Alric Althoff

One Big Idea

My scientific contributions are all, in some way or another, about changing assumptions, and the good things that can happen when we do.

In statistics and machine learning, mathematical optimization, and high-performance algorithm design we (humanity) have gained a lot by leveraging strong assumptions about the problems we are working on. So great have been these gains that many assumptions are now baked into our everyday approaches.

Often, we don't stop to check and see if these assumptions are born out in evidence.

So think about this a little bit today: What do I assume that I haven't checked? How do I benefit, and is it worth it?

About Me

My face

I defended my PhD dissertation, titled Statistical Metrics of Hardware Security, in 2018. My PhD was advised by professor Ryan Kastner at the UC San Diego's Computer Science and Engineering department. I graduated from UC San Diego in 2013 with undergraduate majors in both cognitive science and mathematics, specializing in areas relevant to machine learning, computer vision, and digital signal processing.

My primary areas of interest are statistics, signal processing, and machine learning, focusing on machine learning-based parameter search for hardware/software co-optimization, hardware security, and high-throughput data analysis.

I am grateful to have received financial support during my formal education from UC San Diego, the ARCS Foundation, and the National Science Foundation in the form of San Diego, ARCS, and GRFP fellowships.

I am currently a research scientist working at Leidos, and have previous experience as a software developer and audio engineer.

Selected Areas of Study

  • Phenomenology, sensing, and characterization of computing hardware - from chips to brains
  • Computer hardware security
  • Parallel and heterogeneous computing
  • Machine learning and statistics
  • Streaming algorithms & data structures
  • High-Level Synthesis (HLS) for hardware design
  • Cryptography
  • Compressed sensing for computer vision
  • Optimization & numerical analysis
  • Cognitive & systems neuroscience
  • Computational neurobiology

Research Papers

Alric Althoff, Jeremy Blackstone, and Ryan Kastner, Holistic Power Side-Channel Leakage Assessment:
Towards a Robust Multidimensional MetricInternational Conference on Computer Aided Design (ICCAD), Nov. 2019 (pdf– in press

Quentin Gautier, Alric Althoff, and Ryan Kastner, FPGA Architectures for Real-time Dense SLAM, International Conference on Application-specific Systems, Architectures and Processors (ASAP), Jul. 2019 (pdf, code Best Paper Award 

Dustin Richmond, Alric Althoff, Ryan Kastner, Synthesizable Higher-Order Functions for C++, Transactions on Computer-Aided Design of Integrated Circuits and Systems, Special Issue from ESWeek (CODES+ISSS), 2019 (pdf)

Alric Althoff, Joseph McMahan, Luis Vega, Scott Davidson, Timothy Sherwood, Michael Taylor, Ryan Kastner, Hiding Intermittent Information Leakage with Architectural Support for Blinking, International Symposium on Computer Architecture (ISCA), Jun. 2018 (pdf)

Dajung Lee, Alric Althoff, Dustin Richmond, and Ryan Kastner, A Streaming Clustering Approach Using a Heterogeneous System for Big Data Analysis, International Conference on Computer-Aided Design (ICCAD), Nov. 2017 (pdf)

Alric Althoff and Ryan Kastner, An Architecture for Learning Stream Distributions with Application to RNG Testing, Design Automation Conference (DAC), Jun. 2017 (pdf)

Baolei Mao, Wei Hu, Alric Althoff, Janarbek Matai, Yu Tai, Dejun Mu, Timothy Sherwood, and Ryan Kastner, Quantitative Analysis of Timing Channel Security in Cryptographic Hardware Design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Nov. 2017

Wei Hu, Alric Althoff, Armaiti Ardeshiricham, and Ryan Kastner, Towards Property Driven Hardware Security, Microprocessor Test and Verification Conference (MTV), Dec. 2016 (pdf Invited Paper

Quentin Gautier, Alric Althoff, Pingfan Meng, and Ryan Kastner, Spector: An OpenCL FPGA Benchmark Suite, International Conference on Field-Programmable Technology (FPT), Dec. 2016 (pdf)

Pingfan Meng, Alric Althoff, Quentin Gautier, and Ryan Kastner, Adaptive Threshold Non-Pareto Elimination: Re-thinking Machine Learning for System Level Design Space Exploration on FPGAs, Design Automation and Test in Europe (DATE), Mar. 2016 (pdf)
Janarbek Matai, Dajung Lee, Alric Althoff, and Ryan Kastner, Composable, Parameterizable Templates for High-Level Synthesis, Design Automation and Test in Europe (DATE), Mar. 2016 (pdf)

Ryan Kastner, Wei Hu, and Alric Althoff, Quantifying Hardware Security Using Joint Information Flow Analysis, Design Automation and Test in Europe (DATE), Mar. 2016 (pdf – Invited Paper

Baolei Mao, Wei Hu, Alric Althoff, Janarbek Matai, Jason Oberg, Dejun Mu, Timothy Sherwood, and Ryan Kastner, 
Quantifying Timing-Based Information Flow in Cryptographic Hardware, International Conference on Computer-Aided Design (ICCAD), Nov. 2015 (pdf)

Alric Althoff and Ryan Kastner, 
A Scalable FPGA Architecture for Nonnegative Least 
Squares Problems
, International Conference on Field Programmable Logic and Applications (FPL), Sep. 2015 (pdf)

Christopher Barngrover, 
Alric Althoff, Paul DeGuzman, and Ryan Kastner, A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery, IEEE Journal of Oceanic Engineering, Mar. 2015 (pdf)


John McGarry, Alric Althoff, inventors; Cognex Corporation, assignee. Machine Vision System with Large Depth of Field, filed 3/6/15 Patent Pending.