computational neuroscience phd ucl

Neuroscience

Gatsby computational neuroscience unit.

We established this unit in 1998 at University College London (UCL) to provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with UCL’s other world-class research groups in neuroscience and related areas.

The Gatsby Computational Neuroscience Unit (GCNU) was established with the original aim of “building neurobiologically realistic and computationally sound models of the way that the brain computes”.

GCNU’s core strengths are in computationally and probabilistically-oriented theoretical neuroscience, and statistical machine learning. In total there are approximately 50 researchers, students and support staff. GCNU’s teaching activities are centred on an innovative four-year PhD programme in Computational Neuroscience and Machine Learning.

Following its fourth successful quinquennial review in 2021, the Unit is continuing its research and educational programme in these general directions, broadening and deepening its collaborations both within UCL and outside, and strengthening its theoretical bases in machine learning. The Unit strives to be one of the foremost centres in the world overall, and the GCNU model has been duplicated by various institutions.

In mid-2015, GCNU moved into the Sainsbury Wellcome Centre  building, taking up a central location that connects with the SWC experimental laboratory space and nearby break-out spaces to facilitate discussions and collaborations bewteen theorists and experimentalists.

Research groups

Maneesh sahani.

Director; Professor of Theoretical Neuroscience and Machine Learning

Understanding how information is represented in neural systems, and how this representation underlies computation and learning

Arthur Gretton

Professor of Machine Learning

Machine learning include the design and training of generative models, nonparametric hypothesis testing, and kernel methods

Peter Latham

Professor of Theoretical Neuroscience

Network dynamics and neural coding

Peter Orbanz

Mathematical aspects of machine learning and pattern recognition

Agostina Palmigiano

Research interests in the i) computational principles underlying sensory/motor processing and learning ii) how behavioural information (as choice, reward) across the cortex shapes area-specific computations, and iii) how interspecies differences in circuit architecture and dynamical regimes limit/facilitate learning

Andrew Saxe

Group Leader in Machine Learning applied to Neuroscience (jointly with the Sainsbury Wellcome Centre)

A mathematical toolkit suitable for analysing and describing learning in the brain and mind

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Gatsby Computational Neuroscience Unit MPhil/PhD

Ucl (university college london), different course options.

• Key information

Course Summary

Tuition fees, entry requirements, university information, similar courses at this uni, key information data source : idp connect, qualification type.

PhD/DPhil - Doctor of Philosophy

Subject areas

Neuroscience / Neurobiology

Course type

The Gatsby Unit is a world-class centre for theoretical neuroscience and machine learning. We provide a unique opportunity for a critical mass of theoreticians to interact closely with each other and with other world-class research groups across UCL. Teaching is supplemented with regular research talks, journal clubs and reading groups, extensive seminar programmes and participation in international conferences. As a student you will have ready access to all members of academic staff, not just your immediate supervisors. It is expected that most students will go on to postdoctoral positions in institutions across the world although some may take up posts in industry as senior research scientists or similar.

Most of our graduates go on to postdoctoral positions in academia, although a few students have been known to take up medicine or senior positions in industry. Recent graduates have taken up research posts at institutions such as the University of Cambridge, Princeton, Berkeley, Colorado, École normale supérieure Paris and HHMI Janelia Research Campus and organisations including Microsoft Research, Google Deepmind and the Wellcome Trust.

Employability

Our students are extremely well trained - we offer a very intense, and very rigorous, programme. They therefore tend to get good jobs; over 90% of our graduate students get postdoctoral positions, and then tend to go on to academic positions.

UK fees Course fees for UK students

For this course (per year)

International fees Course fees for EU and international students

Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.

UCL (University College London) is consistently ranked among the top ten universities in the world, conducting leading research across a wide range of subject areas. Throughout its long and prestigious history, it has inspired and educated countless minds and produced 30 Nobel prize recipients. With one campus located in the heart of Bloomsbury and a second campus in vibrant east London, the university is home to around 42,000 students... more

Brain and Mind Sciences MSc

Full time | 2 years | 23-SEP-24

Cognitive Neuroscience MRes

Full time | 1 year | 23-SEP-24

Developmental Neuroscience and Psychopathology MRes

Neuroscience msc, language and cognition mphil/phd.

Full time | 3 years | 23-SEP-24

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Gatsby Computational Neuroscience Unit

Entry requirements.

Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.

Months of entry

Course content.

Students at the Gatsby Unit study toward a PhD in Computational and Theoretical Neuroscience and Machine Learning. You will network extensively with the Sainsbury Wellcome Centre and other UCL experimental groups. Gatsby is part of the Centre for Computational Statistics and Machine Learning, together with UCL Computer Science and Statistical Science. Approximately 90% of alumni have secured academic or industry positions.

The Gatsby Unit is a world-class centre for Computational and Theoretical Neuroscience and Machine Learning. Our research seeks to understand the principles of learning, perception and action in brains and machines by developing mathematical algorithms. We provide a unique opportunity for a critical mass of theoreticians to interact closely and network extensively with each other and with other research groups across UCL, in particular the Sainsbury Wellcome Centre, and the Centre for Computational Statistics and Machine Learning. First year teaching is supplemented with weekly research talks, journal clubs and reading groups, external seminar programmes and participation in international conferences. As a student you will be supported by all members of academic staff, not just your immediate supervisors.

Department specialisms

-Analysis of neural data -Neural dynamics -Neural plasticity -Perceptual processing of auditory and visual input -Neural population coding -Kernel methods -Bayesian statistics -Reinforcement learning -Statistical machine learning -Unsupervised learning -Network and relational data

Qualification, course duration and attendance options

• Campus-based learning is available for this qualification

Course contact details

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Ucl (university college london): gatsby computational neuroscience unit.

Institution
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Study type	Research

Students at the Gatsby Computational Neuroscience Unit study toward a PhD in Theoretical Neuroscience and Machine Learning. You will network extensively with researchers at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour and other UCL experimental groups. The Gatsby Unit is part of the Centre for Computational Statistics and Machine Learning, together with UCL Computer Science and Statistical Science. Approximately 90% of alumni have secured academic or industry positions. Applications to this programme must be submitted directly to the Gatsby Unit via its online portal.

Master of Philosophy - MPhil

Full-time, 4 years starts sep 2024.

Level	RQF Level 7
Entry requirements	Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.English level: Level 3
Location	London, Bloomsbury Gower Street Camden WC1E 6BT

Full-Time, 4 years

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Discovering how the brain gives rise to behaviour

Creating future leaders in neuroscience

Interdisciplinary collaboration on the brain

Prioritising the unexpected: new brain mechanism uncovered.

Researchers at SWC have discovered how two brain areas, neocortex and thalamus, work together to detect discrepancies between what animals expect from their environment and actual events. These findings enhance our understanding of predictive processing in the brain and could offer insights into how brain circuits are altered in autism and schizophrenia spectrum disorders.

Off-target effects of optogenetics

Optogenetics has transformed neuroscience. But as with any groundbreaking technique, it comes with its own set of challenges. Here’s the story of how one such challenge led to the discovery of off-target effects of light stimulation used to activate genetically encoded light channels and how these might be overcome.

TReND-CaMinA: improving access to computational neuroscience and machine learning in Africa

Aimed at enhancing scientific capacity in one of the regions most affected by scientific inequality, the CaMinA summer school seeks to engage students in computational neuroscience and machine learning. SWC PhD student Tom George shares his story.

Brain waves: causes, correlations and controversies

Based at the University of California, San Francisco, Dr Vikaas Sohal's lab investigates the brain circuits underlying fundamental aspects of cognition and emotion. Following his SWC Seminar, Dr Sohal shared his key findings, clinical implications and next steps.

Why join SWC for your PhD?

Receive world-class training in systems neuroscience through our fully-funded 4-year PhD programme. Join our supportive and collaborative environment with close links to the Gatsby Computational Neuroscience Unit. Watch the video as our current SWC PhD students share their experience.

Events & Seminars

Upcoming seminar: professor hiroki r. ueda, upcoming seminar: professor jakob foerster.

Get the Reddit app

/r/neuroscience is dedicated to the academic discussion of the discipline. While we welcome beginners to browse and learn, front page posts are heavily moderated and limited to academic journals and serious discussion. For a more casual option, please see our Beginner Megathread or the less-strict /r/neuro.

List of Good to Great PhD programs for Computational Neuro

I looking at applying to grad school in the next 2-3 years, and I was wondering how I could source as list of mid to top tier schools for CompNeuro.

In my very limited research I have: UWashington Columbia Princeton UChicago NYU UCBerkely UCLA Harvard Yale? Stanford UCSF? UofToronto BU

This list is mostly top-tier, so I'm having trouble finding good but not too competitive programs.

Background: I go to a top-tier school. My GPA is very average, but I've strong research experience.

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THEORETICAL NEUROSCIENCE (2023/2024)

Gatsby computational neuroscience unit, term 2/3.

• Additional material

Information

Theoretical neuroscience is a mathematical approach to understanding neural systems. This course provides a thorough introduction to current approaches in the field.

We will make heavy use of mathematical, statistical, and computational methods. Students should feel comfortable with linear algebra, ordinary differential equations, and probability theory at the level found in Boas ( Mathematical Methods in the Physical Sciences ) or Arfken ( Mathematical Methods for Physicists ).

The course is run primarily for students at the Gatsby Unit. Nevertheless, students, postdocs and faculty members from other UCL departments are welcome to attend on an informal basis. Please contact I-Chun Lin (i.lin 'at' ucl.ac.uk) if you would like to attend.

Peter Latham , Agostina Palmigiano , Maneesh Sahani , Andrew Saxe

Teaching Assistants

Loek Van Rossem Mohadeseh Shafiei Kafraj Yedi Zhang For contact information, see 'PhD Students' at the People page

Dates: Tuesdays and Fridays unless otherwise stated, 9 Jan – 12 Apr 2024 Detailed schedule: here Time: 11:00 - 13:00 Place: Ground Floor Lecture Theatre (SWC building)

Review sessions

Time: Wednesdays, 11:00 - 12:30 (starting 24 January 2024) Place: Gatsby Unit Seminar Room (3rd floor, SWC building)

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12.01.2024	Course schedule updated - please update your calendar accordingly.
11.01.2024	Homework 1 added. Due 2 Feb 2024
06.02.2024	Homework 2 (optional) and 3 added. Due 16 Feb 2024
Homework 1 added. Due 27.01.2023
10.01.2023	Relevant writeups for the first few lectures: 'What you should have learned from my lectures' and 'Dendrites and axons' (access )
09.01.2023	Course website online.

“Some of the most effective learning algorithms are those that combine perspectives from many different models or parameters. This has always seemed a fitting metaphor for effective research. And now ELLIS will provide a new architecture to keep our real-life committee machine functioning --- reinforcing, deepening and enlarging the channels that connect us to colleagues throughout Europe At UCL we're excited to be a part of this movement to grow together. We look forward to sharing new collaborations, workshops, exchanges, joint studentships and more, and to the insight and breakthroughs that will undoubtedly follow. ”

Prof Maneesh Sahani Director, Gatsby Computational Neuroscience Unit

“Advances in AI that benefit people and planet require global cooperation across disciplines and sectors. The ELLIS network is a vital part of that effort and UCL is proud to be a contributor. ”

Prof Geraint Rees UCL Pro-Vice-Provost (AI)

UCL and Inria announce new collaborative research venture

25 February 2021

We are pleased to announce a new joint venture between UCL and Inria, one of the leading French research institutes in computer science and applied mathematics. Inria have launched ‘The Inria London Programme’, an initiative to establish a presence in the UK.

UCL joins ELLIS network

14 September 2020

UCL will join the ELLIS PhD program, a flagship initiative, where PhD students are fully funded and jointly supervised by top AI research groups throughout Europe. The ELLIS PhD program is open to applicants worldwide.

DeepMind/ELLIS CSML Seminar Series

Optimization under the lens of compression learning: trading feasibility to performance, speaker: kostas margellos, event date: 03 may 2024.

In this talk we consider convex optimization problems affected by uncertainty, where uncertainty is represented by means of samples/scenarios. We show how finite sample complexity bounds for the generalization properties of the resulting solutions can be obtained, using tools from statistical learning theory based on probably approximately correct learning. Specifically, we view this problem under a compression learning lens that allows for sharper bounds compared to Vapnik-Chervonenkis results. We next discuss how to trade (probabilistic) feasibility to optimality by introducing a sample discarding procedure. Existing results in this direction are not tight, often leading to a conservative behaviour as far as performance is concerned. We show how to overcome this and achieve a tight quantification of the feasibility-performance trade-off using a sequential methodology for sample discarding. Moreover, we discuss certain aspects of applying such methodology in a multi-agent setting, with each agent having access to a private set of samples.

Computer Science

Benjamin Guedj

Brooks Paige

David Barber

Dimitrios Kanoulas

Emine Yilmaz

Ingemar Cox

John Shawe-Taylor

Lourdes Agapito

Marc Deisenroth

Mark Herbster

Marta Betcke

Massimiliano Pontil

Matt Kusner

Pontus Stenetorp

Carlo Ciliberto

Sebastian Riedel

Federica Sarro

Gatsby computational neuroscience unit.

Arthur Gretton

Ferenc Huszar

Maneesh Sahani

Peter Latham

Peter Orbanz

Andrew Saxe

Department of statistical science.

Alexandros Beskos

Francois-Xavier Briol

Ioanna Manolopoulou

Jim Griffin

Jinghao Xue

Petros Dellaportas

Ricardo Silva

Serge Guillas

Department of electronic and electrical engineering.

Miguel Rodrigues

Ilija Bogunovic

Ucl energy institute.

Victor Ponce Lopez

Department of experimental psychology.

Bradley Love

Benjamin Guedj is a Principal Research Fellow. He is also a tenured scientist with Inria (France), and holds a visiting position at ATI. He is a co-leader (with Peter Grünwald, ELLIS member) of the joint team between CWI and Inria. He is a scientific leader of the Inria@London hub. His research interests include statistical learning theory and algorithms focused on PAC-Bayes theory, concentration inequalities and generalisation bounds, computational statistics and deep learning. He holds two ANR grants (French agency for research) and an H2020 grant in 2018. He is regularly on the program committees of NeurIPS, COLT, ICML, ICLR, AISTATS. He organised (with Francis Bach, ELLIS member, and Pascal Germain) a NeurIPS 2017 workshop, and a tutorial (with John Shawe-Taylor, ELLIS Fellow) at ICML 2019. Local chair for COLT 2022 (in London). He is a member of the board of ECAS, board of the French Statistical Society, and of Inria’s Evaluation Committee. He is currently supervising 5 PhD students and 2 postdocs. Collaborations with Inria, CWI (the Netherlands), Amazon Cambridge, UCLA, among others.

• Department: Computer Science
• Expertise: TBA

Brooks Paige is an Associate Professor and a Turing fellow. His research is on interpretable machine learning and methods for scalable Bayesian inference in probabilistic programming systems. He co-supervises two PhD students and is a co-PI on a Turing UKRI-funded project “Machine learning prediction of plant chemical production”. He participates in long-term collaborative projects in ML for applications in chemistry, climate science, with John Innes Centre, the British Antarctic Survey, and University of Cambridge, among others.

David Barber is Director of the University College London Centrebfor AI, Director of the UKRI Centre for Doctoral Training in Foundational AI. Research on probabilistic modelling (22 NeurIPS papers) in AI and ML, for which his textbook on Bayesian Reasoning and ML is one of the standards in the field. Chief Scientific Officer and co-founder re:infer, a London based startup that uses NLP to analyse unstructure text UK government advisor on AI; action editor for JMLR. Grants: £6.7 million from the UKRI to establish a Centre for Doctoral Training in AI, supported by an additional £5 million from industry. Supervises 10 PhD students at UCL and 1 postdoc. He has collaborated extensively with Bert Kappen (Radboud University), Peter Sollich (Goettingen).

Dimitrios Kanoulas is a lecturer. He leads the Robot Perception and Learning (RPL) lab of the Autonomous System Research Group at UCL.They fous on perception and learning for mobile/articulated robots, new estimation and planning algorithms for robots that navigate, locomote, and manipulate in uncertain natural environments. He was a best Student Paper Award Finalist at IEEE ICARCV 2018 and the Best Interactive Paper Award Winner at IEEE-RAS Humanoids 2017. He co-authored an NSF Career Award (500k, 2012-2015) and a member of the 2020 UK-RAS Strategic Task Group on “Legged Robotics and Locomotion Technical Committee”. He is an Associate editor and is in the program committee of highly ranked conferences/journals, such as ICRA, IROS, IJCAI, and Frontiers. He has one PhD student at the Italian Institute of Technology (IIT) and at least 2 more PhD students at UCL in Sept. 2020. He actively collaborates with (IIT) and the ZOARobotics UK-based robotics start-up.

Emine Yilmaz is a Professor and a Turing Fellow. She is also Amazon Scholar at Amazon Research Cambridge. From 2012 - 2019, she was research consultant for Microsoft Research Cambridge, where she was a full time researcher prior to joining UCL. Her research interests include information retrieval, natural language processing and applications of machine learning. She was awarded the Bloomberg Data Science Research Award (2018), Karen Sparck Jones Award (2015), the Google Faculty Research Award (2014) and the Best paper award ACM CHIIR 2017. She is a co-editor-in-chief of the Information Retrieval Journal, a PC Chair for ECIR 2019, ACM SIGIR 2018 and ACM ICTIR 2017, Practice and Experience Chair for ACM WSDM 2017, Doctoral Consortium Chair for ECIR 2017, and an Executive Committee member of ACM SIGIR. She holds funding from the EPSRC fellowship, EU Horizon 2020 grant. She currently supervises one postdoc and 6 PhD students. She holds collaborations University of Glasgow, Amazon, Microsoft Research and Spotify.

Ingemar J. Cox is a Professor at UCL. He is also a part time professor at the University of Copenhagen. His research focuses on the application of AI, ML, and NLP to large data sets of digital footprints, e.g. Web query logs and Twitter, to infer health information for individuals and populations. With these tools, estimate prevalence and virulence of a disease, and effectiveness of national public health interventions (vaccines and changes to law). Estimates of the prevalence of influenza have been adopted by Public Health England as part of its influenza surveillance programme. Co-I and Deputy Director of an £11M EPSRC IRC called i-sense, “Early Warning Sensing Systems for Infectious Diseases”. He is a co-I and Deputy Director of the £4M follow-on funding from EPSRC. He is a Fellow of the ACM, IEEE, IET, and BCS. In 2019 he was awarded the Tony Kent Strix Award for contributions to information retrieval. In 2015 he received an IEEE Signal Proc. Soc. Sustained Impact Paper Award. He is a co-chair of the 2019 “Health on the Web” track, Web Conference (Formerly WWW2019). He currently supervises two postdocs and 3 Ph.D. students. He has strong collaborations with Microsoft Research.

John Shawe-Taylor is a Professor, an Ellis fellow and a Turing Fellow, He helped to drive a fundamental rebirth in ML, with applications including computer vision, document classification, biology, and medicine (brain scan, immunity and proteome analysis). He has published over 250 papers and two books with over 69000 citations. He is a member of the ELLIS Interactive Learning and Interventional Representations theme. He is a leader of the UCL application for ATI membership, and a former member of ATI PC, and a former Head of CS at UCL (2010-19). In 2014 UCL CS was ranked highest in the UK in the Research Evaluation Framework (REF). He has been a UNESCO Chair of AI since 2018, a leading trustee of Knowledge 4 All Foundation. He is a program co-chair and General co-chair of the NeurIPS. He is an associate editor of JMLR, editor of Information and Inference. He coordinated PASCAL and PASCAL2, EU projects including KerMIT (Kernel Methods for Images and Text), ComPLACS (Composing Learning for Artificial Cognitive Systems). He currently grants from X5Gon (Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site Global OER Network) EU project, and is a PI in the joint US/UK MURI grant, ‘Semantic Information Pursuit for Multimodal Data Analysis’. He supervises two postdocs and five PhD students. He has collaborations with Jozef Stefan Institute Slovenia, Aalto University in Finland, U. of Nantes in France, U. of Valencia in Spain, IIT in Italy, UC at Berkeley, John Hopkins University, U. of Maryland, and UCLA in US.

Lourdes Agapito is a Professor of 3D Vision at UCL, co-founder of London-based startup Synthesia Technologies, and ERC Grant holder (2008-14). Her research interests are in computer vision, graphics and machine learning; including 3D reconstruction from video, 3D shape modelling, weakly supervised learning for 3D vision, human pose estimation and video synthesis. She is program Chair for the top computer vision conferences (CVPR'16, ICCV'21) and Workshops Chair for ECCV'14; Area chair for CVPR (3x), ECCV (2x), ICCV (1x), Associate editor of IEEE PAMI and IJCV. She was the keynote speaker at ICRA'17, the top robotics conference. Her current team consists of six, funded by EU H2020 grant SecondHands and industry. She holds Collaborations with Facebook Reality Labs, ATI, University of Adelaide (Australia). She is co-director of the EPSRC Centre for Doctoral Training in Foundational AI at UCL CS.

Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London and PI of the Statistical Machine Learning Group at UCL. He also holds a visiting faculty position at the University of Johannesburg. Marc's research interests center around data-efficient machine learning, probabilistic modeling, climate science and autonomous decision making.

• Expertise: Gaussian processes, reinforcement learning, approximate inference, data-efficient machine learning

Mark Herbster is an Associate Professor. His research interests are in online learning, semi-supervised learning and matrix completion He is regularly on the program committee for ICML, NeurIPS and has acted as an area chair for NeurIPS (2x), AISTATS (1x), PC. He is currently supervising 3 PhD students and 1 postdoc. He has often collaborated with Università degli Studi di Milano and Università degli studi dell'Insubria.

Marta Betcke is an associate professor in the Department of Computer Science at the University College London. She is also a member of Centre for Inverse Problems and Centre for Medical Image Computing. Between 2010 and 2013, she held an EPSRC Postdoctoral Research Fellowship "Image Reconstruction: the Sparse Way" at UCL to investigate the implications of sparsity on image acquisition and reconstruction in different modalities. Before coming to UCL in the end of 2009, she was a PDRA in the School of Mathematics at the University of Manchester working on novel X-ray CT scanners for airport baggage screening. Hey interest is in broad areas of Inverse Problems, Numerical Analysis and Scientific Computing. In particular in numerical solution of inverse problems, tomographic image reconstruction, compressed sensing, sparsity and compression, applied harmonic analysis and machine learning. She enjoys working on novel imaging technologies and their applications with focus on experimental design and reconstruction problem.

Massimiliano Pontil is an Ellis Fellow, a part-time Professor in the Department of Computer Science, and a Senior Researcher at Istituto Italiano di Tecnologia (IIT). His research interests are in machine learning theory and algorithms, with a recent focus on statistical learning theory, meta-learning, hyperparameter optimization and algorithmic fairness. He was awarded the Best Paper Runner Up Award from ICML 2013, an EPSRC Advanced Research Fellowship in 2006-2011, and the Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism in 2002. He supervises 3 PhD students and 1 postdoc at UCL, and 4 Phd students and 3 postdocs at IIT. He is regularly on the programme committee of the main machine learning conferences (COLT, ICML and NeurIPS), editorial board of the ML Journal, Statistics and Computing, JMLR, and Scientific Advisory Board, MPI for Intelligent Systems.

Matt J. Kusner is an Associate Professor, an Ellis fellow and a Turing Fellow. His research is in simple machine learning models tailored to problem constraints, particularly in causal inference, social justice, privacy preserving learning and chemical design. He was awarded the Turner Dissertation Award for best CS & Engineering PhD. He gives talks at the Royal Society, the London Machine Learning Meetup, and the machine learning podcast Talking Machines. He work is reported in The Guardian, Forbes, and The New Scientist. He is a co-organizer of the NeurIPS 2018 workshop Critiquing and Correcting Trends in Machine Learning, an UAI 2018 Publications Chair, and an invited speaker at the NeurIPS 2017 Press Conference. He supervises 3 PhD students, and has long-term collaborations with NYU, Oxford, Cambridge, MPI for Intelligent Systems Tuebingen, Georgia Tech, Google, BenevolentAI and Frontier Development Lab.

Pontus Stenetorp, a lecturer, leads the University College London NLP group. He is a CO-PI of the European Union Horizon 2020 CLARIFY project (860627). His research is in the intersection between natural language and machine learning, including question answering, information extraction, and semantics. He was awarded the Outstanding paper award at the 2017 Conference of the European Chapter of the Association for Computational Linguistics, a Facebook AI Research (FAIR) award in 2016, and a Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship from 2013 to 2015. He supervises five PhD students and one postdoctoral researcher. He holds long-term collaborations with both the University of Tokyo and Tohoku University and is CS Department Ambassador to Japan.

Laura Toni (Lecturer at UCL) studies the area of machine learning for immersive communications, decision-making strategies under uncertainty, and large-scale signal processing for machine learning. Her current research focuses on applying graph signal processing tools to more data-efficient decision making strategies, with a deep focus on recommendations and reinforcement learning for optimal managing of large-scale systems. She recently received the UCL Future Leadership Award, and the Adobe System academic donation, and the Cisco academic grant. She received her PhD degree from the University of Bologna, Italy, followed by postdoc positions at the University of California at San Diego (UCSD) and at the Swiss Federal Institute of Technology (EPFL).

• Department: Department of Electronic and Electrical Engineering

Miguel Rodrigues (Professor, Turing Fellow) is a Full Professor at University College London, he is also the Director of the MSc in Integrated Machine Learning Systems at University College London, and he is a Turing Fellow. He has also previously held various appointments with various institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto, Portugal. His research work lies in the general areas of information theory, information processing, and machine learning He currently supervises five postdocs and six phd students. He is also currently funded by UKRI, Innovate UK, Royal Society, EU H2020, industry.

Arthur Gretton is a Professor, an Ellis Fellow and Unit Coordinator, and Director, Centre for Computational Statistics and ML (CSML) at UCL).His research interests are in design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He was awarded the Best paper award (NeurIPS 2017), Best student paper runner up (NeurIPS 2009), 9 NeurIPS orals. He is an Associate editor, IEEE TPAMI (until 2013), action Editor JMLR, area chair/Senior area chair of NeurIPS (x3), ICML (x2), COLT, and the RSS Research Section Committee (from 2020). He is a program chair with C. Robert of AISTATS 2016, ICML 2018 tutorials chair with R. Salakhudinov, an ICML 2019 workshops chair with H. Lee, program chair of Dali 2019 with S. Mohammed, K. Muandet, and a co-organiser of the Machine Learning Summer School 2019 with Marc Deisenroth. He currently supervises8 students (incl. 2 from Deepmind) and 2 postdocs. He has ongoing long-term collaborations with ISM Japan, Penn State, ENS Paris-Saclay, MSR New England, MPI for Intelligent Systems. He is a Scientific adviser for Babylon Health.

• Department: Gatsby Computational Neuroscience Unit

Ferenc Huszár is a Honaray lecturer, and studies the probabilistic foundations and principles of data efficient machine learning. His recent work addresses meta-learning, few-shot classification, and causal inference. He is currently a Senior Research Scientist at Twitter where he leads a research team on data-efficient representation learning, as well as championing the company’s efforts on ethical applications of machine learning. He did his PhD at the Computational and Biological Learning Laboratory at Cambridge University in 2013. He has held multiple positions in the technology sector since then. Most notably, Ferenc was principal research scientist at Magic Pony Technology, a startup focussed on applying deep learning to the problem of image compression and low level computer vision.

Maneesh Sahani is a Professor, and focuses on probabilistic inference and learning in natural and machine settings. His research interests include flexible approximate inference in graphical models, structured gaussian-process models and dynamical systems, neural implementations of probabilistic computation, and models of complex behaviour. He was in the Programme committee for N(eur)IPS (04, 06) and Cosyne (07), workshops chair for CNS (99-03) and N(eur)IPS (08), and programme chair (09) and general chair (10) for Cosyne. He is in editorial boards for Neural Computation, and Network and Frontiers. His advisory/review panels include Germany, the Netherlands, Israel, Japan and the USA. He is Lead advisory panel member for Babylon Health. Currently, he supervises 7 PhD students and 4 postdoctoral fellows. His funding includes the Gatsby Foundation, Simons Foundation, NIH, DARPA, BBSRC and the Wellcome Trust. He has collaborations with Stanford, Princeton, ENS, Champalimaud Centre and Hebrew University.

Peter Latham is a Professor. His research interests are mainly in neuroscience; ultimately he is interested in understanding how biologically realistic networks give rise to behavior, and how behavior evolves with learning. He also has strong interests in learning in artificial systems. He is an editor for eLife and PLoS Computational Biology. He currently supervising 6 students (one from DeepMind) and two postdocs. He has ongoing long-term collaborations with Alex Pouget (University of Geneva) and Greg Wayne (DeepMind).

Peter Orbanz is an Associate Professor. He was formerly an Associate Professor at Columbia, and a visiting faculty, MSR New England. His research focuses on network and relational data, Bayesian nonparametrics, and the statistics of highly interdependent variables. He has two graduated PhD students, and currently supervises one PhD and one postdoc. He has been awarded circa one million GBP in funding as a PI from funding bodies in the United Kingdom and the USA. He is regularly an area chair for NeurIPS, ICML, AISTATS. His collaborators include Yee Whye Teh (Oxford), Ismael Castillo (Paris VI), Ryan Adams (Princeton), David Blei (Columbia), and Christian Borgs and Jennifer Chayes (UC Berkeley).

Alexandros Beskos is a Reader in Statistics and a Turing Fellow, H-Index 20. He is a the Leader of the “Stochastic Modelling of Complex Systems” Group. His research includes Methodology & Applications for Monte-Carlo & Optimisation (MCMC, Sequential Monte-Carlo, Hamiltonian Monte-Carlo, Data Assimilation, Inverse Problems, 3D-VAR), Statistical Modelling & Applications in Finance, Epigenetics, Biostatistics, Graphical Models, Atmospheric Sciences, and Econometrics, Copulas. He is Associate Editor of Journal of Statistics & Computing, PI at an EPSRC First Grant (2011-12, £99K), a co-I at a Standard EPSRC Grant (2013-18, £408K) and an ATI Grant (2019-20, £225K). He has been awarded the Leverhulme Trust Prize (2015-20, £100K), 1 of 5 awarded in 2014 in the UK in Mathematics & Statistics. He sas supervised 6 Post-Doctoral Researchers (1 current) and 6 PhD students (3 current). He has collaborations in the USA (Caltech, Harvard), Singapore (NUS), Japan (Osaka University), Spain (Pompeu Fabra); and various UK. He is a visiting Associate Professor at Yale-NUS, Singapore (2020) and was a visitor at the Signal Processing Group, Cambridge (2014-15).

• Department: Department of Statistical Science

Francois-Xavier Briol is a Lecturer/Assistant Professor and a Group leader of "Fundamentals of Statistical Machine Learning, ATI. His research includes theory and methodology for computation and learning in large scale and computationally expensive probabilistic models. He has developed tools in kernel methods, Gaussian processes, Stein's method and Monte Carlo. He has a Discussion paper in the journal of Statistical Science, and spotlights/long talks at ICML and NeurIPS. He currently supervises two PhD students and has long-term collaborations with Cambridge, Imperial College London, Oxford, Warwick and Microsoft Research.

Ioanna Manolopoulou is an Associate Professor and a Turing Fellow. Her research focuses on Bayesian mixture modelling and non-parametric modelling, ML in causal inference, and statistical methods for biased and heterogeneous data. Her work has applications in health data science, ecology, customer science. She was awarded ISBA Mitchell prize (2012), and has published two discussion papers in Bayesian Analysis (2010) and the Journal of the American Statistical Association (2012). She is Associate Editor for Bayesian Analysis, and UCL lead for a cross- institutional PhD programme run by Health Data Research UK and the ATI, funded by the Wellcome Trust. She currently supervises 5 PhD students, and collaborates with Edinburgh University, Arizona State University, and industry (3 PhD students jointly funded by Dunnhumby).

Jim Griffin is a Professor. His research is on Bayesian nonparametric methods, computation for Bayesian inference, functional data analysis, high-dimensional regression, and time series modelling. His work has found applications in biology, economics, the environment, finance, medicine, and elite sporting performance. He supervises two PhD students at UCL and one PhD student at the University of Kent. He is an associate editor of Statistics and Computing, and Bayesian Analysis, and was an associated editor of the Journal of the Royal Statistical Society, Series B from 2013-7. His main collaborators are Mark Steel (Warwick), Eleni Matechou (Kent), Maria Kalli (Kent) and James Hopker (Kent).

Jinghao Xue is an Associate Professor and Turing Fellow. His research themes include statistical machine learning, multivariate and high-dimensional data analysis, statistical pattern recognition, and image analysis. He has been/is on editorial boards of Digital Signal Processing, Neurocomputing. He is a Guest Editor, IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, and Signal Processing Image Communication. He has xix PhD students at UCL, and has collaborations with Tsinghua University, China and Tsukuba University, Japan.

Petros Dellaportas is a Professor and a Turing Fellow. He is also professor, Athens U. of Economics & Business. His research is on Bayesian theory, MCMC theory, Gaussian processes, reinforcement learning and financial modelling. He has been / is on editorial boards of Bayesian analysis (co-editor), Biometrika (AE, 2012-2019), EJS (AE, 2012-2019), Stats & Comp. (AE, 2001-2012), Statistical modeling (AE, 2001-2007), and JRSS D (1996-2000). He has been awarded grants for Causal inference and RL (Just Eat Ltd), Detecting anomalies in networks (ATI), Forecasting with large macroeconomic and financial data with structural change (ATI). He currently supervises 4 PhD and 2 post-doctoral students. He collaborates with Greek academics, and with Michalis Titsias (Deepmind), Alain Durmus (ENS Paris-Saclay), and Constantinos Daskalakis (MIT).

• Expertise: Bayesian statistics, Machine Learnig, Financial econometrics, Dynamic pricing

Ricardo Silva is an Associate Professor, a Turing Fellow, and a member of the Adjunct Faculty, Gatsby Computational Neuroscience Unit. His main interests are in causal inference, graphical models, and probabilistic machine learning. His research has received funding from organisations such as EPSRC, Innovate UK, the Office of Naval Research, Winton Research and Adobe Research. Ricardo has also served in the senior program committee of several top machine learning conferences, including acting as a Senior Area Chair at the NeurIPS conference and being a Program Chair and Conference Chair for the Uncertainty in Artificial Intelligence conference.

Serge Guillas is a Professor of Statistics and a Turing Fellow. He is PI of the "Uncertainty Quantification of multi-scale and multi-physics computer models: Applications to hazard and climate models" with Oxford, Warwick and Exeter, and of “Uncertainty Quantification for tsunamis” (EPSRC HPC), and “Real-Time Advanced Data assimilation for Digital Simulation of Numerical Twins on HPC” 2019-2020 with Leeds, Cambridge, and RIKEN. He founded UQ interest group at ATI. He holds invited professorships and fellowships in the USA, France, and Japan, elected Vice-Chair of SIAM UQ (2015-2016), the leading UQ society. He is Associate Editor of the International Journal for Uncertainty Quantification. His articles are number 1 and 3 most read in SIAM/ASA Journal on Uncertainty Quantification, the flagship journal in UQ. He is the UK Chair of the Uncertainty Working Group of the COST action “Accelerating global science in tsunami hazard and risk analysis” (2019-23, 25 countries). He is project lead, 2019-22 if “Future Indonesian Tsunamis: Towards End-to-end Risk quantification”, funded by three foundations. He currently supervises 5 PhD students and two postdocs.

Carlo Ciliberto's research interests focus on foundational aspects of machine learning within the framework of statistical learning theory. He is particularly interested in the role of “structure” (being it in the form of prior knowledge or structural constraints) in reducing the sample complexity of learning algorithms with the goal of making them more sustainable both computationally and financially. He investigated these questions within the settings of structured prediction, multi-task and meta-learning, with applications to computer vision, robotics and recommendation systems.

• Expertise: Statistical Learning Theory, Kernel methods, Optimal Transport, Multi-Task and Meta-Learning

Sebastian Riedel is a Professor of NLP and ML at UCL, and a research scientist and manager at Facebook AI Research London. His research focuses on how machines can create, share and leverage knowledge, involving Natural Language Understanding, Knowledge Representation, and Reasoning and Integrity. He is an Allen Distinguished Investigator (an international program by the Paul G. Allen Foundation for high-risk, high-reward ideas, $1M award) and has acquired more than £2M of grant funding. He is a program chair of EMNLP 2017 (a main NLP conferences). He collaborates with the University of Cambridge and Sorbonne Universités, among others.

Victor is an Ellis member, a computer scientist, and a PhD research fellow in machine learning at the UCL Energy Institute. His current research focuses on recognizing patterns from multimodal data and has been applied in several domains and AI real-world problems - affective computing, e-health, Earth sciences, and currently in energy systems. He promotes deep learning and evolutionary computation to steer novel approaches for the benefit of society. His main research interests and expertise are transversal and multidisciplinary. He is a former scientific committee member of ChaLearn Looking At People, where he organised challenges and competitions in machine learning and affective computing. As a member of the ChaLearn organization, he worked in close collaboration with Prof. Isabelle Guyon, the co-inventor of the widely used method Support Vector Machines (SVM). He has been co-organiser of events at ICCV, ICMI, ECCV, ICPR, and CCIA conferences. He has participated in peer-reviewing processes for impact factor journals and conference proceedings in JMLR, IEEE TMultimedia, IEEE TPAMI, IET-CV, TTAC, ECCV, ICCV, CVPR, FG, in areas related to computer vision and machine learning with especial interest in behaviour analysis and social computing applications. He was awarded with the best national B.Sc. thesis in computer science in 2010, whose outcomes were published during the realisation of his interdisciplinary M.Sc. in Artificial Intelligence. He has co-supervised and mentored 5+ students at different universities.

• Department: UCL Energy Institute
• Expertise: Pattern Recognition, Deep Learning, Affective Computing, Behaviour Analysis, Computer Vision, Evolutionary Computation.

Brad Love is Professor of Cognitive and Decision Sciences at UCL and a Fellow at the Alan Turing Institute. He works at the intersection of Neuroscience, Experimental Psychology, and Machine Learning. In the recent past, he focused on using brain measures to select between competing models of cognitive function, and theory-driven analyses of naturalistic behavior in large datasets. Currently, his focus is on making deep learning and other complex models more human-like in terms of aligning with behaviour and brain response, as well as using large language models to accelerate scientific discovery.

• Department: Department of Experimental Psychology
• Expertise: TBA.

Ilija Bogunovic serves as an Assistant Professor in Machine Learning Systems Engineering. His research is centered around algorithmic decision-making within the realms of reliable, aligned, and safe AI. Specifically, he delves into the algorithmic challenges of efficiently making decisions and discovering policies, designs, and solutions that can withstand distributional shifts (sim-to-real gap), corruptions, adversaries, and model misspecifications. His work explores how to harness the potential of foundation models to facilitate robust and aligned decision-making. Additionally, within the context of real-world machine learning, he investigates how reliability and robustness considerations can be integrated into interactive data-driven algorithms, encompassing areas such as RL and RLHF, Bayesian optimization/experiment design (AI for science), and multi-agent learning.

Federica Sarro is a Full Professor of Software Engineering at University College London, where she is the Head of the Software System Engineering group and she has established the SOLAR team within the CREST centre. Professor Sarro has extensive academic and industrial expertise in Software Analytics , Search-Based Software Engineering, and Empirical Software Engineering, with a focus on automated software management, optimisation, testing and repair of functional and non-functional properties of modern software systems, including AI-enabled and mobile systems. On these topics she has published over 100 peer-reviewed scholarly articles, and she has given several invited talks at academic and industrial international events. She has also worked in collaboration with several companies including Bloomberg, Google, Meta, and Microsoft. Professor Sarro has obtained numerous awards and generous funding for her research, including the LERO Partnership Fellowship in 2023 and the IEEE TCSE Rising Star Award in 2021 in recognition of her “excellence in Software Engineering research with scholarly and real-world impact. Professor Sarro is a member of the IEEE TCSE Executive Committee, a member of the ELLIS Society, and an ACM Distinguished Speaker.

Andrew Saxe is a Professorial Research Fellow at the Gatsby Computational Neuroscience Unit and the Sainsbury Wellcome Centre. He serves as the Principal Investigator of the UCL Theory of Learning Lab. His research is concentrated on the theory of deep learning and its applications to phenomena in neuroscience and psychology.

2024   |   Janahan joined the lab as a postdoc. 2024   |   Tim's distributional reinforcement learning paper got accepted into Nature Neuroscience. 2023   |   Seb presented his MEG work at Cosyne. 2023   |   Seb published his preprint on a cognitive map for value-guided choice in vmPFC on Biorxiv . 2023   |   William joined the lab as a research assistant. 2023   |   Carl joined the lab as a PhD student. 2023   |   The lab moved from UCL to Oxford. 2023   |   Nour finished her degree with distinction. 2023   |   Seb finished his PhD. 2023   |   Seb presented his MEG work at FENS.

Decision-making is one of the most important cognitive functions of humans and animals. We make hundreds of decisions every day, from the seemingly simple (e.g., what to eat for lunch) to the more complex (e.g., how to best allocate your time). Most of these decisions will be associated with their own costs and benefits (reward, time, probability, effort, etc) which collectively need evaluation and integration to determine each choice’s overall value. However, that is only one side of the story.

When making decisions, it is beneficial to have a model of the environment within which choices are made. For example, knowing the rules or structure of the environment (e.g., knowing the layout of the London transport system) is useful if the environment changes and our behaviour needs to be adjusted (e.g., when your commuter bus/train line is down) to achieve the desired goal (e.g. getting to work). Understanding the possible rules and transitions is also useful for simulating or predicting the necessary behaviour in new environments with similar structure (e.g., applying rules/concepts from the London Underground when using the Paris Underground).

Thus, arguably the main function of the brain and its many cognitive functions (memory, learning, attention) is to support learning appropriate neural models that underlie optimal behavior.

The goal of our lab is to uncover the different computations and functions of different subregions of prefrontal cortex and medial temporal lobe during learning, planning and decision-making, and how these subregions interact to construct models of our environment.

Our working philosophy is that to understand behavior in both health and disease, we must understand the anatomical networks and neural computations/mechanisms that support behavior. To accomplish this, we use a range of methodological approaches including electrophysiology (single neuron, local field potentials), human neuroimaging (fMRI, MEG), and biophysical and computational modeling such as reinforcement or machine learning. We also test causal links between these brain regions and behavior by using reversible inactivation (pharmacological or stimulation) techniques.

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• Wed 9th Oct 2024, 6:00 pm - 9:00 pm AEDT (Opens in new tab)

Science Colloquium 2024 - Medicine with Unimaginable Promise

Event description.

Our annual Science Colloquium this year be held on Wednesday 9 October at the LTC Function Room (Caulfield Campus). This year’s theme is “Medicine with unimaginable promise".

The ability to regenerate damaged body parts and create self-learning neural networks using living nerve tissue and AI may once have seemed like a distant dream. However, this dream is rapidly becoming a reality as Australian researchers continue to push the boundaries of scientific exploration.

At our 2024 Science Colloquium, we are honoured to welcome two distinguished local researchers, Prof. Edwina McGlinn and Prof. Adeel Razi, who will present their cutting-edge research in regenerative medicine and the development of neural networks formed by living nerve cells and trained by AI.

Join us for a thought-provoking session that will explore the exciting possibilities and significant challenges of a future where medicine and science merge in unprecedented ways. This event will take you to the very edge of scientific discovery, offering a glimpse into what is possible in a world that fully embraces the potential of scientific knowledge.

We invite all those intrigued by these ground breaking fields to attend. The evening will feature a buffet dinner and will run from 6:00pm to 9:00pm at the LTC Caulfield Campus on Wednesday, 9 October.

We look forward to reconnecting with familiar faces, making new acquaintances, and engaging in stimulating discussions throughout the night.

Tickets are $82 per person and each campus are providing 50 free places for the first 50 Caulfield Grammar School students who book.

PROFESSOR EDWINA McGLINN

Edwina completed her PhD in developmental and molecular biology at the Institute for Molecular Bioscience, University of Queensland. This work led her to join the laboratory of Professor Clifford Tabin, Department of Genetics, Harvard Medical School, working to understand how 3-dimensional coordination of limb growth occurs in the developing embryo. Edwina was recruited to Monash University, Melbourne as the first EMBL-Australia Group Leader, based at the Australian Regenerative Medicine Institute (ARMI). Edwina was recently promoted to Professor and serves as the Head of Research Excellence and Mentorship at ARMI. Edwina has a strong interest in the promotion of equity, diversity and inclusion (EDI) in science, establishing the first ARMI EDI Committee, current Chair of the Faculty’s EDI committee, and recently joining Monash’s cross-faculty EDI committee that oversees all EDI initiatives across the University.

Research Vision

Edwina’s lab works to understand how early cells of the body, with their broad-ranging potential, become specified to form complex organ structures. By understanding how the body generates tissues and organs in the first place, we will apply this knowledge to advance cell based regenerative medicine strategies.

PROFESSOR ADEEL RAZI

Adeel Razi is Professor of Computational Neuroscience at the Turner Institute for Brain and Mental Health, School of Psychological Sciences, at the Monash University, Australia where he is the Director of the Computational Neuroscience Laboratory. He is a computational neuroscientist who uses both mathematical and experimental methods for his investigations. He leads a highly cross-disciplinary laboratory doing research combining engineering, physics, and machine-learning approaches to answer questions that are motivated by and grounded in neurobiology. This endeavour will enable him to go beyond the traditional boundaries in order to understand how the brain works. His research has implications for building new neuroscience-inspired artificial intelligence systems, treatment of brain diseases and development of new neuro-technologies. His work has been published in journals such as Neuron, Nature Communications, and Proceedings of the National Academy of Sciences , and has been featured in BBC, CNN, Nature, The Economist, The Guardians, The New Yorker, and The Age . He joined Monash after finishing his postdoctoral studies (2012-2018) at the Wellcome Centre for Human Neuroimaging, UCL, UK.

In 2022 a Melbourne-led team had, for the first time, shown that 800,000 brain cells living in a dish can perform goal-directed tasks – in this case the simple tennis-like computer game, Pong. The results of the study are published in the journal Neuron .

“We have shown we can interact with living biological neurons in such a way that compels them to modify their activity, leading to something that resembles intelligence,” says lead author Dr Brett Kagan.

Tickets for good, not greed Humanitix dedicates 100% of profits from booking fees to charity

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Graduate Certificate in Neural Computation and Engineering

The Graduate Certificate Program in Neural Computation and Engineering provides interdisciplinary training for students engaged in quantitative, mathematical, engineering and computational approaches to problems in neuroscience. The Certificate Program allows enrolled students to receive formal recognition for their work, and facilitates connections within the neural computation and engineering community.

The Certificate program is a non-degree granting program; participation requires that a student be already admitted to the University of Washington, working in the biological, physical, computational, mathematical, chemical, engineering or quantitative social sciences.

Required Courses and Activities

The following are all required to receive the Certificate. Successful completion of the Graduate Certificate Program will require a minimum cumulative GPA of 3.0 for courses required for the Certificate and a cumulative GPA of 3.2 or higher.

1.  At least two quarters of AMATH 500 , a theoretical neuroscience journal club. In at least one quarter you will present a paper.

2.  At least two of the following core courses (graded) :

NEURO 545	Quantitative Methods in Neuroscience
CSE/NEURO 528	Computational Neuroscience
AMATH 534	Dynamics of Neurons and Networks
EE/BIOE 560	Neuroengineering

3.  At least two of the following elective courses, totaling at least 7 additional graded credits:

NEURO 502	Sensory and motor systems	5
NEURO 503	Cognitive and integrative neuroscience	4
NEURO 511a	Artiphysiology	3
EE 596b	Practical Introduction to Neural Networks
AMATH 582	Computational methods for data analysis	5
AMATH 522	Computational modeling of biological systems	5
CSE 546/STAT 535	Machine learning	4
EE 505	Probability and random processes	4
STAT 535	Statistical learning	3
AMATH 533/ CSE 529	Neural control of movement	3
AMATH/CSE 589	Intelligent control through optimization and learning	3
EE 518	Digital signal processing	4
EE 546	Applied neural control	3
ENTRE 579	Health Innovation Practicum	2
EE/BIOE 561	Neural Engineering Tech Studio	4

4.  Capstone project:  As a capstone experience, all students will present a 10-15 minute talk, with additional time for questions, at an  annual research symposium  or equivalent event which will demonstrate mastery of a computational or mathematical approach applied to a problem in neuroscience. This work may align with the student’s core thesis work or may be a side project inspired by coursework, course projects or participation in external summer courses. Students will generally present their capstone presentation between the 3rd and 5th years of graduate school. All enrolled students will be expected to attend this yearly event.

Admission requirements and application instructions

Admission is open to students at any stage in their graduate education who will be able to satisfy the requirements by the time of graduation. You should be enrolled in a relevant degree program and have selected a mentor and project within the broad framework of the program. To apply, do the following three things:

• Email  [email protected]  with the following information: your name, graduate program, year started in program, expected date of graduation, thesis mentor’s name.  Please include a copy of your CV, your unofficial graduate transcript, and a short statement about your research interests.
• Have your research advisor(s) send an email to  [email protected] , cc’ed to you, indicating their awareness and support of your application.

School of Electrical and Computer Engineering

College of engineering, collaborative graduate training in computational neural engineering expands.

Funding for the Georgia Tech and Emory University training program in computational neural engineering was recently renewed and increased by the National Institutes of Health, expanding opportunities for students and scholars.

Since it began in 2019, Georgia Tech and Emory University’s Computational Neural-Engineering Training Program has funded and trained doctoral students at the intersection of neuroscience, engineering, computation, and clinical experience.  

“We saw that there was a new kind of neuroscience that was happening, to both understand the mysteries of the brain and nervous system and to treat related diseases and disorders,” says Garrett Stanley , program co-director, professor, and McCamish Foundation Distinguished Chair in the Walter H. Coulter Department of Biomedical Engineering (BME). “The program was created to fill this gap in training, and to provide a community for like-minded scientists and engineers across these disciplines.” 

Combined with support from Georgia Tech and Emory, that community is set to grow with recently renewed and increased funding from the National Institutes of Health (NIH). 

“We’re excited to expand the number of students funded and continue to grow our programs,” says Lena Ting , program co-director, professor, and McCamish Foundation Distinguished Chair in BME. “With this funding, we’ll continue to attract the best and brightest students.” 

Expanding Access  

Through courses, research, professional development, and community outreach, the two-year program provides unprecedented training and community for doctoral students in BME, electrical and computational engineering, neuroscience, machine learning, and beyond.

“Our program is unique in that it combines computation — both how the brain computes and how we can use computational tools to better understand the brain — and engineering of technologies for interfacing with the brain and nervous system,” says Stanley, who also co-directs the Neural Engineering Center with Ting. 

Students are also exposed to neurology, rehabilitation, and other related fields through clinical course requirements. 

“We teach our students alongside physical therapy and occupational therapy students to solve clinically relevant problems,” explains Ting, who teaches several of the courses. “We think early exposure to such clinical problems can accelerate the translation of basic research to the clinic.”  

Originally slated to last five years, funding for the program comes from the T32 program of institutional training grants by the NIH and the National Institute of Biomedical Imaging and Bioengineering. Michael Borich , associate professor in the Emory University School of Medicine, and Chris Rozell , professor and Julian T. Hightower Chair in Georgia Tech’s School of Electrical and Computer Engineering , also serve as directors of the program. 

“The NIH T32 funding mechanism is great because it enables universities to create training programs that span different traditional disciplines,” says Stanley. Without the need to create entirely new academic units, training programs like these provide funding for students conducting interdisciplinary research. Since the funding isn’t tied to a specific research group, it also gives students the flexibility to rotate through multiple labs to find the best fit. “In other words, it’s a game changer.” 

With NIH funding renewed and expanded by 50%, the program will now have the capacity to fund more trainees. 

“I love to see the program grow so more of our students and faculty can benefit,” said Ting. “Thanks to generous funding from Georgia Tech, we will also be able to support international students now, something we couldn’t do in the past.” 

In addition to support from the NIH, Emory University School of Medicine , and the joint Georgia Tech-Emory BME Department, the program is further bolstered by support from Georgia Tech’s College of Engineering and the Office of the Executive Vice President for Research . 

“While the NIH funding enables us to support the salary and tuition for students,” said Stanley, “local support from Georgia Tech and Emory enables us to not only manage the growing program and provide reporting back to the NIH, but also to provide student-initiated training workshops in emerging technical areas, career development activities, training in neuroethics, and social events that help to bring the community together.” 

The community, he said, is the “most exciting and significant part of this. The network of talented people brought together through this program will be valuable and influential for years to come.” 

Audra Davidson Communications Manager Neuro Next Initiative

[email protected]

Related links

Anapurna Germain is an undergraduate student at North Carolina Agricultural and Technical State University studying Biology and Psychology. This summer, Anapurna worked in Dr. Steve Mahler’s laboratory with her PhD candidate mentor Erica Ramirez to explore how specific neurons in the ventral pallidum area of the brain contribute to social behavior, using advanced tissue imaging techniques.

Meet Isaac Clay

Isaac Clay is an undergraduate student from Xavier University of Louisiana and is a Psychology major, minoring in Sociology. Isaac spent his summer conducting research in the laboratory of Dr. Michael Yassa exploring the connection between estradiol and memory impairment using diffusion tensor imaging (DTI), working with his PhD student mentor, Dana Parker.

Meet Déja Lee

Déja Lee is an undergraduate Biology major studying at the University of California, Irvine. This summer, Déja worked with her MD/PhD candidate mentor Bianca Leonard, in the laboratory of Dr. Michael Yassa. Using data from the Adolescent Brain Cognitive Development Study, Deja’s research explored how early life factors, such as adversity experienced by young Black girls influence their resilience later in life.

Meet Destiny Edens

Destiny Edens is an undergraduate student at North Carolina A&T University, majoring in Psychology. This summer, Destiny conducted research in the laboratory of Dr. Aaron Bornstein with their Postdoctoral Scholar mentor, Dr. Dale Zhou. This summer they explored how the descriptions of clinical diagnosis criteria for Autism and Schizophrenia compare to first-hand accounts and how they evolve over time using computational models.

Meet Destiny Green

Destiny Green is an undergraduate student at Howard University majoring in Biology, with a double minor in Chemistry and Psychology. This summer, Destiny conducted research in the laboratory of Dr. Christie Fowler alongside her PhD Candidate mentor, Malia Bautista. During her time in the Fowler Lab, Destiny explored how chronic mild stress and a protein called Lynx2 affect nicotine use and social behavior in mice.

Meet Jada Mars

Jada Mars is an undergraduate student at North Carolina A&T University studying Biology. Jada spent her summer conducting research in the Laboratory of Dr. Katie Thompson-Peer with her graduate student mentor, Mia Brantley. Using drosophila melanogaster also known as the common fruit fly as a model, Jada explored the role of cell adhesion molecules in dendrite regeneration after laser induced injury.

Meet Naomi Cole

Naomi Cole is an undergraduate student at Howard University, where she is majoring in Biology and minoring in both Psychology and Chemistry. This summer, Naomi worked in Dr. Steve Mahler’s laboratory with her PhD student mentor Kate Lawson to study the relationship between the context in which psychedelics are administered affects brain activity, as measured by cFos expression in various brain regions. This research may shed light on how context influences the effectiveness of psychedelic treatments for psychiatric disorders.

Meet Taryn Mallory

Taryn Mallory is an undergraduate student at North Carolina A&T University, majoring in Psychology and Biology. This summer, Taryn conducted research in the laboratory of Dr. Eitan Schectman with her PhD student mentor, Neda Morakabati. Taryn explored the relationship between mood and sleep by assessing how self-reported mood changes from before to after a nap.

Meet Tyra Dennis

Tyra Dennis is an undergraduate student at North Carolina A&T University in the Child Development and Family Relations program, minoring in psychology . Tyra spent her summer conducting research in the laboratory of Dr. Lisa Flanagan with her MD/PhD student mentor, Laura Tennis. She explored how a sugar molecule called GlcNAc and a protein named Yap influence the development and fate of neural stem and progenitor cells. Her work involved using cell culture, immunohistochemistry, and fluorescent microscopy to explore these relationships.

Meet Nadeja Jackson

Nadeja Jackson is an undergraduate student at Howard University, studying Psychology. This summer, Nadeja conducted research in the laboratory of Dr. Aaron Bornstein with her PhD student mentor, Ari Khoudary. Nadeja explored how past experiences shape present decision-making by using programing and computational modeling to assess how expectations, combined with new evidence, guide the decision-making process.

Meet Lauryn Mitchell

Lauryn Mitchell is an undergraduate student at Hampton University majoring in Communicative Sciences and Disorders and minoring in Psychology. This summer, Lauryn conducted research in the laboratory of Dr. Judith Kroll alongside her graduate student mentor Jasmin Hernandez Santacruz. Using electroencephalogram (EEG), Lauryn explored how the human bilingual brain processes language, and the effect of code switching on accessing dominant languages.

About the Program

The Irvine Summer Institute in Neuroscience Program at the University of California, Irvine Center for the Neurobiology of Learning and Memory is an immersive residential neuroscience research program.  Funded by the National Science Foundation's Research Experiences for Undergraduates program and the University of California Office of the President's UC-HBCU (Historically Black Colleges and Universities) Initiative, the program aims to expose undergraduate students from groups underrepresented in science to neuroscience research.

Supporting Partners

The Irvine Summer Institute in Neuroscience is possible thanks to funding from the National Science Foundation Research Experiences for Undergraduates (NSF-REU), the US Department of Defense Awards to Stimulate and Support Undergraduate Research Experiences (ASSURE),  and the University of California - Historically Black Colleges and Universities (UC-HBCU) Initiative from the UC Office of the President.

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Kavli Institute for Neuroscience: Celebrating 20 Years of Scientific Impact

Kavli 20th anniversary symposium.

I-Uen (Yvonne) Hsu, PhD, Kavli postdoctoral fellow in the lab of Rui Chang, is at the microscope studying the "little heart of the brain."

Kavli 20th anniversary logo designed by Kaleigh Kurpiewski.

The Kavli Institute for Neuroscience at Yale invites all Yale affiliates to celebrate the 20th anniversary of the creation of the Institute on Friday, September 20th, 2024. The Kavli Institute for Neuroscience aims to understand how genes, molecules, cells, and neural circuits interact to give rise to brain development and behavior, and to apply this knowledge to advance treatments for brain disorders.

For its 20th anniversary, the Kavli Institute is hosting a one-day celebratory symposium. In alignment with its mission, the symposium will highlight many subfields of neuroscience, and will feature talks from renowned external speakers, Yale faculty, and Yale trainees. We hope you will celebrate with us during this very special event.

Kavli 20th Anniversary Symposium schedule:

8:40 AM- Introduction Stephen Strittmatter, MD, PhD 9:00 AM - Molecular & Cellular Neuroscience Session chair: Shawn Ferguson, PhD Speakers: Kang Shen, PhD - Pietro De Camilli, MD - Maryann Platt, PhD 10:10 AM - Systems Neuroscience and Behavior Session chair: Liang Liang, PhD Speakers: Randy Bruno, PhD - Marina Picciotto, PhD - Lisa Baik, PhD 11:30 AM - Computational Neuroscience Session chair: Anirvan Nandy, PhD Speakers: Ken Miller, PhD - Smita Krishnaswamy, PhD - Erica Busch 12:45 PM - Poster session 1 + lunch 2:00 PM - Disease Session chair: Ellen Hoffman, MD, PhD Speakers: Michel Goedert, MD, PhD - Sreeganga Chandra, PhD - Amanda Bentley-DeSousa, PhD 3:10 PM - Development Session chair: Emilia Favuzzi, PhD Speakers: Oscar Marín, PhD - Nenad Sestan, MD, PhD - Soraya Scuderi, PhD 4:20 PM - Poster Session 2 + Reception

This event is open to all Yale affiliates on a first come first serve basis. There is no sign up required. The TAC auditorium can accommodate 150 people.

Featured in this article

• Pietro De Camilli, MD John Klingenstein Professor of Neuroscience and Professor of Cell Biology; Investigator, Howard Hughes Medical Institute; Director, Program in Cellular Neuroscience, Neurodegeneration and Repair
• Sreeganga Chandra, PhD Associate Professor of Neurology and Neuroscience
• Smita Krishnaswamy, PhD Associate Professor of Genetics and of Computer Science
• Marina Picciotto, PhD Charles B. G. Murphy Professor of Psychiatry and Professor in the Child Study Center, of Neuroscience and of Pharmacology; Director Division of Molecular Psychiatry, Psychiatry; Deputy Chair for Basic Science Research, Dept. of Psychiatry; Director, Interdepartmental Neuroscience Program
• Nenad Sestan, MD, PhD Harvey and Kate Cushing Professor of Neuroscience, and Professor of Comparative Medicine, of Genetics and of Psychiatry; Executive Director, Genome Editing Center
• Maryann Platt Postdoctoral Associate
• Lisa Baik, PhD Postdoctoral Associate, Molecular, Cellular and Developmental Biology
• Amanda Bentley-DeSousa Postdoctoral Associate in Cell Biology
• Soraya Scuderi, PhD Associate Research Scientist - Child Study Center

Kavli 20th Anniversary Symposium

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Max Planck UCL Centre renewed for another five years

29 August 2024

The Max Planck UCL Centre for Computational Psychiatry and Ageing Research has been renewed for another five years, allowing its frontier research on psychiatric disorders and cognitive development to continue at UCL until 2030.

The decision to renew came in July 2024 and was made by the Max Planck Society (MPS) President, Professor Patrick Cramer, after a peer review. The new term of the facility based at Russell Square in Bloomsbury and its partner, the Max Planck Institute for Human Development in Berlin, will run from January 2026 to December 2030.

The Max Planck Society operates over 80 research institutes, mainly in Europe and the US, with most Centres typically undergoing two funding cycles, the initial grant and a renewal. The UCL Centre's upcoming second renewal (third five-year funding period) is considered exceptional.

First established in April 2014, the Max Planck UCL Centre for Computational Psychiatry and Ageing Research is a product of the fruitful partnership between the Max Planck Society and UCL that began in 2011. The Centre continues to drive innovation and discovery in critical fields such as psychiatry, neuroscience and ageing.

Headed by Professor Ray Dolan (UCL Queen Square Institute of Neurology) and Professor Ulman Lindenberger (Max Planck Institute for Human Development), the Centre is dedicated to studying the causes of psychiatric disorders as well as the causes of individual differences in cognitive development, with an emphasis on adulthood and old age.

Computational approaches aim to bridge the gap between neuroscience and the phenomena seen in psychiatric disorders by developing models of how the brain works. The models are then linked to behavioural measurements and brain function changes, offering insights into maintaining cognitive function in old age and improving the recognition and treatment of psychiatric disorders.

Co-Director of the Centre, Professor Ray Dolan, said: “We are delighted to receive this endorsement from the Max Planck Society. We look forward to furthering advances in computational psychiatry and contributing to ever closer ties between UCL and the MPS.”

Professor Geraint Rees, UCL Vice-Provost (Research, Innovation and Global Engagement) said: "We at UCL are immensely proud of our longstanding collaboration with the Max Planck Society. The prospect of another five years of pioneering research and innovation from the Centre fills me with great excitement.

“This renewal is a powerful endorsement of the exceptional work being done here in the field of computational psychiatry, and I am eager to see our partnership with MPS flourish well into the future."

Running alongside the Max Planck UCL Centre is  the International Max Planck Research School on Computational Methods in Psychiatry and Ageing Research (IMPRS COMP2PSYCH) , which currently has funding secured until 2028. IMPRS COMP2PSYCH teaches and trains concepts and methods from computer science and statistics in relation to substantive research questions in psychiatry and lifespan psychology.

And in May 2024, UCL launched a new  Research Visiting Fellowship Scheme  with the Max Planck Law (MPL) network. The scheme is another key milestone in the longstanding relationship between UCL and the Max Planck Society and will enable up to two UCL Laws academics to undertake research visits of one to three months at any of the nine MPL institutes.

• The Max Planck UCL Centre for Computational Psychiatry and Ageing Research
• Professor Ray Dolan's academic profile
• UCL Queen Square Institute of Neurology
• UCL Brain Sciences
• The Max Planck Society
• Brain areas involved in decision acuity identified in a study involving researchers from the Max Planck UCL Centre in 2021

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