جست‌وجوی استاد و پژوهشگر

Hao Zhouمشاهده پروفایل

دانشیار

• Generative AI
• Molecular Modeling
• Protein Engineering
• +۳ مورد دیگر

Hao Zhou is a Research Associate Professor at the Institute for AI Industry Research (AIR), Tsinghua University. His research focuses on developing Generative AI models for discrete symbols such as text, small molecules, and proteins. Previously, he worked as a Research Scientist/Manager at Bytedance AI Lab. Currently, he leads the Generative Symbolic Intelligence group at Tsinghua AIR and co-leads the SIA Lab, a joint laboratory with ByteDance for LLM research. His research interests span molecular representation learning, generative models for scientific discovery, and applications in drug design and protein engineering. Recent work includes advancements in 3D molecular modeling, protein language models, and non-autoregressive sequence generation. These efforts aim to bridge AI techniques with scientific domains like chemistry and biology. Recent publications highlight contributions to areas such as auto-encoder-based molecular representation (Mol-AE), multi-scale protein modeling (ESM All-Atom), and structure-based drug design (MolCRAFT). These papers reflect a focus on improving generative models' accuracy and applicability to real-world scientific challenges. Best Short Paper of INLG (2022) Ranked #1 on WMT EN-DE translation (2021) CCF NLPCC Distinguished Young Scientist (2021) Best Paper Award of ACL (2021) CAAI Doctoral Dissertation Award (2019) He advises several students, including Qiying Yu, Yuxuan Song, and Danqing Wang, and oversees grants related to AI in drug discovery and molecular modeling. His labs are actively hiring postdocs and research assistants, and he encourages student interns to join collaborative projects. Key initiatives include the SIA Lab’s work on LLM research and the Generative Symbolic Intelligence group’s exploration of AI-driven scientific breakthroughs. These efforts emphasize interdisciplinary approaches to tackle complex challenges in AI and life sciences.

• Mental Health Disparities
• Racial Trauma
• Post-Traumatic Stress Disorder (PTSD)
• +۱۳ مورد دیگر

Dr. Monnica T. Williams is a tenured Professor in the School of Psychology at the University of Ottawa, where she holds the Canada Research Chair in Mental Health Innovation and Equity. She is also an Adjunct Scientist at the Institute of Mental Health Research, Royal Ottawa Hospital, and a Research Affiliate at the University of Massachusetts, Lowell. Her academic journey includes prior faculty roles at the University of Pennsylvania, University of Louisville, and University of Connecticut. Ph.D. in Clinical Psychology, University of Virginia (2007) M.A. in Psychology, University of Virginia (2003) B.S. in Electrical Engineering / Biomedical Technology, MIT Additional studies at UCLA Dr. Williams' research centers on mental health disparities, racial trauma, OCD, microaggressions, and psychedelic-assisted therapies. She has published over 300 peer-reviewed articles and authored books on healing racial trauma, antiracist therapy, and psychedelic justice. Her work bridges clinical science with social justice, emphasizing culturally-informed care for BIPOC communities. Her recent publications (2022–2025) reveal a strong focus on racial trauma assessment and treatment, equity in psychedelic research, discrimination in AI, and barriers to mental healthcare for marginalized groups. Themes include validation of trauma scales, development of therapeutic protocols, and systemic critiques of racism in institutions. APA Division 12 Samuel M. Turner Early Career Award (2015) Board-certified in Behavioral and Cognitive Psychology Scientific Advisor, International OCD Foundation Member, Chacruna Institute’s Racial Equity and Access Committee Dr. Williams supervises several graduate students and leads two research labs: the Laboratory for Psychedelic Clinical Research and the Laboratory for Mental Health Disparities. She has received federal and foundation grants and serves as an expert witness on discrimination in legal cases. She also conducts antiracist training workshops and public speaking engagements globally. Her labs focus on developing and testing interventions for racial trauma and advancing equitable psychedelic therapies. She is a vocal advocate for increasing representation of BIPOC individuals in psychology and mental health research.

Rachel Figardمشاهده پروفایل

استادیار

• Engineering Education
• Disability Studies
• Equity and Inclusion in STEM
• +۳ مورد دیگر

Dr. Rachel Figard is an Assistant Professor in the Engineering Education Transformations Institute (EETI) and the School of Environmental, Civil, Agricultural, and Mechanical Engineering at the University of Georgia. Her research focuses on systemic inequities in engineering education, particularly the experiences of disabled students and the implementation of anti-ableism practices. She explores topics like universal design for learning (UDL), disability policies, and intersectional identities in STEM. Institution Affiliations: University of Georgia Roles: Assistant Professor in EETI & ECAM School Her research interests include: Disability equity in engineering education Inclusive curriculum design Policy reform for accessibility Intersectionality of gender and disability Recent publications emphasize analyzing disability-related institutional policies, virtual learning accessibility post-pandemic, and the lived experiences of disabled students. She advocates for proactive design practices that go beyond compliance to foster inclusivity. No scientific awards or grants are explicitly listed in the provided text. She is actively involved in faculty development workshops and curriculum transformation initiatives to address systemic inequities in engineering programs.

• Artificial Intelligence
• Machine Learning
• Engineering Applications
• +۴ مورد دیگر

Professor Adam Sobey holds the Chair of Data-Centric Engineering at the University of Southampton's Maritime Engineering Group. He is currently on an 80% secondment as Director of the Data-Centric Engineering Programme at The Alan Turing Institute, funded by Lloyd's Register Foundation. His research focuses on applying AI and Machine Learning to improve engineering safety, reduce emissions, and advance sustainable practices. He also serves as a Non-Executive Director of Theyr Ltd, a maritime software company. Education: MEng in Aerospace Engineering (2006), PhD in Maritime Engineering (2010). Professional milestones include the 2015 Royal Institute of Naval Architects Jeom Paik Award for structural safety research. Research Interests: Reinforcement Learning in continuous environments, Evolutionary Computation, AI in scenarios with incomplete physics understanding. Key projects include optimizing wind-assisted ship technologies and advancing offshore wind energy systems. Awards: Royal Institute of Naval Architects Jeom Paik Award (2015). Editorial Roles: Editor of Data-Centric Engineering and Associate Editor of Ship and Offshore Structures . Supervision: Advising nine PhD students across engineering and environmental themes. Active in training the next generation of engineers through interdisciplinary collaboration and policy influence.

• Causal Inference
• Data Management
• Machine Learning
• +۳ مورد دیگر

Babak Salimi is an Assistant Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego. Previously, he was a postdoctoral research associate at the University of Washington's Department of Computer Science and Engineering, working with Prof. Dan Suciu. He holds a Ph.D. in Computer Science from Carleton University, advised by Prof. Leopoldo Bertossi. His research focuses on integrating causal inference, theoretical data management, and machine learning to build decision-support systems that enhance interpretability for diverse users. Key areas include causal relational learning, fairness in data-driven decisions, and robust offline evaluation frameworks. He has pioneered work on causal DAG summarization, adversarial data corruption testing, and model-agnostic data valuation systems like KAIROS. Salimi's contributions have been recognized with prestigious awards including the ACM SIGMOD Research Highlight Award and Best Paper Award, and the VLDB Best Demonstration Paper Award. His work bridges foundational data science principles with societal impact, emphasizing ethical AI practices and algorithmic accountability. Education: Ph.D., School of Computer Science, Carleton University Research Lab: HDSI's Causal Data Science Group Key Projects: Development of CausaLens, Zorro, and DEMA

Niloofer Shanavasمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Niloofer Shanavas is an Assistant Professor in the School of Computer Science at the University of Birmingham, Dubai campus. She holds a PhD in Computer Science from Ulster University, UK (2020), and an M.Tech in Computer Science and Engineering with specialization in Information Systems from Rajagiri School of Engineering and Technology, India (2014). Her research focuses on artificial intelligence, machine learning, natural language processing, text mining, and semantic computing . She develops innovative approaches combining graph-based methods, ontologies, and deep learning models to enhance text classification, information extraction, and knowledge representation in both medical and technical domains. Recent publications demonstrate a strong trend in applying advanced NLP techniques—particularly graph-based learning, contextual embeddings, and large language models—to challenging real-world problems such as clinical concept annotation, tender document analysis, and structured entity extraction from unstructured and tabular data. Scientific Awards: No scientific awards mentioned in the provided text. She is actively engaged in research and publication, with recent contributions in 2024 and upcoming works in 2025. Although her advisees and grant funding are not listed, her collaborative work with researchers such as H. Wang, Z. Lin, G. Hawe, and A. Abbas indicates strong research team involvement. Her work suggests leadership in developing knowledge-driven AI systems for complex document understanding. Labs and Research Teams: While specific lab affiliations are not mentioned, her research outputs imply active participation in NLP and AI research groups, particularly focused on semantic computing and document intelligence.

Esperanza Rivera de Torreمشاهده پروفایل

استادیار

• Antibody Engineering
• Venomics
• Antivenom Development
• +۷ مورد دیگر

Esperanza Rivera de Torre is an Assistant Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark. Her research focuses on antibody engineering, venomics, and protein design, particularly in neutralizing venom toxins and cancer therapy. Current affiliation: Department of Biotechnology and Biomedicine (DTU) Academic rank: Assistant Professor Research Interests Specializing in antibody engineering to develop synthetic antivenoms using de novo protein design. Investigating snake venom toxins and their neutralization mechanisms via VHH antibodies. Contributing to protemics and peptide sequencing through AI-driven tools like InstaNovo. Studying structural biology of actinoporins and their interactions with lipid membranes. Active in cross-neutralizing antibodies for tropical diseases and allergens. Advising and Collaborations Supervises multiple PhD students including Ruiz Espi, Møller, and Møiniche. Collaborates on projects like AI-driven protein binder design and allergen epitope characterization. Engages in international collaborations across venom and biomedical research fields.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +۳ مورد دیگر

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

Wesley Tanseyمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +۳ مورد دیگر

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.

• Statistical Machine Learning
• Signal Processing
• Data Fusion
• +۱۷ مورد دیگر

Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.

Chaoqun Dengمشاهده پروفایل

استادیار

• Data Science/Analytics
• Social Media
• Sharing Economy
• +۶ مورد دیگر

Chaoqun Deng is an Assistant Professor at the Zicklin School of Business , Baruch College, City University of New York, affiliated with the Paul H. Chook Department of Information Systems and Statistics . His research bridges Data Science , Sharing Economy , and Digital Strategy . PhD in Management, Rensselaer Polytechnic Institute (2018) MS in Management Science and Engineering, Wuhan University of Technology (2012) BS in Information Systems Management, Wuhan University of Technology (2009) His research interests focus on: Machine Learning and Text Mining for analyzing online reviews and social media Digital Strategy in live streaming platforms and crowdfunding Trust mechanisms in sharing economy platforms like Airbnb His recent articles address topics such as: Impact of visual designs on digital entrepreneurship in live streaming Role of organizational memes in shaping consumer support AI response strategies for customer reviews and their emotional implications Scientific Awards : Teaching Excellence Award (2023) Eugene M. Lang Junior Faculty Research Fellowship (2022-2023) Premier Research Publication Award (2021, 2022, 2023) PSC-CUNY Research Award (2020) AMCIS Doctoral Consortium Fellow (2017) As an active academic , he serves as: Committee Member for Undergraduate Curriculum and Search Committees at Baruch College Faculty Advisor for Data Challenge Competitions Reviewer and Editor for journals like MIS Quarterly , Information Systems Research , and conferences including ICIS and AMCIS He has secured grants from PSC-CUNY (2020) and Eugene Lang Fellowship (2022) for studies on AI in live streaming and consumer feedback analysis .

Luis Alberto Barron Cedenoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۷ مورد دیگر

Luis Alberto Barron Cedeno is an Associate Professor at the Department of Interpretation and Translation, University of Bologna since 2022, where he previously served as Senior Assistant Professor from 2019-2022. He holds a PhD in Computer Science from Universitat Politècnica de València (2012) and has worked at Qatar Computing Research Institute (2014-2019) and TALP Research Center (ERCIM fellowship, 2012-2014). PhD: Universitat Politècnica de València (2012) MSc: AI (Universitat Politècnica de València, 2009) MSc: Computing Science (UNAM, 2007) BEng: Computing (UNAM, 2004) His research focuses on NLP applications for analyzing text qualities like originality (plagiarism detection) and intent (hate speech, propaganda). Recent work includes persuasion techniques in spam emails , misogyny detection using argumentation theory, and Spanish language varieties analysis. He has published over 100 papers with 20+ in top-tier conferences. Key awards include SemEval 2020 Best Task for propaganda detection and ACL 2020 Best Demo . He serves as Specialty Chief Editor for Frontiers in Artificial Intelligence (NLP) since 2023. His students include PhD candidates Arianna Muti (misogyny detection), Katerina Korre (hate speech), and Paolo Gajo (inceldom analysis). 2020: SemEval Best Task 2020: ACL Best Demo 2012: PhD International Mention 2009: Best MSc Thesis 2007: MSc Cum Laude 2004: BEng Cum Laude He organizes the annual CheckThat! lab at CLEF since 2018 and chaired CLEF 2022. His teaching includes NLP courses, computational linguistics, and game localization for translation students.

Sven Kellerمشاهده پروفایل

استاد

• Business Informatics
• Artificial Intelligence
• Blockchain Technologies
• +۵ مورد دیگر

Prof. Dr.-Ing. Sven Keller is a Professor of Business Administration with a focus on Business Informatics at the Technical University of Central Hesse (THM), where he has held a professorship since March 2003. He served as Vice Dean of the Faculty of Economics from March 2013 to February 2016 and as Dean of the Faculty of Economics from March 2016 to March 2022. His academic career began with Industrial Engineering studies at the University of Siegen (1989-1993), followed by work as a Research Associate at the Institute for Manufacturing Automation (1994-2000), where he completed his dissertation on integrated modeling of structures and processes in manufacturing companies. Professor Keller's research spans multiple domains within business informatics, with a strong focus on emerging digital technologies and their business applications. His work particularly emphasizes artificial intelligence, blockchain technologies, digital transformation strategies, and human-computer interaction systems. He has supervised numerous theses exploring applications of AI in service sectors, blockchain implementations, digital voice assistants, and chatbot development. His current research interests include social robotics in healthcare settings, AI-supported drone logistics, and business model innovation for technology applications. His publication record shows consistent contributions from the early 2000s to present, with recent work focusing on AI adoption in regional economies, ethical implications of digitalization, and healthcare technology integration. The trajectory of his publications demonstrates an evolution from traditional manufacturing systems and e-business strategies toward contemporary topics in artificial intelligence and digital transformation. Professor Keller maintains active industry engagement through multiple supervisory board positions, including as Chairman of the Supervisory Board for Volksbank in Südwestfalen eG (since 2019), COCUS AG (since 2020), and CURSOR AG (since 2025). He also co-founded and maintains shareholdings in technology companies including AVENTUM GmbH, medmedias GmbH, and Apptivic GmbH. His supervisory approach emphasizes practical business applications of emerging technologies, with current thesis topics focusing on humanoid robots in healthcare, AI-supported drone logistics, and business models for social robots. Many of his supervised projects involve collaboration with industry partners and address real-world implementation challenges of digital technologies.

Örjan Sandredمشاهده پروفایل

استاد

• Computer-Assisted Composition
• Electro-Acoustic Music
• Rule-Based Composition
• +۳ مورد دیگر

Örjan Sandred is a Professor of Composition at the University of Manitoba's Desautels Faculty of Music, where he founded Studio FLAT for computer music research and production. He teaches composition, computer-assisted composition, and electro-acoustic music while maintaining an active international composition career. Diplomi i Komposition, Royal College of Music Stockholm MFA, Royal College of Music Stockholm Additional studies at McGill University and IRCAM (Paris) His research focuses on Computer-Assisted Composition with emphasis on rule-based systems, live electronics integration, and artificial intelligence applications. Sandred developed the Cluster Engine constraint-solving system for music composition, exploring how computers can process musical ideas through formalized rules. His work bridges acoustic instruments with digital technologies, creating innovative performance systems where musicians interact with responsive computer environments. His recent compositions demonstrate evolving trends in interactive performance systems (Tilting Points, Sketches of Shifting Landscapes), climate change expression (Footprints), and pandemic-era multimedia installations (Sonic Trails: Lockdown). These works consistently integrate live electronics with acoustic instruments through sophisticated Max/MSP patches. Scientific awards include: Guggenheim Fellowship (2022) Fellow of the Royal Society of Canada (2023) DAAD Visiting Professorship at Hochschule für Musik Detmold (2016) Manitoba Arts Council support for major commissions Sandred maintains significant international collaborations with performers and institutions worldwide. His compositions receive regular premieres across Europe, North America, and Asia through commissions from organizations like the Swedish Concert Institute and Théâtre du Châtelet in Paris. He directs Studio FLAT as a hub for computer music research while developing educational resources including his book The Musical Fundamentals of Computer Assisted Composition . Current projects include an installation collaboration with the Royal Winnipeg Ballet and conductors from Sweden/Canada, plus ongoing development of the Cluster Engine system within Max environment through MOZlib.