Find Academic Supervisors

• Mathematics of Data
• Numerical Linear Algebra
• Randomized Algorithms
• +5 more

Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).

Mikaela IacobelliView profile

Associate Professor

• Analysis
• Kinetic theory
• Statistical mechanics
• +7 more

Mikaela Iacobelli is an Associate Professor in the Department of Mathematics at ETH Zürich. During the 2024-25 academic year, she was a von Neumann Fellow at the Institute for Advanced Study in Princeton. Previously, she held faculty positions at Durham University and a research fellowship at the University of Cambridge. Her educational background includes: PhD in Mathematics from Sapienza University of Rome and École Polytechnique in Paris (2015) Master's degree from Sapienza University of Rome (2012) Bachelor's degree from Sapienza University of Rome (2009) Mikaela's research lies at the interface of analysis, kinetic theory, and statistical mechanics. She studies partial differential equations that model the collective behavior of many-particle systems, with a focus on Vlasov-type plasmas and gravitational dynamics. Her current projects range from quasineutral and singular-limit problems for Vlasov-type systems to quantization of measures, ultrafast diffusion, and gradient-flow structures that link microscopic particle models to macroscopic fluid descriptions. She makes extensive use of PDEs techniques, optimal transport, probability, calculus of variations, and Riemannian geometry in her work. Her recent publications demonstrate a strong focus on Vlasov-type equations, particularly examining quasineutral limits, stability properties, and connections to other physical systems like Euler equations and magnetohydrodynamics. She has made significant contributions to understanding Landau damping, quantization problems on manifolds, and the mathematical foundations of plasma physics. Her notable scientific awards include: SNSF Starting Grant (Swiss ERC) Challenges and Breakthroughs in the Mathematics of Plasmas (2025-2030) von Neumann Fellow at the Institute for Advanced Study, Princeton (2024-2025) Invited speaker at the International Congress of Mathematical Physics (2021) CO-PI of the Germaine de Staël Funding Program for French-Swiss cooperation (2021-2023) L'Oréal prize for Women in Science (2015) Mikaela actively mentors postdocs, PhD, Master's, and Bachelor's students. Her current mentees include postdocs Dennis Chemnitz, Rishabh Gvalani, Stefano Rossi, and Simon Becker, as well as PhD students Thérèse Moerschell, Ata Deniz Aydin, and Antoine Gagnebin. She has served as PI for the Starting Research Grant from the University of Rome Sapienza and is currently the PI for the SNSF Starting Grant. She co-organizes several academic seminars including the Zurich Colloquium in Mathematics, the PDE and Mathematical Physics seminar at ETH Zürich and UZH, and the Analysis Seminar. She also serves on various committees including as Chair of the European Mathematical Society Committee for Women in Mathematics.

Giancarlo Ferrari TrecateView profile

Adjunct Professor

• Automatic control
• State estimation
• System identification
• +7 more

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

Zsolt PatakfalviView profile

Associate Professor

• Algebraic Geometry
• Commutative Algebra
• Birational Geometry
• +3 more

Zsolt Patakfalvi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB) within the Department of Mathematics (MATH). He is affiliated with the Chair of Algebraic Geometry (CAG) and the Section of Mathematics for Engineers (SMA-ENS). Additionally, he serves as Director of SMA-GE and holds roles in academic governance bodies like the Conference of Section Directors (CDS) and SB Faculty Management. His research focuses on Algebraic Geometry, particularly in birational geometry, positive characteristic methods, moduli theory, and mixed characteristic algebra. He explores topics such as Hodge theory, singularities, and applications to arithmetic geometry. Notable contributions include work on the minimal model program, test ideals, and counterexamples to classical conjectures in positive characteristics. He supervises doctoral students in areas like algebraic geometry and commutative algebra, including Jefferson Baudin, Léo Navarro Chafloque, and Linus Rösler. His past advisees include Emelie Arvidsson and Quentin Posva. Patakfalvi’s publications frequently address foundational questions in geometry, with recent work extending into perfectoid spaces and globally-regular varieties. He coordinates courses such as 'Algebra III - Rings and Fields' and 'Perfectoid spaces' at EPFL, reflecting his commitment to both research and education. His academic service includes managing educational programs within SB-SMA and contributing to institutional decision-making through CDS membership.

Chen LiuView profile

Assistant Professor

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +9 more

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

Benny SudakovView profile

Professor

• Extremal Graph and Hypergraph Theory
• Ramsey Theory
• Random Structures
• +5 more

Benny Sudakov is a Professor of Mathematics at ETH Zurich, where he conducts research in combinatorics. He has previously held positions at UCLA, Princeton University, and the Institute for Advanced Study. His work is supported by the SNSF grant 200021_196965. Research Interests: His primary research areas include Extremal Graph and Hypergraph Theory, Ramsey Theory, Random Structures, and the application of Algebraic and Probabilistic Methods in Combinatorics, with strong connections to Theoretical Computer Science. He investigates fundamental structural properties of discrete systems, such as the existence of regular subgraphs, extremal configurations, and the behavior of random combinatorial objects. The recent popular science articles on his work highlight a consistent trend of solving long-standing open problems in extremal combinatorics using sophisticated probabilistic and algebraic techniques. His research spans topics like equiangular lines, graph decompositions, and the emergence of cycles in sparse graphs, demonstrating a deep focus on the interplay between structure and randomness. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: He has advised numerous Ph.D. students, many of whom have gone on to become professors at top universities (e.g., Oxford, Stanford, CMU, ETH, Princeton). His research is currently funded by the Swiss National Science Foundation (SNSF). He has organized workshops and seminars, such as the Theory of Combinatorial Algorithms Mittagsseminar at ETH and a workshop at UCLA on Extremal and Probabilistic Combinatorics. Labs and Teams: He is a key member of the combinatorics group at ETH Zurich and co-organizes the Theory of Combinatorial Algorithms Mittagsseminar, a central forum for research discussions in discrete mathematics at the institution.

• Causal Machine Learning
• Shrinkage Methods
• Regularization Methods
• +3 more

Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.

Grigory MikhalkinView profile

Professor

• Geometry
• Topology
• Tropical Geometry
• +2 more

Grigory Mikhalkin is a Full Professor at the University of Geneva, where he has been a faculty member since 2008. He is considered one of the founders of Tropical Geometry, a domain of algebraic geometry governed by (max,+)-calculus where geometric objects degenerate to their piecewise-linear limits. He leads the "ALGEBRA AND GEOMETRY" research group at the university. Mikhalkin studied at Leningrad and Michigan State University under the supervision of Oleg Viro and Selman Akbulut. After receiving his PhD in 1993, he completed postdoctoral training at Princeton, Bonn, Toronto, Berkeley, and Harvard (1993-2000). He served as associate and then full Professor at the University of Utah before moving to the University of Toronto, eventually joining the University of Geneva in 2008. Mikhalkin's primary research areas are Geometry and Topology, with a particular focus on Tropical Geometry. His work bridges algebraic geometry with combinatorial structures, exploring how complex geometric objects can be understood through their piecewise-linear tropical counterparts. This approach has proven fruitful in solving problems in enumerative geometry and has connections to mathematical physics through the study of sandpile models and self-organized criticality. His research group actively explores the connections between tropical geometry, symplectic geometry, and real algebraic geometry, organizing regular seminars including the "Séminaire Fables Géométriques." The recent publications of Professor Mikhalkin demonstrate a strong focus on the intersection of tropical geometry with sandpile models and self-organized criticality. His work has evolved to examine tropical aspects of number theory, lattice sums, and even applications to economics through auction theory. A significant portion of his recent research explores the patterns and structures that emerge in sandpile models across various lattices and dimensions, connecting discrete mathematics with continuum limits through tropical techniques. Prize of the St. Petersburg Mathematical Society (1999) Silver Medal of the Mexican Mathematical Society (2011) Canada Research Chair (2004-2009) Friedrich-Wilhelm-Bessel Research Award of the Alexander-von-Humboldt Foundation (2007-2008) European Research Council Advanced Grant (2010-2015) Chair of Fondation Sciences Mathématiques de Paris (2013-2015) Mikhalkin has successfully advised several PhD students to completion, including Kristin Shaw (2011), Lionel Lang (2014), Nikita Kalinin (2015), Mikhail Shkolnikov (2017), and Johannes Josi (2018). His research has been supported by prestigious grants including the ERC Advanced Grant and the Canada Research Chair. He presented his work at the Bourbaki seminar in 2003 and was selected as a Geometry speaker at the International Congress of Mathematicians in 2006, highlighting the significance of his contributions to the field. Professor Mikhalkin leads the "ALGEBRA AND GEOMETRY" research group at the University of Geneva, which includes current members Thomas Blomme, Francesca Carocci, Aloïs Demory, Gurvan Mével, and Antoine Toussaint. The group has a strong track record of postdoctoral fellows and alumni, including notable researchers such as Ivan Bazhov, Johan Bjorklund, Rémi Crétois, and others. They organize several seminars including the "Séminaire Fables Géométriques" and have historical connections to the Battelle Seminar and Tropical working group Seminar.

Fabio NobileView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Stefania Lottanti von MandachView profile

Senior Lecturer

• Japanese Economic History
• Corporate Governance
• Labor Market Economics
• +3 more

Stefania Lottanti von Mandach is a Senior Lecturer at the University of Zurich , affiliated with the Faculty of Arts and the Department of Business Administration . She has published extensively on Japanese economic history, corporate governance, labor market dynamics, and private equity impacts. Education: Studied Japanese Studies and Business Economics at the University of Zurich (1992–1999), with studies at Kyoto University of Foreign Studies (1996). Professional Experience: Held roles at Golay Buchel Japan KK (1997), Swiss Asia Foundation (1998–1999), Abegglen Management Partners AG (2000–2004), Adveq Management AG (2006–2009), and joined the University of Zurich in 2005, becoming Senior Lecturer in 2011. Her research integrates Japanese economic history with contemporary issues like labor market trends , private equity acquisitions , and retail system transformations . Recent articles analyze telework productivity during the pandemic and stereotype persistence in economic models. Her publications since 2008 span topics including employment growth post-private equity acquisitions, Japanese distribution systems, and family firm succession practices. Collaborative works with Georg D. Blind and others appear in journals such as Social Science Japan Journal and Journal of Economic Interaction and Coordination . She contributes to public economic discourse through Swissinfo.ch and Diamond publications, challenging narratives on Japan's 'lost decades' and analyzing startup culture and executive compensation trends.

Vovka AshkenazyView profile

Lecturer

• Classical Piano Performance
• Piano Pedagogy
• Chamber Music
• +5 more

Vovka Ashkenazy is a distinguished classical pianist and lecturer, currently teaching at Kalaidos Musikhochschule (Zurich), the Conservatory of Italian Switzerland (CSI) in Lugano, the Accademia di Imola Foundation, and the Tiziano Rossetti Academy. He holds a Master of Arts from Belford University and advanced pedagogical certification from France. His academic roles are centered on classical piano performance and interpretation. Education: Master of Arts, Summa Cum Laude, Belford University (2008) Certificat d'Aptitude - Professor d'Enseignement Artistique, France (2002) GRNCM Upper Division, Royal Northern College of Music (BA, 1983) Vovka Ashkenazy's research and artistic interests focus on classical and romantic piano repertoire, with a special emphasis on Russian music, two-piano performance, and chamber music. His pedagogical philosophy emphasizes musical understanding before technical execution, advocating for clarity of sound and emotional expression. He has made significant contributions through performances and arrangements of works by Rachmaninoff, Borodin, Mussorgsky, and Scriabin. His recent artistic output, spanning from 2009 to 2014, reveals a strong focus on collaborative piano performance, particularly in two-piano and piano-and-winds genres. He frequently performs with his father, Vladimir Ashkenazy, and his brother, Dimitri Ashkenazy, exploring Russian romanticism, orchestral transcriptions, and chamber repertoire. His work bridges performance and education, with recordings on DECCA, Chandos, and Cryston labels reflecting both technical mastery and interpretive depth. Scientific and Artistic Awards: Honorary Artistic Adviser, Guangzhou Opera House (2010) Artistic Director, Rina Sala Gallo International Piano Competition, Monza, Italy (2014) Vovka Ashkenazy is actively engaged in mentoring through masterclasses across Asia, Europe, and the Americas. While no formal grant information is available, his leadership roles in international competitions and recordings indicate sustained artistic funding and institutional support. He has contributed to charitable causes such as Action for Children, Cystic Fibrosis Trust, and the Bridges Peace Foundation. He runs a private teaching studio in Pura, Switzerland, offering structured and intensive piano study programs. Laboratories and Performance Ensembles: Private Piano Studio at Casa Camelia, Pura, Switzerland Collaborative Duo with Vladimir Ashkenazy (father) Chamber Ensemble with Dimitri Ashkenazy (brother) Reykjavík Wind Quintet (long-term collaborator) Regular performer at international festivals (Marlboro, Edinburgh, Spoleto)

• Tax Law
• International Tax Law
• Corporate Tax Law
• +7 more

Stephanie Andrea Brawand is a Lecturer at Kalaidos University of Applied Sciences, teaching in the School of Law, specifically in postgraduate programs such as the CAS FH in General Taxation and National Individual Taxation. She also serves as a Judge at the Cantonal Court of Valais, Tax Law Division, reflecting her deep expertise in tax law. Lic. iur., LL.M. International Corporate Taxation Active lecturer at the master’s semester level Owner & Managing Director, Brawand Tax Advisory GmbH (since 2022) Her research focuses on international and corporate tax law, with particular emphasis on cross-border spousal taxation, place of effective management, and German-Swiss tax structures. She frequently publishes in leading Swiss tax journals and contributes to legal commentaries. The recent publications reflect a consistent trend in international tax law, especially on residency, cross-border entities, and digitalization impacts on tax administration. Her work bridges academic rigor and practical jurisprudence, often analyzing case law and legislative frameworks. Scientific Contributions: Co-author of multiple articles in Zsis, ASA, and StHG Commentary Regular speaker and lecturer in continuing legal education While no formal PhD students are listed, her role as a lecturer and judge involves mentoring legal professionals and contributing to academic discourse. She has not received any explicitly mentioned scientific awards. She is actively involved in her own tax advisory firm, which likely informs her applied research and teaching. She is a key figure in Swiss tax law education and practice, integrating judicial experience with academic instruction, particularly in the evolving landscape of digital work and international taxation.

Pascal Mark GygaxView profile

Researcher

• Psycholinguistics
• Gender Studies
• Experimental Linguistics
• +4 more

Pascal Mark Gygax is a faculty member at the University of Fribourg , specifically within the Faculty of Philosophy under the Department of Psychology . His academic career spans psycholinguistics, cognitive psychology, and gender studies, focusing extensively on how language structures influence gender perceptions and cognitive biases. With a research profile marked by experimental methodologies, Gygax has published 64 works, including a 2021 book Does the Brain Think Masculine? that critically examines the psycholinguistic impacts of gender-fair language reforms. Role: Researcher at Department of Psychology, University of Fribourg Key Collaborations: Sandrine Zufferey, Ute Gabriel, Anton Öttl Research Focus: Gygax investigates the cognitive mechanisms behind gender representation in language, particularly how grammatical gender systems (e.g., French, German) perpetuate male-dominated mental models. His work explores: Experimental validation of gender-neutral pronouns Discourse connective processing across age groups Impact of language reform on occupational stereotypes Cross-linguistic comparisons of gender perception Implicit bias in performance evaluation (e.g., magic tricks) Recent Publications (2023-2025) demonstrate methodological rigor in studying: Neological intuition in Romance languages Temporal focus and spatial cognition Connective mastery in L2 contexts Participant variability in experimental linguistics Gender ratio norms across global languages Pragmatic constraints on causal connectives Scientific Contributions: While no explicit awards are listed, Gygax's work has significantly advanced: Development of experimental paradigms for gender perception studies Creation of multilingual datasets on role noun norms Validation of facial feedback mechanisms through collaborative research Understanding of discourse coherence in educational contexts Advising & Collaborations: Though specific students aren't named, Gygax collaborates with: Development of the Routledge Handbook of Experimental Linguistics Leadership in SNSF-funded projects Contributions to debates on inclusive language policies

• Deep Learning
• Distributed Machine Learning
• Federated Learning
• +7 more

Tao Lin is a Tenure-Track Assistant Professor and Principal Investigator of LINs Lab at Westlake University, School of Engineering. He leads cutting-edge research in deep learning optimization, generalization, and robustness, particularly in distributed and federated settings. Prior to this, he was a Ph.D. student at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, under the supervision of Prof. Martin Jaggi and Prof. Babak Falsafi. Doctor of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2017–2022) Master of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2014–2017) Bachelor of Engineering (with honors), College of Electrical Engineering, Zhejiang University, China (2010–2014) His research focuses on the intersection of optimization and generalization in deep learning, leveraging theoretical and empirical insights into loss landscapes and training dynamics to design efficient and robust learning and inference methods. This includes work on decentralized and federated learning under noisy, heterogeneous, and hardware-constrained environments. His work spans algorithmic innovation, theoretical analysis, and practical system integration. The recent publications from his lab demonstrate a strong trend in advancing federated learning, efficient inference for large language models, multimodal foundation models in pathology, and robust training under distribution shifts. Key themes include communication efficiency, model personalization, gradient tracking, and hardware-aware learning. His group has published at top venues including NeurIPS, ICML, ICLR, CVPR, and ECCV, with several papers receiving oral or spotlight presentations. ECCV Best Paper Candidate, 2024 Top 2% Scientists Worldwide 2024 (Stanford University) Doctoral Program Thesis Distinction Award, EPFL, 2022 Outstanding Performance Bonus, EPFL, 2021–2022 Top Reviewer: NeurIPS, ICML, AISTATS He advises multiple Ph.D. and master’s students, including Yongxin Guo, Futing Wang, Peng Sun, and Yuxuan Sun, whose work has been accepted at premier conferences. He has secured competitive grants as PI and participant, including the National Natural Science Foundation of China for Excellent Young Scientists Fund (Overseas) and the Science and Technology Innovation 2030 – Major Project. He also contributes to the community through service as an area chair (NeurIPS, ICML), reviewer for top journals and conferences, and organizer of workshops and academic events. His open-source contributions, such as Post-local SGD, have been integrated into PyTorch. Tao Lin teaches graduate courses such as Research Methodology of Computer Science and Technology and Deep Learning at Westlake University. He is actively involved in academic governance, serving on committees for student seminars, academic exchange, doctoral studies, and teaching leadership. The LINs Lab runs a regular research seminar on Deep Learning and Optimization, fostering a collaborative and dynamic research environment.