Hi there! My name is Dovydas Joksas, aka Yoshke. I’m a PhD student at University College London (UCL) where I investigate machine learning hardware. I also tutor GCSE, A-level and IBDP students in mathematics and physics. Finally, I’ve recently started a business with the goal of developing intelligent diagnostic tools that would aid students’ learning.
You can download my CV from here.
PhD in Electronic Engineering (2018-2022, UCL)
Artificial neural networks (ANNs) have achieved spectacular results over the last decade, but at a cost of low efficiency. Our conventional computer hardware is not well suited to the computations that ANNs perform and so 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 exhibit a greater degree of volatility and uncertainty than the digital computers that we use every day.
Using simulations, I investigate to what extent the accuracy of ANNs is affected when memristor-based hardware is used to implement them. I am also interested in whether the accuracy can be increased not by optimising device performance, but by using different software approaches. I explain the rationale behind the research in less technical terms here, here and here.
- Joksas, D. & Mehonic, A. (2020).
badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays. SoftwareX, 12, 100617. doi: 10.1016/j.softx.2020.100617.
- Joksas, D., Freitas, P., Chai, Z., Ng, W. H., Buckwell, M., Zhang, W. D., … & Mehonic, A. (2020). Committee machines—a universal method to deal with non-idealities in memristor-based neural networks. Nature Communications, 11, 4273. doi: 10.1038/s41467-020-18098-0.
- Mehonic, A., Joksas, D., Ng, W. H., Buckwell, M., & Kenyon, A. J. (2019). Simulation of Inference Accuracy Using Realistic RRAM Devices. Frontiers in neuroscience, 13, 593. doi: 10.3389/fnins.2019.00593.
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).
- Kenyon, A. J., Munde, M. S., Ng, W. H., Buckwell, M., Joksas, D., & Mehonic, A. (2019). The Interplay Between Structure and Function in Redox-Based Resistance Switching. Faraday discussions, 213, 151-163. doi: 10.1039/C8FD00118A.
- Interstellar  (YOU CAN MAKE FUN OF ME, I DON’T CARE)
- There Will Be Blood 
- Taxi Driver 
- Once Upon a Time in the West 
- The Truman Show 
- American Beauty 
- The Big Short 
- Full Metal Jacket 
- Blue Velvet 
- Back to the Future 
- American History X 
- Reservoir Dogs 
- The Prestige 
- One Flew Over the Cuckoo’s Nest 
- The Irishman 
- A Clockwork Orange 
- Mulholland Drive 
- Scent of a Woman 
- Goodfellas 
- The Master 
Favorite songs (performances)
- Strawberry Fields Forever [The Beatles, 1967]
- Mr. Sandman [The Chordettes, 1954]
- Red Light Spells Danger [Billy Ocean, 1976]
- Stop! In the Name of Love [The Supremes, 1965]
- Lah-Di-Dah [Jake Thackray, 1991]
- Happens to the Heart [Leonard Cohen, 2019 (2016)]
- Ain’t No Mountain High Enough [Diana Ross, 1970]
- Eloise [Barry Ryan, 1968]
- Heroes and Villains [The Beach Boys, 1967]
- People Are People [Depeche Mode, 1984]
- 50 Ways to Leave Your Lover [Paul Simon, 1975]
- First We Take Manhattan [Leonard Cohen, 1986]
- Mr. Blue Sky [Electric Light Orchestra, 1977]
- Island in the Sun [Harry Belafonte, 1957]
- Paint It, Black [The Rolling Stones, 1966]
- War [Edwin Starr, 1970]
- She’s a Rainbow [The Rolling Stones, 1967]
- Band of Gold [Freda Payne, 1970]
- Bang Bang (My Baby Shot Me Down) [Nancy Sinatra, 1966]
- Cat People (Putting Out Fire) [David Bowie, 1982]