Hi there! My name is Dovydas.

a researcher UCL I work on security threats to machine learning hardware, e.g., whether memristor-based hardware accelerators can be compromised via adversarial attacks.
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I investigate electric circuits called memristive crossbar arrays and their role as hardware accelerators for machine learning. These circuits allow computing vector-matrix products—a mathematical operation widely used in machine learning—faster and by consuming much less energy. However, they are less precise than their digital counterparts; my PhD focused on understanding these negative effects and ways of minimizing them.

My current focus is cybersecurity threats to such machine learning hardware. Even in conventional (transistor-based) computers, adversarial attacks may be used to confuse machine learning systems, e.g. an attacker might trick an autonomous vehicle into thinking that “60 mph” on a street sign is actually “85 mph” instead. This can cause a lot of problems, thus it is important to understand

  • whether memristor-based hardware is as susceptible to such threats
  • whether effective defense strategies exist


PhD in Electronic Engineering (2018–2022, UCL)

I investigated memristor-based hardware accelerators for machine learning. Artificial neural networks and cognitive computing more generally have achieved amazing results over the last decade. However, conventional computers, which separate memory and computing units, are not well suited to the computations that neural networks perform. As a result, a lot of time and energy is wasted. One of the suggested alternatives is an emerging technology—memristor-based hardware. Although vastly more efficient, memristor-based implementations, compared to traditional digital computers, exhibit more variability. This could present problems.

I used experimental data and simulations to

I explain the rationale behind the research in less technical terms in the following posts:


Original research
  • D. Joksas, E. Wang, N. Barmpatsalos, W. Ng, A. Kenyon, G. Constantinides, and A. Mehonic, Nonideality-aware training for accurate and robust low-power memristive neural networks, Advanced Science, vol. 9, no. 17, p. 2105784, 2022. doi:10.1002/advs.202105784
  • D. Joksas, P. Freitas, Z. Chai, W. Ng, M. Buckwell, C. Li, W. Zhang, Q. Xia, A. Kenyon, and A. Mehonic, Committee machines—a universal method to deal with non-idealities in memristor-based neural networks, Nature Communications, vol. 11, no. 1, 2020. doi:10.1038/s41467-020-18098-0
  • A. Mehonic, D. Joksas, W. Ng, M. Buckwell, and A. Kenyon, Simulation of inference accuracy using realistic RRAM devices, Frontiers in Neuroscience, vol. 13, p. 593, 2019. doi:10.3389/fnins.2019.00593
  • D. Joksas and A. Mehonic, badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays, SoftwareX, vol. 12, p. 100617, 2020. doi:10.1016/j.softx.2020.100617
  • D. Joksas, A. AlMutairi, O. Lee, M. Cubukcu, A. Lombardo, H. Kurebayashi, A. Kenyon, and A. Mehonic, Memristive, spintronic, and 2D-materials-based devices to improve and complement computing hardware, Advanced Intelligent Systems, vol. 4, no. 8, p. 2200068, 2022. doi:10.1002/aisy.202200068
BEng Electronic Engineering (2015–2018, UCL)

The modules that I found the most intellectually rewarding were Photonics and Communication Systems (ELEC215P), Control Systems I (ELEC3003), Numerical Methods (ELEC3030), Quantum Physics (PHAS2222), and Atomic and Molecular Physics (PHAS2224).


Original research
  • A. Kenyon, M. Munde, W. Ng, M. Buckwell, D. Joksas, and A. Mehonic, The interplay between structure and function in redox-based resistance switching, Faraday Discussions, vol. 213, pp. 151–163, 2019. doi:10.1039/C8FD00118A

Erdős Number


My Favorite…


  • Eloise, Barry Ryan [1968]
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  • Oops!... I Did It Again, Britney Spears [2000]
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  • Mr. Sandman, The Chordettes [1954]
  • People Are People, Depeche Mode [1984]
  • You Are, Dolly Parton [1977]
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  • War, Edwin Starr [1970]
  • Fly Me to the Moon, Frank Sinatra [1964]
  • Island in the Sun, Harry Belafonte [1957]
  • Lah-Di-Dah, Jake Thackray [1991]
  • What Becomes of the Brokenhearted, Jimmy Ruffin [1966]
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  • Avalanche, Leonard Cohen [1971]
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  • Happens to the Heart, Leonard Cohen [2019 (2016)]
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  • Uprising, Muse [2009]
  • 50 Ways to Leave Your Lover, Paul Simon [1975]
  • Jessie's Girl, Rick Springfield [1981]
  • Gimmie Shelter, The Rolling Stones [1969]
  • Paint It, Black, The Rolling Stones [1966]
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  • She's a Rainbow, The Rolling Stones [1967]
  • You And Me, Spargo [1980]
  • Stop! In the Name of Love, The Supremes [1965]
  • Waiting Around to Die, Townes Van Zandt [1969]
  • Breakaway, Tracey Ullman [1983]


  • Adaptation [2002]
  • American Beauty [1999]
  • Back to the Future [1985]
  • Barry Lyndon [1975]
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  • Boogie Nights [1997]
  • A Clockwork Orange [1971]
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  • Eternal Sunshine of the Spotless Mind [2004]
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  • The Florida Project [2017]
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  • Goodfellas [1990]
  • Interstellar [2014]
  • The Irishman [2019]
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  • The King of Comedy [1983]
  • La piel que habito [2011]
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  • The Master [2012]
  • Mulholland Drive [2001]
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  • Taxi Driver [1976]
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  • Threads [1984]
  • Trainspotting [1996]
  • The Truman Show [1998]
  • Wild at Heart [1990]