.11.2017, 10:30
- Place:
- Room 401 Taub Bld.
- Affiliation:
- Princeton University

Cryo-EM is an imaging technology that is revolutionizing structural biology; the Nobel Prize in Chemistry 2017 was recently awarded to Jacques Dubochet, Joachim Frank and Richard Henderson "for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution". Cryo-electron microscopes produce a large number of very noisy two-dimensional projection images of individual frozen molecules. Unlike related methods, such as computed tomography (CT), the viewing direction of each image is unknown. The unknown directions, together with extreme levels of noise and additional technical factors, make the determination of the structure of molecules challenging. While other methods for structure determination, such as x-ray crystallography and nuclear magnetic resonance (NMR), measure ensembles of molecules together, cryo-EM produces measurements of individual molecules. Therefore, cryo-EM could potentially be used to study mixtures of different conformations of molecules. Indeed, current algorithms have been very successful at analyzing homogeneous samples, and can recover some distinct conformations mixed in solutions, but, the determination of multiple conformations, and in particular, continuums of similar conformations (continuous heterogeneity), remains one of the open problems in cryo-EM. I will discuss a one-dimensional discrete model problem, Heterogeneous Multireference Alignment, which captures many of the properties of the cryo-EM problem, and I will briefly discuss convex optimization approaches and non-convex optimization approaches for this problem. I will then discuss components which we are introducing in order to address the problem of continuous heterogeneity in cryo-EM: 1. "hyper-molecules," the first mathematical formulation of truly continuously heterogeneous molecules, 2. The optimal representation of objects that are highly concentrated in both the spatial domain and the frequency domain using high-dimensional Prolate spheroidal functions, and 3. Bayesian algorithms for inverse problems with an unsupervised-learning component for recovering such hyper-molecules in cryo-EM. Short Bio: Roy Lederman is a postdoc at the Program in Applied and Computational Mathematics at Princeton University, working with Amit Singer. Before joining Princeton, he was a postdoc at Yale University, where he also completed his PhD in applied mathematics, working with Vladimir Rokhlin and Ronald Coifman. Roy holds a BSc in Electrical Engineering and a BSc in Physics from Tel Aviv University.

### Pixel Club: On the Utility of Context for Object Detection and when it is Lacking

- Speaker:
- Ehud Barnea (Ben-Gurion University)
- Date:
- Tuesday, 28.11.2017, 11:30
- Place:
- Room 337 Taub Bld.

The recurring context in which objects appear holds valuable information that can be employed to predict their existence. This intuitive observation indeed led many researchers to endow appearance-based detection results with explicit reasoning about context. The underlying thesis suggests that with stronger contextual relations, the better improvement in detection capacity one can expect from such a combined approach. In practice, however, the observed improvement in many case is modest at best, and often only marginal. In this work we seek to understand this phenomenon better, in part by pursuing an opposite approach. Instead of going from context to detection score, we try to formulate the score as a function of standard detector results and a contextual relation, an approach that allows to treat the utility of context as an optimization problem in order to obtain the largest gain possible from considering context in the first place. Analyzing different types of context reveals the most helpful ones and shows that in many cases including context can help while in other cases a great improvement is simply impossible or impractical. To better understand these results we then analyze the ability of context to separate correct detections from different types of false detections, revealing that contextual information cannot ameliorate localization errors, which in turn also diminishes the observed improvement obtained by correcting others types of errors. These insights provide further explanations and better understanding regarding the success or failure of utilizing context for object detection.

### CGGC Seminar: Computational Design for the Next Manufacturing Revolution

- Speaker:
- Adriana Schulz (MIT Computer Graphics Group)
- Date:
- Tuesday, 28.11.2017, 12:30
- Place:
- Taub 401

Over the next few decades, we are going to transition to a new economy where highly complex, customizable products are manufactured on demand by flexible robotic systems. This change is already underway in a number of fields. 3D printers are revolutionizing production of metal parts in aerospace, automotive, and medical industries. Whole garment knitting machines allow automated production of complex apparel and shoes. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. Collaborative robots, such as Baxter from Rethink Robotics, allow flexible and automated assembly of complex objects. Overall, these new machines enable batch-one manufacturing of products that have unprecedented complexity.

In my talk, I argue that the field of computational design is essential for the next revolution in manufacturing. This new field has to embrace the following key concepts. First, new design methods have to become more intelligent by utilizing large data repositories and associated machine learning methods. Second, design tools need to transition from declarative to functional, automatically translating functional specifications of an object to manufacturing instructions. Third, workflows need to support concurrent design of shape, materials, control, and software in order to simplify the process and fully utilize the design space. I will showcase how these three concepts are applied by developing new systems for designing robots, drones, and furniture. I will conclude my talk by discussing open problems and challenges for this new emerging research field.### A Query Engine for Probabilistic Preferences

- Speaker:
- Uzi Cohen, M.Sc. Thesis Seminar
- Date:
- Wednesday, 29.11.2017, 11:30
- Place:
- Taub 3
- Advisor:
- Prof. B. Kimelfeld

Models of uncertain preferences, such as Mallows, have been extensively studied due to their plethora of application domains. In a recent work, a conceptual and theoretical framework has been proposed for supporting uncertain preferences as first-class citizens in a relational database. The resulting database is probabilistic, and, consequently, query evaluation entails inference of marginal probabilities of query answers. In this paper, we embark on the challenge of a practical realization of this framework. We first describe an implementation of a query engine that supports querying probabilistic preferences alongside relational data. Our system accommodates preference distributions in the general form of the Repeated Insertion Model (RIM), which generalizes Mallows and other models. We then devise a novel inference algorithm for conjunctive queries over RIM, and show that it significantly outperforms the state of the art in terms of both asymptotic and empirical execution cost. We also develop performance optimizations that are based on sharing computation among different inference tasks in the workload. Finally, we conduct an extensive experimental evaluation and demonstrate that clear performance benefits can be realized by a query engine with built-in probabilistic inference, as compared to a stand-alone implementation with a black-box inference solver.

### CSpecial Talk: Learning to Understand Source Code with Machine Learning

- Speaker:
- Miltos Allamanis (Microsoft Research)
- Date:
- Monday, 4.12.2017, 12:30
- Place:
- Taub 3

Deep Neural Networks are succeeding at a range of natural language tasks such as machine translation and text summarization. Recently, the interdisciplinary field of "big code" promises a new set of learnable statistical static analyses. While machine learning tasks on source code have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this talk, I discuss how graph neural networks that use code's syntactic and semantic structured information can detect variable misuses in code without any external information (e.g. unit tests).

Bio:

Miltos Allamanis (https://miltos.allamanis.com) is a researcher at Microsoft Research, Cambridge. He is interested in applications of machine learning and natural language processing to software engineering and programming languages to create smart software engineering tools for developers. Previously, he was a PhD student at the University of Edinburgh advised by Dr. Charles Sutton.### Faster and Simpler Distributed CONGEST-Algorithms for Testing and Correcting Graph Properties

- Speaker:
- Guy Even - COLLOQUIUM LECTURE
- Date:
- Tuesday, 19.12.2017, 14:30
- Place:
- Room 337 Taub Bld.
- Affiliation:
- Dept. of Electrical Engineering-Systems, Tel-Aviv University
- Host:
- Yuval Filmus

We consider the following problem introduced by [Censor-Hillel et al., DISC 2016]. Design a distributed algorithm (called an $\epsilon$-tester) that tests whether the network over which the algorithm is running satisfies a given property (e.g., acyclic, bipartite) or is $\epsilon$-far from satisfying the property.

If the network satisfies the property, then all processors must accept. If the network is $\epsilon$-far from satisfying the property, then (with probability at least $2/3$) at least one processor must reject. Being $\epsilon$-far from a property means that at least $\epsilon\cdot |E|$ edges need to be deleted or inserted to satisfy the property. Suppose we have an $\epsilon$-tester that runs in $O(Diameter)$ rounds. We show how to transform this tester to an $\epsilon$-tester that runs in $O((\log |V|)/\epsilon))$ rounds. Since cycle-freeness and bipartiteness are easily tested in $O(Diameter)$ rounds, we obtain $\epsilon$-testers for these properties with a logarithmic number of rounds. Moreover, for cycle-freeness, we obtain a \emph{corrector} of the graph that locally corrects the graph so that the corrected graph is acyclic. The corrector deletes at most $\epsilon \cdot |E| + distance(G,P)$ edges and requires $O((\log |V|)/\epsilon))$ rounds. Joint work with Reut Levy and Moti Medina. Short Bio: ========== ============================================= Refreshments will be served from 14:15 Lecture starts at 14:30### On the Expressive Power of ConvNets and RNNs as a Function of their Architecture

- Speaker:
- Amnon Shashua - COLLOQUIUM LECTURE
- Date:
- Tuesday, 2.1.2018, 14:30
- Place:
- Room 337 Taub Bld.
- Affiliation:
- Hebrew University; CEO & CTO, Mobileye; Senior Vice President, Intel Corporation
- Host:
- Yuval Filmus

### Bridging the Gap between End-users and Knowledge Sources: Discovery, Selection and Utilization

- Speaker:
- Yael Amsterdamer - COLLOQUIUM LECTURE
- Date:
- Tuesday, 9.1.2018, 14:30
- Place:
- Room 337 Taub Bld.
- Affiliation:
- Department of Computer Science, Bar Ilan University
- Host:
- Yuval Filmus

TBA

### Personalization is a Two-Way Street

- Speaker:
- Ronny Lempel - COLLOQUIUM LECTURE
- Date:
- Tuesday, 16.1.2018, 14:30
- Place:
- Room 337 Taub Bld.
- Affiliation:
- Outbrain
- Host:
- Yuval Filmus

Recommender systems are first and foremost about matching users with items the systems believe will delight them. The "main street" of personalization is thus about modeling users and items, and matching per user the items predicted to best satisfy the user. This holds for both collaborative filtering and content-based methods. In content discovery engines, difficulties arise from the fact that the content users natively consume on publisher sites does not necessarily match the sponsored content that drives the monetization and sustains those engines. The first part of this talk addresses this gap by sharing lessons learned and by discussing how the gap may be bridged at scale with proper techniques. The second part of the talk focuses on personalization of audiences on behalf of content marketing campaigns. From the marketers' side, optimizing audiences was traditionally done by refining targeting criteria, basically limiting the set of users to be exposed to their campaigns. Marketers then began sharing conversion data with systems, and the systems began optimizing campaign conversions by serving the campaign to users likely to transact with the marketer. Today, a hybrid approach of lookalike modeling combines marketers' targeting criteria with recommendation systems' models to personalize audiences for campaigns, with marketer ROI as the target. This talk was given as a keynote in ACM RecSys 2017. Short bio: ========== Ronny Lempel joined Outbrain in May 2014 as VP of Outbrain's Recommendations Group, where he oversees the computation, delivery and auction mechanisms of the company's recommendations. Prior to joining Outbrain, Ronny spent 6.5 years as a Senior Director at Yahoo Labs. Ronny joined Yahoo in October 2007 to open and establish its Research Lab in Haifa, Israel. During his tenure at Yahoo, Ronny led R&D activities in diverse areas, including Web Search, Web Page Optimization, Recommender Systems and Ad Targeting. In January 2013 Ronny was appointed Yahoo Labs' Chief Data Scientist in addition to his managerial duties. Prior to joining Yahoo, Ronny spent 4.5 years at IBM Research, where his duties included research and development in the area of enterprise search systems. During his tenure at IBM, Ronny managed the Information Retrieval Group at IBM's Haifa Research Lab for two years. Ronny received his PhD, which focused on search engine technology, from the Faculty of Computer Science at Technion, Israel Institute of Technology in early 2003. Ronny has authored over 40 research papers in leading conferences and journals, and holds 18 granted US patents. He regularly serves on program and organization committees of Web-focused conferences, and has taught advanced courses on Search Engine Technologies and Big Data Technologies at Technion. ========================================= Refreshments will be served from 14:15 Lecture starts at 14:30