Seminars

The following are details of lectures held as a part of the CSE seminar series during Jan-May 2018.


May 29, 2018. Matching under preferences: Stability is ubiquitous but popularity is hard to find. Dr. Sushmita Gupta, Postdoctoral researcher at University of Bergen, Norway.
Abstract: Matching under preferences is a research area that finds numerous applications in practice as diverse as assigning colleges to students, workers to firms, kidney donors to recipients and users to servers in a distributed internet service, to just name a few.

In the first part of the talk, I will discuss canonical problems in this area, namely, stable matching and its generalization, popular matching. These problems are at the very heart of the intersection between Computer Science and Economics. In the second part of the talk, I will present a new result that resolved the arguably main open problem in the subarea of popular matching: In an arbitrary graph, deciding if a popular matching exists is NP-complete. The computational complexity of this problem has been unknown for over a decade, during which it was repeatedly posed as a fundamental open question.

Time: 11 am – 12 pm
Venue: Room 7/202



May 9, 2018. Understanding research: Curation, Growth, and Applications. Dr. Mayank Singh, PhD, CSE, Indian Institute of Technology, Kharagpur.
Abstract: Understanding scholarly articles is a key ingredient of impressive research recipe. With the tremendous advancement in internet infrastructure, we are witnessing an ongoing explosion in scholarly information generation. In this talk, I attempt to discuss some of the challenges emanating from scholarly volume overload. In particular, I will look into three different dimensions: (i) metadata, structure, bibliography and experimental performance extraction from scholarly articles, (ii) designing network-assisted ageing growth models for evolving citation networks with novel proposal of temporal signatures, and (iii) leveraging textual and network information to design long-term scientific prediction and venue categorization frameworks.
Time: 3:00 PM – 4:00
Venue: Room 6/202



May 1, 2018. The Billion Building Challenge. Dr. Nipun Batra, Postdoc at University of Virginia.
Abstract: Buildings contribute roughly one-thirds of the total energy consumption. Energy breakdown – providing energy consumption at a per-appliance level can help occupants save up to 15% energy. In this talk, I will propose the building billion challenge – providing an energy breakdown to a billion buildings. Previous approaches required sensor hardware to be installed in each home and thus do not scale well. Towards the challenge, I will present my approach called collaborative sensing which provides an energy breakdown without installing any additional sensors. The key intuition is that the energy consumption variation across buildings can be represented via a low dimensional representation. Our approach leverages transfer learning techniques to scale energy breakdown to regions with none to low instrumentation. Finally, I will talk about providing actionable insights based on the energy breakdown data, and about making energy breakdown research comparable.
Time: 4:30 PM – 5:30
Venue: Room 7/202



April 19th, 2018. Probabilistic Graphical Models. Mr. Ajay Ghanti and Mr. Harish Kashyap.
Speakers Bio: Mr. Ajay Ghanti currently is the CTO of MCG and co-founder of Diagram.AI. He is leading the ML and Data Science teams out of Mysore, India. Mr. Harish Kashyap has a Masters from Northeastern University, Boston in Electrical Engineering. He is currently the founder of Mysuru Consulting Group and Diagram.AI.
Time: 4:30 PM
Venue: 5/203



April 13th, 2018. Mechanism Design for Stochastic Multi-armed Bandit Problems. Shweta Jain, Ph.D. CSA at IISc Bangalore.
Abstract: The multi-armed bandit (MAB) problem is widely studied in the machine learning literature, in the context of online learning. My recent work has studied the MAB problem when the arms are controlled by strategic agents. Dealing with strategic agents warrants the use of principled tools such as game theory and mechanism design in conjunction with online learning, and leads to non-trivial technical challenges. My research offers novel mechanisms for stochastic MAB problems in the various representative, generic settings inspired by applications such as smart grids, crowdsourcing, Internet advertising, and procurement auction. In this talk, I will discuss in detail about one particular problem of designing a low regret, combinatorial, randomized mechanism for the MAB problem, where, we consider a problem of selecting a subset of agents to solve a general combinatorial optimization problem.
Time: 11 am to 12 noon.
Venue: 7/202



April 10th, 2018. Architecting DRAM Based Memory Subsystem For Performance. Dr. Venkata Kalyan Tavva (Kalyan), Hardware Performance Architect at laboratories of IBM Systems.
Abstract: In this talk, to begin with, the fundamentals of memory subsystem are presented. With an improved understanding of the memory subsystem, the talk next dives into the details of two performance improvement techniques. The first technique, namely, Enhanced Fine Granularity Refresh (EFGR), revisits the standard DRAM refresh process to question the fundamental design choices involved during a refresh. EFGR increases the availability of the DRAM banks by refreshing fewer banks of a rank per-refresh and achieves parallel operation of request service and the refresh. Following EFGR, an intuitive DRAM command scheduling technique, namely, just in-time bank access (JIBA), is presented that exploits the DRAM charateristics for power and performance improvement. JIBA reduces the operating power of DRAM by delaying the bank activation operation by taking cues from the current data bus utilization.
Time: 11 am to 12 noon.
Venue: 6/202



April 4th, 2018. Trip Report on GIAN COURSE + SCEC 17 + HiPC 17. Tom Glint Issac, Ph.D., CSE.
Abstract: In this talk, I would like to give a comprehensive presentation on some research problems in Computer Systems. I will share experiences gained by participating in the following: GIAN COURSE + SCEC + HiPC 17.

The GIAN Course on Emerging Computational Devices, Architectures and Systems includes the following topics: Emerging logic and memory devices; Circuit Architecture design using Emerging Logic and Memory, Processor-in-Memory Architectures using emerging devices; Neuromorphic and Brain Inspired Computational Models; Computing Using Coupled Oscillator Systems; and Other Emerging Computational Models.

The Workshop on Software Challenges to Exascale Computing (SCEC 17) was focused on sharing the challenges faced by large problems that require HPC hardware to solve.

The IEEE International Conference on High-Performance Computing (HiPC 2017) is a forum for researchers to present their work on areas of algorithms, system software, and architectures.

Time: 4:30pm –5:30pm,  (Wed)
Venue: 7/202



5th April 2018. Partitioning Problems on Graphs with Bounded Tree-Width. Prof. Subrahmanyam Kalyanasundaram, Department of Computer Science and Engineering, IIT Hyderabad.

Abstract: For an undirected graph G, we consider the following problems: given a fixed graph H, can we partition the vertices of G into two sets A and B such that neither the induced graph G[A] nor G[B] (i) contains H as a subgraph? and (ii) contain H as an induced subgraph?

The problems are NP-complete and are expressible in monadic second order logic (MSOL). The MSOL formulation, together with Courcelle’s theorem implies linear time solvability on graphs with bounded tree-width. This approach yields algorithms with running time f(|\phi|, t) * n, where |\phi| is the length of the MSOL formula, t is the tree-width of the graph and n is the number of vertices of G. The dependency of f(|\phi|, t) on |\phi| can be as bad as a tower of exponentials.

In this talk, we describe explicit combinatorial algorithms for the problems for the case when G has bounded tree-width. Our algorithms run in 2^{O(t^r)} * n time, when H is any fixed graph of order r. This is joint work with N. R. Aravind and Anjeneya Swami Kare.

Time: 3:00 pm to 4:00pm, (Thu)
Venue: 5/202



March 21st, 2018. Introduction to Containers. Mr. Dharmit Shah, Software Engineer at Red Hat.
Abstract: In this talk, we’ll explore Linux containers and how they are taking over the development and deployment story across the software industry. We’ll start with Linux containers, talk about how Docker made them popular and finally how Kubernetes has made them ready for production workloads.
Time: 4:30pm –5:30pm,  (Wed)
Venue: 7/202


24th January 2018. Probabilistic Models and Inference Algorithms for Single-cell Genomics: Applications in Cancer Evolution. Dr. Hamim Zafar, Graduate Researcher, Department of Computer Science, Rice University.
Abstract: Rapidly emerging single-cell DNA sequencing technologies offer promising datasets to further our understanding of diverse facets of cancer biology and genetics. Specifically, it will have a profound impact in resolving the tumor heterogeneity that complicates the diagnosis and treatment of cancer patients and causes relapse and drug resistance. However, novel computational methods are required to perform this task, which is challenged by uncertainties in the underlying evolutionary processes. Moreover, technical artifacts introduced during the sequencing process further complicate this task. Novel computational methods are required for the analysis and interpretation of large-scale single-cell genomic datasets for elucidating tumor heterogeneity and evolution.

In this talk, I will introduce probabilistic models and statistical inference algorithms for elucidating tumor heterogeneity and evolution from single-cell DNA sequencing data. These algorithms probabilistically model the possible mutational histories as well as the sources of uncertainties due to technical artifacts in the data. The mutation discovery algorithm employs a probabilistic model of the technical artifacts and dynamic programming for detecting point mutations from raw single-cell sequencing data. The second method introduces a continuous-time Markov chain to model the underlying mutational events in cancer and infers a tumor phylogeny, a binary leaf-labeled tree that represents the mutational history of a tumor helping guide patient-specific treatment. The final method introduces a tree-structured non-parametric
Bayesian clustering framework to reconstruct the cell subpopulations in a tumor and their mutation content. Using these methods, I analyze several cancer datasets and uncover tumor phylogenies, driver mutations and cell subpopulations that are more biologically plausible than previously reported analyses. To close, I will give a brief outlook on a wider range of future directions towards providing novel computational and data-driven approaches aimed at improvement of patient well-being through advancements in the understanding of biological processes and phenomena.

Time: 4:30pm –5:30pm,  (Wed)
Venue: 6/202


10th January 2018. Markov Chain Monte Carlo simulations for the rational design of molecular systems. Dr. Kaustubh Rane, Assistant Professor, Chemical Engineering.

Abstract: The rational design of molecular systems  involves the systematic manipulation of molecular-scale variables to obtain the desired properties. A famous example is the design of drug molecules in the pharmaceutical industry. The field makes extensive use of computer simulations to predict the results of the above manipulations.

The behavior of a molecular system is governed by the density of molecules at different locations. Here, density refers to how well the molecules are “packed.” The local density fluctuates, resulting in a distribution of densities. In this talk, I will explain the application of Markov Chain Monte Carlo (MCMC) simulations to predict such distributions. We will also discuss the usefulness of density-distributions in rational design and the challenges involved in performing the above MCMC simulations.