Events Listing

Artificial Intelligence for research productivity

Monthly Workshop for Graduate Students.
Faculty and undergraduate students are welcome!

This month's talk is by Professor Jarvis Haupt

Large language models were once synonymous with massive cloud infrastructure and specialized data centers. Today, advances in model architecture, inference engines, and quantization techniques have made it possible to run capable AI systems entirely on consumer hardware, including laptops, desktops, and even mobile devices. This talk explores the technological developments that enabled the rise of local AI models and examines the practical tradeoffs involved with running them. After a brief overview of modern transformer-based language models and the computational resources required for inference, we will discuss the central challenge of local deployment: fitting increasingly capable models within limited memory and compute budgets. Particular attention will be given to quantization methods and the GGUF model format, which have become foundational technologies for efficient local inference. We will survey the current ecosystem of tools for self-hosting and interacting with local models, including popular inference frameworks and desktop applications. Along the way, we will discuss hardware considerations, performance expectations, privacy implications, and opportunities for integrating local AI into research and educational activities.

Organic semiconductors are conjugated molecular materials with highly tunable electrical and optical functionality. These materials have found wide interest as thin film components in optoelectronic and photovoltaic devices, where attractive physical properties are combined with high throughput processing on mechanically flexible substrates enabling novel device form factors. To date, the most successful application of organic semiconductor thin films has been in displays based on organic light-emitting devices (OLEDs). An OLED consists of a vertical thin film stack deposited via a high vacuum, physical vapor deposition process. While these layers are typically amorphous and glassy, ongoing work has revealed the complexity and tunability in property and performance that can come with active engineering of molecular orientation.

Central to this talk is how changes in thin film molecular orientation lead to corresponding changes in the associated transition dipole moment (TDM) and permanent dipole moment (PDM) orientations. The TDM orientation plays a role in determining thin film phenomena including birefringence and OLED efficiency, and manipulation of this parameter is already an active part of display design. This talk will focus more heavily on the less widely investigated case of preferential PDM orientation (termed spontaneous orientation polarization, SOP), its impact on charge and exciton behavior in OLEDs, and ultimately its impact on efficiency and operational lifetime. Emphasis will also be placed on discussing means to manipulate PDM orientation via choice of thin film processing conditions and molecular blending, as well as showing how device architecture can be used to mitigate the impact of SOP on OLED performance. The talk will conclude with a discussion of how tuning of molecular orientation in organic thin films represents a largely open axis for engineering the behavior of this important
materials class.

Celebrating our graduating undergraduate students before their CSE commencement ceremony!

A brunch reception to celebrate our graduating master's and Ph.D. students. 

Note: If you have registered to participate in the University's commencement ceremony, after the reception, please proceed to Mariucci Arena. 

Details on the 2026 commencement ceremony for master's and doctoral degree students.

Professor Sanjay Shakkottai at ECE spring 2026 colloquium

We discuss discrete diffusion models that offer a unified framework for jointly modeling categorical data such as text and images. We present a new model that we have developed for language generation called the Anchored Diffusion Language Model (ADLM). ADLM is grounded in a novel two-stage framework that first predicts distributions over important tokens via an anchor network (e.g., key words or low-frequency words that anchor a sentence), and then predicts the likelihoods of missing tokens conditioned on the anchored predictions. ADLM significantly improves test perplexity on LM1B and OpenWebText, achieving up to 25.4% gains over prior DLMs, and narrows the gap with strong AR baselines. It also achieves state-of-the-art performance in zero-shot generalization across seven benchmarks and surpasses AR models in MAUVE score, which marks the first time a DLM generates better human-like text than an AR model. Beyond diffusion, anchoring boosts performance in AR models and enhances reasoning in math and logic tasks, outperforming existing chain-of-thought approaches.

Project page: https://anchored-diffusion-llm.github.io/

Discover Innovation at the Student Design Showcase

Get ready to be impressed by the creativity and ingenuity of more than 500 talented students! This exciting event highlights experiential learning and features an incredible range of projects from first-year explorers to senior-year trailblazers in electrical, computer and mechanical engineering. Discover what our students bring to life through design!

Learn more details about the design showcase.

Senior process integration engineer Kriti Agarwal of Intel at ECE spring 2026 colloquium

(details coming soon)

Professor Maurizio Porfiri at ECE spring 2026 colloquium

(details coming soon)

Professor Samuel Palermo at ECE spring 2026 colloquium

Growing datacenter bandwidths datacenters requires optical transceivers operating at high data rates. Further increases in bandwidth density is possible with Wavelength-division multiplexing, which architectures based on silicon photonic microring modulators (MRMs) inherently enable. This talk covers high-speed PAM4 transmitter and receiver front-ends implemented in a 28nm CMOS process that are co-designed with these silicon photonic optical devices. The transmitter utilizes an optical DAC approach with two PAM2 AC-coupled pulsed-cascode high-swing output stages to drive the MRM MSB/LSB segments with a 3.42V ppd at 80Gb/s PAM4. The receiver consists of a transimpedance amplifier with sub-Nyquist bandwidth for low input-referred noise and a subsequent continuous-time linear equalizer for bandwidth recovery. Efficient clocking is realized with an LC-oscillator-based quarter-rate digital clock and data recovery system. The RX achieves 100Gb/s PAM4 operation with −6.4 dBm sensitivity.