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| UPCOMING SEMINARS |
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Series: CSA Faculty Colloquium
Title: The GRAMA project: Game theory, Random processes, Artificial intelligence, Machine learning for (Indian) Agriculture - Speaker: Prof. Y. Narahari
CSA, IISc - Date and Time: Tuesday, March 28, 2023, 4:30 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
We, at the Game Theory Lab in the Department of CSA, are currently engaged in a bouquet of projects exploring the use of game theory, optimization, and machine learning to address a few important problems in digital agriculture in the Indian context. These projects include: PREPARE, ACRE, CROPS, PROMISE, PROSPER, and AGRI-VAAHAN (acronyms will be expanded in the talk). PREPARE is concerned with crop price prediction; ACRE, with crop recommendation; CROPS, with crop planning; PROMISE, with procurement of agricultural inputs; and PROSPER, with markets for selling agricultural produce. AGRI-VAAHAN is an AIML pipeline for digital agriculture. In this talk, we provide an overview of these projects, some preliminary results, and work in progress. We present PROMISE in some detail highlighting the problems faced by the farmers in procuring quality inputs at affordable cost; we bring out how “farmer cooperatives” can creatively solve this problem with simple technology using standard tools from game theory and machine learning. These projects are supported by NABARD (National Bank for Agriculture and Rural Development) and BEL (Bharat Electronics Limited).
Speaker Bio:
Y. Narahari is a Professor at the Department of Computer Science and Automation, Indian Institute of Science, Bangalore. The common thread in his current research is to apply game theory, mechanism design, and artificial intelligence techniques to research problems at the interface of computer science and economics. He has authored a book on Game Theory and Mechanism Design (IISc Press and World Scientific). One of his recent interests is to explore the applications of game theory and artificial intelligence to digital agriculture, especially in the Indian context. More details at: https://gtl.csa.iisc.ac.in/hari/.
Host Faculty: Arkaprava Basu
| Series: Department Seminar
Title: Cryptographic Primitives with Hinting Property - Speaker: Sikhar
Advisory Research Scientist
IBM Research India - Date and Time: Friday, March 24, 2023, 3:00 PM
- Venue: CSA Class (Room No. 252, First Floor)
Abstract
A hinting pseudorandom generator (PRG) is a potentially stronger variant of PRG with a ``deterministic`` form of circular security with respect to the seed of the PRG (introduced by Koppula and Waters in CRYPTO 2019). Hinting PRGs enable many cryptographic applications, most notably CCA-secure public-key encryption and trapdoor one-way functions. In this talk, I will cover a recent work where we study cryptographic primitives with the hinting property. Our work introduces a novel and conceptually simpler approach for designing hinting PRGs from certain decisional assumptions over cyclic groups or isogeny-based group actions, which enables simpler security proofs and new instantiations from concrete assumptions as compared to the existing approaches for designing such primitives. In this talk, I will present a detailed treatment of this simple approach for constructing hinting PRGs, including a concrete construction and proof from the DDH assumption over cyclic groups. Our work also introduces several extensions of hinting PRGs, such as: (i) a natural extension of the hinting property to weak pseudorandom functions (which we call hinting wPRFs),and (ii) a stronger version of the hinting property (which we call the functional hinting property) that guarantees security even in the presence of hints about functions of the secret seed/key. We show how to instantiate these extensions by building upon our simple approach to realize hinting PRGs, and also demonstrate that these extensions have stronger implications than plain hinting PRGs, particularly in realizing various notions of KDM-secure encryption. Additionally, we study the cryptographic complexity of hinting PRGs and show the first black-box separation between public-key encryption and hinting PRGs via a simple construction of hinting PRGs given only a random oracle (this black-box separation result also extends to hinting wPRFs). The talk will present a high-level overview of these results. Based on a joint work with Navid Alamati (https://eprint.iacr.org/2022/1770, appeared at ASIACRYPT 2022). Some prior background on cryptography will be useful, but not absolutely necessary.
Speaker Bio:
Sikhar is an advisory research scientist at IBM Research India. His research interests are in theoretical and applied cryptography, with recent focus on quantum-safe cryptographic techniques for decentralized trust, secure computation, and searchable encrypted databases. Prior to joining IBM Research, he was a staff research scientist at Visa Research USA and a postdoctoral fellow in the Applied Cryptography Group, ETH Zurich (hosted by Prof. Kenny Paterson). He received his B.Tech and PhD from the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur.
Host Faculty: Dr. Chaya Ganesh
| Series: Department Seminar
Title: Astronomical Challenges in Atomic Scale Manufacturing - Speaker: Dr. Pradeep Ramachandran
- Date and Time: Friday, March 24, 2023, 10:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Semiconductor manufacturing is approaching the Angstrom-era with innovations such as gate all-around transistors, and chip-to-chip integration technologies enabling the continuation of Moores law. This talk will highlight some of the challenges that these advanced technologies pose to manufacturing semiconductors, and will cover how modern AI & HPC technologies are being leveraged to address these challenges to enable high-volume manufacturing. We will also give a peek into some of the solutions that KLA is pioneering in this space.
Speaker Bio:
Pradeep Ramachandran is the Director and Head of Research at KLAs Artificial Intelligence and Advanced Computing Lab (AI-ACL) based out of IIT Madras Research Park. AI-ACL focuses on doing advanced R&D in AI & HPC technologies to enable KLA to maintain its leadership in process control tools for semiconductor manufacturing. AI-ACL is located in the IIT Madras Research Park in Chennai, India.
Pradeep holds a BTech from IIT Madras and an MS and PhD from the University of Illinois at Urbana Champaign. Prior to joining KLA in 2021, Pradeep worked as an Architect in the memory controller performance simulation and modeling team at Intel, and as Principal Engineer at MulticoreWare Inc, and Uhnder Inc. Pradeeps research interests lie at the intersection of hardware and software and has developed several system-level solutions that leverage the synergies at the boundary.
Host Faculty: Prof. Uday Kumar Reddy B
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PAST SEMINARS |
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Series: Bangalore Theory Seminars
Title: Criticality of AC0-formulae - Speaker: Prahladh Harsha,
Tata Institute of Fundamental Research,
Mumbai - Date and Time: Monday, March 20, 2023, 4:00 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Hastads celebrated switching lemma gives inverse-exponential bounds (In terms of t) on the probability that an k-DNF when hit by a p-restriction requires decision-trees of depth larger than t. The switching lemma has proved to be extremely powerful, since its discovery, leading to optimal size lower bounds for AC0-circuits [Hastad 1986] and AC0 formulae [Rossman 2015] against the parity function.
More recently, the search for optimal correlation bounds against parity led to the notion of criticality [Rossman 2019]. The criticality of a Boolean function
f:{0,1}n → {0,1} is the minimum λ≥1 such that for all positive integers t, we have
Prρ∼Rp[DTdepth(f|ρ)≥t]≤(pλ)t.
Hastad (2014) proved that size S and depth (d+1) AC0-circuits have criticiality at most O((logS)d) leading to optimal correlation bounds of AC0-circuits against parity. Rossman (2019) subsequently proved that size S and depth (d+1) AC0-formulae, which are regular (i.e., all gates of same depth have fan-in) having criticality at most O((logS/d)d).
In this work, we strengthen and unify all the above results by proving that any (not necessarily regular) AC0-formula of size S and depth (d+1) have criticality at most O((logS/d)d). The criticality bound implies tight correlation bounds against parity, tight Fourier concentration results and improved #SAT algorithm for AC0-formulae.
The talk is based on joint works with Tulasimohan Molli and Ashutosh Shankar.
Microsoft teams link:
Teams link
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
We are grateful to the Kirani family for generously supporting the theory seminar series
Hosts: Rameesh Paul, Aditya Subramanian, Aditya Abhay Lonkar and Rahul Madhavan
| Series: Department Seminar
Title: Foundations of Lattice-based Cryptography - Speaker: Dr. Rajendra Kumar, Weizmann Institute of Science, Israel
- Date and Time: Thursday, March 16, 2023, 11:30 AM
- Venue: CSA Lecture Hall (Room No. 117, Ground Floor)
Abstract
Public key cryptography is essential for internet security, and RSA and Diffie-Hellman are the most widely used public-key cryptosystems for internet traffic. However, recent progress in building quantum computers threatens RSA and Diffie-Hellman's security, as they are vulnerable to quantum adversaries. To address this, organizations like the National Institute of Standards and Technology (NIST) and the European Telecommunications Standards Institute (ETSI) have started standardizing and deploying cryptosystems that are secure against quantum attacks. Recently, NIST has chosen Kyber and Dilithium, lattice-based candidates, as primary algorithms for security against quantum adversaries. The security of these cryptosystems crucially relies on the assumption that the best-known algorithms for the lattice problems cannot be significantly improved.
In this talk, I will discuss the connections between the security of lattice-based cryptosystems and the hardness of lattice problems. I will talk about classical and quantum algorithms for lattice problems. I will also discuss the works on the fine-grained security of lattice-based Crypto.
Speaker Bio:
Rajendra Kumar is a Postdoctoral Fellow at the Weizmann Institute of Science, Israel. He completed his Ph.D. under the Joint degree program of the Indian Institute of Technology Kanpur, and the National University of Singapore. He is broadly interested in algorithms, quantum computing, fine-grained complexity and cryptography, with special interests in lattice algorithms and reductions. In particular, he gave the fastest quantum algorithm for the Shortest Vector Problem in lattices.
Host Faculty: Chandan Saha
| Series: Bangalore Theory Seminars
Title: Private Convex Optimization via Exponential Mechanism - Speaker: Sivakanth Gopi
MSR, Redmond - Date and Time: Thursday, March 16, 2023, 10:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
We study differentially private optimization of (non-smooth) convex functions F(x)=E_i[f_i(x)]. The classic exponential mechanism minimizes F(x) by sampling from pi(x) ~ exp(-kF(x)), but achieves a suboptimal privacy vs utility tradeoff. We show that modifying the exponential mechanism by adding an ell_2^2 regularizer to F(x) and sampling from pi(x) ~ exp(-k(F(x)+mu ||x||_2^2/2)) recovers both optimal empirical risk and population loss under (eps,delta)-DP. We also give an algorithm to efficiently sample from the exponential mechanism using optimal number of oracle queries to f_i(x).
We prove that the regularized exponential mechanism satisfies Gaussian Differential Privacy; our privacy bound is optimal (with tight constants), as it includes the analysis of Gaussian mechanism as a special case. The privacy proof uses isoperimetric inequality for strongly log-concave measures.
Joint work with Yin Tat Lee and Daogao Liu. The link to the paper is at https://arxiv.org/pdf/2203.00263.pdf.
Speaker Website: https://www.microsoft.com/en-us/research/people/sigopi/
Microsoft teams link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
We are grateful to the Kirani family for generously supporting the theory seminar series
Hosts: Rahul Madhavan, Rameesh Paul, Aditya Subramanian and Aditya Abhay Lonkar
| Series: Department Seminar
Title: A Case for Correctly Rounded Math Libraries - Speaker: Prof. Santosh Nagarakatte
Associate Professor
Rutgers University - Date and Time: Monday, March 13, 2023, 11:30 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
This talk will provide an overview of the RLIBM project where we are
building a collection of correctly rounded elementary functions for
multiple representations and rounding modes. Historically, polynomial
approximations for elementary functions have been designed by
approximating the real value. In contrast, we make a case for
approximating the correctly rounded result of an elementary function
rather than the real value of an elementary function in the RLIBM
project. Once we approximate the correctly rounded result, there is an
interval of real values around the correctly rounded result such that
producing a real value in this interval rounds to the correct
result. This interval is the freedom that the polynomial approximation
has for an input, which is larger than the ones with the mini-max
approach. Using these intervals, we structure the problem of
generating polynomial approximations that produce correctly rounded
results for all inputs as a linear programming problem. The results
from the RLIBM project makes a strong case for mandating correctly
rounded results at least for any representation that has fewer than or
equal to 32-bits.
Speaker Bio:
Santosh Nagarakatte is an Associate Professor at Rutgers
University. He obtained his PhD from the University of Pennsylvania in
2012. His research interests are in Hardware-Software Interfaces
spanning Programming Languages, Compilers, Software Engineering, and
Computer Architecture. His papers have been selected as IEEE MICRO Top
Picks papers of computer architecture conferences in 2010 and 2013. He
received the NSF CAREER Award in 2015, ACM SIGPLAN PLDI 2015
Distinguished Paper Award, ACM SIGSOFT ICSE 2016 Distinguished Paper
Award, and 2018 Communications of the ACM Research Highlights paper
for his research on LLVM compiler verification. His PhD student David
Menendezs dissertation on LLVM verification was awarded the 2018 ACM
SIGPLAN John C Reynolds Outstanding Dissertation Award. His papers on
correctly rounded elementary functions have been recognized with the
ACM SIGPLAN PLDI 2021 Distinguished Paper Award and the ACM SIGPLAN
POPL 2022 Distinguished Paper Award. His PhD student Jay Lims
dissertation on correctly rounded elementary functions was awarded the
2022 ACM SIGPLAN John C Reynolds Outstanding Dissertation Award.
Host Faculty: Prof. Vinod Ganapathy
| Series: Bangalore Theory Seminars
Title: Causal Fairness Analysis - Speaker: Drago Plecko
ETH Zurich - Date and Time: Thursday, March 09, 2023, 6:30 PM
- Venue: Microsoft teams
Abstract
Decision-making systems based on AI and machine learning have been used throughout a wide range of real-world scenarios, including healthcare, law enforcement, education, and finance. It is no longer far-fetched to envision a future where autonomous systems will drive entire business decisions and, more broadly, support large-scale decision-making infrastructure to solve societys most challenging problems. Issues of unfairness and discrimination are pervasive when decisions are being made by humans, and remain (or are potentially amplified) when decisions are made using machines with little transparency, accountability, and fairness.
In this paper, we introduce a framework for *causal fairness analysis* with the intent of filling in this gap, i.e., understanding, modelling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present in the observed data with the underlying, often unobserved, collection of causal mechanisms that generate the disparity in the first place, a challenge we call the Fundamental Problem of Causal Fairness Analysis (FPCFA). In order to solve the FPCFA, we study the problem of decomposing variations and empirical measures of fairness that attribute such variations to structural mechanisms and different units of the population. Our effort culminates in the Fairness Map, the first systematic attempt to organize and explain the relationship between various criteria found in the literature. Finally, we study which causal assumptions are minimally needed for performing causal fairness analysis and propose the Fairness Cookbook, which allows one to assess the existence of disparate impact and disparate treatment.
Joint work with Elias Bareinboim. The link to the paper is at https://causalai.net/r90.pdf.
Speaker Website: https://people.math.ethz.ch/~pleckod/
Microsoft teams link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
We are grateful to the Kirani family for generously supporting the theory seminar series
Hosts: Rahul Madhavan, Rameesh Paul, Aditya Subramanian and Aditya Abhay Lonkar
| Series: Department Seminar
Title: Fusing AI and Formal Methods for Automated Synthesis - Speaker: Priyanka Golia, National University of Singapore and Indian Institute of Technology Kanpur
- Date and Time: Monday, March 06, 2023, 11:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
We entrust large parts of our daily lives to computer systems, which are becoming increasingly more complex. Developing scalable yet trustworthy techniques for designing and verifying such systems is an important problem. In this talk, our focus will be on automated synthesis, a technique that uses formal specifications to automatically generate systems (such as functions, programs, or circuits) that provably satisfy the requirements of the specification. I will introduce a state-of-the-art functional synthesis algorithm that leverages artificial intelligence to provide an initial guess for the system and then uses formal methods to repair and verify the guess to synthesize a system that is correct by construction. I will conclude by exploring the potential for combining AI and formal methods to address real-world scenarios.
Speaker Bio:
Priyanka Golia is a final year Ph.D. candidate at NUS, Singapore and IIT Kanpur. Her research interests lie at the intersection of formal methods and artificial intelligence. In particular, her dissertation work has focused on designing scalable automated synthesis and testing techniques.
Her work has been awarded Best Paper Nomination at ICCAD-21 and Best Paper Candidate at DATE-23. She was named one of the EECS Rising Stars in 2022. She has co-presented a tutorial on Automated Synthesis: Towards the Holy Grail of AI at AAAI-22 and IJCAI-22, and she is co-authoring an upcoming book (on invitation from NOW publishers) on functional synthesis.
Host Faculty: R Govindarajan
| Series: CSA Open Day Keynote Talk
Title: Towards Sustainable Agriculture prioritizing Global South using Machine Learning - Speaker: Radhika Dua,
Pre-doctoral Researcher,
Google Research India - Date and Time: Saturday, March 04, 2023, 3:00 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
The agricultural sector, which is a major source of employment in the global south, is unfortunately the second largest contributor to greenhouse gas emissions globally after energy. Farmers are vulnerable to climate-related problems such as droughts, floods, and crop failure due to extreme temperatures. To mitigate these problems, it is crucial to develop innovative technological solutions that cater to the specific climate and socio-economic needs of the agricultural sector, thereby enabling it to advance rapidly in developing countries. Identifying different land features, such as fields, trees, and dug wells, and analyzing them to optimize water consumption, crop yields, and soil carbon sequestration is essential. Therefore, we concentrate on three areas: Agricultural Landscape Understanding (ALU) for automatic identification of land features, Agriculture Monitoring and Event Detection (AMED) for automatic crop monitoring, and Soil Carbon Sequestration for a deeper understanding of the soil organic carbon change.
Speaker Bio:
Radhika is a pre-doctoral researcher at Google Research India. She is passionate about leveraging the capabilities of machine learning to solve real-world problems. She recently completed her Masters in Artificial Intelligence from KAIST, South Korea, where she worked on enhancing the reliability of deep neural networks against natural and synthetic distribution shifts. Radhika was selected as a Grace Hopper Celebration India (GHCI) Scholar. Before joining Google, she also spent time at Brown University and IIT Hyderabad as a visiting researcher, which gave her valuable exposure to different research environments.
Host Faculty: The Chair, Dept. of CSA
| Series: CSA Open Day Keynote talk
Title: How do recommendation systems work? And, what are their privacy implications? - Speaker: Prof. Murali Annavaram
Rukmini Gopalakrishnachar visiting Chair Professor
Deans Chair Professor in the ECE
and CS departments at USC - Date and Time: Saturday, March 04, 2023, 10:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Have you ever wondered how good Spotify, Netflix, Amazon, .. give you such great recommendations for what to listen, watch, purchase, and live our lives? The magic behind their recommendations are the deep learning machine learning models. These models capture seemingly end-less amounts of information about our online behavior and transform these behaviors into embeddings for future recommendations. These machine learning models are massive (think of terabytes of data), trained on equally imposing set of training samples, called sparse features. Every click, purchase, and even a mouse hover on a website is a sparse feature for training the model. In this talk I will first provide an overview of how current generation recommendation systems work. If these models can recommend so well, then, they must also know a lot about us. In fact, they do. By simply observing features such as click history and object interactions an attacker can de-anonymize users with extremely high probability, or track users across different interaction sessions. For instance, if you tell me what are the last two items you purchased online, I can track you with 97% accuracy. All of which is to say there is a lot of interesting privacy research that needs to get done.
Speaker Bio:
Murali Annavaram is a Deans Chair Professor in the ECE and CS departments at USC. He currently holds the Smt. Rukmini - Shri. Gopalakrishnachar Visiting Chair Professorship at IISc. He is the founding director of the REAL@USC-Meta centre that is focused on research and education in AI, and the co-PI on the NSF DISCoVER Expedition on superconducting computing. His groups KnightShift research won the 2012 IEEE Micro Top Picks most influential research paper award. His work at Intel lead to the first 3D microarchitecture design, and also influenced Intels TurboBoost technology. Murali co-authored Parallel Computer Organization and Design, a widely used textbook in computer architecture. He is a Fellow of IEEE and Senior Member of ACM.
Host Faculty: The Chair, Dept. of CSA
| Series: Bangalore Theory Seminars
Title: Utilizing the CLT Structure in Stochastic Approximations of Sampling Algorithms - Speaker: Dheeraj Nagaraj
Google Research - Date and Time: Friday, March 03, 2023, 11:00 AM
- Venue: CSA Lecture Hall (Room No. 112, Ground Floor)
Abstract
We consider stochastic approximations of sampling algorithms like Langevin Monte Carlo and Interacting Particle Dynamics with random batches. These are heavily deployed in Bayesian inference, and the physical sciences. The noise induced by random batches is approximately Gaussian (due to the Central Limit Theorem) while the Brownian motion driving the algorithm is exactly Gaussian.
We utilize this structure to provide improved guarantees for sampling algorithms under significantly weaker assumptions. We also propose covariance correction, which rescales the brownian motion to approximately remove the random batch error. We show that covariance corrected algorithms enjoy even better convergence.
Joint work with: Aniket Das (Google) and Anant Raj (INRIA and UIUC)
Speaker Website https://dheerajmn.mit.edu/
Microsoft Teams link
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
We are grateful to the Kirani family for generously supporting the theory seminar series
Hosts: Rahul Madhavan, Rameesh Paul, Aditya Subramanian and Aditya Abhay Lonkar
| Series: Department Seminar
Title: A decentralised algorithm for minimizing multi-agent congestion cost on a network - Speaker: Prof. N. Hemachandra
Industrial Engineering and Operations Research
Indian Institute of Technology Bombay - Date and Time: Friday, March 03, 2023, 11:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Consider a model wherein a given set of agents need to reach the goal node of a network. The cost for each agent on any link depends on the congestion on that link as well as on a cost component that is private to that agent. We propose a multi-agent congestion cost minimization (MACCM) algorithm for minimizing the total cost incurred by the agents. Our algorithm is fully decentralised, uses linear function approximations that addresses privacy of agents costs as well as scalability aspects and achieves sub-linear regret. Each agent maintains an estimate of the global objective function and the algorithm relies on a multi-agent version of extended value-iteration. We illustrate computations on a hard instance. Our model is a generalisation of a classical learning problem, the stochastic shortest path problem. This is a joint work with Prashant Trivedi.
Speaker Bio:
N. Hemachandra is a Professor at Industrial Engineering and Operations Research, IIT Bombay. His academic interests are learning algorithms, sequential decision models including RL, MABs, and Markov decision models, queueing theory, game theory, etc. and their applications to pricing and resource allocation problems arising in supply and value chains, communication networks, logistics, etc.
Host Faculty: Dr. Gugan Thoppe
| Series: Department Seminar
Title: Understanding Performance of Internet Video using Network Measurement Data - Speaker: Dr. Tarun Mangla, Univ. of Chicago
- Date and Time: Wednesday, March 01, 2023, 11:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Internet video is used extensively for entertainment, education, work, and telehealth, making it the largest contributor to Internet traffic. Due to these two factors, i.e., popularity and resource utilization, it is crucial to understand the network dynamics of Internet video. Understanding network behavior of the video, however, has several challenges, including (1). complex interactions among entities on the network path (e.g., CDNs, ISPs) and within the vertical network stack, (2). limited information available to some stakeholders (e.g., network operators), and (3). heterogeneity in network and user contexts. In this talk, I will cover two of my research directions that use network data to provide insights into the dynamic behavior of the Internet video applications. The first is on using passive measurements to enable network operators to understand Quality of Experience metrics for HTTP-based adaptive streaming (e.g., Netflix, YouTube). The second is on using empirical network data to understand performance and network utilization of video conferencing applications (e.g., Zoom). The talk will conclude with a brief discussion of my ongoing work on mapping broadband inequity and future research plans.
Speaker Bio:
Tarun is a Postdoctoral Scholar at University of Chicago (UChicago) working with Nick Feamster. He is interested in using data-driven methods to understand and improve performance and accessibility of the Internet. Before joining UChicago, Tarun received his Ph.D. from Georgia Tech in 2020, where he was advised by Mostafa Ammar and Ellen Zegura. He received his bachelor’s degree from IIT Delhi in 2014. He is a recipient of the Best Paper Award at IFIP TMA, 2018.
Host Faculty: Prof. Vinod Ganapathy
| Series: Department Seminar
Title: What is common to robots, proteins, genomics and video games? - Speaker: Kartic Subr
University of Edinburgh - Date and Time: Tuesday, February 28, 2023, 3:30 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
The once familiar story of machine learning, specifically deep-learning, facilitating inordinate progress widely across disciplines quickly evolved into the realisation that they are data hungry and difficult to explain or analyse. In my research, I explore the benefits of incorporating a physics model within the learner to alleviate some of these problems. Although this could be included under the popular buzz-phrase Physics Inspired AI, my approach has been to use rapid (hence approximate) physics models by learning the discrepancy between their approximations and an accurate (hence computationally intensive) simulation model.
The Computer Graphics community is curiously comfortable with the dichotomy between accurate versus timely algorithms that solve the same computational problems (usually physical simulation) under different constraints. In my research, I explore both of these strands, each with a different goal. On the one hand, I strive to develop formalisms with the goal of assessing inaccuracies of existing models. On the other, I investigate applications of `quick and dirty approximations for applications that impose a strict time-budget. In this talk, I will provide an overview of these goals and the tension between them. After an introduction to Edinburgh and the School of Informatics as an exciting venue for visiting students, I will present accuracy and timeliness as contrasting notions of error and their relative importance across applications. I will describe my general research directions using examples including a recent (SIGGRAPH 22) paper on the analysis of error in light transport and previous works on the use of approximate physics simulation for robotic manipulation.
In this talk I will be sharing problems, that I am excited by, in protein design and genomics and some progress that we have made. I will touch upon recent work from my group on approximate learning of an NP-hard problem [2], our method for protein design [3] that is in the top-three methods available and a recent paper that uses functional analysis to explain why fixed sampling methods (such as NeRFs) will hit a fundamental roadblock when used to approximate light transport [1]. If time permits, I will also present some of our work on spectral coarsening of simplicial complexes [4].
I will end my talk with some insights on potential routes for Indian researchers who are interested in academic jobs in the UK. This is particularly relevant if you are finishing your PhDs and are looking to apply for a post-doctoral research position.
[1] https://homepages.inf.ed.ac.uk/ksubr/research.html#SIGG22
[2] https://homepages.inf.ed.ac.uk/ksubr/research.html#AAAI22
[3] https://arxiv.org/pdf/2109.07925.pdf
[4] https://arxiv.org/abs/2207.01146
Speaker Bio:
Kartic Subr is a Royal Society University Research Fellow and Senior Lecturer (Assistant Prof) of Computer Graphics at the University of Edinburgh, UK. Kartics research addresses the development of approximate methods for computationally expensive procedures such as physical simulation. More recently he has also explored the use of neural approximations for their application to robotic manipulation. His previous employers include Heriot Watt University, Disney Research, University College London and INRIA-Grenoble. He obtained his PhD (2008, UC Irvine) under the supervision of James Arvo.
Host Faculty: Prof. Vijay Natarajan
| Series: Bangalore Theory Seminars
Title: 2D Expansion in Random Geometric Graphs - Speaker: Sidhanth Mohanty
UC Berkeley - Date and Time: Friday, February 24, 2023, 11:00 AM
- Venue: Microsoft Teams - ON-LINE
Abstract
High-dimensional expansion is a generalization of expansion to hypergraphs where the expansion of certain random walks are witnessed by local neighborhoods.
This talk is on the topic of constructing natural probability distributions over sparse high-dimensional expanders (HDXes). On one hand, (standard/1-dimensional) sparse expanders are plentiful, since a constant degree random graph is an expander with high probability. On the other hand, most natural random models over hypergraphs fail to exhibit 2-dimensional expansion unless the average degree exceeds sqrt(number of vertices).
However, sparse HDXes are known to exist due to algebraic and number theory based construction. We describe some progress towards constructing sparse HDXes based on random geometrics graphs on the unit sphere.
The talk is based on joint work with Siqi Liu (Berkeley), Tselil Schramm (Stanford), and Elizabeth Yang (Berkeley).
Microsoft teams link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
We are grateful to the Kirani family for generously supporting the theory seminar series
Hosts: Rameesh Paul, Aditya Subramanian, Aditya Abhay Lonkar and Rahul Madhavan
| Series: Department Seminar
Title: Time-SpaceTradeoffs for Collisions in Hash Functions - Speaker: Akshima
NYU Shanghai - Date and Time: Tuesday, February 21, 2023, 4:00 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Cryptographic hash functions are functions that take arbitrary length inputs and output fixed length digest. They are one of the most important cryptographic primitives and widely used in applications today. Apart from the compression requirement, the applications using these functions could need additional properties to be provably secure. One such, perhaps the most important property is collision resistance.
This property has been well studied for uniform adversaries. However, uniform adversaries fail to capture many real-world adversaries. Hence, several recent works have studied the collision resistance property for non-uniform adversaries. Analyzing non-uniform adversaries presents several challenges. That is why Dodis et al in their EUROCRYPT 18 paper presented a reduction to another (easier to analyze) model named Bit-fixing model.
In our CRYPTO 20 paper, we showed that adversaries in this Bit-fixing model are too strong when the length of the collisions are bounded. We also showed a reduction to the Multi-instance model, which helped us obtain better results for restricted parameter ranges. In our recent CRYPTO 22 paper, we further explored the relation between the Bit-fixing model and the Multi-instance model and further improved the results with our new findings.
The talk will include some preliminary definitions, detailed description of these models, results and a high level idea of the techniques from all the relevant works.
Speaker Bio:
Dr. Akshima is currently a Postdoctoral fellow at NYU Shanghai. She has completed her PhD from University of Chicago in 2022. Prior to this, she was a PhD candidate at Rutgers university from 2016 to 2018. She completed her bachelors degree from IIIT Delhi in 2016.
Her homepage is: https://sites.google.com/view/akshima-
Her research primarily focuses on studying the security of cryptographic primitives. Recently, her work focuses on understanding the role of memory in the security of cryptographic primitives. As part of her thesis, she has studied non-uniform algorithms against popular security properties of hash functions. The objective was to understand how much better can any algorithm that is capable of performing pre-computation and storing a bounded amount of information about the cryptographic primitive (under attack) do? On the flip side, she is interested in the question whether some cryptographic applications can be provably more secure against adversaries with lesser memory? Apart from that she has been interested in Searchable encryption. In addition, she is also working on automation of formal verification techniques for compression based proofs.
Host Faculty: Prof. Vinod Ganapathy
| Series: M.Tech (Research) Colloquium
Title: Fragile Interpretations and Intepretable Models in NLP - Speaker: Ayushi Kohli,
M.Tech (Research) student,
Dept. of CSA, - Faculty Advisor: Dr. V. Susheela Devi
- Date and Time: Friday, February 17, 2023, 12:00 PM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Deployment of deep learning models in critical areas is still an issue of concern, as the cost of making a wrong decision is very high in these areas. As a result, the final decision in these settings is human-centric. Also, these models act as black boxes, and we are unaware of their internal workings. Therefore the models must be explainable to know their internal workings. So, if the model is explainable, is it safe to deploy it in real-world settings involving huge risks?
Our work centers around the concept of fragile interpretations considering the models robustness and the robustness of interpretations. We have proposed an algorithm that perturbs the input text and generates adversarial examples with the same prediction as the input but with different interpretations. Through our experiments, we have provided a detailed analysis of whether these interpretations are reliable and whether to trust the model or the interpretations. We have provided the reason for the fragility in the case of NLP. Taking this into account, we have proposed two interpretable models, one for a multi-task offensive language detection task and the other for a sentence-pair similarity detection task.
| Series: M.Tech (Research) Colloquium
Title: Towards Robustness of Neural Legal Judgment System - Speaker: Rohit Raj,
M.Tech (Research) student,
Dept. of CSA - Faculty Advisor: Dr. V. Susheela Devi
- Date and Time: Friday, February 17, 2023, 11:00 AM
- Venue: CSA Seminar Hall (Room No. 254, First Floor)
Abstract
Legal Judgment Prediction (LJP) implements Natural Language Processing (NLP) techniques to predict judgment results based on fact description. It can play a vital role as a legal assistant and benefits legal practitioners and regular citizens. Recently, the rapid advances of transformer-based pretrained language models led to considerable improvement in this area. However, empirical results show that existing LJP systems are not robust to adversaries and noise. Also, they cannot handle large-length legal documents. In this work, we explore the robustness and efficiency of LJP systems even in a low data regime.
In the first part, we empirically verified that existing state-of-the-art LJP systems are not robust. We further provide our novel architecture for LJP tasks which can handle extensive text lengths and adversarial examples. Our model performs better than state-of-the-art models, even in the presence of adversarial examples of the legal domain.
In the second part, we investigate the approach for the LJP system in a low data regime. We provide a novel architecture using a few-shot approach that is also robust to adversaries. We conducted extensive experiments on American, European, and Indian legal datasets in the few-shot scenario. Our model, though trained using the few-shot approach, performs as well as state-of-the-art models which are trained using large datasets in the legal domain.
| Series: Distinguished Department Seminar
Title: Machine Learning and Logic: Fast and Slow Thinking - Speaker: Moshe Y. Vardi
Rice University - Date and Time: Thursday, February 16, 2023, 6:30 PM
- Venue: Microsoft teams
Abstract
Computer science seems to be undergoing a paradigm shift. Much of earlier research was conducted in the framework of well-understood formal models. In contrast, some of the hottest trends today shun formal models and rely on massive data sets and machine learning. A cannonical example of this change is the shift in AI from logic programming to deep learning. I will argue that the correct metaphore for this development is not paradigm shift, but paradigm expansion. Just as General Relativity augments Newtonian Mechanics, rather than replace it -- we went to the moon, after all, using Newtonian Mechanics -- data-driven computing augments model-driven computing. In the context of Artificial Intelligence, machine learning and logic correspond to the two modes of human thinking: fast thinking and slow thinking. The challenge today is to integrate the model-driven and data-driven paradigms. I will describe one approach to such an integration -- making logic more quantitative.
For more details please visit: https://www.csa.iisc.ac.in/theoryseminars/?talk=20230216_MosheVardi
Link to online talk:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZGE3NDg5NzktMWQ0Zi00MzFmLTg5OTgtMTMyYWM4MWQyYjI2%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%227c84465e-c38b-4d7a-9a9d-ff0dfa3638b3%22%7d
Host Faculty: Prof Deepak DSouza
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