Events Listing

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.

Professor Andrew Kent at ECE spring 2026 colloquium

Magnetic tunnel junctions (MTJs) are widely used as nonvolatile memory elements, but they can also serve as controllable, high-rate sources of random bits. In this talk, I will describe experimental studies of perpendicularly magnetized MTJs that are magnetically stable at room temperature. Instead of relying on spontaneous thermal magnetization fluctuations (superparamagnetism), stochastic behavior is generated on demand by actuating the device with nanosecond electrical pulses in the ballistic spin-transfer regime. This approach enables precise control of the switching probability. I will present measurements showing high-rate (up to 100 MHz/MTJ), reproducible generation of random bit streams and random telegraph noise. By interfacing individual pMTJs with custom electronics and a field-programmable gate array (FPGA), we generate truly random numbers that pass the full NIST statistical test suite with no post-processing. I will also show how such actuated stochastic MTJs (A-sMTJs) can be electrically connected in simple circuits to generate tunable, circuit-mediated interactions that map onto effective Ising couplings. Finally, I will discuss the potential of stochastic MTJs or physics-inspired computing systems, including their use for solving combinatorial optimization problems.

Professor Rosana Esteller at ECE 2026 Spring Colloquium

Neuromodulation is shifting from conventional open-loop therapy toward intelligent systems that integrate sensing, signal processing, modeling, and adaptive stimulation. This talk frames neuromodulation as an engineering problem in which biomarker algorithms estimate physiologic state, closed-loop systems use feedback to adjust therapy, and dynamic stimulation strategies expand control beyond conventional static pulse trains. Drawing on translational work in implantable neurotechnology, the presentation will discuss the design of biomarker-driven algorithms, practical constraints in real-time closed-loop implementation, and the biophysical rationale for time-varying stimulation. Together, these approaches highlight how engineering can enable more precise, personalized, and effective neuromodulation therapies.

Professor Zhichao Cao at ECE 2026 Spring Colloquium

In this talk, Professor Zhichao Cao will explore the symbiotic relationship between AI and data systems, showing how Large Language Models (LLMs) both automate data systems and drive new data system designs. He will present two representative projects: (1) StorageXTuner, the first LLM agent–driven framework that automatically tunes performance for diverse data systems such as RocksDB, LevelDB, MySQL, and CacheLib, outperforming traditional tuning methods; (2) M2Cache, a system that makes LLM inference sustainable and accessible on outdated or low-end hardware through a co-design of dynamic mixed-precision inference and a predictive multi-level cache across HBM, DRAM, and SSDs, greatly improving efficiency and reducing carbon footprint. Finally, Cao will conclude with his vision for future research at the intersection of AI and data systems.