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Nicholas EvansView profile

Professor

• Endangered Languages
• Linguistic Typology
• Historical and Contact Linguistics
• +5 more

Nicholas Evans is Distinguished Professor of Linguistics and Director of the ARC Centre of Excellence for the Dynamics of Language (CoEDL) at the Australian National University’s School of Culture, History & Language. His work bridges fieldwork-based language documentation with theoretical questions in typology, cultural evolution, and social cognition. Focus on endangered Australian and Papuan languages Director of ARC Laureate Project on 'The Wellsprings of Linguistic Diversity' Co-leader of SCOPIC (Social Cognition Parallax Corpus) study Collaborator in global linguistic diversity initiatives His research explores how micro-level community multilingualism shapes macro-level linguistic diversity, with fieldwork spanning seven years in remote Indigenous communities. Recent projects include PARABANK (paradigm syncretism analysis) and Southern New Guinea language studies, particularly Nen and Yam family languages. Scientific recognition includes the Ken Hale Award (Linguistic Society of America), Anneliese Maier Forschungspreis, and fellowships in the Australian Academy of Humanities, Australian Social Sciences Academy, and the British Academy.

Kevin JamiesonView profile

Associate Professor

• Machine Learning
• Active Learning
• Multi-Armed Bandits
• +4 more

Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .

Konrad SchindlerView profile

Professor

• Photogrammetry
• Remote Sensing
• Computer Vision
• +3 more

Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.

Polina GollandView profile

Professor

• Biomedical Image Analysis
• Medical Vision
• AI & Machine Learning
• +8 more

Polina Golland is a Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and a Principal Investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on developing novel techniques for biomedical image analysis and understanding, particularly in medical vision, AI/ML, and health care applications. She leads the Medical Vision Group and collaborates with the Vision Group at CSAIL. Her work emphasizes statistical modeling of medical images, shape modeling, and predictive analytics for biological processes. Current projects include fetal MRI analysis, cardiac MRI segmentation, and quantitative assessment of pulmonary edema in chest X-rays. She has secured grants from NIH, MIT-IBM Watson AI Lab, and other institutions to support her research. Dr. Golland teaches courses on inference, probability, and probabilistic systems. She advises graduate students in MIT's EECS program and has mentored numerous postdocs and researchers. Her lab focuses on translating advanced imaging techniques into clinical workflows, with applications in neuroimaging, fetal health monitoring, and cardiovascular disease analysis. Notable collaborations include work with Harvard Medical School affiliates, Brigham and Women's Hospital, and the MIT Jameel Clinic. Her research aims to bridge computational methods with clinical needs, improving diagnostic tools and treatment planning through machine learning and medical imaging innovation.

Guillermo AmadorView profile

Assistant Professor

• Biophysics
• Biomaterials
• Biomechanics
• +7 more

Dr. Guillermo Amador is an Assistant Professor in the Experimental Zoology department at Wageningen University & Research. His research focuses on understanding how animals interact with complex environments through locomotion, adhesion, and fluid dynamics. He investigates biological systems like insects, plants, and marine organisms to inspire bio-engineered solutions for robotics, microfluidics, and material science. Amador received his PhD in Mechanical Engineering from Georgia Institute of Technology (USA), followed by postdoctoral research at the Max Planck Institute for Intelligent Systems (Germany) and a Marie Sklodowska-Curie fellowship at TU Delft (Netherlands). His expertise spans biophysics, biomaterials, and biomechanics, with a focus on self-cleaning mechanisms and bioadhesion. He collaborates with the 4TU consortium on Dutch Soft Robotics to develop bio-inspired designs. His work bridges fundamental biology with engineering applications, emphasizing interdisciplinary approaches to solve challenges in robotics and environmental science. Amador teaches courses including Biomimetics and Functional Zoology , integrating his research into education. His research highlights include studies on cuttlefish suction cups, stick insect adhesion, and pollen transport mechanisms in pollinators.

Andrew StuartView profile

Professor

• Applied and Computational Mathematics
• Bayesian Inverse Problems
• Data Assimilation
• +6 more

Andrew Stuart is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), joining in 2016. He previously held faculty positions at the University of Warwick (1999–2016), Stanford University (1992–1999), and Bath University (1989–1992). He earned his PhD from the University of Oxford's Computing Laboratory in 1986. Professor Stuart's research focuses on applied and computational mathematics , particularly Bayesian inverse problems , data assimilation for dynamical systems , and stochastic modeling . His work bridges mathematical theory, algorithm development, and applications in geophysics, materials science, and biological systems. His recent publications emphasize operator learning , machine learning for PDEs , and uncertainty quantification . Key areas include ensemble Kalman methods , Gaussian processes , and neural operators for solving and learning from complex systems. Scientific awards include the Vannevar Bush Faculty Fellowship and election to the Royal Society of Great Britain . He advises graduate students in applied mathematics, computational science, and geophysics, including Edoardo Calvello , Hojjat Kaveh , and Florian Wolf .

Arman CohanView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Arman Cohan is an Assistant Professor in the Department of Computer Science at Yale University, where he leads the Yale NLP Lab since its founding in January 2023. His research spans natural language processing and machine learning with emphasis on language modeling, representation learning, retrieval systems, and specialized domain applications including scientific discovery and AI for science. His primary research interests include: Natural Language Processing Machine Learning Large Language Models Information Retrieval AI for Science Scientific Problem-Solving Recent publications (2025) demonstrate intense focus on evaluating and advancing LLM capabilities across multimodal reasoning, scientific claim verification, financial domain applications, and biological modeling. The lab consistently produces high-impact work accepted at top-tier conferences including ACL, EMNLP, and ICLR, with 11 papers at ACL 2025 alone. Scientific awards include: Best Paper Award at AI4Research Workshop (IJCAI 2024) Outstanding Paper Award at EACL 2023 Best Paper Award at ACL 2024 for Olmo language model research Professor Cohan actively advises PhD students including Kaili Liu, Jacob Dunefsky, Alan Li, Yilun Zhao, and has graduated researchers such as Linyong Nan (now at Zoom) and Ansong Ni (now at Meta). The lab maintains strong industry partnerships and receives substantial research funding as evidenced by its prolific output and conference presence. The Yale NLP Lab hosts the annual New England NLP Workshop and regularly features speakers from leading institutions including Meta AI, Allen Institute for AI, and DeepMind, fostering a collaborative environment for advancing NLP research.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Arti SinghView profile

Assistant Professor

• Plant breeding
• Soybean diseases
• Genomics
• +3 more

Arti Singh is an Assistant Professor in the Department of Agronomy at Iowa State University. Her research focuses on plant breeding, soybean diseases, genomics, and phenomics, with a strong emphasis on integrating artificial intelligence and high-throughput technologies into agricultural systems. She leads projects involving AI-driven disease identification, precision agriculture, and crop improvement strategies. Her expertise includes developing machine learning models for real-time weed and insect classification (e.g., WeedNet and InsectNet), deploying drones and ground robots for crop phenotyping, and leveraging genomic data to map traits like flowering time and disease resistance in legumes. Singh collaborates on initiatives like the AIIRA Institute for Resilient Agriculture and the BioTrove biodiversity dataset. Singh’s work spans plant stress phenotyping, digital twin technologies for plant sciences, and multi-sensor phenotyping for early disease detection. Her research bridges computational methods with traditional agronomy, aiming to enhance crop resilience and sustainability in the face of environmental challenges. Her recent projects include optimizing robotic navigation for precision agriculture, improving soybean yield estimation via video analysis, and dissecting genetic architectures of traits in mungbean and soybean using GWAS and genomic tools. She actively contributes to conferences and publishes in high-impact journals, advancing both foundational and applied aspects of agricultural science.

Tommi JaakkolaView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +7 more

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

• Bioinformatics
• Machine Learning
• Deep Learning
• +5 more

Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

Romain LopezView profile

Assistant Professor

• Single-cell omics
• Spatial transcriptomics
• Deep generative models
• +5 more

Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University, with a joint appointment in the Courant Institute of Mathematical Sciences and the Department of Biology. He will be joining NYU in September 2025, bringing expertise at the intersection of machine learning and computational biology. Prior to joining NYU, he was a Postdoctoral Fellow at Genentech and Stanford Medicine from 2021 to 2025, working with Jonathan Pritchard and Aviv Regev. Dr. Lopez received his educational training at prestigious institutions: PhD in Computer Science (2021) from the University of California, Berkeley, advised by Mike Jordan and Nir Yosef M.S. in Applied Mathematics (2016) from École polytechnique, Palaiseau, France Dr. Lopez's research focuses on developing machine learning methods to understand biological systems at the cellular level. His work bridges computational techniques with biological applications, particularly in single-cell and spatial omics analysis. He pioneered probabilistic approaches for single-cell analysis with scVI and co-developed scvi-tools, now widely adopted tools in the field. His research spans deep generative models, causal inference, perturbation modeling, and representation learning for biological data. His publication record demonstrates a consistent trajectory of innovation in computational biology, with recent work focusing on spatial biology, disentangled representations of cellular perturbations, and causal modeling of cellular responses. He has made significant contributions to the field of single-cell analysis, developing methods that help scientists interpret complex cellular data and predict how cells respond to various perturbations. Dr. Lopez has received numerous honors and awards for his research: Best Paper Award from the ICML Workshop on AI for Science (2024) Best Paper Award Honorable Mention from the AAAI Conference on Artificial Intelligence (2021) Best Student Poster Award from the ICML Workshop on Computational Biology (2019) UC Berkeley EECS Departmental Graduate Fellowship (2016) Carnot Foundation Fellowship (2016) Monahan Foundation Fellowship (2016) French National Defence Medal, Bronze Echelon (2014) At NYU, Dr. Lopez will lead the Biological Machine Learning group, which develops probabilistic machine learning methods to uncover biological mechanisms governing cellular behavior and disease. His lab focuses on creating tools that transform complex cellular data into biological insights, with applications in understanding cancer, immune responses, and fundamental cellular processes. His work has significant implications for precision medicine and drug discovery.

Francesca StradoliniView profile

Lecturer

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +3 more

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Shreyas SenView profile

Associate Professor

• VLSI and Circuit Design
• Human Body Communication (HBC)
• Hardware Security
• +5 more

Shreyas Sen is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, jointly affiliated with the School of Biomedical Engineering. He directs the Center for Internet of Bodies (C-IoB) and leads the SPARC Lab, focusing on circuits/systems for secure, energy-efficient body-centric communication and biomedical applications. Education: B.E. (Jadavpur University, 2006), M.S. & Ph.D. (Georgia Tech, 2009-2011). His research spans Human Body Communication (HBC), hardware security, IoT/IoB systems, and low-power sensing. Notable innovations include Electro-Quasistatic HBC (EQS-HBC) technology, which enables covert body-wire communication with 100x lower energy than conventional methods. Research Themes: Secure communication for medical wearables Physical layer security in IoT Ultra-low-power biosensors Side-channel attack mitigation in cryptographic ICs Publications: Over 200 papers in top venues (ISSCC, DAC, CICC, HOST) with focus on HBC systems, electromagnetic security, and biomedical circuits. Recent work explores EQS-HBC privacy properties (Nature Scientific Reports, 2019) and 350x current-domain signature attenuation for AES security (ISSCC 2020). Awards: NSF CAREER Award (2020) AFOSR Young Investigator (2016) MIT TR35 India (2018) 9 Best Paper Awards Intel Outstanding Researcher (2020) He advises over 20 students and leads industry collaborations through Ixana Inc. (founded 2020), commercializing EQS-HBC technology. Current efforts include EQS-based authentication systems and radiation dosimetry wearables.

• Environmental Health Engineering
• Wastewater-Based Epidemiology
• Bioremediation
• +7 more

Rolf Halden is a Professor and Center Director at Arizona State University, holding appointments in the School of Sustainable Engineering and the Built Environment, the Biodesign Center for Environmental Health Engineering, and the Global Futures Scientists and Scholars program. He is a leading expert in environmental health engineering, wastewater-based epidemiology, and bioremediation. Education: Ph.D. in Civil (Environmental) Engineering, University of Minnesota (1997) M.S. in Civil (Environmental) Engineering, University of Minnesota (1994) M.S. in Biology, Technical University of Braunschweig, Germany (1992) Postdoctoral Fellow, Environmental Science, Lawrence Livermore National Laboratory (1998) Research Focus: Halden’s research integrates environmental chemistry, public health, and sustainability to understand how anthropogenic chemicals affect ecological and human health. His lab uses advanced mass spectrometry (LC-MS/MS, MALDI-TOF) to track pollutants such as PPCPs, microplastics, pesticides, and dioxins across air, water, soil, and biological systems. He pioneered the use of wastewater treatment plants as public health observatories, enabling real-time tracking of disease, drug use, and environmental exposures. Scientific Awards & Recognition: Leadership Award, Arizona State University (2018) Rocky Mountain Emmy Award (2018) Leroy E. Burney Lecturer, Johns Hopkins School of Public Health (2011) ACS Expert Program Member (2014–present) NIEHS Superfund R01 Working Group National Leader (2012–2014) Labs & Teams: Halden is the Founding Director of the Biodesign Center for Environmental Health Engineering at ASU, where he leads a multidisciplinary team focused on environmental proteomics, bioremediation, and diagnostic tool development. He also co-leads the Human Health Observatory , a national wastewater monitoring network. Grants & Funding: Halden has secured major funding from NSF, NIH, EPA, DoD, and private industry. Notable projects include the development of in-situ groundwater monitoring devices, national wastewater surveillance of SARS-CoV-2, and studies on microplastics and neurodegenerative disease.