Hi there! My name is Dovydas Joksas, aka Yoshke. I’m a PhD candidate 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 founded Ab Initio AI—a startup aimed at developing intelligent diagnostic tools that aid students’ learning.
You can download my CV from here.
PhD in Electronic Engineering (2018–Present, 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]