Ankush Achwani

Computer Engineering senior at UMass Amherst. Securing machine learning and large-scale, real-world systems.

I'm a senior in Computer Engineering at UMass Amherst (Commonwealth Honors College). I'm interested in making machine learning and other large-scale, real-world systems secure and trustworthy, from cellular networks and phone infrastructure down to small devices. So far I've worked on post-quantum security for 5G and embedded devices, attacks on small quantized models, and embedded ML for health sensing. I also TA and coordinate courses in probability, ML math, and security, and I'm cofounder and CTO of StageLink. I'm applying to PhD programs for Fall 2027.

Email / CV / GitHub / LinkedIn

aachwani [at] umass [dot] edu  ยท  ankushachwani [at] gmail [dot] com

Portrait of Ankush Achwani

Selected work

Published signature sizes on a log scale: ES256 64 bytes, Falcon-512 666, ML-DSA-44 2,420, ML-DSA-65 3,309, SLH-DSA-128s 7,856 and SLH-DSA-128f 17,088, against the 1,300-byte limit for SIP over UDP.

Post-quantum signatures for STIR/SHAKEN in 5G

Manuscript in preparation

Post-quantum signatures are too large for SIP call setup, so simply replacing ECDSA in caller-ID authentication makes calls fail or slow down. This work develops a post-quantum design that matched today's ECDSA performance in testing on real SIP stacks, where the alternatives fall short.

Role: Lead researcher

Time per polynomial product on the ESP32 for six cases. A tuned NTT is faster than the RSA unit in five; the RSA unit wins only ML-DSA c times s.

Polynomial products for post-quantum cryptography on the ESP32 RSA unit

Released

Runs ML-KEM, ML-DSA and Falcon products on the chip's big-integer multiplier. 4.0 to 4.6x faster than the naive packed path, but a tuned NTT still wins five of six products.

Role: Lead researcher / code and data

Mean accuracy against targeted bit flips for int8 models. DS-CNN-S falls to about the 15 percent target within 6 flips. TC-ResNet8 is still at 73 percent after 30 flips. Random flips leave accuracy near 94 percent.

Bit-flip attacks on microcontroller-scale keyword spotting

Released

Targeted bit flips broke DS-CNN-S in a mean of 5.3 to 8.0 flips, while TC-ResNet8 held out past a 30-flip cap. Architecture mattered more than bit-width. White-box software simulation.

Role: Lead researcher / code and data

Membership-inference attack AUC for five keyword-spotting models at int8 and 4-bit. All values sit between 0.51 and 0.53, near chance, and the two bit-widths nearly coincide.

Does 4-bit quantization change membership-inference leakage in keyword spotting?

Released

Across five small models, loss-attack AUC stays at 0.513 to 0.533 with no detectable difference between int8 and 4-bit. Membership is assumed from the standard split.

Role: Lead researcher / code and data

Summed inference time for ten models: 171 seconds unoptimized, 102 with no copies, 63 with a direct driver, 44 with all changes, against 15 seconds on the CPU kernel.

Cryptonite: neural-network inference on the ESP32 RSA accelerator

Manuscript in preparation

Course project. Runs keyword-spotting networks as big-integer multiplies. Data-path changes made it 3.93x faster across ten models, though still 0.33x the speed of an optimized CPU kernel.

Role: Group project, no fixed roles

Still from the StageLink video: the logo, the line Your booking agent, in software, and a dashboard with demo data.

StageLink

Artist app live, access by waitlist

Booking and show-running software for live music, with an artist side and a venue side on one system, so one booking stays a single thread from first pitch to final payout. The artist app is live; the venue app is built and not yet released.

Role: Cofounder and CTO / join the waitlist / video, 10 s

News