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Colloquium

Balancing Heterogeneity and Programmability Across Computing Scales

Date and Time
Monday, February 12, 2024 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Host
Kai Li, Margaret Martonosi, Jonathan Cohen

Abhishek Bhattacharjee
Hardware heterogeneity is everywhere, from the high-performance server chips that comprise our data centers to the milliwatt-scale chips on board our biomedical devices. The central thesis of my talk is that hardware heterogeneity breaks through traditional computing abstractions to enable orders of magnitude performance improvements, but that these performance improvements are useful to software developers only when hardware continues to remain easy to program. I will discuss ongoing research in my group on balancing hardware heterogeneity with abstractions/interfaces to enable programmability/flexibility. As exemplars of this question, I will focus on the benefits and challenges of building shared address spaces between general-purpose CPUs and domain-specific hardware accelerators. I will also discuss my work on building flexible neural interfaces driven by a collection of programmable ASICs. My talk will highlight cross-cutting lessons learned and their implications on future accelerator-rich computer systems.

Bio: Abhishek Bhattacharjee is a Professor of Computer Science at Yale University, and is also affiliated with Yale's Wu Tsai Institute for the Brain Sciences as well as Yale's Center for Brain & Mind Health. He is interested in the hardware/software interface. Abhishek's research on address translation has shipped in over one billion AMD Zen CPU cores, over tens of millions of NVIDIA GPUs, over two billion Linux kernel downloads, and has also helped the group tasked with deciding the RISC-V page table format. For these contributions, Abhishek was the recipient of the 2023 ACM SIGARCH Maurice Wilkes Award. Abhishek teaches courses on computer architecture, operating systems, and compilers. In recognition of his teaching and mentoring of undergraduate and graduate students, Abhishek was the recipient of the 2022 Yale Engineering Ackerman Award.


To request accommodations for a disability, please contact Emily Lawrence at emilyl@cs.princeton.edu at least one week prior to the event.
Live stream only available to Princeton University students, faculty, and staff.  Webinar registration here.

BioE Colloquium: Machine learning for discovery: Deciphering the logic of RNA

Date and Time
Thursday, October 5, 2023 - 4:00pm to 5:00pm
Location
Louis A. Simpson International Building B60B & B60C
Type
Colloquium
Speaker
Oded Regev, from Courant Institute of Mathematical Sciences of New York University

Talk details here

To request disability-related accommodations, please contact Jessica Varela at jv2026@princeton.edu no later than three working days prior to the event.

The Role of Archaic Admixture in Human Evolution

Date and Time
Tuesday, September 5, 2023 - 4:30pm to 5:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Host
Ben Raphael

Sriram Sankararaman
Over the past decade, the ability to sequence genomes from both present-day and archaic humans (including our closest evolutionary relatives, the Neanderthals) has transformed our understanding of human history. Analyzing these genome sequences paints a picture of human history in which present-day humans migrated out of Africa but exchanged genes with multiple archaic human populations.

I will describe statistical methods that identify segments of DNA inherited from archaic humans that are surviving in our genomes today and how these maps of introgressed archaic DNA are providing insights into human migration and biology.  Despite this progress, our understanding of the contribution of archaic introgression to populations in Africa remains limited, in part due to the challenges in obtaining ancient DNA in Africa. Leveraging recently developed approaches that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit substantial genetic ancestry from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. Finally, we combine maps of introgressed Neanderthal DNA with phenotypic datasets collected in hundreds of thousands of individuals to assess the contribution of introgressed Neanderthal DNA to complex traits.

I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor.

Bio: Sriram Sankararaman is a professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA. His research interests lie at the interface of computer science, statistics and biology. His lab develops machine learning algorithms to analyze genomic data and biomedical data with the broad goal of understanding the interplay between evolution, genomes and traits. 

He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a postdoctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of a NSF Career Award, NIH Pathway to Independence Award, and fellowships from Microsoft Research, the Sloan Foundation, the Okawa Foundation and the Simons Institute.

Dialog with Robots: Perceptually Grounded Communication with Lifelong Learning

Date and Time
Friday, May 6, 2022 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Host
Danqi Chen

Raymond Mooney
Developing robots that can accept instructions from and collaborate with human users is greatly enhanced by an ability to engage in natural language dialog. Unlike most other dialog scenarios, this requires grounding the semantic analysis of language in perception and action in the world. Although deep-learning has greatly enhanced methods for such grounded language understanding, it is difficult to ensure that the data used to train such models covers all of the concepts that a robot might encounter in practice. Therefore, we have developed methods that can continue to learn from dialog with users during ordinary use by acquiring additional targeted training data from the responses to intentionally designed clarification and active learning queries. These methods use reinforcement learning to automatically acquire dialog strategies that support both effective immediate task completion as well as learning that improves future performance. Using both experiments in simulation and with real robots, we have demonstrated that these methods exhibit life-long learning that improves long-term performance.

Bio: Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 180 published research papers, primarily in the areas of machine learning and natural language processing. He was the President of the International Machine Learning Society from 2008-2011, program co-chair for AAAI 2006, general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He is a Fellow of AAAI, ACM, and ACL and the recipient of the Classic Paper award from AAAI-19 and best paper awards from AAAI-96, KDD-04, ICML-05 and ACL-07. 


This talk will be recorded and live-streamed at https://mediacentrallive.princeton.edu/

Graduate Certificate in Computational Science & Engineering Colloquium

Date and Time
Thursday, April 28, 2022 - 2:00pm to 4:00pm
Location
Lewis Library 120
Type
Colloquium
Host

One of the graduate certificate requirements is for students to give a seminar on their dissertation research before graduation, typically in the last year once significant results can be reported. This research seminar occurs as part of a colloquium with other program participants and is organized by PICSciE.

Each research seminar is approximately 20 minutes in length with additional time for questions from the audience and is accessible to the broader University community with an interest in computational science and engineering. 

The University community is invited to participate as audience members in the colloquium. Students enrolled in the program are highly encouraged to attend.

Efficient Verification of Computation

Date and Time
Thursday, February 17, 2022 - 12:30pm to 1:30pm
Location
Live-stream online (off campus)
Type
Colloquium
Speaker
Yael Tauman Kalai, from Microsoft Research and MIT
Host
Ran Raz

Recording available here.


Yael Tauman Kalai
Efficient verification of computation is fundamental to computer science, and is at the heart of the P vs. NP question. Recently it has had growing practical significance, especially with the increasing popularity of blockchain technologies and cloud computing.  In this talk, I will present schemes for verifying the correctness of a computation. I will discuss both their practical aspects and their impact on quantum complexity, hardness of approximation, and the complexity of Nash equilibrium.

Bio: Yael Tauman Kalai received her BA (1997) from the Hebrew University in Jerusalem, MA (2001) under the supervision of Adi Shamir at the Weizmann Institute, and PhD (2006) under the supervision of Shafi Goldwasser at MIT. After postdoctoral positions at Microsoft Research and the Weizmann Institute, she is now a Principal Senior Researcher at Microsoft Research New England and an adjunct professor at MIT.  Her research focuses on cryptography.


To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu, at least one week prior to the event.

This talk will be recorded.

Qualcomm: Past, Present and Future of 5G Millimeter Wave

Date and Time
Thursday, June 17, 2021 - 1:30pm to 2:30pm
Location
Zoom Webinar (off campus)
Type
Colloquium
Speaker
Ozge Koymen, from Qualcomm
Host

Please register here


Abstract: After more than a decade of advanced R&D and ecosystem trials, commercial 5G mmWave service is now available in more than 55 U.S. cities and 160 areas in Japan. Looking forward, we expect 5G mmWave to expand into new geographic regions across the globe, and new device types and tiers will emerge to take full advantage of mmWave’s virtually unlimited capacity. On the research front, Qualcomm continues to push the technology boundaries of mmWave for 5G/6G by bringing new capabilities and enhancements. Join this seminar to:

  • Review the key technical achievements and milestones at Qualcomm that enabled the commercialization of 5G mmWave systems.
  • See our vision for 5G mmWave and the new opportunities it poises to bring for the broader ecosystem.
  • Learn about the mmWave capabilities and enhancements coming in 3GPP Release -17 and beyond (e.g. Integrated Access and Backhaul, 60GHz and beyond, IIOT, etc.).
  • Track the latest update on the global commercial rollout of 5G mmWave networks and devices.

Bio: Ozge Koymen is a Senior Director of Technology at Qualcomm Technologies, Inc. where he has been since 2006. He has led the 5G millimeter-wave program within Qualcomm R&D since early 2015, from early conceptual evaluation to commercial deployment. His previous areas as a technical contributor includes Wireless Backhaul, Small Cells, LTE-D, LTE and UMB. Prior to Qualcomm, he was a member of Flarion Technologies developing a pioneering OFDMA cellular system, Flash-OFDM, during 2003-2006. His earlier work experience includes full-time and consulting work for Impinj, Inc. (2000-2003) and TRW (1996-2000). He received the B.S. in Electrical and Computer Engineering from Carnegie Mellon University in 1996 and the M.S. and Ph.D. in Electrical Engineering from Stanford University in 1997 and 2003, respectively.

JAX: Accelerated machine learning research via composable function transformations in Python

Date and Time
Tuesday, October 15, 2019 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Host
Ryan Adams

Dougal Maclaurin
JAX is a system for high-performance machine learning research. It offers the familiarity of Python+NumPy and the speed of hardware accelerators, and it enables the definition and the composition of function transformations useful for machine learning programs. In particular, these transformations include automatic differentiation, automatic batching, end-to-end-compilation (via XLA), and parallelizing over multiple accelerators. They are the key to JAX's power and to its relative simplicity.

JAX had its initial open-source release in December 2018 (https://github.com/google/jax). It is currently being used by several groups of researchers for a wide range of advanced applications, from studying spectra of neural networks, to probabilistic programming and Monte Carlo methods, and scientific applications in physics and biology. Users appreciate JAX most of all for its ease of use and flexibility.

Bio: Dougal Maclaurin is a research scientist at Google. He works on programming languages and systems for machine learning, particularly the Python library JAX. He started Autograd, a system for automatic differentiation in Python, which has inspired the design of several systems, including PyTorch, MinPy, Torch Autograd and Julia Autograd. He is a co-founder of Day Zero Diagnostics, a startup developing a sequencing-based diagnostic for drug-resistant infections. He received his Ph.D. from Harvard in 2016, working with Ryan Adams on the development of methods for machine learning. His work on scalable MCMC, "Firefly Monte Carlo", was recognized with the Best Paper award at UAI 2014.


Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Challenges in Cloud Networking

Date and Time
Wednesday, September 18, 2019 - 1:30pm to 2:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Speaker
Muhammad Jehangir Amjad, from Google
Host
Jennifer Rexford

Muhammad Jehangir Amjad
Google's products, e.g. Search, YouTube, Gmail, etc. are used by billions of people the world over. Google Cloud hosts some of the most popular services in the world.  Building systems that scale to support all these applications is among the greatest challenges at the company. Underlying all these systems is the network which must enable low-latency, high throughput, high capacity, high availability and secure access to compute and storage. While Google has been enormously successful in achieving these goals, cloud networking faces exciting new challenges today. With the demise of Moore's Law and explosive data growth, the implications for the performance, reliability, predictability and cost efficiency on networking, from hardware to communication protocols, will be profound.  

This talk will focus on highlighting some of the challenges in networking alluded to above. Additionally, we will discuss the current and future challenges in network telemetry systems and Google's approach to overcoming these challenges via statistical inference which allows us to estimate that which cannot be measured.

Bio:
Muhammad Jehangir Amjad is a Software Engineer in the Network Infrastructure team at Google working on inference and statistical learning on data produced by network telemetry systems. He joined Google from MIT where he has an appointment as a Lecturer of Machine Learning in CSAIL. Jehangir received his PhD from the Operations Research Center (ORC) and Laboratory of Information and Decision Systems (LIDS) at MIT, under the supervision of Prof Devavrat Shah. He received his BSE in Electrical Engineering from Princeton University.

To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Security and Privacy Guarantees in Machine Learning with Differential Privacy

Date and Time
Tuesday, September 17, 2019 - 12:30pm to 1:30pm
Location
Computer Science Small Auditorium (Room 105)
Type
Colloquium
Host
Amit Levy

Machine learning (ML) is becoming a critical foundation for how we construct the code driving our applications, cars, and life-changing financial decisions.  Yet, it is often brittle and unstable, making decisions that are hard to understand and can be exploited.  As one example, tiny changes to an input can cause dramatic changes in predictions; this results in decisions that surprise, appear unfair, or enable attack vectors such as adversarial examples.  As another example, models trained on users' data have been shown to encode not only general trends from large datasets but also very specific, personal information from these datasets, such as social security numbers and credit card numbers from emails; this threatens to expose users' secrets through ML predictions or parameters.  Over the years, researchers have proposed various approaches to address these rather distinct security, privacy, and transparency challenges.  Most of the work has been best effort, which is insufficient if ML is to become a rigorous basis for how we construct our code.

This talk positions differential privacy (DP) -- a theory developed by the privacy community -- as a versatile foundation for building into ML much-needed guarantees of not only privacy but also of security, stability, and transparency.  As supporting evidence, I first present PixelDP, a scalable certified defense against adversarial examples that leverages DP theory to guarantee a level of robustness against this attack.  I then present Sage, a DP ML platform that bounds the leakage of personal secrets through ML models while addressing some of the most pressing challenges of DP, such as the "running out of privacy budget" problem.  Both PixelDP and Sage are designed from a pragmatic systems perspective and illustrate that DP theory is powerful but requires adaptation to achieve practical guarantees for ML workloads.

Bio:
Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington.  For her work in cloud and mobile data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a "Brilliant 10" Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

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