Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .
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.
Dimitrios Skalkos serves as a Professor at the Department of Chemistry, University of Ioannina, holding the specific title of Professor of Innovation Management in Food Businesses. His office is located in building X2, room X2-206c on the Ioannina Campus (45110 Ioannina), and he leads the Food Chemistry Laboratory. He earned his Basic Degree in Chemistry from the University of Ioannina (1980-1985) followed by a Doctoral Degree from the University of Toledo, USA (1986-1989) with dissertation research on "Di- and Tetrahydroporphyrins: Syntheses and Antitumor Activities". Skalkos's research spans Food Business Management, Food Innovation, and Innovation Management, with specialized focus on Innovation & Entrepreneurship, Aromatic Plants, Innovative Foods, and Strategic Marketing. His work bridges academic research with industry applications in food systems, emphasizing Greek agricultural products and pandemic-era consumer behavior. His 36 publications (2020-2024) reveal strong trends in post-pandemic food innovation, consumer perception of traditional Greek foods, functional food development (particularly olive oil, cheese, and grape by-products), and sustainable agri-food supply chains. Key thematic clusters include traceability systems, bioactive properties of novel foods, and economic impacts on food choices. He has actively supervised Master's and PhD students through departmental presentations, directed 23 research programs, and organized 15 seminars and conferences. His laboratory website ( https://foods.chem.uoi.gr ) showcases ongoing food chemistry innovations.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
Prof. Senthold Asseng is a Professor of Digital Agriculture at the Technical University of Munich (TUM), leading the Hans Eisenmann Forum for Agricultural Sciences since 2021. His research focuses on climate-plant-soil systems modeling, addressing global food security, sustainable agriculture, and digital technologies like vertical farming and autonomous robotics. He holds a PhD from Humboldt University Berlin and habilitation from TUM, with prior roles at CSIRO Australia and the University of Florida (as Full Professor and Director of the Florida Climate Institute). His honors include AAAS Fellow, Web of Science Highly Cited Researcher, and multiple teaching/mentorship awards. Education: BSc/MSc in Agronomy & Horticulture, Humboldt University Berlin (1989-1990) PhD in Agronomy, Humboldt University Berlin (1994) Habilitation in Agronomy, TUM (2004) Research Interests: Climate change impacts on crops, digital agriculture technologies, systems analysis for autonomous farming, and vertical farming sustainability. His work integrates modeling, robotics, and environmental control to enhance agricultural resilience and productivity. Awards: AAAS Fellow (2019) Highly Cited Researcher (2019) UF Research Foundation Professor (2016-2019) Grants & Leadership: Led interdisciplinary projects like AgMIP-Wheat, SECC, and FAO advisory roles. Active in editorial roles for journals like Global Change Biology and Environmental Research Letters . Labs/Teams: Chair of Digital Agriculture at TUM, collaborating on projects like Smartfield and Proteins4Singapore , focusing on automation, controlled environment agriculture, and climate-smart solutions.
Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.
Hamed Zamani is an Associate Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, where he also serves as Associate Director of the Center for Intelligent Information Retrieval (CIIR). He joined UMass Amherst in 2020 after working as a researcher at Microsoft. His research focuses on designing and evaluating statistical and machine learning models for information access systems, including search engines, recommender systems, and question answering. Education: PhD in Computer Science, University of Massachusetts Amherst MS in Computer Engineering, University of Tehran BS in Computer Engineering, University of Tehran Zamani's current research explores neural information retrieval, conversational search, and retrieval-enhanced machine learning. He develops efficient neural models for core IR tasks and emerging areas like conversational information seeking. His work bridges information retrieval with large language models to enhance capabilities in understanding complex queries and generating relevant responses. His recent publications demonstrate a strong focus on retrieval-augmented generation, personalized information access, and efficient neural ranking models. There's a clear trend toward integrating large language models with information retrieval systems, optimizing multi-agent frameworks, and developing evaluation metrics for generative AI applications in search contexts. Scientific Awards: NSF CAREER Award ACM SIGIR Early Career Excellence in Research & Community Engagement Awards (2023) UMass CICS Outstanding Dissertation Award Paper awards at SIGIR (2022, 2023, 2024), CIKM (2020), ICTIR (2019) Microsoft Research Award (AI and New Future of Work program) Amazon Research Award (Optimization of Retrieval-Enhanced ML Models) Zamani actively advises PhD students and postdoctoral researchers, with his students receiving prestigious awards including NSF Graduate Research Fellowships and SIGIR Best Paper awards. He leads the CIIR Talk Series, hosting IR researchers to share recent findings. His Alexa Prize TaskBot Challenge team was selected for two consecutive years, advancing task-oriented dialogue systems. He directs research at the Center for Intelligent Information Retrieval (CIIR), where he oversees projects in neural retrieval models, conversational AI, and retrieval-augmented generation. The center serves as a hub for developing next-generation information access systems with industry and academic collaborators.
Dr. Lilong Chai serves as an Associate Professor & Engineering Specialist in the Department of Poultry Science at the University of Georgia's College of Agricultural and Environmental Sciences, with affiliate status at the UGA Institute of Integrative Precision Agriculture. His work integrates engineering principles with animal science to advance sustainable poultry production systems through climate-resilient practices and precision farming technologies. His academic foundation includes a Ph.D. in Agricultural and Bio-environmental Engineering from China Agricultural University (2005-2011), B.S. from Anhui Agricultural University (2001-2005), joint Ph.D. studies at Purdue University (2008-2010), and postdoctoral research at Iowa State University (2015-2018) and Agriculture and Agri-Food Canada (2012-2015). Dr. Chai's research program centers on precision poultry farming, climate-smart animal production, and animal welfare enhancement. He pioneers applications of deep learning, computer vision, and environmental engineering to develop real-time monitoring systems for poultry behavior, health indicators, and housing conditions. His work addresses critical industry challenges including floor egg management, footpad dermatitis detection, air quality control, and disease prevention in cage-free systems, emphasizing practical solutions that balance productivity with ethical animal husbandry. Analysis of his 2023-2025 publications reveals dominant themes in AI-driven behavioral monitoring (dustbathing, perching, foraging), thermal imaging for welfare assessment, and sustainable waste management. These studies consistently bridge agricultural engineering, veterinary science, and data analytics to create scalable precision farming tools applicable across commercial poultry operations. His scientific recognition includes 20 major awards such as: Educational Aids Blue Ribbon Award from ASABE (2024) NACAA Communications Award for Precision Poultry Farming Education (2023) Georgia Research Alliance's Georgia Greater Yield selection (2023) ASABE Outstanding Associate Editor Award (2022) Dr. Chai has secured $5 million through 40 competitive grants from USDA-NIFA, NSF, and international agencies as PI/Co-PI. He actively translates research into practice through leadership roles including Coordinator of the Georgia Precision Poultry Farming Conference, Chair of ASABE's Environmental Air Quality Committee, and reviewer for major research foundations. His extension work directly impacts industry stakeholders through annual training programs serving Georgia's poultry sector. His research infrastructure operates within UGA's Poultry Science Department and the Institute of Integrative Precision Agriculture, where he collaborates with interdisciplinary teams to develop next-generation monitoring systems integrating robotics, thermal imaging, and foundation models for real-world poultry applications.
Associate Professor Wenhua Zhao is a globally recognized expert in offshore hydrodynamics and renewable energy technologies at The University of Queensland , School of Civil Engineering. With over 110 publications and 30 million AUD in secured research funding, his work bridges theoretical and practical advancements in marine engineering. Research focuses on Clean Energy , Artificial Intelligence , and Climate Change , specifically floating wind energy, floating solar, offshore aquaculture, and green hydrogen production. His 15 most recent articles (2024-2025) emphasize wave-structure interactions, gap resonance dynamics, and AI-driven wave prediction, published in top journals like Journal of Fluid Mechanics and Ocean Engineering . Scientific awards include the prestigious ARC Future Fellowship (2024-2028) and DECRA Fellowship (2019-2022) , recognizing his contributions to academia and industry. He teaches the 'Design of Offshore Energy Systems' course , training hundreds of students in coastal and ocean engineering, and serves as Deputy Editor for Ocean Engineering and Associate Editor for ASME's Journal of OMAE . Available for research supervision, Zhao actively collaborates with editorial boards of Applied Ocean Research and other Q1 journals.
Kumar Varoon Agrawal is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding the Gaznat Chair for Advanced Separations. He is affiliated with the School of Basic Sciences (SB), the Institute of Chemical Sciences and Engineering (ISIC), and the Laboratory of Advanced Separations (LAS) in Sion, Switzerland. Additionally, he contributes to the Swiss Doctoral School in Chemical and Bioengineering (SCGC) and serves as Vice President of the Confédération des Chimistes et des Génie Chimique (CCE). Research Focus: Material Chemistry & Engineering at the Ångström scale for high-performance inorganic and hybrid membranes, emphasizing energy-efficient molecular separations. Teaching: Courses include Fundamentals of separation processes , Diffusion and mass transfer , and Chemical engineering product design . Scientific Contributions: His 15 most recent publications (2025-2020) span topics like graphene pore engineering , 2D material synthesis , carbon capture , and gas separation membranes , with keywords such as Nanotechnology , Materials Science , and Molecular Transport . Subfields include Atomic-Scale Pores , Membrane Stability , and Industrial Scalability . Students and Collaborations: He advises 10 current PhD students and has mentored 9 past PhD candidates in areas like graphene membranes , ion separation , and MOF films . He is an Academic Referent for the EPFL Carbon Team and a committee member for the EDCH Doctoral Program in Chemistry and Chemical Engineering.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Prof. April Wang is an Assistant Professor in the Department of Computer Science at ETH Zurich. Her work focuses on Educational Technology, Human-Computer Interaction, and AI-driven tools for programming education. She is affiliated with the Professur Educational Technology group and teaches courses such as Human Computer Interaction and Ethics in Computing. Her research explores interactive learning environments, collaborative data science workflows, and AI-augmented educational systems. Her research interests span computational notebooks, live coding interfaces, and human-AI collaboration in software development. Notable contributions include frameworks like DBox for algorithm learning and EDBooks for interactive programming narratives. She also investigates challenges in real-time collaboration tools and automated documentation systems for data scientists. Recent work emphasizes understanding user needs in automated copilot systems and resolving conflicts in collaborative notebook environments. Her projects often bridge theoretical HCI principles with practical educational tools, aiming to enhance both instructor efficacy and student engagement. Prof. Wang’s teaching portfolio includes courses on user-centered interface design and ethical scientific practices. She actively contributes to advancing interdisciplinary computational literacy through initiatives like datAR, which uses everyday objects to teach data concepts.
Netina Tan is University Scholar and Associate Professor of Political Science at McMaster University, where she conducts research on authoritarian resilience, digital technology, and political representation of women and ethnic minorities, with a focus on East and Southeast Asia. Her scholarly work has gained international recognition, with publications referenced in 24 Wikipedia pages and covered by 32 news outlets. Dr. Tan earned her PhD in Comparative Politics from the University of British Columbia, a Master's in Political Science from the University of Regina, a Master's in Southeast Asian Studies from the National University of Singapore, and a Bachelor of Arts in Political Science and Sociology from the National University of Singapore. She was also a Social Sciences and Humanities Research Council (SSHRC) Postdoctoral Fellow at the University of Toronto. Her research interests center on authoritarianism, democratization, and representation in Asian contexts, with particular attention to how digital technology affects political systems. She investigates how women and ethnic minorities gain political representation in authoritarian and semi-authoritarian regimes across Southeast Asia, examining electoral systems, quota mechanisms, and party strategies that either facilitate or hinder inclusion. Analysis of her recent publications reveals consistent focus on democratic backsliding in Southeast Asia, especially Singapore; the relationship between digital technology and authoritarian resilience; electoral manipulation tactics in Asian contexts; and gender representation through quotas and electoral systems. Her work bridges comparative politics, gender studies, and technology studies with regional expertise in Southeast Asia. University Scholar at McMaster University Dr. Tan has advised graduate students including Cassandra Preece, with whom she has co-authored publications. Her research has attracted significant scholarly attention with hundreds of readers on academic platforms like Mendeley. She regularly contributes expert commentary to media outlets on Southeast Asian politics, particularly regarding elections in Cambodia, Malaysia, and Singapore. Her scholarly activities include directing research projects on electoral malpractice in Asia and gender equality in Myanmar politics, with recent books including 'Putting Women Up Gender Equality and Politics in Myanmar' (2024) and 'Electoral Malpractice in Asia Bending the Rules' (2023).
Eric Slessarev is an Assistant Professor at Yale University , affiliated with the Department of Ecology and Evolutionary Biology and the Yale Center for Natural Carbon Capture. His research focuses on soil biogeochemistry, particularly how soil properties influence carbon and nutrient cycling in terrestrial ecosystems, with applications to climate change mitigation strategies like enhanced rock weathering and perennial grass cultivation. Teaches Ecology of Landforms and General Ecology Labs located at KGL 318 (research) and KGL 417 (office) His recent publications highlight interdisciplinary approaches to understanding mineral-organic matter interactions, microbial controls on carbon cycling, and policy implications for soil-based carbon removal strategies. Key methodologies include global data synthesis, isotope tracing, and experimental manipulation of soil-plant systems across depth profiles. Notably, his 2025 work demonstrates drought impacts on carbon persistence, microbial harnessing for CO2 removal, and economic modeling of reversible carbon storage. 2024 studies explore deep-rooted plant effects on carbon fractions, calcium's role in mollisol formation, and policy optimization for carbon sequestration.
Zsolt Kira serves as an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology's College of Computing, with additional affiliations at the Georgia Tech Research Institute and as Associate Director of ML@GT. He leads the Robotics Perception and Learning (RIPL) Lab, driving research at the intersection of machine learning and robotics. Dr. Kira earned his Ph.D. in 2010 under Professor Ron Arkin, establishing foundational expertise in robotics and AI before transitioning to his current academic role after industry experience at SRI International Sarnoff. His research pioneers beyond supervised learning through unsupervised, semi-supervised, self-supervised, and continual/lifelong learning frameworks, while advancing distributed perception via multi-modal fusion and cross-robot information integration. This dual focus addresses core challenges in robotic autonomy and sensor processing. Analysis of his 15 most recent publications reveals dominant trends in embodied AI systems, robust foundation model adaptation, and multimodal learning architectures. Key themes include neural radiance field applications, reinforcement learning for locomotion, and novel benchmarks for memory evaluation in agents. While specific advisees and grants aren't documented in the source material, his RIPL Lab leadership implies active graduate mentorship and research funding acquisition. The lab's work directly enables next-generation robotic systems through algorithmic innovation in perception and learning. The RIPL Lab operates as a hub for developing machine learning techniques that solve difficult perception problems in robotics, with particular emphasis on unsupervised learning paradigms and distributed multi-robot systems that push the boundaries of autonomous operation.
