Austin was born in San Francisco and has grown up in Bay Area. He is a 4th year undergraduate at UC Berkeley studying electrical engineering and computer science (EECS). At Berkeley, he is involved with undergraduate research in a micro-robotics lab as well as a computer vision group. His research interests include deep learning, robotics, and computer vision and especially the intersection of those three subjects. In addition to research, Austin has enjoyed his time as a teaching assistant in an introductory electrical engineering course at Berkeley. Outside of academics, Austin enjoys running, biking, and exploring the outdoors.
I am a Bay Area native, attending UC Berkeley for my undergraduate degree in EECS, my master’s degree in EECS and now as a PhD candidate advised under Professor Steve Conolly. My research is developing hardware for Magnetic Particle Imaging (MPI). MPI is a radiation-free, positive contrast, tracer imaging modality with great promise for a variety of applications: stem cell and immune cell tracking, as well as lung, gut bleed, traumatic brain injury, and tumor imaging. My project is to design and implement an optimized front end hardware and electronics that I hope will enable even higher resolution, higher sensitivity, and quantitative, robust imaging for the clinic. Outside of research, I enjoy moving heavy things at the gym, running, and trying new healthy recipes in the kitchen.
I’m a 4th year undergraduate at UC Berkeley studying EECS and Business Administration as a part of the Management, Entrepreneurship, and Technology (M.E.T.) Program. Living systems and light fascinate me, especially at the nano and microscopic scale. My current work concentrates on creating scalable platforms for synthetic biology with techniques from computational imaging, robotics, and deep learning. I work with Professor Laura Waller and previously have worked at Insitro, the Chan Zuckerberg Biohub, and Stanford Medicine.
Jasmine was born and raised in the city of Diamond Bar in LA County, California. Her journey at Berkeley began as an undergraduate studying Bioengineering with a minor in electrical engineering and computer sciences (EECS). After graduating in 2019, Jasmine started her PhD in EECS with a research focus on printed electronics advised by Professor Ana Claudia Arias. Her thesis project investigates the use of “greener” and less toxic solvents in the processing of organic semiconductor devices. In particular, she is focusing on large-area printed photovoltaics and photodiodes. Outside of research, Jasmine enjoys cooking, hiking, and biking to tasty bakeries in the Bay.
Kaylo Littlejohn is a 3rd year Electrical Engineering and Computer Sciences Ph.D. student creating speech brain-machine interfaces in the Berkeley AI Research Lab and Chang Lab at UCSF. He is advised by Professor Gopala Anumanchipalli and Professor Edward Chang. His research is focused on decoding speech and movement from persons with paralysis using brain-computer interfaces. Kaylo received his B.S. degree in Electrical Engineering from Columbia University in 2020. During his time at Columbia, Kaylo developed VR 3D real time closed loop brain-machine interface paradigms under Dr. Paul Sajda. Now Kaylo works on the BCI Restoration of Arm and Voice (BRAVO) clinical trial and is broadly interested in real-time speech synthesis and machine learning applied to developing assistive communication devices.
I am a Computer Science PhD student advised by Prof. Nilah Ioannidis. I work on problems at the intersection of machine learning and computational genomics. More specifically, I am interested in understanding and controlling gene expression by building prediction models that can be used to understand the regulatory functions of sequences and for interpreting the effects of genomic variants. I also work on understanding splicing and its regulation. My broader research goals are to use machine learning to understand, diagnose and treat human diseases. I worked on problems in natural language processing and computational neuroscience before starting my PhD.
Thomas Krendl Gilbert is an interdisciplinary Ph.D. candidate in Machine Ethics and Epistemology at UC Berkeley, and an incoming postdoc with the Digital Life Initiative at Cornell Tech in fall 2021. With prior training in philosophy, sociology, and political theory, he designed this degree program to investigate the ethical and political predicaments that emerge when artificial intelligence reshapes the context of organizational decision-making. His recent work investigates how specific algorithmic learning procedures (such as reinforcement learning) reframe classical ethical questions and recall the foundations of democratic political philosophy, namely the significance of popular sovereignty and dissent for resolving normative uncertainty and modeling human preferences. This work has concrete implications for the design of AI systems that are fair for distinct subpopulations, safe when enmeshed with institutional practices, and accountable to public concerns, including medium-term applications like automated vehicles.
Marius Wiggert is originally from Germany where he studied Engineering Science, Philosophy, and Technology Management during his undergraduate. He is an outdoor enthusiast, enjoys camping on mountain tops, skiing, kitesurfing, and is an avid learner. In his EECS PhD at Berkeley, Marius focuses on developing methods that enhance the type of systems which we can reliably operate in and control. As he feels deeply connected to nature specifically the ocean and mountains, he initiated and won funding for his main research project: developing algorithms to reliably control underactuated seaweed-growing platforms in the ocean. To make seaweed-based carbon-sequestration as affordable as possible the platforms have limited energy and thrust smaller than ocean currents. This inspired the idea of the platforms “hitchhiking” on non-linear ocean currents to achieve steering over distances of hundreds of kilometers.
I am a PhD Candidate at the University of California, Berkeley working at the intersection of machine learning and robotics. He is a member of the Department of Electrical Engineering and Computer Sciences, formally advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab. Nathan also has worked extensively with and been advised by Roberto Calandra at Facebook AI Research. Nathan has joined Facebook AI and DeepMind for internships exploring his research interests.
Nathan is an active member of the Graduates for Engaged and Extended Scholarship in Computing and Engineering (GEESE) working to understand how technology interfaces with society, writing frequently at [https://robotic.substack.com]. During his Ph.D., he was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism.
I’m a second year PhD student at UC Berkeley’s [BAIR](https://bair.berkeley.edu/) and [CHAI](https://humancompatible.ai/), working with [Anca Dragan](https://people.eecs.berkeley.edu/~anca/) and [Stuart Russell](http://people.eecs.berkeley.edu/~russell/).
I’m broadly interested in ensuring human-AI systems work as intended and can be beneficial for the people involved: specifically, I’ve worked on improving the quality and robustness of agents trained to collaborate with humans, and am interested in the effects of recommender systems on users’ preferences and beliefs.