Colloquia and Seminars
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Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.
Academic Calendar at Technion site.
Upcoming Colloquia & Seminars
Wednesday, 21.4.2021, 12:30
Technion CS open day 2021 invites outstanding undergraduates from all universities to learn about the Computer Science Department and register for Winter Semester 2021-22.
The event will be held online by ZOOM
- ID MEETING NO. 96244586510, on Wednesday, April 21, 2021. between 12:30-13:45.
The program will include review on curriculum, research and life at the Technion CS Department:
12:30-12:40 CS Dean, Prof. Dan Geiger
12:40-12:55 Vice Dean, Prof. Gill Barequet
12:55-13:20 Dr. Kira Radinsky, Head of Diagnostic Robotics: Digital Healthcare - The Next Frontier
13:20-13:30 Mr. Gil Ben-Shachar and Ms. Stav Perle (Ph.D. students): Life in the Computer Science Faculty
13:30 Questions and answers
Attendance at the open day requires pre-registration
More details and program
Liron Bronfman, M.Sc. Thesis Seminar
Wednesday, 21.4.2021, 14:00
For password to lecture, please contact: firstname.lastname@example.org
Advisor: Dr. Ron Rothblum
The connection between information theoretic proof systems and cryptography has been extremely fruitful. In this thesis, we further explore this connection, showing both new limitations and opportunities.
In the talk we will focus on the new opportunities and show constructions of computational relaxations of objects that are known to be essentially impossible to achieve information theoretically. In particular, we show cryptographic analogs of:
(1) PCPs whose length is proportional to the witness size.
(2) Instance compression, which allows, for example, to efficiently and generically reduce the size of a given formula on m clauses and n variables (with m >>n) to a formula of size poly(n,log(m)).
We will discuss the applicability of these relaxations and raise questions for future research.
Wednesday, 21.4.2021, 17:30
Fady Massarwi (CS, Technion)
Zoom Lecture: 91344952941
For password to lecture please contact email@example.com
This talk presents some of the geometrical aspects involved in treating irregular heart beat rhythm (Arrythmia) using Carto 3 System. Carto 3 is a product of Biosense-Webster, a global leader in the science of diagnosing and treating heart rhythm disorders. CARTO 3 System enables accurate visualization of multiple catheters in a patient’s heart and pinpoints exact location/orientation of a catheter. During arrythmia procedure, a 3D electro-anatomical reconstruction of the heart is built and color coded with the electrical activity in the heart. In this talk, we’ll introduce mesh processing algorithms and discuss industrial challenges encountered in the process of building and coloring geometrical reconstructions of the heart.
Interested parties ca email firstname.lastname@example.org
for the zoom link.
Lior Ben-Yamin, M.Sc. Thesis Seminar
Thursday, 29.4.2021, 14:30
For password to lecture, please contact: email@example.com
Advisor: Prof. H. Shachnai
We consider scheduling real-time jobs in the classic flow shop model. The input is a set of n jobs, each consisting of m segments to be processed on m machines in the specified order. Each job also has a release time, a due date, and a weight. The objective is to maximize the throughput, i.e., to find a subset of the jobs that have the maximum total weight and can complete processing on the m machines within their time windows. This problem has numerous real-life applications ranging from manufacturing to cloud and embedded computing platforms, already in the special case where m=2.
Previous work in the flow shop model has focused on makespan, flow time, or tardiness objectives. However, little is known for the flow shop model in the real-time setting. In this work, we give the first nontrivial results for this problem and present a pseudo-polynomial time (2m+1)-approximation algorithm for throughput maximization on $m \geq 2$ machines, where m is a constant. This ratio is essentially tight due to a known hardness of approximation result. For the two-machine case, we give a polynomial-time $9+\eps$-approximation algorithms, where $\eps = O(1/n)$. Better bounds are derived for some restricted subclasses of inputs with two machines, as well as the no-wait flow shop model.
Shoval Lagziel, Ph.D. Thesis Seminar
Thursday, 29.4.2021, 15:30
For password to lecture, please contact: firstname.lastname@example.org
Advisor: Prof. Tomer Shlomi
Metabolic reprogramming is a hallmark of cancer, providing novel means to selectively target cancer cells, for precision medicine and early diagnosis. Understanding tumor-specific metabolic alterations facilitates the identification of induced dependency on specific enzymes whose inhibition selectively targets cancer cells. In addition, the altered metabolic activity of cancer cells, involving the consumption of metabolic nutrients and the secretion of byproducts from the tumor leaves metabolic traces that can be utilized for diagnostic purposes. Here, we explored two main directions based on the metabolic reprogramming of cancer: (1) construction of models suggesting potential metabolic mechanisms for the dependency on metabolic genes, (2) early cancer diagnosis based on fast and sensitive metabolomics of blood samples.
Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer proliferation and survival. Previous works integrated loss-of-function screens with cancer cell line molecular characterization to reveal the underlying mechanisms for cancer dependence on specific genes; however, explaining cancer dependence on metabolic genes was shown to be especially challenging. Considering that metabolic activity is highly dependent on nutrient availability, analyzing publicly available omics datasets, we have shown that utilizing different media types for culturing cancer cell lines has a major effect on intracellular metabolite levels and metabolic gene dependencies â€“ calling for future analyses of published omics datasets such as that of the CCLE to account for this confounding effect. Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network enabled us to improve our understanding of cancer cell line-specific dependence on metabolic genes using machine learning models.
Early diagnosis of cancer greatly increases the chances for successful treatment of cancer. Major ongoing efforts are made to develop highly sensitive, cost-effective screening methods via a variety of molecular biomarkers. Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are time-consuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected into the ionization source. However, it was previously shown to provide a reduced sensitivity and reproducibility. We developed two rapid mass-spectrometry based metabolomics methods, FI-MS based and LC-MS based, enabling a reproducible detection and quantitation of thousands of metabolites within less than one minute per sample. The developed approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens. Applying the developed metabolomics method to hundreds of serum samples from cancer patients and healthy controls, utilizing machine learning techniques, we have demonstrated the potential and applicability of this approach for population-wide cancer screening.